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1	Artificial Intelligence and Data Science	ABS-17
Optimizing Engine Efficiency: An Artificial Neural Network Approach for Fuel Consumption Reduction through Engine Remapping Agung Nugroho*, Randy Cahya Kurnianto, Tabah Priangkoso Wahid Hasyim University Abstract In the pursuit of reducing fuel consumption and carbon emissions, the automotive industry has been exploring ways to enhance engine efficiency. One popular technique is engine remapping, which involves modifying electronic engine settings to optimize performance and fuel efficiency. However, manual engine remapping can be complex, time-consuming, and may not always yield the desired efficiency improvements. As a result, the use of Artificial Neural Network (ANN) simulation has emerged as a promising alternative for optimizing engine remapping. This paper presents a study on the use of ANN simulation in engine remapping to achieve more efficient fuel consumption. The research aims to optimize fuel efficiency by predicting ignition timing mapping using ANN modeling. The study utilizes the TRAINGDA feed-forward backpropagation training method to develop an ANN model and achieve a 10% increase in mileage compared to standard data. The research builds upon previous studies that have demonstrated the effectiveness of ANN in improving fuel efficiency and engine performance. The methodology involves conducting tests on a chassis dynamometer to simulate highway driving conditions. The initial vehicle data is recorded, and fuel consumption tests are performed at various speeds. The fuel consumption results are then used as input data for the ANN program, which predicts optimal ignition timing values. The resulting ignition timing map is incorporated into the engine control unit (ECU) for further testing and evaluation. The study^s results indicate that the ANN method effectively reduces fuel consumption at speeds ranging from 10 km/h to 40 km/h. By retarding the ignition timing by 2&#730-, the fuel efficiency is improved compared to the standard map. However, at a speed of 50 km/h, the standard ignition timing data is found to yield optimal fuel consumption. The analysis demonstrates a strong correlation between predicted values from the ANN model and experimental measurements, as well as a significant relationship between ignition timing and vehicle speed. In conclusion, the use of ANN simulation in engine remapping offers a promising approach to optimize fuel efficiency and improve overall engine performance. The study highlights the potential benefits of ANN modeling in achieving fuel consumption reduction and suggests avenues for further research in this field. Keywords: Engine efficiency, Fuel consumption, Engine remapping, Artificial Neural Network (ANN), Ignition timing, Chassis dynamometer. Share Link | Plain Format | Corresponding Author (Agung Nugroho)

2	Artificial Intelligence and Data Science	ABS-24
Development of a Face Mask Type Detection with Multiclass Classification using Artificial Intelligence on Python Anindya Ananada Hapsari, Halimatuz Zuhriah, Devan Junesco Vresdian, Onki Alexander, Brainvendra Widi Dionova, Untung Suprihadi Universitas Global Jakarta Abstract This research presents a system based on a machine learning system with artificial intelligence. The approach presented uses a camera to detect people who are not wearing face masks and it is hoped that it can detect type from mask. The purpose of this study is to be able to compare whether a computer system can correctly distinguish different types and types of masks using multiclass classification with artificial intelligence and training datasets on the system. And testing with different amounts of data does it affect the detection sensitivity of the system. The main contributions of this research are creating a prototype face mask detector which is implemented in three phases to help detect the presence of a face mask detector in real-time using images and video streams. The data used is a dataset consisting of images using two dataset contain two categories and four categories type of mask. This study also tried to conduct training on a dataset of several different types of masks. The model created has used deep-learning methods to develop classifiers and collect photos of individuals wearing masks and not wearing masks. Mask and non-mask class. Which is then implemented in Python with Google Colab environment along with the Open-CV modules, Keras, NumPy, tensor flow, sci-py, and matplotlib. Trials will be carried out with the dataset training process first using CNN and then collecting the data system accuracy. Keywords: Artificial Intelligence- CNN- Face Mask Detection- Multiclass Classification- Python Share Link | Plain Format | Corresponding Author (Anindya Ananda Hapsari)

3	Artificial Intelligence and Data Science	ABS-26
Convective Clouds Classification Based on Weather Forecast for Air Traffic Flow Management in Kualanamu Airport 1.Sunardi, 2.Syahrul Humaidi, 3. Marhaposan Situmorang, 4.Marzuki Sinambela. Department of Physics - Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara, Medan, Indonesia Politeknik Penerbangan Palembang, Sumatera Selatan, Indonesia Abstract Weather conditions are important information in flight. Extreme weather such as heavy rain, strong winds, and fog can disrupt flight activities, ranging from delays to plane crashes. This study aims to classify and predict extreme weather events at Kualanamu Airport as information for determining Air Traffic Flow Management (ATFM). ATFM is an air traffic management system that is oriented towards optimizing resources, which aims to improve safety, ensure smooth operation, deal with limitations, and harmonize flight operations and traffic. This research was conducted using the Weather Research and Forecasting (WRF) numerical model, Himawari 8 satellite imagery, and observation data from the Kualanamu Meteorological Station on a case study of heavy rain events on 17 January 2021. The results showed that the WRF model was able to predict extreme weather events so that they could be anticipated earlier by the airline traffic operator (Air Traffic Controller). The cloud growth observed in the Himawari 8 satellite image adds information to the location of extreme weather events in the short term. Keywords: ATFM, weather forecasting, aviation safety Share Link | Plain Format | Corresponding Author (Sunardi Sunardi)

4	Artificial Intelligence and Data Science	ABS-27
Improving Stunting Prediction in Children: Evaluating Ensemble Algorithms with SMOTE and Feature Selection Agus Byna (a*),Fadhiyah Noor Anisa (b), Nurhaeni (a) a) Information System Department, Science and Technology Faculty, Universitas Sari Mulia, Jl. Pramuka no. 2, Banjarmasin, Indonesia *agusbyna[at]unism.ac.id b) Midwifery Department, Health Faculty, Universitas Sari Mulia, Jl. Pramuka no. 2, Banjarmasin, Indonesia Abstract Childhood stunting presents a critical challenge to the welfare and health of numerous developing countries, Indonesia included. The phenomenon arises from various factors, including insufficient, excessive, or imbalanced intake of vital energy and nutrients crucial for proper child growth. To address this issue, our study endeavors to develop a predictive model utilizing Machine Learning (ML) techniques. We focus on evaluating three ensemble algorithms on the Banjarmasin Demographic Health dataset to forecast stunting in children under five accurately. To maximize prediction accuracy, we employ SMOTE (Synthetic Minority Over-sampling Technique) and Feature Selection techniques in conjunction with the three algorithms. By doing so, we aim to enhance the performance of our models and attain the most reliable results. Our dataset comprises 457 instances of stunted children, and we carefully select thirteen pertinent features to incorporate into twelve distinct models. Upon thorough analysis, we find that the Decision Tree model with SMOTE and Feature Selection emerges as the most accurate, achieving an impressive 90% accuracy score during testing on 70% of the training data. In contrast, the Random Forest model with SMOTE performs less effectively as the weakest predictor for stunting. As a result of our discoveries, we confidently assert that the Decision Tree model with SMOTE and Feature Selection outperforms the other eleven models utilized in this study to predict stunting status among children under five in Banjarmasin. We intend to expand our research by incorporating more features and data. Additionally, we will explore alternative models, potentially leveraging a combination of Machine Learning and Deep Learning techniques to enhance the predictive capabilities for childhood stunting further. These advancements promise to refine interventions and policy decisions to address this pressing issue and improve the well-being of young children in Indonesia and beyond. Keywords: Stunting, Machine Learning, Decision Tree, SMOTE,Feature Selection Share Link | Plain Format | Corresponding Author (Agus Byna)

5	Artificial Intelligence and Data Science	ABS-28
The Extended Kalman Filter Method for Covid-19 Spread Prediction in Indonesia and East Java Helisyah Nur Fadhilah, Amalia Nur Alifah, Vessa Rizky Oktavia Institut Teknologi Telkom Surabaya, Surabaya, Indonesia Institut Teknologi Telkom Surabaya, Surabaya, Indonesia Institut Teknologi Telkom Surabaya, Surabaya, Indonesia Abstract The COVID-19 pandemic has caused significant loss of human life worldwide and presented unprecedented challenges to public health, food systems, work, and many other sectors. In this paper, we make short-term predictions beyond the actual data we have using the Extended Kalman Filter (EKF) method. Basically EKF has 2 stages in estimating, the prediction stage and the correction stage. To get short term prediction results, in this paper we make modifications at the correction stage. The COVID-19 mathematical model used in this paper is the SIRD model to see the effect of mobility restriction programs on infection cases. The simulation outcomes demonstrate that mobility restriction programs in Indonesia and East Java can lower infections. Keywords: Extended Kalman Filter, SIRD Model, Covid-19 Share Link | Plain Format | Corresponding Author (Helisyah Nur Fadhilah)

6	Artificial Intelligence and Data Science	ABS-35
Herb Compounds Screening as Meningitis Inhibitor Candidates using Neural Network and Random Forest Methods Riska Aprilia(1), Mohammad Hamim Zajuli Al Faroby(2,*), Muhammad Adib Kamali(1), Muhammad Dzulfikar Fauzi(3) (1) Information Technology Study Program, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Indonesia (2)Data Science Study Program, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Indonesia *Corresponding author: alfaroby[at]ittelkom-sby.ac.id (3) Informatics Study Program, Faculty of Information Technology and Business, Institut Teknologi Telkom Surabaya, Indonesia Abstract Meningitis is an inflammation of the meninges that occurs in the protective lining of the brain and spinal cord caused by bacterial, viral, or fungal infections. This disease is difficult to recognize because it has initial symptoms like the flu where the patient has a fever and headache. Current efforts to prevent the disease by strengthening antibodies. Meanwhile, drug candidates for the treatment of this disease still have not found optimal results in reducing mortality due to meningitis. This study aims to find and analyses herbal compound candidates that might be inhibitors of meningitis. Compound data was acquired from a validated open database. The data acquired are smiles of the chemical bond structure of the compounds. In the data processing process, compound feature extraction is required by applying the concept of molecular fingerprint. The results of feature extraction are used as datasets to build classification models by applying the Multilayer Perceptron (MLP) and Random Forest algorithms. The two models are compared, and a more robust model is selected to be used as a prediction model for herbal compound search. The MLP model has a better accuracy of 0.97 compared to the Random Forest model. The results of screening using the MLP learning model obtained Symphytine, cis-Linalool oxide and 3-O-Methylcalopocarpin compounds have the highest probability compared to thousands of other herbal compounds. This candidate compound can be used as a recommendation for drug discovery to treat patients who contract Meningitis. Keywords: Multilayer Perceptron- Random Forest- Herb Compound Screening- Meningitis- Molecular Fingerprint. Share Link | Plain Format | Corresponding Author (Mohammad Hamim Zajuli Al Faroby)

7	Artificial Intelligence and Data Science	ABS-43
Quality Evaluation on Higher Education Research Articles Publication using Artificial Intelligence Dedy Kurniadi- Rahmat Gernowo- Bayu Surarso Doktor Sistem Informasi, Universitas Diponegoro Abstract Evaluating the quality of research article publications from universities is essential, given the sharp increase in international article publications. This quality assessment is a complex and critical process in the world of higher education to identify universities contributing to global-level research. One primary aspect of assessing university quality based on international publication output is the quantity of scholarly publications produced by the university. Universities that are actively engaged in research and produce numerous publications tend to have a greater contribution. However, it^s important to note that a high quantity of publications does not necessarily guarantee high publication quality. Data for this research was collected from the Scopus database using the Application Programming Interface (API). To determine publication quality, a journal rank database was constructed using Integration Scimago Journal Rank data. The research utilized the Promethee II algorithm to identify the best alternatives among Indonesian universities, ranking them based on the quality of their international publications. The results of this research yielded an integrated system with Scopus and Scimago Journal Rank databases, ranking the top 25 universities in Indonesia. From the research, a total of 326,000 indexed articles from these 25 universities were obtained. Subsequently, Promethee II ranking was applied, resulting in the University of Indonesia securing the top position with a net flow value of 0.85. Following that, the University of Diponegoro ranked second with a net flow value of 0.15. The third position was held by Binus University with a net flow value of -0.0158267 among the 25 universities in Indonesia. Keywords: Quality Systems, API, Artificial Intelligence, Net Flow, Rank Share Link | Plain Format | Corresponding Author (Dedy Kurniadi)

8	Artificial Intelligence and Data Science	ABS-44
Software Evolution Analysis Based on Software Changelog Rafli Azra Virendra Azhari*, Muhamad Khafidz Haikal, Muhamad Rizki Triyanto, Muhammad Naufal Pratama, Septian Luthfia Sanni, Aris Puji Widodo, Edy Suharto Department Of Informatics, Faculty of Science and Mathematics, Diponegoro University Jalan Prof. Jacob Rais, Tembalang Semarang - 50275, Jawa Tengah, Indonesia *rafliazra[at]students.undip.ac.id Abstract Evolution is phenomena that are difficult to predict, including in the field of software. The evolution of software involves numerous factors and variables, such as technological advancements, the acceleration of computing power, and the need to adapt to users^ requirements in their respective eras. This can lead to exponential growth in the complexity of existing software. In order to help software developers in developing their software in conjunction with maintaining and evolving their software to become better software, this paper will analyze a software^s development and evolution to illustrate its evolutionary history. The software history evolution can be caught from the software^s changelog messages. The software evolution analysis is based on Chapin Model and Lehman^s law. The result from this paper can be utilized for further software development. Keywords: Changelog, Evolution, Maintenance, Redis, Software. Share Link | Plain Format | Corresponding Author (Rafli Azra Virendra Azhari)

9	Artificial Intelligence and Data Science	ABS-49
Tourism Recommendation System using User Based Collaborative Filtering Kurniawan Eka Permana, Abdullah Basuki Rahmat, Eka Mala Sari Rochman, Aery Rachmad, Sigit Susanto Putro Department of Informatic, Faculty of Engineering, University of Trunojoyo Madura Abstract This study investigates the design and evaluation of a tourism recommendation system based on user-based collaborative filtering. The study makes use of the Indonesia tourism destination dataset obtained from Kaggle, which includes user ratings for a diverse range of tourist destinations around Indonesia. The dataset, which includes ratings from 300 users on a scale of 1 to 5, serves as the foundation for the proposed methodology. The approach is made up of several key stages, beginning with data loading and transformation into a matrix format. To improve suggestion accuracy, user ratings are normalized based on the average rating of each user, taking into consideration individual rating tendencies. Then, cosine similarity measurements are used to identify people with similar tastes, with missing values addressed before calculating similarity scores. The method suggests tourism destinations to users during the recommendation phase using a weighted average of user similarity scores and place ratings. The performance of the recommendation system is evaluated by calculating Root Mean Square Error (RMSE) and analyzing prediction accuracy. Several scenarios are thoroughly investigated, each with a different number of top neighbors considered for predictions. Surprisingly, the scenario containing the top 20 neighbors produces the best results, with the lowest RMSE of 0.3646, showing much improved prediction accuracy. The methodology has potential for additional developments in tourism recommendation systems, with the RMSE metric confirming the system^s effectiveness Keywords: Recommendation System- Collaborative Filtering - User Based Collaborative Filtering Share Link | Plain Format | Corresponding Author (kurniawan eka permana)

10	Artificial Intelligence and Data Science	ABS-52
CheckJump: An Approach to Real-Time Pathfinding for 2D Grid-Based Platformer Games Ibnu Athaillah (a*), Moch. Kholil (b), Rafika Akhsani (c), Ismanto Ismanto (d), Heri Waspada (e), Muchamad Saiful Muluk (f) Department of Multimedia Akademi Komunitas Negeri Putra Sang Fajar Blitar Jl. Dr. Sutomo 29, Blitar 66133, Indonesia Abstract This research proposed a solution for game developers that are building a 2D grid platformer game that requires characters that are able to intelligently find a way to navigate levels through various movements. This research is based on an unannounced platformer game that is currently under development and uses the defined input handling and jump mechanics. The pathfinding task is done by utilizing the A* algorithm and movement sequence with Finite State Machine. Vertical movement predictions can be made with precision by using the built-in Physics system to replicate physics-affected movements outside of the real-time frame. This solution is tested on varied levels, each with a different size and complexity. In terms of capability, NPC has been shown to be capable of performing a sequence of horizontal or vertical movements in order to reach its target. In terms of performance, there isn^t any noticeable impact on memory usage. However, the number of extra frames required for mapping is proportional to the number of cells in the level. Fortunately, this is a task that can be done sparingly without impacting functionality. The pathfinding process itself, including the NPC movement, does not have any noticeable performance impact. This technique has the flexibility of real-time adjustments to changes in the level layout and can be used as an option for developers looking for a platformer pathfinding solution. Keywords: Platformer Game- Non-Player Character- Pathfinding- Share Link | Plain Format | Corresponding Author (Ibnu Athaillah)

11	Artificial Intelligence and Data Science	ABS-58
Identification of Tuberculosis with the Fuzzy Sugeno Method and Diet Recommendations Using the Naive Bayes Method Eka Mala Sari Rochman, Retno Tri Lestari, Muhammad Ali Syakur, Hermawan Bin Fauzan. Kurniawan Eka Permana, Aeri Rachmad University of Trunojoyo Madura Abstract The development of tuberculosis, according to the World Health Organization (WHO) in 2014, stated that it was estimated to affect 9.6 million people, with 12% of them being HIV-positive. Tuberculosis is a directly communicable disease caused by the tuberculosis bacterium (Mycobacterium Tuberculosis). Tuberculosis bacteria can be transmitted through physical contact, the air, the sputum of patients, and so on. Currently, many people are unaware of the early symptoms and dangers of tuberculosis, so an expert system is needed to diagnose tuberculosis early and provide dietary recommendations that can help expedite patient treatment. In this disease diagnosis expert system, the Fuzzy Sugeno method is used to make decisions by answering questions related to symptoms. Additionally, to ensure that patients have good nutritional status, dietary recommendations are provided using the Naive Bayes method to tailor diets according to the nutritional needs of tuberculosis patients. Using the Fuzzy Sugeno method for disease diagnosis resulted in an accuracy rate of 85.35%, and the Naive Bayes method for dietary habit recommendations produced the highest accuracy rate of 89.28% in k-fold = 3, with an average accuracy rate calculated across all folds of 78.57%. Keywords: Mycobacterium Tuberculosis, Naive Bayes method, Fuzzy Sugeno, expert system Share Link | Plain Format | Corresponding Author (Eka Mala Sari Rochman)

12	Artificial Intelligence and Data Science	ABS-59
Garbage classification using Depthwise Separable Convolution with data augmentation Budi Dwi Satoto(a*), Achmad Yasid(a), Faroid(a), Aghus Setio Bakti(a), Muhammad Yusuf(a), Budi Irmawati(b) (a) Information system department University of Trunojoyo Madura Bangkalan, East Java, Indonesia budids[at]trunojoyo.ac.id (b) Informatics Engineering Department University of Mataram West Nusa Tenggara, Indonesia budi-i[at]unram.ac.id Abstract Tourism destinations are often beautiful and valuable natural areas. Good waste management helps maintain the cleanliness and beauty of the natural environment, minimizes negative impacts on the ecosystem, and ensures the sustainability of tourist destinations. Additionally, waste management creates opportunities for the recycling industry. By separating, collecting, and processing recyclable waste, such as paper, plastic, metal, and glass, this industry creates jobs and produces products that can be sold. Tools are needed to facilitate visually sorting waste in tourism areas. It can be done with the help of deep learning. The contribution is to use a combination of Depthwise Separable Convolution architectural concepts, hoping that computing will be lighter, maintain accuracy, and remain stable. The model is relatively small, which makes it suitable for mobile devices with limited computing power and storage. The dataset consists of six classes: Cardboard, glass, metal, paper, plastic, and trash. Because of data limitations, augmentation techniques are used. The test results show an average model accuracy of 98.29% with a training computing time to obtain a model of 45 minutes. MSE 0.0343, RMSE 0.1852, and MAE 0.0229. Testing with new experimental data takes an average of 1-2 seconds Keywords: Garbage classification, Deep learning, Depthwise Separable Convolution, Data augmentation Share Link | Plain Format | Corresponding Author (Budi Dwi Satoto)

13	Artificial Intelligence and Data Science	ABS-61
The Development of a Matchmaking System Through The Use of Reinforcement Learning For Pet Match (PATCH) Radisa Hussien Rachmadi and Maria Seraphina Astriani Computer Science Department, School of Computing and Creative Arts, Bina Nusantara University, Jakarta, Indonesia 11480 Abstract Pets are sometimes considered as an extended family- they deserve all the love and attention just as they show to their owners. During the Covid-19 pandemic, there was a surge in pet adoptions as research shows that pets can help with depression and loneliness. Now that the pandemic has slowly faded, and people are coming out of their homes more often, pets are often left neglected which is why it is needed to create a solution to cater to connecting pets for either breeding purposes or playdate purposes. The author and his team decided to develop a mobile application that will serve as a platform to connect pet owners through a matchmaking style application integrated with machine learning. Developed using Neural Networks and Proximal Policy Optimization (PPO) to predict user^s best possible matches based on the user^s swipes. Keywords: reinforcement learning, pet, matchmaking, machine learning Share Link | Plain Format | Corresponding Author (Maria Seraphina Astriani)

14	Artificial Intelligence and Data Science	ABS-63
Implementation of Forecasting Ginger Harvest using Seasonal Autoregressive Integrated Moving Average Method Achmad Jauhari, Devie Rosa Anamisa and Fifin Ayu Mufarroha University of Trunojoyo Madura Abstract Herbal plant farming is one of the holders of an essential role in the economy of Madura. Therefore, the Madurese government is very concerned about ginger farmers developing ginger production to meet market demand so that the Madurese economy increases. In addition, 2019 data from the Central Statistics Agency show that the herbal farming sector in Madura has been achieved by ginger farming with the most significant number of commodities compared to other herbal plants. However, in recent years, ginger yields have not been able to meet the very high market demand. Therefore, to meet consumer demand for the availability of ginger, this study uses forecasting analysis. The method used in this study is the Seasonal-Autoregressive Integrated Moving Average (SARIMA) hybrid method. This method is a pretty good method for modeling forecasting. The data used in this study are data on ginger production and harvested area from January 2015 to December 2019. And the results of this study are in the form of yield forecasting data for the following year. The test results with a data range of five produce small MAPE and RMSE, namely 43.94% and 14579.338. This shows that the SARIMA method has been able to predict future crop yields and can be used as a reference for the government in determining crop yields according to market demand. Keywords: Forecasting, Ginger, SARIMA, Madura Share Link | Plain Format | Corresponding Author (Achmad Jauhari)

15	Artificial Intelligence and Data Science	ABS-64
Utilizing Single Exponential Smoothing for Early Detection and Forecasting of Stunting Cases in Madura Fifin Ayu Mufarroha, Devie Rosa Anamisa, Achmad Jauhari, and Triyas Septiyanto University of Trunojoyo Madura Abstract Stunting is still a serious public health concern in Indonesia. Stunting can have a negative impact on children^s health and development, as well as their productivity and learning abilities as adults. Efforts to eliminate stunting in Indonesia have been made in recent years, however there are still many cases that were not identified early and were not appropriately treated. Predicting the health of stunted individuals is thus one approach to this problem. Forecasting can benefit from the Single Exponential Smoothing technique. This method may be useful for diagnosing cases of stunting early and providing appropriate preventative actions. The purpose of this research is to create a prediction model for the number of stunted patients using the Single Exponential Smoothing method. This study relied on nutritional status data for children from 2018 to 2021. The Single Exponential Smoothing technique is used to anticipate future data by taking patterns in past data into consideration. The alpha value chosen was 0.5 by repeating the method and 0.1 as the second alpha value. This has caused the error value to drop by 10%. The outcomes of this study are expected to help connected parties design programs to address nutritional concerns more effectively and efficiently, enhance the quality of local community health, and aid in future planning and decision making in efforts to eliminate stunting. Keywords: Forecasting, Stunting, Single Exponential Smooting, Madura Share Link | Plain Format | Corresponding Author (Fifin Ayu Mufarroha)

16	Artificial Intelligence and Data Science	ABS-65
Performance Analysis of Naive Bayes and Fuzzy K-Nearest Neighbor Methods for Malnutrition Status Classification Systems Devie Rosa Anamisa, Fifin Ayu Mufarroha, Achmad Jauhari, Muhammad Yusuf, Normalita Eka Ariyanti, and Muhammad Hanif Santoso University of Trunojoyo Madura Abstract Malnutrition Status is a nutritional measurement of toddlers^ nutritional needs as indicated by age, weight, upper arm circumference, height, and health status resulting from a balance between daily nutritional needs and intake. Toddlers require adequate nutritional intake in quantity and quality because young children usually have high physical activity levels. Apart from that, efforts to reduce malnutrition status are a top priority in the Health Development program in Indonesia. Therefore, the Government needs a system that can help identify the nutritional status of toddlers early for prevention and treatment based on the classification of their nutritional status, thereby making it easier to collect data on toddlers who experience stunted nutritional status to provide education on increasing stunting nutritional levels. In this study, we classify nutritional status in toddlers by comparing the Naive Bayes (NB) and Fuzzy K-Nearest Neighbor (FKNN) methods. The performance of the two methods was compared to find out which method performed better in classifying malnutrition status. Based on the research results, comparing the performance between the FKNN and NB methods with testing using accuracy as the main benchmark for malnutrition status classification performance. The results showed that the FKNN method was superior in accuracy with a quite large margin of 7.5%. The conclusion is that in classifying toddlers^ nutritional status, the FKNN method outperforms NB. Keywords: Share Link | Plain Format | Corresponding Author (Devie Rosa Anamisa)

17	Artificial Intelligence and Data Science	ABS-69
Transformation of Indonesian National Television Media: Implementation of Artificial Intelligence Presenters in News Delivery and Program Hosting Ari Cahyo Nugroho, Ahmad Budi Setiawan, Amri Dunan, Bambang Mudjiyanto National Research and Innovation Agency Abstract In the rapidly evolving digital era, television media has undergone a fundamental transformation in response to contemporary demands. This transformation is reflected in the application of AI Presenters, which combine traditional television elements with AI technology to create interactive experiences. The concept of mediamorphosis emphasizes the changes in the form and content of media as responses to evolving technology and culture. The utilization of AI Presenters brings about changes in content format and presentation, delivering a more personalized and responsive experience. New Media theory highlights the active role of users within the media ecosystem. AI Presenters introduce a new dimension of interaction, where the audience can engage with AI technology, fostering deeper engagement. Thus, this research focuses on how the implementation of AI Presenters mirrors the fundamental transformation in Indonesian national television media through the lens of these theories. Employing qualitative methods and theoretical analysis, this study investigates changes in content production, news presentation, and interactions between humans and AI technology. By observing these changes within the context of Media Convergence theory, mediamorphosis, and New Media, this research strives to comprehend the dynamics of change in Indonesian national television media. The outcomes of this research provide profound insights into how media transformation through AI Presenter implementation influences television^s image, human-AI interaction, and broadcasting industry dynamics. The cultural, social, and economic implications of these changes are also examined within the framework of these theories. This study holds the potential to contribute significantly to the understanding of media changes in an increasingly complex and dynamic digital era. Keywords: Television Media Transformation- Artificial Intelligence Presenters- News- Program Hosting Share Link | Plain Format | Corresponding Author (Ahmad Budi Setiawan)

18	Artificial Intelligence and Data Science	ABS-70
Survey on Brain Tumor Detection Using Manhattan Distance-Based Techniques in Content-Based Image Retrieval Suhendro Y. Irianto, Sri Karnila,Dona Yuliawati, Muhammad Galih Institute Informatics and Business Darmajaya Abstract This research paper delves into the domain of Content-Based Image Retrieval (CBIR) with a specific focus on predicting brain cancer, particularly brain tumors. Brain cancer is a condition that triggers abnormal activity in the vital organ, the human brain, and can become life-threatening if it spreads to other parts of the body. While cancer can affect individuals across different age groups, it disproportionately impacts those in their productive years. Recent advancements in medical science have brought new hope to patients through improved diagnostic techniques and medications. However, diagnosing brain cancer has presented challenges, including time consumption, inconsistency, inaccuracy, and high costs. This paper aims to address these challenges by harnessing the power of artificial intelligence, particularly in the field of computer vision, and specifically utilizing CBIR methods. The study utilizes a dataset comprising more than 21,000 brain CT-scan images collected from Malahayati Hospital in Lampung, Indonesia. The effectiveness of CBIR is assessed using the Manhattan distance metric. The research findings reveal a segmentation-based accuracy of 80.39%, with a corresponding error rate of 2.66%. The primary objective of this study is to explore an innovative approach driven by artificial intelligence to enhance the precision and efficiency of brain cancer diagnosis, ultimately leading to improved patient outcomes. Keywords: Brain tumor, CBIR, Manhattan distance metrics Share Link | Plain Format | Corresponding Author (Suhendro Irianto)

19	Artificial Intelligence and Data Science	ABS-85
A K-Medoids Clustering Approach to Controlling Assistance Fund Allocation in Madura Achmad Jauhari, Ika Oktavia Suzanti, Arifatul Maghfiroh, Amelia Nur Septiyasari, Devie Rosa Anamisa, and Fifin Ayu Mufarroha University of Trunojoyo Madura Abstract Stunting is a condition in which the body does not develop optimally in children as a result of chronic starvation. Stunting is a serious health issue in Madura, with a relatively high frequency. As a result, the local government gives support finances to families experiencing these difficulties. The goal of delivering humanitarian finances is to enhance children^s health and avoid future stunting in youngsters. Aside from that, the stunting assistance financing program in Madura is projected to help overcome the problem of stunting in children while also improving the community^s health and welfare. However, aid finances must be properly classified and administered in order to deliver the greatest benefit to families in need. As a result, the K-Medoids Clustering approach was used to categorize recipients of stunting aid finances in Madura. To address stunting in Bangkalan Regency, data on 14 qualifying criteria for obtaining relief funding was utilized. K-Medoids clustering is used to classify patients based on their stunting status. This simple and convergent method divides data points into clusters, allowing for efficient allocation of funds. This approach helps identify priority groups for interventions to reduce malnutrition rates and helps identify clusters and locations for providing assistance funds. The K-Medoids Clustering approach tries to divide the population into two groups: the cluster not receiving assistance (C1) and the cluster receiving assistance (C2). As a result, 3 sub-districts were declared unfit to receive assistance and 9 sub-districts were recipients of assistance. Keywords: Stunting, Clustering, K-Medoids, Assistance Fund Share Link | Plain Format | Corresponding Author (Achmad Jauhari)

20	Chemical Engineering	ABS-2
Contoh Submission Anggota 1, Anggota 2 Universitas Wahid Hasyim Abstract isi Abstrak Keywords: kata kunci Share Link | Plain Format | Corresponding Author (Safaah Nurfaizin)

21	Chemical Engineering	ABS-89
Ultrasound-Assisted Extraction of Sappan Wood-Kinetic Modeling Laeli Kurniasari, Mohammad Djaeni, Andri Cahyo Kumoro Universitas Wahid Hasyim, Universitas Diponegoro Abstract Secang or sappan wood (Caesalpinia sappan L) contains many phytochemical compounds and has been used in foods, beverages and also in medicinal industries. Secang contains Brazilin, the main phytochemical compound and also contains xanthone, sappanchalcone and coumarin. Ultrasound-Assisted Extraction (UAE) is a recent extraction method that is considered suitable for taking phytochemical compounds and kinetic of extraction process is one important point to study. This research investigate kinetic models of sappan wood UAE process. There are four kinetic model observed, pseudo first order, second order, peleg model and power model. Extraction was conducted from 4-60 min in 500C using ethanol 60% as solvent. Result shows that UAE reduce the extraction time and increase extraction yield. All four models has high conformity with experimental data (R2>0.95). Moreover, second order and peleg model are better fit with R2 0.9798, while pseudo first order has lowest R2 0.9539 Keywords: sappan wood, UAE, kinetic model, peleg model, second order Share Link | Plain Format | Corresponding Author (Laeli Kurniasari)

22	Chemical Engineering	ABS-91
Potential Compound Content with Aphrodisiac Effects of Mangrove Plants for Herbal Therapy Rita Dwi Ratnani, Ahmad Muhyi, Agus Ismanto, Forita Dyah Arianti Department of Chemical Engineering, Wahid Hasyim University, Semarang, Indonesia Department of Medical education, Wahid Hasyim University, Semarang, Indonesia Applied Zoology Research Center of National Research and Innovation Agency (BRIN), Cibinong, Bogor 16911, Indonesia Research Center for Sustainable Production System and Life Cycle Assessment, National Research and Innovation Agency (BRIN), Jakarta 10340, Indonesia Abstract Abstract. Erectile dysfunction is something that men are very afraid of. One of the causes of erectile dysfunction is suffering from diabetes mellitus. The search for plant-based herbal medicines has become a priority in this modern era. This is sought after because it is identified as having the least side effects compared to chemical drugs. Aphrodisiac or libido booster due to erectile dysfunction can be obtained from mangrove plants. This study aims to explore more in-depth information from previous research on the content of active aphrodisiac compounds in the mangrove species Avicenna marina, Avicennia officinalis, Acanthus ilicifolius. The results of the study show that this species has great potential to provide libido-generating bioactive compounds. This is because it contains alkaloids, flavonoids and saponins. These compounds are found in almost all parts of the plant, including the leaves, fruit, skin, stems and roots. Further research to examine in more depth the effects of aphrodisiacs as well as optimal formulations and superior processing methods is needed. This is in the hope of finding nature-based herbs. Keywords: Aphrodisiac, Avicenna marina, Avicennia officinalis, Acanthus ilicifolius, flavonoids. Share Link | Plain Format | Corresponding Author (Rita Dwi Ratnani)

23	Chemical Engineering	ABS-93
Analysis of the stockpiling process at Drum Yard PT. ABC Based on Sampling Aspects Ika Gita Lestari 1, a) Oksil Venriza 2,b) Program Studi Logistik Minyak dan Gas, Politkenik Energi dan Mineral Akamigas, Jl. Gajah Mada 38, Cepu Blora, Jawa Tengah, 58312, Indonesia Abstract PT. ABC, in maintaining the stock of raw materials, especially additives, stockpiles additives in the drum yard. Drum stockpiles in drum yards are very abundant and make annual sampling difficult. This additive annual sampling does not pay attention to number production batch so that additives of the same type in one shipment are considered to have the same quality. To find out the cause of problems, this method uses a fishbone diagram. The large number of additives with slow moving and non moving movements makes the annual sampling process more extensive. When stock data does not include production batch number, annual sampling will not pay attention to this. The annual sampling process will be carried out randomly and three samples will be taken per locater without paying attention to differences in available batch numbers. This random sampling check means that dead stock additives will be completely eliminated. When sampling can be carried out according to the standards, it can minimize the number of offspec additives and these additives can be reused when the quality control results are onspec. Keywords: Additive, Stockpiling, Drum yard, FSN (Fast, Slow, Non moving), Sampling. Share Link | Plain Format | Corresponding Author (Ika Gita Lestari)

24	Chemical Engineering	ABS-94
A Brief Review On Aging And Its Combination With Bibliometric Analysis On Cellular Senescence Elimination As Part Of Anti Aging Strategies Indah Hartati1a) Vita Paramita2b) Farikha Maharani1c) Lusiana3d) Laeli Kurniasari1e) 1Department of Chemical Engineering, Faculty of Enginering, Wahid Hasyim University, Indonesia 2 Department of Industrial Chemical Engineering Technology Vocational School, Diponegoro University, , Indonesia 3Department of Physical Education, Health and Recreation, Faculty of Teaching and Education, Wahid Hasyim University , Indonesia Abstract It is estimated that in 2050, the growth of the aging population in the world is growing faster than the one of the younger age people. Consequently, the high growth rate of elderly population has become a major challenge due to its strong correlation on social and economics condition. Therefore in this work, the definition of aging, hallmarks of aging, aging theory, as well as anti aging strategies are reviewed. The review was combined with bibliometric analysis on publication having ^anti aging strategies^ as well as ^senolytics and cellular senescence^ keywords. Aging, a process of cellular deterioration, is associated with oxidative stress and the accumulation of toxic peroxidation products. The developments of the aging hallmarks proposed are summarized. Regarding the aging theory exist there are Chinese medicine based theory as well as modern theory. Among the aging theory proposed, free radical theory plays a significant role in the aging process. Bibliometric analysis of anti aging strategies informs that cellular senescence is emphasized while dasatinib, fisetin, quercetin, and navitoclax as the four senolytics agents appear on the network visualization of keyword occurrence in publication with ^senolytics and cellular senescence^ keywords. Among the four senolytics agents, two natural based compounds of senolytics agent i.e quercetin and fisetin. Due to the varied sources, both of fisetin and quarcetin would play a great role in the development of senolytics agent obtained from natural resources. The development of isolation methods will also emphasize further research, especially in the finding green chemicals and green separation and isolation routes. Keywords: aging, cellular senescence, anti aging, review, bibliometric Share Link | Plain Format | Corresponding Author (Indah Hartati)

25	Information Industry and Management	ABS-12
Improving the Integration of Customer and Company Requirements through QFD for Solution Design and Supplier Selection Praneeth Galhena, Kongkiti Phusavat Department of Industrial Engineering, Kasetsart University, Thailand. Abstract Continuous changing technology and customer requirements are challenging companies to be competitive in order to be ahead of business. QFD was identified as a tool that support companies identify customer requirements and convert them to design or product requirements, yet there are drawbacks in the model which makes it sophisticated and undesirable for certain business environments. The objective of the current research is to revise the traditional QFD by addressing its drawbacks and enabling stakeholder requirement identification, prioritization, technical requirement identification and supplier selection through a reliable decision making process, where the end goal is to encourage workplace learning by allowing the company employees to indulge in the revised QFD process and satisfy the stakeholder requirements by providing the best product, service or solution. The roadmap to achieving this target is proposed through an extended methodology that integrates the revised QFD, AHP and focus group discussion. AHP was identified as an ideal technique that can support requirement prioritization decision making by minimizing the subjectivity of individual decision making. This research has utilized an access control case illustration to validate the proposed framework stepwise. The findings were able to highlight the differences between the traditional and revised QFD. Furthermore, future works have been encouraged to improve the methodology, benefits and further development of modernized QFD. In conclusion, the main implication of this research is, utilization of the revised QFD framework for stakeholder requirement identification, solution design and supplier selection can enhance workplace learning, leading to a productive organization. Keywords: QFD- AHP- Stakeholder Requirements- Workplace Learning- Supplier Selection Share Link | Plain Format | Corresponding Author (Praneeth Peumal Galhena)

26	Information Industry and Management	ABS-18
Critical Product Features Identification In Online Reviews Using Sentiment Analysis and Kano Model For Product Quality Improvement Murahartawaty Arief (a*) Noor Azah Samsudin (b) a,b) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Persiaran Tun Dr. Ismail, Parit Raja, Batu Pahat, Johor, Malaysia. Abstract Keywords: Critical Product Features- Product Quality Improvement, Online Reviews, Sentiment Analysis, Kano Model Share Link | Plain Format | Corresponding Author (Murahartawaty Arief)

27	Information Industry and Management	ABS-20
Utility Analysis Of The Use Of Hydrant Dispensers In The Aviation Fuel Distribution Process At The Pertamina SHIPS Unit Zizi Aida, Oksil Venriza Akamigas Energy and Minerals Polytechnic Cepu Abstract Air transportation is a transportation that is in great demand by the public, so there is an increase in the number of arrivals and departures of both domestic and international flights, especially at Soekarno-Hatta International Airport. This is a benchmark for the Pertamina SHIPS Unit in preparing Avtur filling facilities and facilities at each terminal, one of which is a hydrant dispenser. This study was conducted to know how each type of flight affects the use of hydrant dispensers and the usefulness of the hydrant dispenser. The data used for this study are historical Avtur thrupu data in the period April 2022-March 2023 and Daily Objective Throughput data for the period April 2022-March 2023. With these data the author can ,conduct an assessment using the ^Minitab^ software which involves 2 methods that are thought to examine the effect of aviation on hydrant dispensers for s1 year, namely regression and correlation. The results of the average utility of hydrant dispenser usage show that the use of hydrant dispensers in terminal 1 is 80%, terminal 2 is 93.33%, and terminal 3 has 100%. With this it can be concluded that there is excessive utilization that affects the distribution of Avtur. Keywords: Hydrant dispenser, Regression, Correlation, Utility Share Link | Plain Format | Corresponding Author (Zizi Aida)

28	Information Industry and Management	ABS-22
Management of K-Means Clustering Algorithms To Manage Traffic Violation Area Darul Prayogo Politeknik Ilmu Pelayaran Semarang Abstract This research to study Traffic accidents make the death rate very high. As a step to minimize the rate of traffic accidents, a study of clustering is needed- clustering itself is the grouping of data into several clusters. The foundation for building a theoretical framework is the content of the clustering algorithm, group traffic, and violation area. Cluster results are influenced based on the initial central value of the given cluster. The K-means Clustering algorithm research results from 35 regions in Central Java resulted in 5 areas included areas that do not need to be given socialization. In contrast, 13 sites are included in the areas that need to be considered, and 17 other regions included in the area need to be socialized to provide knowledge to the public about the importance of orderly traffic. The implementation of K-Means Clustering can be a reasonably suitable medium to show areas with a level of public awareness of regulations in orderly traffic that is still minimal. It has a considerable risk of accidents. Based on the author^s experience when performing the final task, there are several suggestions for the development of the following system, namely: Add in-app features to show more accurate data- 2. The data used is still limited to monthly periods- in the future, it can still be developed for more specific data such as daily, weekly, monthly, and yearly- 3. The parameters used in this study still correspond to the event description data- it is recommended to consider data with more specific parameters. The K-Means Clustering method can group data based on similar data breach characteristics within a region- 2. The implementation of K-Means Clustering can be a reasonably suitable medium to show areas with a level of public awareness of regulations in orderly traffic that is still minimal. It has a considerable risk of accidents. Based on the author^s experience when performing the final task, there are several suggestions for the development of the following system. This research is expected to be used to manage the level of traffic violations in an area. This management is carried out to reduce the risk of traffic accidents and to give more attention to certain regions of traffic management Keywords: Management, Accident, Traffic, K-means, Clustering Share Link | Plain Format | Corresponding Author (Darul Prayogo)

29	Information Industry and Management	ABS-25
Mapping the Blockchain^s Decentralized Finance Characteristics Andry Alamsyah(a), Gede Natha Wijaya Kusuma(b), Dian Puteri Ramadhani(c) a) School of Economics and Business Telkom University Bandung, Indonesia andrya[at]telkomuniversity.ac.id b) School of Economics and Business Telkom University Bandung, Indonesia nathawijaya[at]student.telkomuniversity.ac.id c) School of Economics and Business Telkom University Bandung, Indonesia dianpramadhani[at]telkomuniversity.ac.id Abstract The integration of blockchain technology, particularly through Decentralized Finance (DeFi), has reshaped the finance industry. By January 2023, the DeFi crypto market reached a -46.21 billion market capitalization, catering to 6,686,500 user bases. DeFi outperforms Traditional Finance (TradFi) with its lower fees, inclusivity, faster processing, accessibility, transparency, programmability, security, and absence of intermediaries. For the end user, DeFi advantage lies in the self-custodial of their own asset, the ability of peer-to-peer transactions, and leveraging programmability for complete control and creativity. However, despite the rapid growth of DeFi, there is a notable scarcity of comprehensive research on DeFi mapping, specifically in terms of its benefits, risks, financial services, and technology. This study aims to understand DeFi mapping characteristics and bridge the research gap in DeFi knowledge and mapping. Keywords: Digital Economics, Decentralized Finance, Financial Institution, Traditional Finance, Mapping Characteristics, Blockchain Share Link | Plain Format | Corresponding Author (Gede Natha Wijaya Kusuma)

30	Information Industry and Management	ABS-72
Effectiveness of Energy Conservation Program in the Industry Sector in Improving the Quality of Human Resources Aep Saefullah (a*), Muhammad Arief Noor (b), Fuad Gagarin Siregar (c) a) STIE Ganesha Jakarta Jalan Raya Legoso No 31, Pisangan, Ciputat Timur, Tangerang Selatan 15412, Indonesia *aep[at]stieganesha.ac.id b-c) STIE Ganesha Jakarta Jalan Raya Legoso No 31, Pisangan, Ciputat Timur, Tangerang Selatan 15412, Indonesia Abstract Energy conservation is critical in the industrial sector to maintaining the balance among the availability of energy and environmental protection. The effectiveness of energy-saving initiatives in the industrial sector has yet to be fully assessed, particularly with regard to the level of human resource quality. The study^s goal is to analyze the efficacy of vitality conservation programs in the mechanical division in enhancing the human resources quality. The investigate was conducted for 4 months from April to July 2023 to 100 individuals working in the industrial sector in Indonesia who implemented the energy conservation program. This investigation utilized an expressive qualitative approach. Information was collected through a literature survey, observation and discussions. The results show that an effective energy conservation program can make strides in the quality of human assets in the industrial segment. The program influences employees^ awareness and skills in using energy efficiently, thus benefiting both the environment and the company^s productivity. Company management support is a major factor within the victory of the vitality preservation program. The study was limited to 100 employees and the conclusions cannot be generalized. The implication of the ponder is that the execution of vitality preservation programs in the industrial sector not only provides benefits in energy savings and reduces environmental impacts, but can also improve the quality of human resources. Industrial companies should continue to encourage and support effective energy conservation programs to create employees who are environmentally minded and contribute positively to the sustainability of the industry Keywords: Energy Conservation- Industry- Human Resources Share Link | Plain Format | Corresponding Author (Aep Saefullah)

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