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Anjar Wanto
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JOMLAI: Journal of Machine Learning and Artificial Intelligence
Published by Yayasan Literasi Sains Indonesia
ISSN : 28289102     EISSN : 28289099     DOI : 10.55123/jomlai
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Focus and Scope JOMLAI: Journal of Machine Learning and Artificial Intelligence is a scientific journal related to machine learning and artificial intelligence that contains scientific writings on pure research and applied research in the field of machine learning and artificial intelligence as well as an overview of the development of theories, methods, and related applied sciences. Topics cover the following areas (but are not limited to): Software engineering Hardware Engineering Information Security System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Computer Engineering Image processing Genetic Algorithm Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Other relevant study topics Noted: Articles have primary citations and have never been published online or printed before
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
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Search results for , issue "Vol. 4 No. 3 (2025): September 2025" : 6 Documents clear
Classification of Village Development Index in North Sumatra Using the Support Vector Machine (SVM) Method Yayang Arum Kemangi; Daniel Desmanto Sihombing; Permaisuri Siregar; Sella Ujani; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6117

Abstract

The classification of the Village Development Index (IDM) status is a fundamental component in formulating targeted and effective village development policies. However, the conventional classification process is often slow and inefficient, thereby reducing the data's relevance for dynamic decision-making. This research aims to design and evaluate an automatic classification model for the IDM status in 5,417 villages in North Sumatra Province using the Support Vector Machine (SVM) method. By utilizing secondary data from 2024, this model uses three main sub-indices—the Social Resilience Index (IKS), the Economic Resilience Index (IKE), and the Environmental Resilience Index (IKL)—as predictor variables to map villages into five status categories. The implementation of the SVM model with a Radial Basis Function (RBF) kernel was chosen to handle the complex non-linear relationships between variables. The evaluation results on the test data show superior performance, with an overall accuracy rate reaching 96.77%. The model's performance proved to be very strong, particularly in identifying the 'Developing' class with a perfect recall (1.00) and the 'Independent' class with perfect precision (1.00). Although minor challenges were found in distinguishing between adjacent classes such as 'Disadvantaged' and 'Developing', the high F1-score across all classes confirms a good balance between precision and recall. This study concludes that the SVM method is a highly reliable and valid approach for automating IDM classification, and it offers significant implications as a fast and accurate evidence-based decision support tool for local government
Diagnosis of Gastric Disease Based on Artificial Neural Network with Hebb Rule Algorithm Victor Asido Elyakim P; Alyah Octafia; Yemima Pepayosa Sembiring; Dony Jordan Pangomoan Sirait; Angga Priandi
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6543

Abstract

Gastric disorders are among the most common health problems faced by society, often caused by irregular eating habits, unhealthy lifestyles, and high stress levels. The symptoms are diverse, ranging from abdominal pain and nausea to weight loss, making accurate and timely diagnosis essential to prevent more serious complications. This study aims to develop a diagnostic system for gastric diseases using Artificial Neural Networks (ANN) with the Hebb Rule algorithm, a learning principle that strengthens the connections between neurons when they are activated simultaneously. The research utilized binary-encoded data consisting of ten types of gastric diseases and twenty associated symptoms to establish patterns of correlation between symptoms and diagnoses. The results demonstrate that the system successfully recognized all test data with outcomes consistent with the expected targets, proving that the Hebb Rule is effective in mapping symptom-disease relationships even when applied to simple binary data. These findings highlight the practicality and efficiency of the Hebb Rule in building an intelligent diagnostic framework, while also showing its potential for further development with more complex datasets, such as symptom severity levels or laboratory test results. Ultimately, this research contributes to the advancement of smart medical systems that can support both healthcare professionals and the general public in performing early detection of gastric diseases quickly, accurately, and effectively.
Analysis of Airline Passenger Satisfaction Using the Rough Set Method Alisa Putri Amanda Nasution; Auralia Izmi; Aprillya Zahra Iswandy Lubis; Haya Atiqah Tampubolon; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.5946

Abstract

This study analyzes airline passenger satisfaction using the Rough Set method, an effective approach in handling complex data without requiring additional information such as probability. The main factors influencing customer satisfaction are identified based on data collected through questionnaires and analyzed using the attribute reduction method. The results show that flight punctuality, cabin crew service quality, and flight class type have a significant influence on customer satisfaction. From the survey results, 72% of respondents stated that they were satisfied, 18% were quite satisfied, and 10% were dissatisfied, with dissatisfaction generally related to flight delays and lack of comfortable facilities. The application of the Rough Set method has been proven to be able to identify passenger satisfaction patterns more accurately, so that it can be used by airlines to improve their service strategies.
Optimized Vessel Scheduling Model Using Multilayer Perceptron Algorithm Henry Onyebuchukwu Ordu; Joseph Tochukwu Odemenem
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6031

Abstract

Efficient vessel scheduling is crucial to the performance and profitability of maritime terminals, yet conventional approaches often struggle to accommodate the dynamic, nonlinear interactions among vessel arrivals, cargo handling requirements, and berth availability. This study presents a Multilayer Perceptron (MLP)–based scheduling framework that models these complex relationships and delivers actionable berth assignments in real time. Leveraging an integrated dataset of historical arrival and departure timestamps, cargo throughput, and occupancy records, the MLP model was trained on 80% of the data and rigorously tested on the remaining 20% Performance was assessed using metrics such as vessel turnaround time, berth utilization rate, and scheduling accuracy. Experimental results reveal that our MLP-driven scheduler achieves a 15% reduction in average turnaround time and a 12% increase in berth utilization. Remarkably, the neural network maintains high levels of schedule adherence even under peak-demand scenarios, minimizing idle berth time and streamlining cargo flow. These findings underscore the adaptability of advanced machine learning techniques to the evolving demands of port operations.
Public Sentiment Analysis of the Agrarian Conflict between PT TPL and the Toba Simalungun Indigenous Community Using the SVM Method Dian Yusri Andira; Deswita Maharani Harahap; Vibiola Br Damanik; Indah Frian Sari; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6116

Abstract

The agrarian conflict between PT Toba Pulp Lestari and the Toba Simalungun indigenous community has generated diverse public opinions on social media. This study aims to analyze public sentiment regarding the conflict using the Support Vector Machine (SVM) method based on TikTok comment data. A total of 1,751 comments were collected via the TikTok API and processed through cleaning, normalization, stopword removal, and stemming. Sentiment labeling was performed automatically with a lexical-based approach, followed by feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM model was used to classify public sentiment into two main categories, namely positive and negative. The results of the testing showed that the SVM model was able to achieve an accuracy of 80%, with excellent performance in detecting negative sentiment. Additional analysis through wordcloud visualization shows the dominant words in each sentiment category, which reinforces the model's classification results. The findingsof this study provide an objective picture of public opinion patterns on social media, while also demonstrating the potential application of machine learning-based sentiment analysis methods to understand public perceptions of other social issues in the future.
Design and Development of a Web-Based Boarding House Information System Iqbal Aditya Ferryanto; Ruswanti, Diyah; Susilo, Dahlan
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 3 (2025): September 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i3.6486

Abstract

Web-based information systems serve an essential function in promoting efficient and efficient information management in the digital era.  The objective of this research is to create a web-based information system for boarding houses with 6 boarding house research objects. to address challenges often encountered in manual data management, such as data inaccuracies and difficulties in accessing information. The development process employs the Waterfall methodology, which includes phases including communication, planning, planning, execution, and upkeep. The coding language applied for in creating websites is JavaScript and uses a MySQL database. Where This system offers features tailored to the needs of boarding house seeker, house owners, and administrators. Testing using the System Usability Scale (SUS) involving 38 respondents resulted in a rating of 78.16, categorized as good usability (Grade B). These results indicate that the system provides a satisfactory user experience and supports increased efficiency in managing boarding house operations.

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