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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 82 Documents
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Trend of Increasing Health Budget in Indonesia's State Budget During COVID-19 Pandemic Victor Asido Elyakim P; Bagas Adi Nata; M.Alfathan Haris; Mirza Afif Pradivta; Muhammad Rizky Ramadhan
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5958

Abstract

This research aims to analyze trends and allocation of Indonesia's health budget during 2010-2022 period based on state budget realization and projection data. The analysis was conducted on three main components of the health budget, namely central government expenditure through Ministries/Institutions, transfers to regions and village funds, and health financing. Based on the analysis results, the health budget experienced significant increase from IDR 29.89 trillion in 2010 to IDR 255.39 trillion in 2022, with the ratio to total state expenditure increasing from 2.9% to 9.4%. The Ministry of Health became the largest allocation recipient in the central government expenditure category, while the regional transfer component showed consistent growth through Special Allocation Fund (DAK) for Health and Operational Health Assistance (BOK). Drastic increases in health budget occurred during 2020-2022 period, caused by COVID-19 pandemic response through various special programs such as health budget reserves, earmarked General Allocation Fund (DAU), and village funds for COVID-19 response. The results of this research provide a comprehensive overview of the Indonesian government's commitment in the health sector and can serve as a basis for evaluating the effectiveness of health budget allocation in the future.
Detection of Mental Health Tendencies Using Naïve Bayes Based on Social Media Activity Jeremi Sibarani; Ratih Manalu; Dongan Parulian Hutasoit; Wilman Arif Telaumbanua; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5959

Abstract

The development of social media has had a significant impact on individual mental health. This study aims to detect mental health trends based on user activity on social media using the Naïve Bayes algorithm. The data used is sourced from the Kaggle platform and collected through web scraping techniques with keywords related to mental health and social media activity. The analysis process includes data preprocessing, classification using Naïve Bayes, and evaluation of model performance by dividing training and test data at a ratio of 60:40, 70:30, and 80:20. The results showed that the Naïve Bayes method was able to classify mental health tendencies with the highest accuracy of 75.17% at a ratio of 60:40. Precision and recall were higher for the “Troubled” category compared to the “Good” category, showing the effectiveness of the model in detecting indications of mental disorders. However, there is still a prediction imbalance that affects the overall accuracy. These findings suggest that the Naïve Bayes algorithm can be a tool in social media-based mental health early detection, which can be used by health practitioners and researchers to design more appropriate intervention strategies.
Application of the Gaussian Mixture Models (GMM) Algorithm to Identify Error Patterns in Compilation Ayu Utari Nasution; Eko Prima Ambarita; Nurhidayanti, Nurhidayanti; Heba Elsisy Fadlia; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5960

Abstract

In software development, the compilation process is a critical step that transforms source code into executable programs. These compilation errors can vary from simple syntax errors to more complex semantic errors, which often require a lot of time and effort to identify and fix. As a result, identifying recurring error patterns in the compilation process is important to improve software development efficiency. This study aims to explore the application of GMM in identifying error patterns in the compilation process. The results of this study indicate that the value of 0.58 on the Silhouette Score indicates that the clustering performed by GMM is quite good at identifying error patterns in compilation. The clusters are divided into 3, namely, Cluster 0 may indicate types of errors that occur more quickly (possibly related to syntax errors), with fewer lines of code and lower error frequency. Cluster 1 may represent more complex and less frequent errors (e.g., linker or runtime errors), with more lines of code. Cluster 2 may contain errors with different patterns, such as higher compilation duration or more frequent error frequency.
Annual Rainfall Prediction in Indonesia Using A Hybrid Artificial Neural Network and Fuzzy Algorithm Model Siti Asiah; Wanda Riana; Dika Chryston Purba; M Ilham Azharsum; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5964

Abstract

Rainfall is an essential meteorological parameter that affects various sectors of life. Accurately predicting rainfall has become crucial, and artificial intelligence-based models are increasingly popular in this field. Artificial Neural Networks (ANNs) have been widely used due to their ability to identify non-linear patterns in complex data. However, ANN-based predictions have limitations in optimally handling uncertainty or data variability. To address this issue, this study proposes a hybrid model that combines ANNs with fuzzy algorithms. Fuzzy algorithms are capable of managing uncertainty and providing flexible decision-making. This research proposes a hybrid model that integrates Artificial Neural Networks (ANNs) and fuzzy algorithms to predict annual rainfall based on meteorological data from 2019 to 2024. ANNs are used to detect non-linear patterns in temperature, humidity, and atmospheric pressure data, while fuzzy algorithms handle the uncertainty in input data. The model was tested using data from local meteorological stations and evaluated using MAE, RMSE, and the coefficient of determination (R²) metrics. The evaluation results show that the hybrid model achieved the best performance, with an MAE of 3.17 mm, RMSE of 3.4 mm, and R² of 0.98. These findings indicate that the combination of ANN and fuzzy logic significantly improves the accuracy of rainfall prediction compared to individual methods. This model has the potential to be applied in early warning systems and more precise climate management.
Effect of Population Density on Sex Ratio in North Sumatera Province Rahma Dhea Safitri; Rizky Nurhasanah; Regita Audyna Siregar; Victor Asido Elyakim P; Yuegilion Pranayama Purba
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.6012

Abstract

This study evaluates the effect of population density on the sex ratio in North Sumatra Province based on 2025 data. This study collected data on population size, land area, and sex ratio from each district and city in the province. Population density was calculated as the result of dividing the population by the land area, then its relationship with the distribution of male and female ratios in each region was analyzed. Data were quantitatively analyzed using statistical correlation approaches, such as Pearson correlation and simple linear regression, to identify potential relationships between density levels and sex ratio differences. The research results indicate that the relationship between population density and the sex ratio in North Sumatra Province is very weak and not statistically significant. The Pearson correlation coefficient value of -0.114 and the coefficient of determination (R²) of 0.013 show that only about 1.3% of the variation in the sex ratio can be explained by population density. These findings indicate that other factors, such as age composition, selective migration, economic structure, as well as socio-cultural factors and local policies, are more dominant in shaping the sex ratio between regions. Nevertheless, these findings provide initial insights into the social dynamics of the population as a basis for more comprehensive regional planning and demography-based development policies.
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.
Comparative Evaluation of MLR and SVM Algorithms for DKI Jakarta Air Quality Prediction Arfany Dhimas Muftareza
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5369

Abstract

This research explores the application of Machine Learning using Multiple Linear Regression (MLR) and Support Vector Machine (SVM) algorithms to predict air quality categories in Jakarta based on key pollutant parameters, such as PM10, PM2.5, NO2, CO, SO2, and O3. The dataset used comes from ISPU data measured from five Air quality monitoring stations in DKI Jakarta Province in 2021. The research process includes data collection, data cleaning, model implementation using the scikit-learn library, and model performance evaluation using Accuracy, R-Squared, RMSE, and MAE metrics. The results of model performance evaluation show that SVM performs better than MLR, as evidenced by higher accuracy value (91.78% vs. 90.41%), higher R-squared value (69.63% vs. 64.56%), lower RMSE value (0.2867 vs. 0.3097), and lower MAE value (0.0822 vs. 0.0959), indicating that the error in SVM model is smaller than MLR. This study proves the effectiveness of machine learning-based models in providing accurate air quality category predictions, although there are still challenges in predicting the “Good” category that require further development, such as balancing data and advanced feature engineering to improve the prediction accuracy of all categories.
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.

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