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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 96 Documents
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Comparison of Weighted Moving Average and Single Exponential Smoothing Forecasting on Manila Duck Eggs Production Gusman Simon
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 1 (2026): Maret 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i1.7846

Abstract

As a waterfowl, the Manila duck is a potential livestock option and a source of animal protein for the community. Its population growth is still considered low, and its egg production capacity only serves to support the food supply. Population growth and public awareness of the importance of nutrition drive the demand for animal protein consumption. The highest growth in duck egg consumption was observed between 2014 and 2018. In 2021 and 2022, the production and consumption of Manila duck eggs increased per capita every week. However, in 2024, the production rate of Manila duck eggs decreased. One way to anticipate the risk of declining Manila duck egg production is through predictive analysis. Based on the data patterns obtained from the Central Statistics Agency, the weighted moving average (WMA) forecasting method and single exponential smoothing (SES) were selected. The MAPE value of the WMA method for the last two periods 6.742%, last three periods 6.444%, and last four periods 6.814%. The MAPE value of the SES method is 6.466%. The lowest MAPE value is from the 3-period WMA method. To minimize the MAPE value, the Solver add-in application attached in MS Excel is used to determine the weight value of each period in the WMA method, as well as the smoothing coefficient value of the SES method.
A Diabetes Prediction Model Based on BMI, HbA1c, Age, and Blood Glucose Using KNN Avelina Garcia Wong; Devin Hernando; Marcelyn Wijaya; Thomas Herpin; Vinola Lorencia; Ade Maulana
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i2.5429

Abstract

Diabetes is a chronic disease with a globally increasing prevalence and poses a serious health risk if not detected early. This study aims to develop a simple yet accurate classification model to predict diabetes risk using the K-Nearest Neighbors (KNN) algorithm. The model utilizes four key clinical parameters: body mass index (BMI), blood glucose level, glycated hemoglobin (HbA1c), and age. The dataset used in this study was obtained from Kaggle and consists of 50,066 records. The data underwent preprocessing stages including normalization, class balancing, and feature selection before being split into training (70%), testing (20%), and validation (10%) sets. Experimental results demonstrate that the KNN model with k=4 achieves an accuracy of 83% on the validation set and 82% on the test set. Although the model shows stable and reasonably good performance, further improvement is needed to achieve better class balance in prediction. Future work may involve exploring more advanced machine learning techniques to enhance predictive capabilities and ensure fair classification across different risk groups.
Early Detection of Cardiovascular Disease Risk Using the K-Nearest Neighbors Algorithm Leo Fernandy; Leonardo Leonardo; Stanley Lim; Vincent Liawis; Ade Maulana
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i2.5430

Abstract

Health is a fundamental aspect in determining the quality of human life. Along with changes in lifestyle and environmental conditions, various new challenges have emerged in the healthcare sector. Technological advancements, particularly in artificial intelligence, have opened significant opportunities for the development of more accurate and efficient healthcare systems. One of the most rapidly growing applications of AI is machine learning for disease prediction. This study aims to develop a model for predicting the risk of cardiovascular disease using the K-Nearest Neighbors (KNN) algorithm. The “Cardiovascular Disease” dataset from Kaggle, consisting of 68,205 entries and 17 medical attributes, was used as the basis. The research stages included data preprocessing (cleaning, categorical transformation, and normalization), selection of key features, model training, and performance evaluation. The dataset was split into 80% training data and 20% testing data. The experiment showed that k = 41 achieved the highest accuracy of 73%. Evaluation using precision, recall, and f1-score indicated fairly good performance, particularly in identifying high-risk patients. This model has the potential to serve as a decision-support tool for early detection of cardiovascular disease, enabling more accurate and preventive medical actions..
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. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i2.6605

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.
Optimization of the Random Forest Algorithm Using GridSearchCV for Household Energy Consumption Classification Adinda Nabila; Azharda Afriaci; Haya Atika Syafi'ah
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i2.8571

Abstract

The high fluctuation in electricity usage and the complexity of historical data distribution in Internet of Things (IoT)-based smart home environments frequently trigger significant uncertainty in household energy efficiency management. Without an intelligent predictive system, energy consumption surges become exceedingly difficult to control accurately. Therefore, this study aims to develop a high-accuracy predictive classification model architecture to map energy consumption levels into three main categories (Low, Medium, High) by leveraging the Random Forest algorithm. The experimental process was conducted systematically, beginning with the evaluation of a baseline model, followed by an optimization phase integrating the GridSearchCV method based on 5-fold cross-validation for exhaustive hyperparameter tuning on the IoT Smarthome Energy Dataset. Performance was comprehensively evaluated using a confusion matrix and gap accuracy analysis to ensure model robustness. Experimental results proved that the hyperparameter optimization process successfully boosted the global accuracy rate significantly from an initial baseline of 98.92% to 99.46%, while effectively reducing the number of misclassifications. Furthermore, the feature importance analysis provided a transparent scientific interpretation, where the future_consumption_kWh feature was identified as having the most dominant influence at 61.75% on the model's decision structure. In conclusion, this optimized Random Forest architecture is proven to be highly robust and ready to be implemented as a real-scale automatic energy-saving instrument.
Heavy Equipment Management Information System at PT Gajah Unggul International Kevin Fernando Cahyadi; Triana Elizabeth
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i2.9071

Abstract

PT Gajah Unggul Internasional is a company engaged in loading-unloading services and heavy equipment rental that still manages operational data using Microsoft Excel and WhatsApp communication, resulting in several problems such as inaccurate equipment status, delays in maintenance based on Hour Meter (HM), difficulties in contract monitoring, and unstructured recording of heavy equipment expenses and revenues. This study aims to design and develop a web-based Heavy Equipment Operational Management and Monitoring Information System to support more effective and integrated operational data management. The system development method used is Unified Process (UP) with the stages of inception, elaboration, construction, and transition. The system was developed using the Laravel framework and MySQL database with features including heavy equipment management, contract management, mobilization and demobilization, service monitoring, HM recording, expense and revenue management, equipment scheduling, and heavy equipment performance analysis. The results of this study indicate that the system is able to assist the company in monitoring heavy equipment operations more effectively, accelerate data management processes, and support decision making based on available operational information.

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