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All Journal International Journal of Electrical and Computer Engineering Jurnal INKOM Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri MUST: Journal of Mathematics Education, Science and Technology Techno Nusa Mandiri : Journal of Computing and Information Technology Techne : Jurnal Ilmiah Elektroteknika Jurnal Pertanian Agros Jurnal Informatika Universitas Pamulang Indonesian Physical Review INTI Nusa Mandiri International Journal of Economics, Business and Accounting Research (IJEBAR) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) International Journal of Advances in Data and Information Systems Jurnal Pengabdian kepada Masyarakat Nusantara Insyst : Journal of Intelligent System and Computation Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Agus Subekti
Balai Pengkajian Teknologi Pertanian Kalimantan Barat Jalan Budi Utomo No. 45, Siantan Hulu, Kotak Pos 6150, Pontianak 78061, Kalimantan Barat, Indonesia
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A Comparative Methodological Study of Automated Machine Learning for Multiclass Stunting Prediction Using Anthropometric Data Joharini, Joharini; Subekti, Agus
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15886

Abstract

Stunting remains a major public health challenge among children under five years old and requires reliable early screening to support timely nutritional interventions, particularly in resource-limited healthcare settings. However, many existing stunting prediction studies rely on complex socio-economic variables and manually selected machine learning models, which limits reproducibility and practical deployment. This study proposes an automated machine learning (AutoML)–based framework for multiclass stunting prediction using routinely collected anthropometric data. The prediction task is formulated as a multiclass classification problem encompassing normal growth, stunted, severely stunted, and above-normal nutritional status. The proposed framework integrates standardized preprocessing, systematic model comparison, stratified 10-fold cross-validation, and controlled hyperparameter optimization, evaluated under SMOTE and non-SMOTE preprocessing scenarios. Experimental results demonstrate that reliable multiclass prediction can be achieved without socio-economic variables. Under SMOTE preprocessing, the optimized k-Nearest Neighbors model improves minority-class sensitivity, increasing accuracy from 0.9806 to 0.9820 with an MCC of 0.9688, while under non-SMOTE conditions, Random Forest achieves robust performance with an accuracy of 0.9985 and an MCC of 0.9975 without resampling. Confusion matrix, ROC, and learning curve analyses confirm strong discriminative capability and stable generalization for both models. Overall, the findings indicate that the proposed AutoML-based framework provides a practical, scalable, and reproducible solution for early multiclass stunting screening using anthropometric data alone.
VISUAL HISTORICAL DATA-BASED TRAFFIC MOVEMENT AND DENSITY PATTERN EXTRACTION FOR ADAPTIVE PATTERN DETECTION BASE ON VEHICLE TYPE Angellia, Filda; Merlina, Nita; Subekti, Agus; Handayanto, Rahmadya Trias
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1715

Abstract

Traffic congestion in urban areas has become a crucial issue, impacting time efficiency, energy consumption, and quality of life. One of the main causes of difficulties in traffic management is the lack of optimal predictive systems capable of detecting and adaptively responding to vehicle movement patterns. This study proposes a historical digital image-based approach to extract traffic movement patterns and density based on vehicle type and dimensions. The developed model utilizes historical traffic video footage from CCTV systems as a visual data source, which is then processed using the YOLOv5 algorithm to detect the number, size, and type of vehicles. After the detection process, vehicle information is converted into a sequential format that reflects vehicle movement in the temporal dimension. This data is then analyzed using a Long Short-Term Memory (LSTM) model to generate traffic density prediction patterns. This study also compares the performance of LSTM with other algorithms such as Random Forest and XGBoost in terms of prediction accuracy. Model evaluation is conducted using MSE and RMSE metrics to measure accuracy against actual data.The research results show that the integration of dimension-based vehicle detection with a visual historical data-driven prediction approach can improve the accuracy and flexibility of modeling future traffic conditions. This approach significantly contributes to the development of intelligent transportation systems that can adapt to dynamic environmental conditions and traffic patterns
Deep Learning Approach for Earthquake Detection and Classification using MobileNet V2 Transfer Learning Siregar, Giel Utami Putri; Subekti, Agus; Rozaq, Hasri Akbar Awal; Yildiz, Oktay
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1400

Abstract

Earthquake detection is a critical component of disaster management, as early identification of seismic events can help mitigate potential damage and support timely response efforts. This study evaluates the application of deep learning for binary earthquake detection using a lightweight Convolutional Neural Network (CNN) based on the MobileNetV2 architecture. The experiments were conducted using seismic waveform data from the Stanford Earthquake Dataset (STEAD), which were transformed into time–frequency representations through the Short-Time Fourier Transform (STFT). Spectrogram images derived from the seismic signals were used as input to the CNN models. Transfer learning was applied to MobileNetV2 to adapt the pretrained architecture to the earthquake detection task. The proposed approach achieved an accuracy of 99%, precision of 100%, recall of 97.96%, and an F1-score of 98.97% on the test dataset. In terms of model complexity, MobileNetV2 has 7,176,600 total parameters and 1,639,538 trainable parameters, indicating a favorable balance between performance and computational efficiency. For comparative evaluation, MobileNetV2 was benchmarked against several commonly used CNN architectures, including CNN Vanilla, MobileNetV1, VGG16, and ResNet, under the same experimental conditions. The results indicate that MobileNetV2 provides competitive detection performance while maintaining a significantly smaller model size. Although real-time deployment on mobile devices was not implemented in this study, the findings suggest that lightweight CNN architectures, such as MobileNetV2, hold promise for future earthquake detection systems operating in resource-constrained environments.
Co-Authors A.A. Ketut Agung Cahyawan W Abdullah Umar, Abdullah Aditya, Dedy Yusuf Agung Tjahjo Nugroho Akhmad Syukron Andra, Muhammad Bagus Ari Setiawan Arumjeni Mitayani Aryono Adhi, Aryono BUDIYONO Cahyono, Bowo Eko Chaeriah Bin Ali Wael, Chaeriah Bin Dayat Kurniawan Devi Wulandari Diantika, Sri Edy Supriyanto Emy Setyaningsih Eni Heni Hermaliani, Eni Heni Faisal, Anas Filda Angellia Haerul Fatah Heriyani, Novia Hidayat Teguh Wiyono Hiya Nalatissifa Indriyanti Indriyanti, Indriyanti Joharini, Joharini Kahar Muzakhar Khultsum, Umi Kirso, Kirso Kusmayadi, Kusmayadi Kusumah, Maulana S. Lukman, Arif M. Rangga Ramadhan Saelan Mardiana, Vita Awalia Mareanus Lase Marta Dinata, Mochamad Mardi MUHAMMAD HARIS Muhammad, Nawafil Nasrullah Armi Nita Merlina, Nita Nufus, Fina Sifaul Nurkahfi, Galih Nugraha Nursulistyono, Yuda Nurul Priyantari Prasetya, Dwi Sabda Budi Purwoko Adhi Rahmadya Trias Handayanto Rian Hidayat Rozaq, Hasri Akbar Awal Satyawan, Arief Suryadi Setiawan, Boy Siregar, Giel Utami Putri Siti Helmyati Sugara, Bayu Suprihati Supriyadi Supriyadi Suryanto, Wiwit Suyoto Suyoto Wahyudi Wahyudi Winarsa, Rudju YILDIZ, Oktay