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All Journal Jurnal Dehasen Untuk Negeri Jurnal Media Computer Science
Ahmad Arib Al Farisy
Universitas Dehasen Bengkulu
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Public Health Other

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Pemanfaatan Artificial Intellegence Dalam Pembuatan Karya Intelektual Siswanto Siswanto; Desi Mahdalena; Nofi Qurniat; Ahmad Arib Al Farisy; Rizki Amelia
Jurnal Dehasen Untuk Negeri Vol 3 No 2 (2024): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jdun.v3i2.11081

Abstract

This community service activity aims to improve students' understanding of artificial intelligence utilization in creating intellectual works at Madrasah Aliyah Pancasila Bengkulu. The main issue is the low utilization of digital technology, especially artificial intelligence, in supporting learning processes. The solution offered is educational socialization using lectures, discussions, and evaluation methods. The activity was conducted through observation, preparation, implementation, and evaluation stages. The results indicate an increase in students' knowledge regarding the use of artificial intelligence and its positive and negative impacts. Students also showed high enthusiasm during the activity. Therefore, this program contributes to improving digital literacy and preparing students for rapid technological advancements.
Predicting Stunting Levels in Toddlers Using Machine Learning Based On Health Data and Image Processing Gathan Raihan Al Saleh; Juju Jumadi; Ahmad Arib Al Farisy
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12124

Abstract

Stunting is one of the most prevalent chronic nutritional disorders affecting toddlers, with significant impacts on their physical growth and cognitive development. The limited quality and completeness of health records collected manually at community health posts (Posyandu) present challenges for the early identification of stunting. This study aimed to develop a predictive model for stunting levels in toddlers using a machine learning approach based on health data and image processing. The theoretical framework incorporated Machine Learning, Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Image Processing, and Ensemble Learning using the Hard Voting technique. This study employed a quantitative experimental design following the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The data were obtained through observations and the collection of child health records at the Posyandu in Lubuk Sahung Village, Seluma Regency, Bengkulu Province, complemented by a public dataset from Kaggle and body images of children under five. The research process included data preprocessing, image feature extraction using CNN, feature fusion of health data and image features, model training using SVM and Random Forest, and model integration through Ensemble Voting. The results demonstrated that the SVM and Ensemble Voting models achieved the best predictive performance, with an accuracy of 91.67%, precision of 100.00%, recall of 84.62%, and an F1-score of 91.67%, while the Random Forest model achieved an accuracy of 83.33%. Based on the generalization analysis, the SVM model exhibited the highest stability, with a training–testing accuracy gap of only 4.16%. These findings indicate that integrating health data with image-derived features can effectively support the prediction of stunting levels and serve as a reliable decision-support tool for the early monitoring of children's nutritional status.
Co-Authors Desi Mahdalena Gathan Raihan Al Saleh Jumadi, Juju Nofi Qurniat Rizki Amelia Siswanto Siswanto