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All Journal Jurnal Media Infotama International Journal of Artificial Intelligence Research JMM (Jurnal Masyarakat Mandiri) JUTIM (Jurnal Teknik Informatika Musirawas) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Dehasen Untuk Negeri Jurnal Media Computer Science Jurnal Dehasen Mengabdi Jurnal Komputer Indonesia Jurnal Komputer, Informasi dan Teknologi
Jumadi, Juju
Program Studi Informatika, Universitas Dehasen Bengkulu
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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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.
Performance Analysis of Discrete Cosine Transform and Discrete Wavelet Transform Methods on Digital Images Based on Peak Signal-to-Noise Ratio and Mean Squared Error Parameters Muetya Oktaviani; Juju Jumadi; Ockhy Jey Fhiter. W
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.v5i4.12141

Abstract

As the demand for image data storage and distribution continues to increase, compression techniques are required to reduce file size without significantly degrading image quality. One of the most widely used approaches in image compression is frequency-domain transformation, including the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT). This study aims to evaluate and compare the performance of DCT and DWT methods on digital images based on the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) parameters. In addition, the study also considers the compression ratio as a supporting parameter in the evaluation process. The results indicate that the DWT method produced an average MSE value of 63.9381, which was lower than that of DCT method at 70.0780. Meanwhile, DCT method achieved an average PSNR value of 31.21 dB, which was higher than that of DWT method at 30.52 dB. In terms of compression ratio, the DCT method also demonstrated better performance, with an average value of 4.2954, whereas DWT method produced an average value of 2.0275. Based on the testing results, it can be concluded that DWT method performs better in minimizing image distortion based on the MSE parameter, whereas the DCT method is superior in producing reconstructed image quality and compression efficiency based on PSNR values and compression ratio.
Co-Authors Abdussalam Al Akbar Ahmad Arib Al Farisy Ahmad Asyhari Andre pranista Annisa Putri Pratiwi Defri Wiranata Dellya Septari Devi Sartika Devi Sartika Dewi Suranti Edwin S Giovani Edy Rahmat Efran Fernando Ade Pratama Eky Heryendy HR Fredricka, Jhoanne Gathan Raihan Al Saleh Gita Dahlia Lestari Gustian Febri Syah Putra Hengki Jon Saputra Herlina Latipa Sari Ila Yati Beti Intan Diba Aulia Sari Jusuf Wahyudi Jusuf Wahyudi Kanedi, Indra Kanedi Khairil Khairil Lena Elfianty Lianda, Deri Liza Yulianti M Dicky Imansyah Mesterjon Mesterjon Muetya Oktaviani Natali Mevian Deaz Nofi Qurniati Ockhy Jey Fhiter. W Oki Ardiansyah Olvi Mariani Rahmat Pidi Putra Redo Osi Putra Reno Supardi Ricky Zulfiandry Ridho Rahman Kurnianto Ridwan Ridwan Rodi Rodi Ronaldi Aprilianto Ronaldo Praja Saputra Sallaby, Achmad Fikri Sapri , Sapri Shintia Cantika Siswanto Siswanto Venny Novita Sari Yupianti, Yupianti