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Widya Eka Pranata
Universitas Muhammadiyah Gorontalo, Gorontalo
Computer Science & IT

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Penerapan Algoritma Naive Bayes Untuk Sistem Klasifikasi Status Gizi Bayi Balita Mohamad Ilyas Abas; Rizal Lamusu; Widya Eka Pranata; Syahrial Syahrial; Irawan Ibrahim; Wahyudin Hasyim; Verliana Kiayi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.508

Abstract

Infants and toddlers are in a critical period of rapid growth and development, often referred to as the "golden age." During this stage, regular nutritional assessments are essential to monitor health status and detect potential nutritional problems early. This study aims to classify the nutritional status of infants and toddlers using the Naïve Bayes algorithm, a probabilistic classification method based on Bayes' theorem with a strong assumption of attribute independence. The main attributes used in the classification system include age, weight, and height. The dataset consists of 700 records of infants and toddlers collected from previous observations. The results show that the Naïve Bayes algorithm can be effectively implemented for nutritional status classification, achieving a system accuracy of 88.14%. This indicates that the method performs well and has the potential to be utilized in decision support systems for child health monitoring.
Identifikasi Penyakit Padi Berdasarkan Citra Daun Menggunakan Arsitektur Convolutional Neural Network Kustom Andre Gunawan Polontalo; Mohamad Ilyas Abas; Widya Eka Pranata
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.809

Abstract

Rice production in Indonesia often declines due to leaf diseases that are difficult to detect early using conventional methods. This study aims to identify rice leaf diseases based on leaf images using a Convolutional Neural Network (CNN). The dataset was obtained from an online repository (Kaggle) containing labeled images of rice leaves across several disease categories. A custom CNN model was designed and trained after applying image preprocessing (resizing to 224×224 pixels), normalization, and data augmentation to reduce overfitting. The training was conducted in the Google Colab environment using TensorFlow with train–test splits of 70:30, 80:20, and 90:10 to analyze model performance. The best result achieved a training accuracy of 83.02% and a testing accuracy of 77.33%. Furthermore, the model was compared with several widely used architectures in the literature, including ResNet50, VGG16, and EfficientNetB0. The findings indicate that the proposed custom CNN model provides competitive classification performance for early detection of rice leaf diseases and has the potential to serve as a decision-support system for farmers in rapid and efficient disease management.
Co-Authors Andre Gunawan Polontalo Ibrahim, Irawan Mohamad Ilyas Abas Rizal Lamusu Syahrial Syahrial Verliana Kiayi Wahyudin Hasyim