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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Intechno Journal : Information Technology Journal
Agastya, I Made Artha
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Journal : journal of applied informatics and computing

 1 
Penerapan Algoritma Machine Learning Untuk Sistem Prediksi Penyakit Osteoporosis Wiryawan Sujana, Rajendra Artanto; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8448

Abstract

Osteoporosis is a condition characterized by decreased bone density, leading to fragile and easily fractured bones. This disease is a significant concern as it can cause disability, fractures, and death, particularly in the elderly population. Early detection of osteoporosis is crucial to prevent disease progression through timely interventions. This study aims to develop a machine learning-based prediction system capable of detecting osteoporosis using three different algorithms, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The study involves analyzing and comparing the performance of these algorithms based on evaluation metrics such as Accuracy, Precision, Recall, and F1 Score. The data used is processed in two formats, namely ordinal and one-hot encoding, to assess the impact of encoding techniques on model performance. The results show that the Gradient Boosting algorithm performs the best on both types of data, with the highest Accuracy of 91.07% on the one-hot encoded data. Meanwhile, SVM and Random Forest also demonstrate competitive performance but with slightly lower results. This study concludes that Gradient Boosting is the most effective algorithm for osteoporosis prediction in this research. These findings can serve as a foundation for further development in the early detection of osteoporosis and support more effective and efficient prevention and treatment efforts.
Betta Fish Identification System Based On Convolutional Neural Network Saputra, Gilang Ardhi; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8449

Abstract

This study developed an automated identification system based on the Convolutional Neural Network (CNN) to classify Betta Splendens, a fish species with high economic value in Indonesia. The system aims to improve accuracy and efficiency in the identification process. The research was divided into several experiments, where the data was split into 320 images for training, 80 for validation, and 100 for testing. We used two optimizers, Adam and RMSprop. The Adam optimizer experiments conducted two stages with learning rates of 0.0001 and 0.001, each with 100 and 200 epochs. The results showed that a lower learning rate (0.0001) with 200 epochs yielded the best test accuracy of 71%, while a learning rate of 0.001 caused accuracy to stagnate at 66%, indicating potential overfitting. The RMSprop optimizer with a learning rate of 0.00001 demonstrated good stability, though with slightly lower accuracy than Adam. This study highlights the importance of selecting the appropriate learning rate and number of epochs to achieve an optimal balance between training, validation, and testing accuracy, ensuring the model generalizes well to new data.
Evaluation of SMOTE Technique in the Comparison of XGBoost and Random Forest Algorithms for Liver Disease Prediction Rohman, Wahyutri Nur; Agastya, I Made Artha
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.10239

Abstract

In many countries, including Indonesia, liver disease remains a major cause of morbidity and mortality. Early detection plays a crucial role in improving treatment outcomes. This study evaluates the performance of two widely used machine learning models Random Forest and XGBoost for predicting liver disease, employing the SMOTE balancing technique to address class imbalance. The primary objectives are to enhance model fairness, reduce overfitting, and improve sensitivity toward the minority class. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. The XGBoost model achieved an average accuracy of 99.74%, precision of 99.77%, recall of 99.75%, and F1-score of 99.72%, while the Random Forest model attained an average accuracy of 99.82%, precision of 99.89%, recall of 99.75%, and F1-score of 99.75%. Both models demonstrated excellent predictive capability, with Random Forest slightly outperforming XGBoost. These results highlight the importance of data balancing and robust model validation in developing reliable machine learning models for healthcare decision-making.
Co-Authors Adhani, Muhammad Azmi Afrig Aminuddin Agus Suryadi Arief Setyanto Dini Oktarina Dwi Handayani Kusrini, K Kusrini, Kusrini López, Alba Puelles Martínez-Béjar, Rodrigo Maulana, A. Salky Mohamad Firdaus, Mohamad Mohammad Haris Muzakki, Mohammad Haris Noor Akhmad Setiawan Nugroho, Agung Pradipto, Dimas Prasetyo, Martinus Juan Pratama, Muhammad Egy Ramadhan, Akhdan Ferdiansyah Robert Marco, Robert Rohman, Wahyutri Nur Saputra, Gilang Ardhi Sukaria, Petra Nugra Teguh Bharata Adji Wicaksono, Dika Wiryawan Sujana, Rajendra Artanto