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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Rabbani, Haidar
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Pengembangan Sistem Pendukung Keputusan Untuk Prediksi Diabetes Aldyno, Achmad Farhan; Junaidi, Faiza Ulinnuha; Rabbani, Haidar; Oda, Ahlam Nauf; Rifai, Achmad Pratama
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 2 (2024): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i2.787

Abstract

Diabetes is one of the major health issues worldwide, affecting 10.5% of the total adult population (20-79 years old). Often referred to as the silent killer, nearly half of those affected by diabetes are unaware of their condition. Diabetes is categorized into several types, namely type 1 diabetes mellitus, type 2 diabetes mellitus, and gestational diabetes. Detection of diabetes can be carried out through various methods, including blood sugar level tests, Hemoglobin A1c (HbA1c) tests, oral glucose tolerance tests, as well as physical examinations and medical history reviews by doctors. Interpreting the results of these tests can be used to identify the potential for an individual to have diabetes, employing a machine learning approach as a decision support system for doctors to make informed decisions, and also providing patients with reminders to consult with a doctor. In the machine learning model we've developed, we trained and tested algorithms using the 'Diabetes prediction dataset,' consisting of 8 variables: age, gender, Body Mass Index (BMI), hypertension, heart disease, smoking history, HbA1c level, and blood glucose level. The algorithm employed was the Artificial Neural Network (ANN) with the optimizer using Stochastic Gradient Descent (SGD). This application is intended to serve as a decision support system for doctors and the general public. It's designed using Anvil for 8 types of input variables, providing 2 output variables: the percentage of an individual's potential to have diabetes and suggestions for preventing such risks.
Classification of single origin Indonesian coffee beans using convolutional neural network Rifai, Achmad Pratama; Sari, Wangi Pandan; Rabbani, Haidar; Safitri, Tari Hardiani; Hajad, Makbul; Sutoyo, Edi; Nguyen, Huu-Tho
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5140-5156

Abstract

This research aims to develop a coffee bean type detection model using convolutional neural networks (CNN), leveraging a dataset of 14,525 images from 116 types of Indonesian coffee beans. Pre-processing steps including resizing, rescaling, and augmentation were applied to improve the dataset quality. The dataset was split into training, validation, and testing sets with proportions of 80%, 10%, and 10%, respectively. Two model development approaches were used: transfer learning with Inception V3 in two scenarios and a model built from scratch. The transfer learning Inception V3 model in scenario 1 achieved the best performance, with a test accuracy of 0.87 and optimal evaluation metrics across precision, recall, and F1-score. This model was fine-tuned using pretrained weights, allowing it to adapt effectively to the coffee bean dataset. The results highlight that transfer learning, especially with Inception V3, provides a robust method for classifying coffee beans, offering potential applications in the coffee industry for improving classification efficiency and accuracy. The study demonstrates how deep learning can enhance the objectivity and precision of coffee bean classification, contributing to greater consistency in product sorting and quality assessment.
Co-Authors Achmad Pratama Rifai Aldyno, Achmad Farhan Hajad, Makbul Junaidi, Faiza Ulinnuha Nguyen, Huu-Tho Oda, Ahlam Nauf Safitri, Tari Hardiani Sutoyo, Edi Wangi Pandan Sari