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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Dan Sistem Informasi Bisnis
Usman Sudibyo
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Computer Science & IT Education Electrical & Electronics Engineering

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Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware Fauzi Adi Rafrastaraa; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Etika Kartikadarma; Usman Sudibyo
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.854

Abstract

Malware is a type of software designed to harm various devices. As malware evolves and diversifies, traditional signature-based detection methods have become less effective against advanced types such as polymorphic, metamorphic, and oligomorphic malware. To address this challenge, machine learning-based malware detection has emerged as a promising solution. In this study, we evaluated the performance of several machine learning algorithms in detecting malware and applied Principal Component Analysis (PCA) to the best-performing algorithm to reduce the number of features and improve performance. Our results showed that the Random Forest algorithm outperformed Adaboost, Neural Network, Support Vector Machine, and k-Nearest Neighbor algorithms with an accuracy and recall rate of 98.3%. By applying PCA, we were able to further improve the performance of Random Forest to 98.7% for both accuracy and recall while reducing the number of features from 1084 to 32.
On the Effectiveness of Lightweight CNN Architectures for Fine-Grained Coffee Bean Classification Akbar Muhamad Burhanudhin; Usman Sudibyo; Eka Putra Agus Meindiawan
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34044

Abstract

Distinguishing coffee bean varieties remains a significant challenge in the agricultural industry due to high inter-class similarity and the subtle morphological differences between species. This study aims to conduct a comparative evaluation of MobileNetV2 and EfficientNetB0 for fine-grained coffee bean classification, specifically investigating how efficiency-oriented architectural mechanisms such as depthwise separable convolution and compound scaling influence feature extraction. The research employed a quantitative experimental method using a private dataset of 2,400 images comprising Arabica, Robusta, and Liberica varieties. Data preprocessing included resizing to 224×224 pixels and augmentation, followed by training the two architectures using transfer learning under a controlled experimental framework. The results showed that EfficientNetB0 achieved superior performance with a testing accuracy of 99.17%, while MobileNetV2 attained a competitive accuracy of 98.33% with lower computational complexity. These results demonstrate that while EfficientNetB0 is optimal for high-precision industrial sorting, MobileNetV2 offers a highly efficient alternative for resource-constrained mobile applications. This study provides a scalable framework for automating quality control, effectively balancing architectural efficiency with the sensitivity required for accurate coffee variety identification.
Co-Authors Akbar Muhamad Burhanudhin Dwi Puji Prabowo Eka Putra Agus Meindiawan Etika Kartikadarma Fauzi Adi Rafrastara Ricardus Anggi Pramunendar