Article Per Year (5 Year)
p-Index From 2021 - 2026
4.934
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Teknik Informatika (JUTIF) PARAHITA : Jurnal Pengabdian kepada Masyarakat Sevanam: Jurnal Pengabdian Masyarakat AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Pengabdian Masyarakat Inovasi Indonesia
Sasongko, Theopilus Bayu
Informatics, Faculty Of Computer Science, Universitas Amikom Yogyakarta, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization) Rosyida, Anistya; Sasongko, Theopilus Bayu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1716

Abstract

Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%. 
Peningkatan Manajemen E-Learning dan Keterlibatan Pengguna dalam Implementasi Moodle di SMK Nasional Berbah, Yogyakarta Hadinegoro, Arifiyanto; Sasongko, Theopilus Bayu; Ningrum, Fauzia Anis Sekar; Rahim, Abd. Mizwar A.; Fikri, Muhammad Ainul; Huda, Amirudin Khorul
Jurnal Pengabdian Masyarakat Inovasi Indonesia Vol 1 No 2 (2023): JPMII - Desember 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jpmii.290

Abstract

Dokumen ini membahas implementasi aplikasi Moodle dalam manajemen pembelajaran dan administrasi di SMK Nasional Berbah, Yogyakarta. Tujuan dari penggunaan Moodle adalah untuk meningkatkan kreativitas dan produktivitas di lingkungan sekolah melalui platform e-learning. Meskipun SMK Nasional Berbah telah berusaha menciptakan media e-learning yang bermanfaat, mereka masih menghadapi beberapa kendala dalam pembuatan konten dan optimalisasi penggunaan e-learning. Untuk mengatasi kendala tersebut, dilakukan kegiatan sosialisasi dan pelatihan manajemen pengguna bagi karyawan dan staf khusus IT SMK Nasional Berbah. Kegiatan ini melibatkan 16 karyawan, tim staf e-learning, 2 pemateri, dan 3 pendamping pemateri dari Universitas Amikom. Fokus utama kegiatan ini adalah memberikan bimbingan teknis dalam pengelolaan pengguna LMS Moodle. Dalam pengabdian kepada masyarakat, dilakukan kolaborasi antara SMK Nasional Berbah dan Program Studi Informatika Universitas Amikom Yogyakarta. Kendala yang dihadapi dalam pelaksanaan pengabdian meliputi keterbatasan bandwidth server LMS Moodle, ketersediaan koneksi internet yang kurang memadai, dan kurangnya keterlibatan tim teknis dalam proses sosialisasi. Sebagai tindak lanjut, direncanakan langkah-langkah peningkatan yang melibatkan guru-guru sebagai pengguna utama Moodle. Hasil akhir dari kegiatan ini adalah para guru dan siswa mampu menggunakan Moodle sebagai wadah dalam kegiatan belajar mengajar.  Dengan demikian, implementasi Moodle di SMK Nasional Berbah dapat lebih efektif dan memberikan manfaat yang maksimal dalam proses pembelajaran dan administrasi di sekolah tersebut.
Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation Abd Mizwar A. Rahim; Theopilus Bayu Sasongko
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4035

Abstract

This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants.
Implementation of SSL-Vision Transformer (ViT) for Multi-Lung Disease Classification on X-Ray Images Baasith, Rafi Haqul; Sasongko, Theopilus Bayu; Hadinegoro, Arifiyanto; Saputro, Uyock Anggoro
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11844

Abstract

Chest X-ray imaging is one of the most widely used modalities for lung disease screening; however, manual interpretation remains challenging due to overlapping pathological patterns and the frequent presence of multiple coexisting abnormalities. In recent years, Vision Transformer (ViT) models have demonstrated strong potential for medical image analysis by capturing global contextual relationships. Nevertheless, their performance is highly dependent on large-scale labeled datasets, which are costly and difficult to obtain in clinical settings. To address this limitation, this study proposes a Self-Supervised Learning Vision Transformer (SSL-ViT) framework for multi-label lung disease classification using the CheXpert-v1.0-small dataset. The proposed approach leverages self-supervised pretraining to learn robust and transferable visual representations from unlabeled chest X-ray images prior to supervised fine-tuning. A total of twelve clinically relevant thoracic disease labels are retained, while non-disease labels are excluded to enhance interpretability and reduce confounding effects. Experimental results demonstrate that SSL-ViT achieves a high recall of 0.73 and a peak AUC of 0.75 on the test set, indicating strong sensitivity in detecting pathological cases. Compared to the baseline ViT model, SSL-ViT exhibits a recall-oriented performance profile that is particularly suitable for screening applications, where minimizing false negatives is critical. Furthermore, Grad-CAM visualizations confirm that the model focuses on anatomically meaningful lung regions, supporting its clinical relevance. These findings suggest that SSL-enhanced Vision Transformers provide a robust and effective solution for multi-label chest X-ray screening tasks.
Co-Authors Abd Mizwar A. Rahim Abdul Mizwar A. Rahim AGIT AMRULLAH Agus Fatkhurohman Agus Fatkhurohman Alifqi Radjavani Amrullah, Yusuf Amri Anas Fikri Hanif Anas Fikri Hanif Arif Dwi Laksito Arifin, Oki Arifiyanto Hadinegoro, Arifiyanto Arnandito, Seno Asrawi, Hannan Baasith, Rafi Haqul Dewantoro, Agus Erfina Nurussa’adah Fajar, Rizqi Al Fikri, Muhammad Ainul Ginanjar Wiro Sasmito Haryoko, Haryoko Hasbi Huda, Amirudin Khorul Joshua Agung Nurcahyo Joshua Agung Nurcahyo Khairul Rohman Muhammad Fikri Hidayatullah Muhammad Taqiyuddin MUhammad Yusrizal Ningrum, Fauzia Anis Sekar Radjavani, Alifqi Rahim, Abd. Mizwar A. Restu Bumi Apit Rivi Neritarani Rivi Neritarani Rosyida, Anistya Safitri, Anisa Ika Saputro, Uyock Anggoro Satya Tegar Kusuma Senli Yusdiantoro Yusdiantoro Setyanto, Joko Simarmata, Asyer Aprinando Pratama Subagyo, Ageng Ramdhan Yusdiantoro, Senli Yusdiantoro