Article Per Year (5 Year)
p-Index From 2021 - 2026
5.096
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Abdimasku : Jurnal Pengabdian Masyarakat Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Innovation in Research of Informatics (INNOVATICS) Journal of Soft Computing Exploration Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Information System Exploration and Research Journal of Electronics Technology Exploration (JOeTEx) Scientific Journal of Informatics Jurnal Panjar : Pengabdian Bidang Pembelajaran Journal of Vocational, Informatics and Computer Education Journal of Soft Computing Exploration
Jumanto Unjung
Department Of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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

Found 2 Documents
Search
Journal : journal of soft computing exploration

 1 
Enhancing diabetes classification performance using XGBoost integrated with SMOTE and bayesian hyperparameter optimization Muhammad Nurul Ihyaul Ulum; Jumanto Unjung
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.3

Abstract

Diabetes mellitus is a long-term metabolic disorder that is becoming more common around the world. Finding people at risk early can help prevent serious health problems and improve patient outcomes. Machine learning is often used to predict diabetes, but imbalanced medical data can make it harder for models to spot positive cases. In this study, we created a diabetes classification model by combining the Extreme Gradient Boosting (XGBoost) algorithm with the Synthetic Minority Over-sampling Technique (SMOTE), and we used Bayesian Optimization to fine-tune the model’s settings. We worked with the Pima Indians Diabetes Dataset, which has 768 patient records and eight clinical features. Our steps included preprocessing the data, splitting it into training and testing sets, using SMOTE to balance the training data classes, training the XGBoost model, and performing hyperparameter tuning using Bayesian Optimization with Stratified 5-Fold Cross-Validation to determine the optimal parameter configuration. The final model reached an accuracy of 0.88, a precision of 0.79, a recall of 0.91, an F1-score of 0.84, and a ROC-AUC of 0.955. These results show that our approach can identify diabetes cases more effectively while keeping strong overall performance.
A comparative benchmark of vision transformer architectures for chili leaf disease classification Acihmah Sidauruk; Danang Wijayanto; I Made Artha Agastya; Jumanto Unjung; Mulia Sulistiyono
Journal of Soft Computing Exploration Vol. 7 No. 3 (2026): September 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i3.62

Abstract

Chili plant disease detection represents a critical component for enhancing agricultural productivity. Although Convolutional Neural Networks (CNN) have demonstrated promising results, they encounter limitations in capturing global contextual relationships within images. However, existing Vision Transformer studies on plant disease commonly assess only a single architecture, leaving the relative performance of different Vision Transformer families on chili disease data largely unexamined. This research aims to conduct a comparative benchmark analysis of five Vision Transformer-based architectures ViT, Swin Transformer, MaxViT, DINOv2, and EVA-02 to identify the most optimal model for chili plant disease classification. The methodology begins with data preprocessing and augmentation on a chili leaf dataset comprising five classes: healthy, leaf curl, leaf spot, whitefly, and yellowish. Each model is then fine-tuned under consistent training configurations with early stopping to prevent overfitting, and evaluated using accuracy, precision, recall, F1-score, and AUC. The results indicate that DINOv2 achieves superior performance with 96% accuracy, 96% precision, 96% recall, 96% F1-score, and 99% AUC, along with the highest training efficiency through convergence at epoch 12, outperforming ViT (92%), Swin (88%), MaxViT (88%), EVA-02 (86%), and previous CNN-based approaches. These findings confirm the superior potential of Vision Transformers, particularly self-supervised models, as a promising alternative for agricultural disease detection applications. The main contribution of this study is the first unified, head-to-head benchmark of five distinct Vision Transformer families for chili leaf disease classification, providing practical guidance on model selection in terms of both accuracy and training efficiency.
Co-Authors Abas Setiawan Acihmah Sidauruk Adi S Ahmad, Kamilah Al Qohar, Bagus Alamsyah - Anisa, Devi Nurul Budi Prasetiyo Budi Prasetiyo Budi Prasetiyo, Budi Cahyo Yuwono Ceorido Ghalib Wibowo Chrisandito Sebastian Erlangga Bia DAA Pertiwi Danang Wijayanto Darmawan, Aditya Yoga Devi Ajeng Efrilianda Dicky Budhi Setyawan Dimas Fanny Hebrasianto Permadi Dullah, Ahmad Ubai Dwika Ananda Agustina Pertiwi Dwika Ananda Agustina Pertiwi Endang Sugiharti, Endang Erika Afriana Farih, Habib al Franki SW Hakim, M. Faris Al Heri Ardiyanto I Made Artha Agastya Karina Permata Andhini Khalidah, Saidatul M. Faris Al Hakim M. Faris Al Hakim Maylinna Rahayu Ningsih Much Aziz Muslim Much Aziz Muslim Much Aziz Muslim Much Aziz Muslim Much. Aziz Muslim Muhammad Nurul Ihyaul Ulum Mulia Sulistiyono Nina Fitriani Ningsih, Maylinna Rahayu Nurriski, Yopi Julia Nurul Hidayat Pertiwi, Dwika Ananda Agustina Prabowo Yudho Jayanto Purwoko, Agus Putri Utami, Putri Qohar, Bagus Al Rianto, Nur Azis Kurnia Riza Arifudin Riza Arifudin Rofik Rofik Rofik Rofik Rofik Rofik, Rofik Roshan Aland Hakim Sanjani, Fathimah Az Zahra Shabaha, Achmad Rozin Sugianto Parulian Simanjuntak Syamsudin, Hilmi Tanga, Yulizchia Malica Pinkan Tiara Lailatul Nikmah Ummi Nur Laila Sulistyani Utami, Annisaa Wibowo, Kevyn Alifian Hernanda Yana, Natasya Siska Fitri Zaenal Abidin