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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

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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.
Optimizing Stacking Ensemble Models for Customer Churn Prediction in the Telecommunications Industry Rofik Rofik; Jumanto Unjung; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1783

Abstract

One of the biggest challenges in the telecommunications industry is predicting churn, which is the condition when a customer unsubscribes and switches to another service provider. In an era of competitive market conditions, retaining customers is much more efficient than acquiring new customers. Conventional prediction models are often unable to capture the complexity of customer behavior patterns, resulting in a lower accuracy than optimal. This study aims to optimize customer churn prediction performance by developing a stacking ensemble model that combines several classification algorithms to improve model performance. Fourteen algorithms were tested, and the six algorithms with the best accuracy were selected as base learners, while Logistic Regression was selected as the meta-learner. The stacking model testing was carried out sequentially through a combination of 6 algorithms with the same meta-learner algorithm. Testing was also carried out with and without using the SMOTE data balancing method to evaluate the effect of data balancing on the prediction results. The results of this study show that the combination of the Adaboost, Ridge Classifier, and Logistic Regression algorithms can produce the highest accuracy of 82.97%, which exceeds the prediction performance of a single algorithm. This research contributes to demonstrating an effective stacking ensemble configuration for predicting customer churn in the telecommunications industry and emphasizes that the selection of the right algorithm combination has a greater impact on model performance than the number of algorithms used.
Strengthening the Preparedness of Elementary School Physical Education Teachers in Injury Prevention and Initial Emergency Response Adi S; Cahyo Yuwono; Sugianto Parulian Simanjuntak; Dicky Budhi Setyawan; Jumanto; Ummi Nur Laila Sulistyani; Erika Afriana; Karina Permata Andhini
Jurnal Panjar: Pengabdian Bidang Pembelajaran Vol. 6 No. 1 (2026)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/panjar.v6i1.52281

Abstract

The limited preparedness of elementary school Physical Education (PE) teachers in managing injuries and emergency situations remains a significant challenge for ensuring student safety. This community service programme aimed to strengthen teachers’ competencies in injury prevention, emergency response, and school safety management through an integrated training and mentoring approach. The programme involved 33 PE teachers from the Physical Education Teachers’ Working Group (KKG PJOK) in Gunungpati Sub-district, Semarang, Indonesia. Activities included needs assessment, training on school safety management, injury prevention, first aid, Emergency Cardiac Care (ECC), cardiopulmonary resuscitation (CPR), simulation-based practice, the development of school safety Standard Operating Procedures (SOPs), and the implementation of SIGAPEdu as a digital injury reporting system. Programme effectiveness was evaluated using pre-test and post-test assessments, practical performance observations, and monitoring of programme outputs. The results demonstrated substantial improvements in teachers’ knowledge and practical competencies. The mean knowledge score increased from 58.24 ± 9.17 in the pre-test to 86.73 ± 6.84 in the post-test, representing an overall improvement of 48.9%. The proportion of teachers capable of performing basic CPR increased from 12.1% to 84.8%, exceeding the programme target of 80%. Furthermore, school-based safety SOPs were developed and implemented, three safety-oriented PE teaching materials were produced, and SIGAPEdu was adopted by 72.7% of participants, surpassing the predetermined target. These outcomes indicate that the programme successfully strengthened teachers’ emergency preparedness, promoted the integration of safety principles into PE learning, and supported the development of data-driven school safety practices. Continuous implementation of similar programmes is recommended to foster safer and higher-quality learning environments in schools. 
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