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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Edu Komputika Journal JOIV : International Journal on Informatics Visualization SINTECH (Science and Information Technology) Journal Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization JOMPA ABDI: Jurnal Pengabdian Masyarakat
Kusuma, Bagus Adhi
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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A Slab Multi-Fold Classification Technique on A Mixed Pixel Hyperspectral Image Purwadi, -; Abu, Nor Azman; Mohd, Othman; Kusuma, Bagus Adhi; Ahmad, Asmala
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3432

Abstract

Hyperspectral imaging offers a significant edge over standard RGB and multispectral images for land classification. It captures a wider range of electromagnetic waves, producing more detailed images than previous methods. This allows objects to be identified and distinguished with high certainty due to hyperspectral capabilities. However, the large data volume makes reducing the computational workload challenging. Imbalanced data and suboptimal hyperparameter settings can reduce classification accuracy. Hyperspectral image classification is computationally demanding, especially with mixed-pixel issues in high-resolution images. This study uses EO-1 satellite imagery with a 30-meter resolution affected by mixed pixels. It introduces a new classification approach to effectively use hyperspectral remote sensing at this resolution. The process includes satellite image preprocessing—geometric correction, image enhancement with FLAASH, and geometric and atmospheric corrections. To lessen the computational burden, a slab approach partitions the 242 spectral bands into segments, extracting features from each, resulting in fewer total features. These features are then input into a support vector machine (SVM) for five-class classification. Parameters like polynomial order, kernel scale, and kernel type are tuned for optimal accuracy. A novel SLAB Multi-Fold technique is proposed. Results indicate that the slab method combined with SVM achieves a maximum accuracy of 51.39%. The best results came from slab 2, with a polynomial order of 8 and k=4, using both linear and Gaussian kernels. These findings offer valuable insights for future research on satellite image classification, especially when tuning multiple hyperparameters within this SLAB approach. Future work could compare these results with higher-resolution images and different datasets to better evaluate the technique's accuracy.
IMPLEMENTATION OF YOU ONLY LOOK ONCE V8 ALGORITHM IN POTATO LEAF DISEASE DETECTION SYSTEM Ekhsanto, Bagus kurniawan; Kusuma, Bagus Adhi; Kuncoro, Adam Prayogo
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2104

Abstract

Agriculture is an important foundation of the national economy, as effective development in this sector will support overall economic stability. Potato itself is one of the world's staple foods after rice, wheat and corn. This crop belongs to the category of horticulture which is widely planted and developed by people to meet their needs. On the farm of Bibit sida kangen Kalibening, Banjarnegara which is one of the farms that grow potatoes has constraints related to potato diseases which result in decreased productivity of crops. Therefore, the main purpose of this system is to provide fast and accurate disease detection capability on the farm of Bibit sida kangen Kalibening, Banjarnegara, so that it can help farmers in reducing losses caused by disease attacks on plants. By utilizing YOU ONLY LOOK ONCE V8 (YOLOv8) technology, this system can recognize and classify potato leaf disease types, including early_blight, late_blight, and healthy plants, with a high level of accuracy. Through evaluation using precision and recall matrices, the results show a significant success rate, with precision accuracy for early_blight of 87%, healthy plants of 81%, and late_blight of 97%, respectively. Meanwhile, the recall results for the three categories also reached 87%, 81%, and 97% respectively. With an overall accuracy of 88%, these findings confirm that the developed detection system is successful in identifying potato leaf diseases with high accuracy. This indicates the great potential of this system in assisting farmers in managing the condition of their potato crops, which in turn can improve farmers' productivity and welfare.
Rule-Based Expert System for Personalized GERD Food Recommendations Using Forward Chaining and Certainty Factor in Indonesia Maulia, Alifani; Purwadi, Purwadi; Kusuma, Bagus Adhi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5477

Abstract

This study develops a personalized and transparent rule-based expert system to support dietary decision-making for Indonesian patients with gastroesophageal reflux disease (GERD), addressing a critical gap in existing expert-system applications that focus mainly on diagnosis rather than daily diet management. The system integrates knowledge derived from the Indonesian GERD Consensus (2022) and its 2024 addendum with local nutritional evidence to construct if–then rules that classify foods into safe, limit, and avoid categories. A forward-chaining inference engine processes user-specific inputs—including symptoms, trigger sensitivities, eating behaviors, and dietary restrictions—while the Certainty Factor (CF) model quantifies confidence levels to accommodate individual tolerance variability. The system was implemented using Python and deployed through a Gradio-based wizard interface, enabling stepwise data collection and producing Top-N food recommendations with explainable “reason traces.” Functional evaluations across mild, moderate, and severe profiles demonstrated consistent alignment with national dietary guidelines, steering users toward low-fat, non-spicy, soft-textured, and clear-broth menu options, while eliminating high-risk trigger foods. Preliminary expert validation indicated high agreement with guideline principles, emphasizing the system’s interpretability and practical relevance. This research contributes to the field of health informatics by operationalizing forward chaining and CF for personalized dietary support, offering an auditable and computationally efficient alternative to black-box recommendation systems. Future developments include expanding the food dataset, refining CF calibration, and conducting structured clinical validation to enhance performance and applicability in real-world mHealth environments.
Co-Authors Abu, Nor Azman Adam Prayogo Kuncoro Ahmad, Asmala Anggraini, Nova Arsi, Primandani Baihaqi, Wiga Maulana Dhanar Intan Surya Saputra Ekhsanto, Bagus kurniawan Fandy Setyo Utomo Farchani, Saofikh Bagus Habibah, Ariska Nurul Heryanti, Linda Imam Tahyudin Isnaini, Khairunnisak Nur Junianto, Haris Khasanah, Fitrotul Lestari, Tri Endah Widi Magnolia, Cindy Maulia, Alifani Mohd, Othman Nandang Hermanto Nugroho, Rizki Nurhopipah, Ade Primandani Arsi Pungkas Subarkah Purwadi Purwadi Purwadi, - Putra, Bernardus Septian Cahya Riandini, Dini Riyanto Riyanto Rujianto Eko Saputro Waluyo, Retno