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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 Journal of Applied Data Sciences JOMPA ABDI: Jurnal Pengabdian Masyarakat Jurnal Medika: Medika
Kusuma, Bagus Adhi
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST IN SENTIMENT ANALYSIS OF DISDUKCAPIL APPLICATION REVIEWS Junianto, Haris; Saputro, Rujianto Eko; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.1802

Abstract

Civil registration administration institutions such as Disdukcapil have an important role in carrying out government functions, in supporting the smooth running of administrative services the Government presents the Disdukcapil Mobile Application platform which aims to provide efficient and fast services to the community regarding various population administration needs. Sentiment analysis of user reviews on the Play Store for the Disdukcapil application is needed to understand user perceptions and needs, as well as to improve service quality and application development. In this study, researchers conducted sentiment analysis using 2 algorithms, namely: Logistic Regression and Random Forest, which after comparing by testing the two algorithms with test data of 18810 user review data from PlayStore, obtained the performance results of each algorithm as follows: 90% accuracy, 91% precision, 89% recall, and f1 90% for the performance results of the Logistic Regression algorithm, while for the performance results of the Random Forest algorithm accuracy 89%, precision 92%, recall 86% and f1-score 89%. From these results the Logical Regression algorithm has better performance than the Random Forest algorithm.
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 Aulia Hamdi 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 Khofiyah, Salma Ngarifatul 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 Septiana Putri, Refida Waluyo, Retno