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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Information System Journal (INFOS) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Pengabdian Mitra Masyarakat (JPMM) Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Advanced Science Computing and Engineering SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Scientific Journal of Informatics SWAGATI: Journal of Community Service Edu Komputika Journal Jurnal Pengabdian Masyarakat Inovasi Indonesia
Yoga Pristyanto
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Provincial Clustering using GARCH-based Chili Price Volatility Features Yogata Rama Guninta; Bety Wulan Sari; Yoga Pristyanto
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6521

Abstract

Bird's eye chili is a strategic food commodity in Indonesia whose prices are highly susceptible to interregional fluctuations due to differences in distribution systems and supply chain conditions. These fluctuations often occur over short time horizons, making analyses based on monthly or annual data less capable of capturing short-term price spikes that have the greatest impact on consumers' purchasing power and price stabilization policies. Previous studies have generally clustered regions based on nominal or average prices, which do not adequately represent the dynamics of daily price movements. This study aims to cluster Indonesian provinces according to the volatility characteristics of bird's eye chili prices by proposing a clustering approach that utilizes GARCH(1,1)-based conditional volatility features to represent daily price dynamics, combined with the K-Means algorithm for cluster formation. Daily bird's eye chili price data from 34 provinces covering the period from April 2024 to April 2026 were obtained from the National Strategic Food Price Information Center (PIHPS). The price data were transformed into daily returns and modeled using GARCH(1,1) to estimate the average conditional volatility of each province, which was subsequently used as the clustering feature. The optimal number of clusters was determined using the Elbow Method and the Silhouette Score. The evaluation results identified five as the optimal number of clusters, achieving a silhouette score of 0.65 and classifying the 34 provinces into five volatility categories: very high, high, moderate, low, and very low. The findings reveal that provinces with higher price levels do not necessarily belong to the highest volatility cluster, indicating that a volatility-based approach provides additional insights into price dynamics beyond those captured by nominal price-based clustering. These results can support regional food price volatility monitoring and serve as a reference for developing data-driven decision support systems for food price management.
Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Donni Prabowo; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6449

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.
Open-Set Recognition for Potato Leaf Disease Identification Using OpenMax Ike Verawati; Mambaul Hisam; Yoga Pristyanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6525

Abstract

Traditional methods for identifying potato leaf diseases rely on manual visual inspection, which is prone to human error and inefficiency. While machine learning models have improved automation, conventional closed-set classifiers fail to recognize unknown diseases outside their training scope, limiting real-world applicability. This study addresses this gap by implementing Open-Set Recognition (OSR) using the OpenMax framework to classify known potato leaf diseases while effectively rejecting unknown pathologies. Leveraging the Xception architecture with dual learning schedulers (ReduceLROnPlateau and StepLR), we optimized OpenMax parameters, including distance metrics (Euclidean, Eucos) and rejection thresholds. After rigorous tuning, the model achieved 86.8% accuracy and 86.4% F1-score under an openness score of 18.3%, with optimal performance using Euclidean distance and a 0.95 threshold. The results demonstrate robust discrimination between known classes (potato late blight, early blight, healthy leaves) and visually similar unknown classes (e.g., tomato diseases, healthy bell peppers). This work enhances AI-driven agricultural diagnostics by bridging the gap between closed-set precision and open-set practicality, offering a scalable solution for real-world disease identification where novel pathogens may emerge.
Co-Authors Acihmah Sidauruk Aditya Yoga Pratama Afrig Aminuddin Aisha Shakila Iedwan Akhmad Dahlan Alfian Ramadhan Alvin Rahman Al Musyaffa Andi Sunyoto Angelina Putri Ariani Anggi Thoat Ariyanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggita, Sharazita Dyah Anna Baita Ariefhan Maulana arif nur rohman Arif Nur Rohman Arif Nur Rohman Asti Astuti, Ika Atik Nurmasani ATIK NURMASANI Atik Nurmasani Barus, Herianta Bety Wulan Sari Bety Wulan Sari, Bety Wulan Bligania Bligania Cherfly Kaope Dewi Ayu Murtiningsih Donni Prabowo Donni Prabowo, Donni Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Eza Nanda Fadhilah Dwi Ananda Fajri, Ika Nur Fauzy, Marwan Noor Gagah Gumelar Gita Cahyani Hanif Al Fatta Heri Sismoro Hidayat, Kardilah Rohmat Ibnu Hadi Purwanto Ibnu Hadi Purwanto Ibrahim Aji Fajar Romadhon Iedwan, Aisha Shakila Ike Verawati Ikmah Ikmah Irfan Pratama Irma Rofni Wulandari Istikomah Khoiruddin, Lukman Kono, Maria Fatima Kristianti, Fanny Novatriana LILIS DWI FARIDA Lucky Adhikrisna Wirasakti Mambaul Hisam Marcheilla Trecya Anindita Mauliza, Nia Mukarabiman, Zulfikar Mulia Sulistiyono Natasaskara, Nandana Ayudya Nia Mauliza Nia Mauliza Norhikmah Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Nurwijayanti Purwati, Sintia Eka Putra, Frahma Aditya Rahman Saputra, Rahman Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Rohman, Arif Nur Romadhon, Ibrahim Aji Fajar Rospita, Andri Sabella, Cindy Dinda Sifa’ul Husna, Siti Okta Sumarni Adi Utama, Hastari Windarni, Vikky Aprelia Wirantanu, Dipa Wirasakti, Lucky Adhikrisna Wiwi Widayani Yanuar Nur Kholik Yogata Rama Guninta Yudiyanto, Muhammad Resa Arif Yuli Astuti Zein, Aditya Ahmad