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All Journal Telematika JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal INTECOMS: Journal of Information Technology and Computer Science METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) Journal on Education CCIT (Creative Communication and Innovative Technology) Journal Jurnal Tecnoscienza JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Infotek : Jurnal Informatika dan Teknologi Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan EXPLORE Indonesian Journal Computer Science (ijcs) Explore Jurnal Pengabdian Masyarakat (JURDIMAS)
Anggit Dwi Hartanto
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 Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method Hery Priandoko; Alva Hendi Muhammad; Anggit Dwi Hartanto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.219

Abstract

Indonesia consists of several regions that have the potential to meet food needs. One of the main sectors that meet food needs is the agricultural sector. The agricultural sector is a sector that needs significant attention from the central and regional governments in meeting national food needs. Food needs are currently often scarce so people find it difficult to obtain these food needs. The problem of dependence on food needs can endanger the availability of the country's food supply. Importing food crop commodities is one solution to maintaining food availability in Indonesia. Imports of food crop commodities carried out by Indonesia show that the amount of food commodity availability cannot meet national food needs. In Indonesia, some regions have food crop commodity production so that they can help in the availability of these food needs. From the existing problems, researchers tried to conduct research by mapping the regions or areas in Indonesia to find out which regions have food crop commodity production. In this study, the mapping that will be used is using the hierarchical cluster method. The hierarchical cluster method that will be used is the agglomerative hierarchical cluster method with the average linkage method. The results of this study will be formed into 3 clusters with the following details: high cluster, medium cluster, and low cluster. The highest cluster obtained 2 members, namely the Provinces of East Java and Central Java. The Medium Cluster obtained 1 member, namely the Province of West Java. The Low Cluster obtained 31 members, namely the Provinces of Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu, Lampung, Bangka Belitung Islands, Riau Islands, DKI Jakarta, DI. Yogyakarta, Banten, Bali, West Nusa Tenggara, East Nusa Tenggara, West Kalimantan, Central Kalimantan, South Kalimantan, East Kalimantan, North Kalimantan, North Sulawesi, Central Sulawesi, South Sulawesi, Southeast Sulawesi, Gorontalo, West Sulawesi, Maluku, North Maluku, West Papua, and Papua.
A Multispectral YOLOv8-Based System for Real-Time Object Detection and Distance Estimation in Blind Navigation Ema Utami; Erwin Syahrudin; Anggit Dwi Hartanto; Suwanto Raharjo
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28373

Abstract

Developing reliable real-time navigation systems for visually impaired individuals remains challenging, particularly in dynamic and low-light environments. This study proposes an integrated framework combining YOLOv8, OpenCV-based monocular distance estimation, and RGB–NIR multispectral imaging to enhance detection robustness and distance awareness. A dataset of 1,700 annotated images collected from diverse indoor and outdoor environments was used for training and evaluation using preprocessing techniques such as resizing, normalization, and data augmentation. System performance was evaluated using Precision, Recall, F1-Score, mean Average Precision (mAP), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Frames Per Second (FPS). Experimental results show that YOLOv8x achieved the best performance with an F1-Score of 0.91, mAP@50 of 0.74, MAE of 0.15 m, RMSE of 0.20 m, and a processing speed of 22 FPS. Multispectral RGB–NIR integration further improved low-light performance, increasing the F1-Score from 0.83 to 0.89 and reducing MAE from 0.28 m to 0.19 m with only a minor reduction in speed. These findings demonstrate that the proposed system provides an effective balance between accuracy and real-time performance for assistive navigation applications.
Stock Price Prediction Using SVR: A Feature Engineering and Hyperparameter Tuning Approach Alfian Ramadhan; Yoga Pristyanto; Anggit Dwi Hartanto; Donni Prabowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7249

Abstract

Stock price prediction in Indonesia's volatile mining sector poses significant forecasting challenges driven by commodity price dynamics and structural market shifts. This study proposes a systematic prediction framework for PT Indo Tambangraya Megah Tbk (ITMG.JK) integrating technical and market-derived non-technical feature engineering, LightGBM-based feature selection, multilevel TimeSeriesSplit cross-validation, and hyperparameter optimization. Support Vector Regression (SVR) is benchmarked against LightGBM, XGBoost, and Random Forest under 5-fold, 10-fold, and 15-fold schemes. SVR achieves the best performance at 10-fold, with RMSE of 0.0121, MAE of 0.0090, MAPE of 1.1457%, and R² of 0.9249. Generalization experiments across four additional stocks in banking, automotive, and mining sectors confirm SVR's robustness, maintaining R² above 0.89 and MAPE below 2.65% in all cases while tree-based models produce negative R² on certain datasets. Statistical validation via Wilcoxon signed-rank test (p < 0.05) and Cohen's d (|d| > 0.8) confirms the significance of SVR's advantage. These findings indicate that SVR consistently outperforms the evaluated models under the proposed experimental framework.
Co-Authors Ahmad Fauzi Akhmad Efendi Al Abdul Rohim Alfian Ramadhan Alva Hendi Muhammad Alvina Felicia Watratan Anik Sri Widowati Biva Candra Lutfi Adiatma Bobby Candera Lim Candra Adipradana Deas Pradana Donni Prabowo Edy Hardianto Rifeni Ema Utami Eny Nurnilawati Erwin Syahrudin Fajar Surya Putro Fakhrur Wiradhika Halim Bayuaji Sumarna Hanafi Hanafi Hartatik Hery Priandoko Idris Idris Ivan Rifky Hendrawan Jangkung Tri Nygroho Kusrini Muhammad Ilham Fadila Muhammad Ridlo Arifandi Mulia Sulistiyono Omar Muhammad Altoumi Alsyaibani Pamela Hapsari Putri Rumini Rumini Suwanto Raharjo Taufiq Nashrullah Triana Triana Wahyu Hidayat wahyuni, wenti ayu Winarnie Yoga Pristyanto