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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 682 Documents
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Utilization of the AgriTrack Information System to Strengthen Smart Farming Practices in Small-Scale Hydroponic Enterprises Sholeha, Eka Wahyu; Supriyanto, Arif; Utomo, Hendrik Setyo; Firmansyah, Eka Ridhoni August; Aisyah, Aisyah; Hidayat, Ardhi; Mardhiyatirrahmah, Liny
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1385

Abstract

The implementation of smart farming in small-scale hydroponic enterprises is often constrained by high automation costs and technological complexity. This study examines the utilization of the AgriTrack information system as a practical approach to strengthening smart farming practices through structured digital data management. AgriTrack was utilized in a small-scale hydroponic farm using a Software Development Life Cycle (SDLC) Waterfall approach, encompassing system configuration, operational deployment, and evaluation through functional testing and user acceptance testing. The system applies a cycle-based relational data model to manage cultivation records from sowing to harvesting and integrates automated scheduling with Telegram Bot notifications. Testing results indicate a 100% success rate across core operational functions, while user evaluation shows that routine cultivation data recording time was reduced from several minutes to under one minute per entry. Notification delivery was consistently observed within approximately one minute after scheduled triggers, supporting timely operational decisions. These findings demonstrate that AgriTrack effectively strengthens smart farming practices in MSME-scale hydroponic enterprises by improving efficiency and accountability, while providing a scalable foundation for gradual adoption of advanced technologies such as IoT and data analytics.
Comparative Analysis of Random Forest, Logistic Regression and SVM for Stunting Prediction Using Anthropometric Data Widyawati, Shalsa Bela Dwi; Purwadi, Purwadi; Yunita, Ika Romadoni
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1387

Abstract

Stunting remains a critical nutritional issue in Indonesia, significantly impacting the physical and cognitive development of children under five. Prompt and accurate detection of nutritional status is essential for early intervention. This study aims to predict toddlers' nutritional health using the Random Forest algorithm, based on age and height data. From an initial dataset of 120,998 anthropometric records, preprocessing steps—such as duplicate removal and nutritional status recategorization—resulted in a final dataset of 39,425 entries. The research methodology includes data collection, preprocessing, exploratory analysis, model training, handling class imbalance, and performance evaluation using accuracy, precision, recall, and F1-score. The study also compares the Random Forest model with Logistic Regression and Support Vector Machine (SVM). Results show that Random Forest outperforms the other models, achieving perfect classification metrics: Accuracy (1.00), Recall (1.00), F1-Score (1.00), and Cross-validation Accuracy (99.74%). These outcomes highlight Random Forest's robustness in classifying under-five nutrition data, making it an effective tool for rapid and reliable stunting risk detection. This research supports efforts to reduce Indonesia's stunting rate to below 20% by 2024, contributing to national health improvement strategies through technology-driven early diagnosis.

Page 69 of 69 | Total Record : 682

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