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All Journal Journal of Information Systems and Informatics
Ahmad Yusuf
Politeknik Negeri Banjarmasin
Computer Science & IT

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A Lightweight Classical Machine Learning Pipeline for Rice NPK Deficiency Classification Using Hand-Crafted Feature Fusion Dhiyaussalam; Kun Nursyaiful Priyo Pamungkas; Wanvy Arifha Saputra; Ahmad Yusuf
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1486

Abstract

Many high-accuracy deep learning solutions for plant nutrient deficiency remain impractical in resource-limited settings due to computational cost and limited explainability. This study proposes a lightweight classical machine learning pipeline for rice leaf NPK (nitrogen, phosphorus, potassium) deficiency classification on the publicly available Kaggle Nutrient-Deficiency-Symptoms-in-Rice dataset (1,156 images); all results should be interpreted in this dataset context rather than as field-validated performance. The pipeline applies HSV-based leaf segmentation to reduce background influence. It extracts a 126-dimensional feature set combining masked color moments, HSV histograms, vegetation indices, LBP and GLCM texture descriptors, and spatial symptom ratios. Hyperparameters are tuned via RandomizedSearchCV with 5-fold StratifiedKFold and macro-F1 scoring; final evaluation uses a held-out 80/20 stratified test set kept separate throughout tuning. XGBoost achieves the best test performance (accuracy 0.9267; macro-F1 0.9233), followed by SVM-RBF (0.9224; 0.9187) and Random Forest. Feature importance analysis confirms that color moments dominate class separability, with texture and spatial features providing complementary support. The dominant remaining error is phosphorus–potassium confusion. The novelty lies in integrating leaf-focused preprocessing with a structured, low-cost feature representation suitable for mobile or edge deployment.
Capability Gap Analysis in IT Governance for a Logistics Company Using COBIT 2019 Ahmad Yusuf; Wanvy Arifha Saputra; Jamilah Jamilah
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.832

Abstract

This paper aims to evaluate the IT governance capability levels within a logistics company using the COBIT 2019 framework, focusing on identifying gaps between the current state (as-is) and the desired state (to-be). The methodology involves an assessment of three key Governance and Management Objectives: stakeholder engagement (EDM05), IT asset management (BAI09), and IT compliance (MEA03). The results indicate that all objectives are currently at capability level 1, highlighting the early stages of implementation and the need for substantial improvement. The study concludes that addressing these gaps through the COBIT 2019 framework will significantly enhance the company’s IT governance, leading to improved operational efficiency, stronger risk management, and better alignment with strategic business goals. These findings provide actionable insights for advancing IT governance maturity, contributing to the long-term success and competitiveness of the company in the logistics sector.
Predicting Respiratory Conditions Using Random Forest and XGBoost Dhiyaussalam Dhiyaussalam; Ahmad Yusuf; Isna Wardiah; Nitami Lestari Putri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1124

Abstract

This study examines the performance of Random Forest and XGBoost in predicting the diagnosis and severity of respiratory diseases using a simulated dataset of 2,000 patient records. The models were tested on two classification tasks: identifying disease types (e.g., pneumonia, influenza) and classifying severity levels (mild, moderate, severe). Both models achieved perfect accuracy in severity classification, with 1.0000 ± 0.0000 cross-validation scores, demonstrating strong stability under balanced class distributions. However, in the diagnosis task, Random Forest underperformed on minority classes, particularly pneumonia, with a recall of 0.18 and F1-score of 0.31. XGBoost, on the other hand, achieved superior results across all classes, including minority cases, with 0.9825 ± 0.0170 cross-validation accuracy and perfect test set performance. These findings highlight XGBoost’s robustness in handling imbalanced and multiclass medical data, making it a promising candidate for clinical decision support. Future work should address class imbalance and explore explainability techniques to improve trust and transparency in real-world applications.
Co-Authors Dhiyaussalam Dhiyaussalam Dhiyaussalam Isna Wardiah Jamilah Jamilah Kun Nursyaiful Priyo Pamungkas Nitami Lestari Putri Wanvy Arifha Saputra