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All Journal Journal of Information Systems Engineering and Business Intelligence Jutech: Jurnal Teknologi Informasi
Rafi, Haris
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Computer Science & IT

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Model-based Decision Support System Using a System Dynamics Approach to Increase Corn Productivity Suryani, Erma; Rafi, Haris; Utamima, Amalia
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.139-151

Abstract

Background: As the population increases, the need for corn products also increases. Corn is needed for various purposes, such as food consumption, industry, and animal feed. Therefore, increasing corn production is crucial to support food availability and the food industry. Objective: The objective of this project is to create a model to increase corn farming productivity using scenarios from drip irrigation systems and farmer field school programs. Methods: A system dynamics approach is utilized to model the complexity and nonlinear behaviour of the corn farming system. In addition, several scenarios are formulated to achieve the objective of increasing corn productivity. Results: Simulation results showed that adopting a drip irrigation system and operating a farmer field school program would increase corn productivity. Conclusion: The corn farming system model was successfully developed in this research. The scenario of implementing a drip irrigation system and the farmer field school program allowed farmers to increase corn productivity. Through the scenario of implementing a drip irrigation system, farmers can save water use, thereby reducing the impact of drought. Meanwhile, the scenario of the farmer field school program enables farmers to manage agriculture effectively. This study suggests that further research could consider the byproducts of corn production to increase the profits of corn farmers.   Keywords: Corn Farming, Decision Support System, Modeling, Simulation, System Dynamics
Predictive Maintenance Berbasis Machine Learning dalam Smart Manufacturing Rafi, Haris
Jurnal Teknologi Informasi (JUTECH) Vol 6 No 2 (2025): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v6i2.3333

Abstract

The concept of predictive maintenance represents a significant change in traditional maintenance methods. The use of machine learning in manufacturing machine maintenance has the potential to offer unprecedented opportunities for predicting problems by uncovering hidden patterns in vast data sets. This study aims to examine four machine learning models in classifying maintenance needs in a smart manufacturing environment. Machine learning models such as Logistic Regression, Random Forest, XGBoost, and Multi-layer Perceptrong (MLP) are trained with 5-fold cross-validation. The dataset used is a public dataset from the kaggle website, which consists of 10000 rows and 13 features with the maintenance_required feature as the target feature. The model training results are evaluated using various metrics, such as accuracy, precision, recall, f1-score, and ROC-AUC. The test results show that Random Forest provides the best performance with an accuracy of 98.37%, precision of 99.97%, recall of 91.72%, f1-score of 95.67%, and ROC-AUC of 95.95%. The tree-based ensemble method Random Forest is able to capture patterns in the data better than linear and neural models. This indicates that Random Forest is a reliable model for detecting machine maintenance requirements. Further research can consider increasing dataset capacity, integration with deep learning techniques, examining the perspective of multivariate time-series structures.
Co-Authors Amalia Utamima Erma Suryani