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All Journal Journal of Information Systems and Informatics Journal of Innovation Information Technology and Application (JINITA)
Fadiana
Telkom University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Sensor-Driven Nutrient Monitoring Using a Two-Layer Machine Learning Model for Sugarcane Fertilization Recommendation Fadiana; Didi Supriyadi; Daniel Yeri Kristiyanto; Isnaeni Nurul Agita
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.v8i2.1547

Abstract

The growth of sugarcane requires optimal environmental conditions and the availability of balanced nutrients. However, fulfilling nutrition is a challenge because it requires targeted observation. The study proposes a machine learning-based decision support model using a predictive empirical approach to monitor nutrient needs and recommend fertilizer dosages. The proposed approach integrates field data with a two-layer modeling framework to support fertilization decision-making. The classification model predicts the status of nutrient adequacy, while the regression model estimates the level of fertilizer application. The target label (y) is generated through feature extraction using a rule-based empirical formula derived from the threshold of agronomic parameters. The nutrients analyzed included macronutrients (nitrogen, phosphorus, potassium) and micronutrients (iron, zinc, copper). Model development involves selecting the best-performing algorithm using recall for classification and RMSE and R² for regression. The results of the cross-validation showed that the Gradient Boosting algorithm achieved the most consistent performance, with a recall of 0.99 during training and >0.98 in holdout testing. The regression model also showed low RMSE and high R² values, especially for micronutrient estimation. The proposed model contributes to data-driven fertilization optimization.
A Hybrid K-Means-Random Forest Approach for Optimizing CO Air Pollution Prediction Fadiana; Ria Andara; Azaila Dwi; Rona Nisa
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2997

Abstract

Air quality is becoming an increasingly worrying global issue due to increasing air pollution. The increase in air pollution in the environment encourages the presence of innovative solutions in terms of countermeasures and prevention. This study compares regression algorithms (Random Forest Regression, Linear Regression, SVR, Decision Tree Regression, and KNN Regression) to find the best prediction model. Feature development is carried out using a clustering algorithm (K-means) to produce new features that are able to support the optimization of the model search process and prediction results. Model quality measurement was carried out by applying Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R2 metrics. The results showed that the best model was RFR, which excelled at the R2 approach = 0.75, MSE = 0.008823, RMSE = 0.093931, MAE = 0.060096, and ROC-AUC = 0.951. These findings suggest the effectiveness and quality of prediction models in supporting efforts to develop an early warning system based on ensemble learning dashboards. This research contributes practically to the application of machine learning in air pollution mitigation, as well as supporting the achievement of SDGs 3: Good Health and Well-Being through the provision of a healthier environment.
Co-Authors Azaila Dwi Daniel Yeri Kristiyanto Didi Supriyadi Isnaeni Nurul Agita Ria Andara Rona Nisa