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All Journal JUITA : Jurnal Informatika Journal of Information Systems and Informatics
Didi Supriyadi
Telkom University
Computer Science & IT

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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.
Customer Segmentation in an Internet Service Provider: A K-Means Case Study of Telecommunication Company Merleen Januar; Didi Supriyadi
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1631

Abstract

PT Lintas Jaringan Nusantara, an internet service provider, faces challenges in utilizing customer data, which is mainly used for administrative purposes such as billing and support, limiting deeper analysis. This study applies K-Means clustering under the CRISP-DM framework for customer segmentation-based service-oriented attributes: internet package, price, and NAS location, using 972 customer records. Categorical attributes were transformed using frequency encoding and manual mapping. Model evaluation using the Elbow Method suggested 3 clusters, while the Silhouette Coefficient indicated that 10 clusters were optimal, improving the score from 0.5471 to 0.7704. The resulting clusters show variations in customer characteristics and provide an exploratory overview of grouping patterns. However, the 10 clusters solution should not yet operationally validated, as stakeholder validation involving marketing and customer service teams is still required to assess interpretability, business relevance, and practical applicability. Further validation using additional customer data or alternative datasets is also recommended. Overall, the findings serve as an initial analytical step to support future data-driven decision-making.
Multi-Class Mental Health Classification Based on DASS-21 and Perceived Social Support Using Machine Learning Algorithms Winny Dwita Sumbayak; Didi Supriyadi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30073

Abstract

Mental health issues among students require data-driven approaches for early identification. This study aims to classify students’ mental health levels using the Depression Anxiety Stress Scale (DASS-21) and perceived social support, measured by the Multidimensional Scale of Perceived Social Support (MSPSS), via machine learning algorithms. A supervised classification approach was employed using Random Forest, Support Vector Machine, and Logistic Regression on data collected from 450 respondents. The data were processed through scoring, labeling, encoding, balancing, and stratified 80:20 splitting. Model evaluation was conducted using hold-out testing and 5-fold cross-validation to ensure robust and reliable performance estimation. The results indicate that Random Forest achieved the best performance, with an accuracy of 0.97 on the test set, outperforming Support Vector Machine (0.81) and Logistic Regression (0.83). Improvements in recall and F1-score for minority classes demonstrate the effectiveness of the balancing process. These findings highlight the potential of machine learning for student mental health classification, although further validation on larger and more diverse datasets is required.
Co-Authors Daniel Yeri Kristiyanto Fadiana Isnaeni Nurul Agita Merleen Januar Winny Dwita Sumbayak