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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JSiI (Jurnal Sistem Informasi) Jurnal Pengabdian Masyarakat Tekno
Rizka Tiaharyadini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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DEVELOPMENT OF A PREDICTIVE MODEL FOR EARLY CHILDHOOD LEARNING SUCCESS BASED ON ENSEMBLE LEARNING WITH INTEGRATION OF PSYCHOLOGICAL AND DEMOGRAPHIC DATA Zaqi Kurniawan; Rizka Tiaharyadini; Arief Wibowo
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.9956

Abstract

Early chilhood learning serves as a crucial foundation for cognitive and emotional development, significantly influencing future academic success. The use of machine learning technologies presents chances to improve the effectiveness and scalability of educational practices in the digital age. By creating an ensemble learning-based model which includes both demographic and psychological data. This study overcomes the shortcomings of earlier research, which frequently ignores the psychological elements operating learning outcomes. The F1-Score, Accuracy, Precision, and Recall measures are used in this study to evaluate prediction using Random Forests and Gradient Boosting Machines. With an F1-Score of 89%, Accuracy of 92 %, Precision of 90%, and Recall of 88%, the Random Forest model exceeded Gradient Boosting, proving its ability to manage data complexity while finding a balance between precision and recall. The results show while demographic characteristics like age, gender, and parental occupation have little impact on early learning achievement, academic performance and attendance are the most important predictors. This emphasizes the necessity of focused tactics to improve academic achievement and classroom engagement. The study is limited by the representativeness of the dataset and the limited extent of psychological data, notwithstanding its contributions. To improve the interpretability and use of prediction models in early childhood education, future research should address these constraints by integrating qualitative methodologies, utilizing sophisticated machine learning techniques, and considering larger psychological factors
Peningkatan Minat dan Kesiapan Akademik Siswa melalui Simulasi Edukatif Budi Luhur College Rizka Tiaharyadini; Zaqi Kurniawan; Windhy Widhyanty
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 6 No 1 (2025): Juni 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v6i1.6671

Abstract

The transition from high school to higher education is a critical phase that demands academic readiness, mental preparedness, and strong student motivation. However, many high school students still have limited understanding of university life and the realities of the academic process. The lack of exposure to campus environments contributes to low motivation and inadequate preparation for continuing their studies, highlighting the need for a more contextual and interactive educational approach. In response, the Budi Luhur College 2025 program was designed to provide direct experience through lecture simulations, computer laboratory practicums, and faculty excursions. The program engaged 160 Grade XI students from SMA Budi Luhur, conducted over 14 sessions from February 14 to May 30, 2025. Evaluation results showed a significant increase in student satisfaction, with the number of students rating the program as “good/very good” rising from 38 at the beginning to 47 by the end. Additionally, 82% of participants reported improved understanding of campus life, and 75% stated they felt more prepared to pursue higher education. The most influential satisfaction factors were direct lecture experience (31.2%) and faculty excursions (31.2%). These findings suggest that the participatory and simulation-based approach used in the program is effective in enhancing students’ academic orientation and motivation. This success presents opportunities for future program expansion, either as an annual institutional initiative or as a sustainable collaborative community service model.
ANALYZING CLIMATE IMPACTS ON RICE PRODUCTION IN SUMATRA THROUGH SPATIOTEMPORAL MACHINE LEARNING MODELS Zaqi Kurniawan; Rizka Tiaharyadini; Puguh Jayadi; Windhy widhyanty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7344

Abstract

Climate variability poses a major challenge to rice production in Sumatra, a key contributor to Indonesia’s food security. This study aims to analyze spatiotemporal climate impacts on rice yields by integrating climatic, geographical, and agricultural datasets. Historical records from 1993–2024, including rainfall, temperature, humidity, and rice production statistics, were collected from BMKG, BPS, and the Ministry of Agriculture. After preprocessing and feature selection, six machine learning algorithms—Linear Regression, Random Forest, Gradient Boosting, Support Vector Regression, Decision Tree, and K-Nearest Neighbors—were evaluated for predictive performance. Results show significant spatial heterogeneity: rainfall strongly affects yields in Aceh and North Sumatra, while temperature stress is critical in southern provinces. Among the tested models, Random Forest achieved the best accuracy (R² = 0.985), outperforming other algorithms. These findings highlight the importance of localized adaptation strategies and demonstrate the potential of ensemble machine learning to support climate-resilient rice production.
Co-Authors Arief Wibowo Puguh Jayadi Windhy Widhyanty Zaqi Kurniawan