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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Mitra Mahajana: Jurnal Pengabdian Masyarakat
Hamdikatama, Bimantyoso
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Industrial & Manufacturing Engineering Public Health Social Sciences

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Comparison of The Performance of SVR, KNN and Decision Tree Methods in Predicting Rice Production Hamdikatama, Bimantyoso; Kusrini, Kusrini; Setyanto, Arief
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.10133

Abstract

Rice holds importance in Indonesia as a commodity driving the economy and improving societal well-being, however, its production encounters obstacles attributed to the effects of drastic climate variations. This study sought to evaluate how Support Vector Regression (SVR) k Nearest Neighbors (KNN) and Decision Tree models perform in forecasting rice yields while considering variables related to climate change. The research process included stages such, as gathering and cleaning the information before exploring and analyzing it to apply metrics and implement algorithms like Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) and R² Score, for evaluation purposes. The findings obtained from the study indicate that the Decision Tree technique is efficient, achieving a minimal deviation rate of 0%. This outcome implies that the model effectively grasped the core patterns within the dataset while reducing errors effectively. The KNN model displayed performance levels and suggested room, for enhancement with parameter adjustments; however, SVM Regression was deemed fitting for the datasets needs. The results emphasize the significance of choosing the algorithm for modeling in agriculture and stress the necessity, for additional research to confirm these findings in various datasets.
EMPOWERING RURAL EDUCATORS THROUGH AI LITERACY: CHATGPT TRAINING AT SD NEGERI 3 SIBETAN KARANGASEM BALI Hamdikatama, Bimantyoso; Kusrini, Kusrini; Utami, Ema
Mitra Mahajana: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Volume 6 Nomor 2 Juli 2025
Publisher : LPPM Universitas Flores

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/mahajana.v6i2.5853

Abstract

The advancement of Artificial Intelligence (AI) has had a significant impact on various sectors, including education. However, the adoption of AI in Indonesia remains uneven, particularly in remote and rural areas. This study aims to assess the effectiveness of a training program on the use of ChatGPT as a teaching aid for elementary school teachers at SD Negeri 3 Sibetan, Karangasem, Bali. The training was designed to enhance teachers' understanding, practical skills, and perceptions of AI integration in education. Using a quantitative approach with a one-group pretest-posttest experimental design, data were collected through conceptual knowledge tests, practical skill observations, and perception questionnaires. The results revealed a significant increase in teachers' knowledge, with average posttest scores rising from 32.2 to 78.0. Additionally, practical skills improved notably, as indicated by a posttest average score of 73.0. Positive perception also increased, with 71% of participants expressing enthusiasm for using ChatGPT in the classroom. Despite limited infrastructure, the training successfully introduced AI-based tools to rural educators, demonstrating the transformative potential of AI in promoting equitable, innovative, and interactive education. This study contributes to the discourse on AI in education and underscores the importance of contextualised teacher training in rural settings.
Co-Authors Arief Setyanto Ema Utami Kusrini, Kusrini