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All Journal JETL (Journal Of Education, Teaching and Learning) MIND (Multimedia Artificial Intelligent Networking Database) Journal JTP - Jurnal Teknologi Pendidikan REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Sulawesi Tenggara Educational Journal Indonesian Journal of Community Services JSES
Sulfikar Sallu
Fakultas Teknologi Informasi, Universitas Sembilanbelas November Kolaka, Indonesia
Religion Aerospace Engineering Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Social Sciences Other

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Random Forest Machine Learning Analysis of Generative AI’s Impact on Learning Effectiveness in Indonesian Higher Education Sallu, Sulfikar; Hendriadi, Hendriadi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5365

Abstract

Generative Artificial Intelligence (GenAI) has rapidly penetrated Indonesian higher education, creating opportunities for learning innovation while raising concerns about effectiveness and academic integrity. This study develops a machine learning–based quantitative model to analyze the impact of GenAI usage on learning effectiveness, with a particular focus on Informatics students as key digital literacy stakeholders. Data were collected from a simulated survey of 300 students, covering demographics, GPA, exam scores, GenAI usage patterns, digital literacy, motivation, self-efficacy, academic integrity, and institutional support. Preprocessing steps included normalization of continuous variables, one-hot encoding of categorical variables, and feature selection using Recursive Feature Elimination (RFE). Six machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network—were compared to identify the best predictive model. Results show that Random Forest achieved the highest performance, with 87% accuracy and an AUC greater than 0.90, significantly outperforming other algorithms. The most influential predictors were digital literacy, institutional policies, and frequency of GenAI usage, while demographic variables contributed minimally. These findings suggest that GenAI can enhance learning effectiveness in Informatics education when supported by critical digital literacy and ethical awareness. The novelty of this study lies in integrating survey-based educational data with Random Forest machine learning to empirically model GenAI’s role in Indonesian higher education. The results provide practical implications for policymakers, educators, and institutions to design AI-integrated learning strategies that maximize innovation while safeguarding academic integrity.
Implementation of Waterfall Method in Model Development to Improve Learning Quality of Computer Network Courses Sallu, Sulfikar; Harsono, Yhonanda; Fajarianto, Otto
JTP - Jurnal Teknologi Pendidikan Vol. 25 No. 3 (2023): Jurnal Teknologi Pendidikan
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jtp.v25i3.44418

Abstract

This research aims to improve the learning quality of Computer Network course through the implementation of Waterfall method in the development of learning model. Waterfall method, with its focus on systematic and sequential approach in software development, is adapted to design and implement effective learning structure. This study uses qualitative research design with data collection through observation, interview, and documentation. Data analysis was conducted using content analysis method to evaluate the effectiveness of Waterfall-based learning model implementation. The results show that the implementation of Waterfall method facilitates structured planning, systematic development of learning materials, and continuous evaluation, which overall contribute to the improvement of learning quality. The developed learning model encourages students' active participation and improves the understanding of key concepts in Computer Networking. This research confirms that the Waterfall method can be effectively used outside the context of software development, particularly in improving the quality of learning in the academic field.
Sentinel-2 NDVI Analysis Using GEE and QGIS for Green Open Space Sustainability Assessment in Kendari City Sufrianto, Sufrianto; Yaacob Zubir, Siti Sara; Jassin, Andi Makkawaru Isazarni; Brata, Joko Tri; Danggi, Erni; Sallu, Sulfikar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5409

Abstract

Rapid urbanization has profoundly transformed land cover in many growing cities, leading to a substantial decline in Green Open Space (GOS) and a progressive deterioration of ecological functions. The continuous conversion of vegetated zones into impervious and built-up surfaces has reduced the city’s ability to absorb carbon, regulate local microclimates, and maintain overall ecological resilience. Consequently, assessing the sustainability and spatial distribution of GOS is crucial for ensuring environmentally balanced urban development and resilience to future land-use pressures. This study aims to evaluate the sustainability of urban green spaces in Kendari City through an integrated geospatial approach that combines remote sensing and open-source cloud computing technologies. Sentinel-2 Level-2A imagery was analyzed in Google Earth Engine (GEE) using the QA60 band for cloud masking and spatial clipping to accurately define the study boundaries. Normalized Difference Vegetation Index (NDVI) values were subsequently processed and classified in QGIS using a reclassification technique to distinguish vegetation density categories. The results indicate that 56.7% of the total land area, equivalent to 15,213 hectares, exhibits high greenness, reflecting dense and healthy vegetation, whereas 32.3% consists of low or non-vegetated surfaces dominated by built-up and barren lands. These findings reveal substantial spatial disparities in vegetation coverage and underscore the importance of sustainable land management and green infrastructure policies. Furthermore, this research contributes to the advancement of geospatial informatics by developing an open, reproducible workflow that integrates cloud-based computation and open-source GIS for urban ecological monitoring and sustainability assessment.
Co-Authors Ahmad Fathoni Ashari Ashari Brata, Joko Tri Darlispo n Erni Danggi Erwin Eka Saputra Harsono, Yhonanda Hasbi Hendriadi, Hendriadi Irawati Jassin, Andi Makkawaru Isazarni n, Darlispo Noorhasanah Zainuddin Otto Fajarianto Parisu, Chairan Zibar L. Qammaddin Qammaddin Qammaddin Qammaddin Rabiah Adawiyah Raehang Raehang Raehang Raehang, Raehang Sufrianto, Sufrianto Wahidin Wahidin Yaacob Zubir, Siti Sara