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All Journal Jurnal Pengajaran MIPA Journal of Natural Science and Integration JURNAL PETIK Journal of Education Technology International Journal of Electronics and Communications Systems Digital Transformation Technology (Digitech) Jurnal Guru Komputer Jurnal Pendidikan Teknologi Informasi dan Komunikasi Journal of Environment and Sustainability Education
Eki Nugraha
Universitas Pendidikan Indonesia
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering Environmental Science Library & Information Science Physics Social Sciences Other

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School Feasibility Analysis and Grade Improvement Strategies Using the Random Forest Algorithm Aliyya, Farrel Rahma; Farizi, Syahandhika Naufal; Riza, Lala Septem; Megasari, Rani; Nugraha, Eki; Wahyudin, Asep
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 2 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i2.475

Abstract

Background of Study: Educational disparities across Indonesian provinces persist, particularly in infrastructure, teacher quality, and dropout rates, necessitating data-driven analysis for equitable improvements.Aims: This study investigates school feasibility and proposes strategies to enhance provincial education performance using the Random Forest algorithm.Methods: Aggregated provincial education data covering student numbers, dropout rates, teacher qualifications, and classroom conditions were transformed into derivative indicators. A binary classification (Feasible/Not Feasible) based on national dropout median was applied. The model was developed using R with six systematic steps, including training and evaluation of a Random Forest model (ntree = 100, mtry = 3) using accuracy, sensitivity, and specificity.Result: The model accurately classified school feasibility. Key predictors included teacher quality, student-teacher ratios, and classroom conditions. Several provinces were identified as “Not Feasible.”Conclusion: Machine learning proves effective for education policy support. The study offers targeted recommendations such as improving infrastructure, enhancing teacher training, and reducing dropouts to promote equitable education in Indonesia.
Hybrid Database Architecture for Retail Big Data Analytics: PostgreSQL vs MongoDB Performance Analysis Noor, Tubagus Firman Iskandar; Nugraha, Eki; Maknun, Johar; Kustiawan, Iwan; Kurşun, Engin
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.28108

Abstract

The rapid growth of retail big data has intensified the challenge of selecting a database architecture that can balance analytical performance and resource efficiency, particularly in data-intensive retail environments. This study aims to conduct a comparative performance analysis between PostgreSQL 16 and MongoDB 8.0 in the context of implementing big data analytics in the retail industry. An experimental quantitative approach is used, utilizing a large-scale, real-world retail sales and inventory dataset to benchmark PostgreSQL 16 and MongoDB 8.0 across a range of representative analytical workloads. Results show MongoDB is 28-31% faster in query processing, but PostgreSQL is 13-17% more efficient in resource usage (CPU, RAM, Storage I/O) and requires 6x less storage. These results indicate that MongoDB consistently achieves faster execution times for read-intensive analytical queries, especially in large-scale aggregation operations. Conversely, PostgreSQL exhibits superior storage efficiency and lower computational resource consumption due to its normalized relational architecture. These findings reveal a fundamental trade-off between analytical speed and infrastructure efficiency in retail big data systems. This research contributes to the development of hybrid data architecture strategies for big data analytics in the retail industry, supporting performance optimization and informed decision-making in data-rich environments
Co-Authors A. Suhandi Achmad Samsudin Aim Abdulkarim Akbar, Anthonio Aliyya, Farrel Rahma Asep Wahyudin Didin Wahyudin, Didin Enjang Ali Nurdin Faizal, Mochammad Yusuf Farizi, Syahandhika Naufal Fratiwi, Nuzulira Janeusse Gintara, Andre Rangga Hadi Nasbey Hera Novia Hikmawan, Rizki Iwan Kustiawan Jasmine Tsania Johar Maknun, Johar Kapıcı, Hasan Özgür Kurşun, Engin Lala Septem Riza Maqruf, Ahmad Nazir, Shah Ningsih, Dena Tresna Ningsih, Siti Widya Noor, Tubagus Firman Iskandar Nurdini Nurdini, Nurdini Nursalman, Muhammad Putro, Budi Laksono Rani Megasari Sonjaya, Rebina Putri Sözbilir, Mustafa Wahyudin Wawan Setiawan Zahran, Muhammad