Article Per Year (5 Year)
p-Index From 2020 - 2025
6.054
P-Index
This Author published in this journals
All Journal DE JURE Jurnal Asy-Syari'ah El-Mashlahah JURNAL MAHKAMAH JMPM: Jurnal Matematika dan Pendidikan Matematika MONETER Legal Standing : Jurnal Ilmu Hukum Building of Informatics, Technology and Science Ulumuddin : Jurnal Ilmu-ilmu Keislaman Dinasti International Journal of Education Management and Social Science PAKAR Pendidikan AL IQTISHOD: Jurnal Pemikiran dan Penelitian Ekonomi Islam VARIA HUKUM: Jurnal Forum Studi Hukum dan Kemasyarakatan Jurnal Res Justitia : Jurnal Ilmu Hukum Action Research Literate (ARL) Locus: Jurnal Konsep Ilmu Hukum Journal Of Sharia Banking Indonesian Journal of Multidisciplinary Science International Journal Mathla'ul Anwar of Halal Issues Jurnal Iman dan Spiritualitas CASHFLOW : CURRENT ADVANCED RESEARCH ON SHARIA FINANCE AND ECONOMIC WORLDWIDE HUKMY : Jurnal Hukum At-Tamwil: Journal of Islamic Economics and Finance Journal of Artificial Intelligence and Digital Business Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research Nanggroe: Journal Of Scholarly Service International Journal of Law and Society ISLAMICA : Jurnal Ilmu-Ilmu Agama Islam Nanggroe: Journal of Scholarly Service Strata Social and Humanities Studies METRIK SERIAL TEKNOLOGI DAN SAINS Mawaddah: Jurnal Hukum Keluarga Islam Zona Law And Public Administration Indonesia Tifani : Jurnal Penelitian dan Pengabdian Kepada Masyarakat Jurnal Baut Dan Manufaktur Cakrawala: Journal of Religious Studies and Global Society Madania: Jurnal Kajian Keislaman
Syahrul Anwar, Syahrul
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

Published : 48 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Ekonomi, Teknologi dan Bisnis

 1 
Predicting School Dropout Risk Using Machine Learning Models: A Comparative Study of Random Forest, Gradient Boosting, and Neural Network Anwar, Syahrul
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 4 No. 6 (2025): JETBIS : Journal of Economics, Technology and Business
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v4i6.193

Abstract

Dropping out of school is a serious challenge in the education system that negatively impacts individual and social development. Early identification of students at risk of dropping out of school is crucial to prevent its long-term impact. This study aims to develop and compare a model for predicting the risk of dropping out of school using a machine learning approach. The three models compared in the study were Random Forest, Gradient Boosting, and Neural Network, with data covering 1000 students and features such as socioeconomic status, academic performance, parental engagement, distance to school, and educational resources. The results of the evaluation showed that the Random Forest model performed best with an accuracy of 93%, followed by Neural Network (92%) and Gradient Boosting (90%). The feature importance analysis revealed that socioeconomic status, parental involvement, and academic achievement were the dominant factors in predicting the risk of dropping out. These findings demonstrate the potential of applying machine learning as an early warning system for more targeted interventions in improving student retention. Further research is recommended to include psychological variables and longitudinal data as well as develop information technology-based systems for real implementation in schools.
Predicting School Dropout Risk Using Machine Learning Models: A Comparative Study of Random Forest, Gradient Boosting, and Neural Network Anwar, Syahrul
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 4 No. 6 (2025): Jurnal Ekonomi, Teknologi dan Bisnis
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/jetbis.v4i6.193

Abstract

Dropping out of school is a serious challenge in the education system that negatively impacts individual and social development. Early identification of students at risk of dropping out of school is crucial to prevent its long-term impact. This study aims to develop and compare a model for predicting the risk of dropping out of school using a machine learning approach. The three models compared in the study were Random Forest, Gradient Boosting, and Neural Network, with data covering 1000 students and features such as socioeconomic status, academic performance, parental engagement, distance to school, and educational resources. The results of the evaluation showed that the Random Forest model performed best with an accuracy of 93%, followed by Neural Network (92%) and Gradient Boosting (90%). The feature importance analysis revealed that socioeconomic status, parental involvement, and academic achievement were the dominant factors in predicting the risk of dropping out. These findings demonstrate the potential of applying machine learning as an early warning system for more targeted interventions in improving student retention. Further research is recommended to include psychological variables and longitudinal data as well as develop information technology-based systems for real implementation in schools.
Co-Authors ., Rachmatullaily Abdullah, Fadli Daud Aden Rosadi Aen, I. Nurul Ahmad Fathonih, Ahmad Ahmad Ridwan Aisah, Putri Maharani Rahma Alif, Muhammad Nur Almurni, Muhammad Furqon Anwari, Amalia Nur Arifiansah, Arifiansah Asyroflie, Farrel Badarulzaman, Muhammad Hafiz Cahyani, Putri Tri Christina Juliane, Christina Darmawan, Muhammad Abdi Dede Kania Diah Sugiarti, Lilis Didi Sumardi, Didi Dody Prayitno Dona, Dona Dwi Yulianto Eli Suryani Ending Solehudin Faiq, Faiq Faizal, Enceng Arif Faturokhman, Aziz Fauzan . Fauzan Ali Rasyid Fauzi Ahmad Muda Fauziah, Dita Anggun Fauziansah, Silvanus Fitrianto, Bambang Fitriyani Fitriyani Habibah Arifin, Desi Siti Hakim , Arief Rahman Hardiati, Neni Hermawan, Dudung Hikmawati, Nina Kurnia Humaira Siti Salma, Shofya I Nurol Aen Ibnu Elmi AS Pelu Imam Muttaqin, Faruqi Jaenudin Jaenudin Jauhari, Moh. Ahsanuddin Jefry Tarantang Koidin Kuraesin, Siti Kusuma, Fajar Ichsan Lena Ishelmiani Ziarahah Madani, Farid Malik, Deden Abdul Maryadi Maryadi Mastur, Atep Maulana Hasanuddin, Maulana Moh. Rizal Umami Mohd Anuar Ramli Muhammad Nuruddien Muhammad Yusup, Rangga munir, dede Najmudin, Deden Najmudin, Deden Ningrum, Novita Ardiyanti Novianti, Erika Nurhikmah, Aulia Nurjaman, Muhamad Izazi Nursamsi, Widia Prasetia, Riky Prasetiadi, Yan Septiawan Qiftia, Maryathul Ramdani Wahyu Sururie Ridwan, Ahmad Hasan Rifqi Lidzikrirrofiqi, Muhammad Rizal Umami, Moh. Saepuloh, Usep Safitriani, Mida Saiful Anwar Sakinah, Nailus Sari, Mulyani Indah Sephia, Reyhan septiadi, yans Sujana, Ahmad Sulistiyo, Budi Supriatna, Encup Syahidin, Rosyad Wijaya, Adje Yusup Junaedi Zulvia, Ransya Ayu