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All Journal JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Scientific Journal of Engineering Research
Abdul Fadlil
Department of Electrical Engineering, Universitas Ahmad Dahlan
Dentistry Education Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Empowering Teachers in Muhammadiyah Boarding School Yogyakarta toward Safer Digital Behavior through Smartphone Security Education Aris Rakhmadi; Hero Wintolo; Esi Putri Silmina; Dewi Soyusiawaty; Sunardi; Abdul Fadlil
JURPIKAT Vol 6 No 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2843

Abstract

Abstract: This community-service program was implemented through the Program Pemberdayaan Umat (PRODAMAT) of Universitas Ahmad Dahlan with the aim of enhancing digital literacy and cybersecurity awareness among teachers at Muhammadiyah Boarding School (MBS) Yogyakarta. The activity focused on smartphone account security education through practical steps such as password management, two-factor authentication (2FA), and phishing awareness. A participatory approach was applied through training involving 15 teachers and staff, combining interactive discussions, demonstrations, and pretest–posttest evaluation. The results showed an increase in the average knowledge score from 4.63 to 4.90, digital awareness from 4.05 to 4.45, and intention and safe digital behavior from 4.35 to 4.73. These improvements reflect positive changes in participants’ understanding, awareness, and behavior toward digital security. The program highlights the importance of integrating technological skills with ethical and religious values to promote sustainable digital empowerment in Islamic educational environments.
Comparison of Machine Learning Algorithms for Stunting Classification Muhajir Yunus; Muhammad Kunta Biddinika; Abdul Fadlil
Scientific Journal of Engineering Research Vol. 1 No. 2 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i2.2025.9

Abstract

Indonesia is one of the countries with medium stunting data over the past decade, around 21.6%. Stunting prevention is a national program in Indonesia, and stunting reduction in children is the first of the six goals in the Global Nutrition Target for 2025. Based on SSGI data in 2022, the prevalence of stunting in Gorontalo Province is 23.8% and is in the high category. Stunting prevention is an early effort to improve the ability and quality of human resources. This study compared two Machine Learning algorithms for stunting classification in children, namely the Naive Bayes method and Decision Tree C4.5 using Python by dividing the training and testing data a total ratio of 80:20. The performance of each algorithm was evaluated using a dataset of child health information based on z-score calculation data with a total of 224 records, consisting of 4 attributes and 1 label, namely gender, age, weight, height and nutritional status. The results of the research that have been conducted show that the Decision Tree C4.5 algorithm achieves the highest accuracy in the classification of stunting events with an accuracy of 87% while for the Naïve Bayes algorithm produces a low accuracy of 71% so that for this study the Decision tree C4.5 algorithm is the best algorithm for the classification of stunting events. These findings suggest this algorithm can be a valuable tool for classifying children's stunting.
Co-Authors Aris Rakhmadi Dewi Soyusiawaty Esi Putri Silmina Hero Wintolo Muhajir Yunus Muhammad Kunta Biddinika Sunardi