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All Journal Jurnal Pengabdian Masyarakat Tapis Berseri Nuansa Informatika Bulletin of Intelligent Machines and Algorithms
Arif Bakti Nugraha
Universitas Informatika dan Bisnis Indonesia
Computer Science & IT Education

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Implementasi Sistem Informasi Undangan Digital Berbasis WEB Arif Bakti Nugraha; Aren Kurnia
NUANSA INFORMATIKA Vol. 18 No. 2 (2024): Nuansa Informatika 18.2 Juli 2024
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v18i2.208

Abstract

A wedding invitation is a letter in the form of a card asking the recipient to attend a wedding. But now, wedding invitations can also be distributed via social media in the form of a web which is commonly called digital invitations. This study discusses the registration process, inputting forms, package selection, and payment. In addition, there is an admin interface whose job is to manage transactions which include managing payments, and managing incoming orders in the form of user data that will be included in the invitation web design.
Membangun Infrastruktur Jaringan Bagi Siswa SMKN 13 Bandung Imannudin Akbar; Arif Bakti Nugraha; R.Yadi Rakhman; Erlang Anggara Widjaksono
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edition April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v5i1.179

Abstract

This Community Service Program (PkM) aims to equip students of SMKN 13 Bandung with practical skills in Debian server administration. The activity was conducted through an intensive workshop emphasizing hands-on practice, aimed at enhancing students' abilities to build a network infrastructure based on Debian 12. This activity not only provided theoretical understanding but also prepared students for the Competency Skills Test (UKK) and challenges in the workforce. Through this program, students gained skills in configuring network services such as DNS, DHCP, Web Server, and Database Server using the Command Line Interface (CLI). Evaluations showed significant improvement in students' knowledge and skills, as well as their readiness to face exams and enter the workforce. This program is expected to enhance the quality of graduates from SMKN 13 Bandung, meeting the demands of the information technology industry.
Ensemble Learning for Early Warning Systems in Higher Education: A Comparative Study of Student Attrition Muhamad Achya Arifudin; Elia Setiana; Arif Bakti Nugraha
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i3.19

Abstract

Student attrition poses a substantial challenge to higher education institutions, affecting their reputation and financial sustainability. Conventional single machine learning models often exhibit limited sensitivity when analyzing educational data, which is typically marked by severe class imbalance favoring graduating students over dropouts. This study introduces an Early Warning System based on a Hybrid Stacking Ensemble framework to improve student attrition prediction. The approach leverages complementary biases from Bagging and Boosting as base learners, which are then combined using a Logistic Regression meta-learner to refine prediction weights. To counteract class imbalance and majority-class bias, the Synthetic Minority Over-sampling Technique was employed during preprocessing. Empirical evaluations reveal that the Hybrid Stacking Ensemble attains a classification accuracy of 88.81% and a Recall of 80.99%, surpassing standalone models and other ensemble methods. Feature importance rankings highlight second-semester academic performance and administrative-financial factors—particularly tuition payment punctuality—as key dropout predictors. These results affirm the value of integrating diverse classifiers to discern intricate, nonlinear student behavior patterns. In essence, this work establishes a reliable, evidence-based framework enabling administrators to shift from reactive to proactive, precision-targeted strategies that foster student retention and institutional success.
Co-Authors Akbar, Imannudin Aren Kurnia Erlang Anggara Widjaksono Muhamad Achya Arifudin R.Yadi Rakhman Setiana, Elia