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Eko Siswo Adi Sahputra
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Digital Transformation of Student Assignment Management through a Mobile-Friendly E-Learning System Integrated with WhatsApp Gateway and Google Drive Syahrul Anwar; Abdul Robi Padri; Ade Bani Riyan; Eko Siswo Adi Sahputra
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v11i6.64930

Abstract

Objective: To develop a mobile-friendly e-learning system for managing student assignments that integrates with a WhatsApp Gateway and Google Drive, aiming to translate digital transformation in higher education into simple, rapid, and integrated assignment management processes. Methods: This study employs a Research and Development (R&D) approach using the ADDIE model, which includes needs analysis, architectural design, prototype development, limited implementation, and evaluation of usability and functionality. The proposed system features assignment creation, automatic WhatsApp reminders, Google Drive file integration, submission status tracking, and mobile-first dashboards. The evaluation is conducted through expert validation, black-box testing, System Usability Scale (SUS) measurement, and Likert-based user perception questionnaires. Results: The implementation of the system is expected to yield improved on-time assignment submissions, a reduction in late submissions, a more efficient workflow for lecturers, and the establishment of an economical yet reliable small-scale personal Learning Management System (LMS) model. Conclusion: This research provides significant practical contributions for educational institutions seeking a lightweight, compliant, and readily adoptable digital assignment management system that enhances learning flexibility and academic engagement.
Pendekatan Kecerdasan Buatan Hibrida dalam Meningkatkan Akurasi Prediksi Churn pada Big Data Ade Bani Riyan; Syahrul Anwar; Eko Siswo Adi Sahputra
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 8 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i8.9094

Abstract

The explosion of digital data has given birth to the era of big data, which presents great opportunities as well as significant challenges in knowledge extraction. Traditional data mining processes often face obstacles in terms of accuracy and efficiency when faced with massive data volume, variety, and speed. This study aims to propose and evaluate a hybrid model based on Artificial Intelligence (AI) to improve the performance of the data mining process on large-scale data sets. The proposed model integrates the power of Random Forest's algorithm in handling structured data and resistance to overfitting, with the ability of Neural Networks to model complex non-linear relationships. The research uses a case study on customer churn data from the e-commerce industry which contains 1.5 million records, with comprehensive data mining process stages, ranging from data preprocessing, feature engineering, to model implementation. The results of the evaluation showed that the hybrid model achieved an accuracy of 94.7% and an AUC (Area Under the Curve) value of 0.97, significantly outperforming the Random Forest (91.2% accuracy, 0.93 AUC) and Artificial Neural Network (92.5% accuracy, 0.95 AUC) models. Although hybrid models require slightly higher computational times, the substantial increase in accuracy provides a strong justification for their use in critical business scenarios. This study provides empirical evidence that the hybrid AI approach is an effective and promising strategy to address the challenges of big data analysis, particularly in critical business scenarios where predictive accuracy is a top priority.
Co-Authors Abdul Robi Padri Riyan, Ade Bani Syahrul Anwar