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All Journal Journal of Technology and Computer (JOTECHCOM) Jurnal Pengabdian dan Inovasi Masyarakat
Sinaga, Roseri
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Religion Computer Science & IT Economics, Econometrics & Finance Education Health Professions Law, Crime, Criminology & Criminal Justice Mathematics

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Pemanfaatan Aplikasi Digital UMKM di Kabupaten Dairi Sinaga, Roseri; Simangunsong, Herbet; Simanullang, Jasael
CivicAction: Jurnal Pengabdian dan Inovasi Masyarakat Vol. 1 No. 3 (2025): Artikel Pengabdian Kepada Masyarakat
Publisher : SORATEKNO PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/civicaction.v1i3.262

Abstract

Dalam era digital yang terus berkembang, pemilik Usaha Mikro, Kecil, dan Menengah (UMKM) di Kabupaten Dairi memiliki kesempatan besar untuk meningkatkan kehadiran dan jangkauan online mereka. Dengan memanfaatkan alat pemasaran digital seperti pemasaran media sosial, optimasi mesin pencari, dan kampanye email, UMKM dapat mempromosikan produk serta layanan mereka secara lebih efektif. Strategi ini bisa diintegrasikan dengan aplikasi bisnis, termasuk sistem manajemen inventaris dan perangkat lunak manajemen hubungan pelanggan, yang bertujuan untuk merampingkan operasi dan meningkatkan efisiensi. Pendekatan terintegrasi ini tidak hanya membantu UMKM menarik dan mempertahankan pelanggan, tetapi juga berpotensi untuk meningkatkan penjualan dan pendapatan. Dengan memanfaatkan platform e-commerce dan pasar online, pemilik UMKM di Kabupaten Dairi dapat memperluas jangkauan mereka melampaui komunitas lokal. Mengadopsi strategi pemasaran digital dengan cara yang komprehensif dapat memastikan pertumbuhan berkelanjutan dan kesuksesan di tengah persaingan global.
Predicting AI-Assisted Student Academic Improvement Using K-Nearest Neighbors Simangunsong, Herbet; Sinaga, Roseri; Simanullang, Jasael
Journal of Technology and Computer Vol. 3 No. 2 (2026): May 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

The rapid integration of Artificial Intelligence (AI) assistants in higher education necessitates empirical methods to evaluate their actual impact on student academic performance. A significant challenge in Educational Data Mining (EDM) is interpreting these impacts accurately, especially given the inherently imbalanced nature of educational datasets. This study proposes a predictive framework utilizing the K-Nearest Neighbors (KNN) algorithm to classify student academic improvement based on AI usage patterns and Learning Management System (LMS) engagement. To ensure model robustness, the Boruta algorithm was applied for feature selection, alongside the SMOTE-Tomek technique to address class imbalance. The optimized KNN model achieved a high predictive performance with an F1-Score of 86.8% and a Balanced Accuracy of 86.1%. Furthermore, the integration of Explainable AI (XAI) via SHapley Additive exPlanations (SHAP) revealed that using AI for conceptual clarification, rather than direct task completion, is the primary driver of academic success. The findings demonstrate that this explainable KNN framework effectively identifies at-risk students, providing educators with transparent, actionable insights to deliver personalized interventions and foster responsible AI usage.
Co-Authors Herbet Simangunsong Jasael Simanullang