Claim Missing Document
Check
Articles

Found 33 Documents
Search

Digitalizing MSME Financial Recording in Medan: The Development and Implementation of a User-Centered POS System Barus, Okky Putra; Maulana, Ade; Sinaga, Triandes
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 2 (2025): November 2025
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v9i2.2143

Abstract

This community engagement project aimed to address operational challenges faced by Micro, Small, and Medium Enterprises (MSMEs) through an integrated Point of Sales (POS) system. A case study at Akane Beauty Studio and Salon used a User-Centered Design (UCD) and Rapid Application Development (RAD) approach, managed under the PMBOK framework. The implementation yielded a significant impact on operational efficiency, notably reducing transaction time from over one minute to under one minute. A comparison of the workflow also showed improvement, with a manual process that previously took four minutes now being completed in two minutes. User Acceptance Testing (UAT) confirmed the system was fully functional and easy to use. This project demonstrates that a user-centric digital solution can tangibly improve the efficiency, data accuracy, and business sustainability of MSMEs.
Penerapan Digital Marketing dan IoT pada Arjuna Farm untuk Peningkatan Penjualan dan Presisi Ternak Jefri Junifer Pangaribuan; Ade Maulana; Okky Putra Barus; Stephanie; Rosita Darianty
Jurnal Malikussaleh Mengabdi Vol. 5 No. 1 (2026): Jurnal Malikussaleh Mengabdi, Januari 2026
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v5i1.23033

Abstract

Penelitian pengabdian kepada masyarakat ini bertujuan mengatasi permasalahan manajemen usaha dan pemasaran pada Arjuna Farm, sebuah UMKM peternakan domba kambing di Kabupaten Deli Serdang, Sumatera Utara. Meskipun telah mengimplementasikan sistem Point of Sales dan Internet of Things untuk pencatatan data ternak, mitra masih menghadapi kendala penimbangan bobot ternak yang kurang presisi serta penurunan engagement pemasaran digital sebesar 40-70%, mengakibatkan penjualan domba yang kurang optimal (±50 ekor/bulan dibandingkan ±3.238 kelahiran di Medan). Solusi yang diimplementasikan meliputi pengembangan timbangan digital berbasis Internet of Things yang terintegrasi dengan sistem Point of Sales guna mencapai Precision Livestock Farming, dengan target peningkatan pendapatan mitra 5% setiap bulan. Selain itu, kegiatan juga berfokus pada peningkatan kemampuan pemasaran digital mitra melalui pelatihan, investasi biaya pemasaran, dan pendampingan pembuatan campaign selama delapan bulan, dengan target peningkatan penjualan 20%. Hasil kegiatan menunjukkan keberhasilan implementasi timbangan digital terintegrasi yang memungkinkan pengukuran bobot ternak presisi, serta peningkatan traffic media sosial dan penjualan setelah lima bulan implementasi iklan digital. Kegiatan ini diharapkan dapat meningkatkan efisiensi operasional dan daya saing Arjuna Farm.
Analisis Kualitas Wine Menggunakan Machine Learning dengan Pendekatan SMOTE dan Seleksi Fitur Triandes Sinaga; Kevin Bastian Sirait; Jefri Junifer Pangaribuan; Okky Putra Barus; Romindo Romindo
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5436

Abstract

Conventional wine quality assessment remains reliant on subjective expert judgment, which introduces potential bias and inconsistency in quality control processes. This study aims to develop an objective and automated machine learning-based classification model to enhance the accuracy of wine quality prediction. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, along with ANOVA F-test-based feature selection to optimize model performance. The White Wine Quality dataset from the UCI Machine Learning Repository (4,898 samples, 11 numerical features) was utilized to evaluate five classification algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Before SMOTE application, the Random Forest model achieved an accuracy of only 67.55%. After implementing SMOTE and parameter tuning, the Random Forest (Tuned) model demonstrated the best performance with 90.29% accuracy, 89.99% precision, 90.29% recall, and 89,97%.  % F1-score. Additionally, Decision Tree and KNN algorithms also exhibited notable improvements. SMOTE effectively balanced extreme minority class representations (quality levels 3 and 9). The most influential features in quality classification were alcohol content, density, and chlorides. These findings indicate that the proposed framework offers a reliable, objective, and scalable solution for automated wine quality control in industrial production environments.