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All Journal JURIKOM (Jurnal Riset Komputer) JPM: JURNAL PENGABDIAN MASYARAKAT
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Politeknik Negeri Medan, Medan
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Pemanfaatan Google Bisnis dan Media Sosial untuk Optimalisasi Promosi dan Pencatatan Usaha Mikro Ismael Ismael; Donny Sanjaya; Aulia Rahman Dalimunthe; Nugroho Syahputra; Cut Try Utari
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 4 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i4.2788

Abstract

Digital transformation has become a crucial strategy for enhancing the competitiveness and sustainability of Micro, Small, and Medium Enterprises (MSMEs), particularly in rural areas. Bengkel Las Siti Hajar, a micro-enterprise located in Sunggal District, Deli Serdang Regency, faces several challenges in business management, including conventional promotion methods, limited digital branding, and manual bookkeeping practices that reduce efficiency and accuracy. This Community Partnership Service Program (PKM) aims to strengthen the partner’s capacity to utilize simple digital technologies to support business promotion, financial record-keeping, and daily operations. The implementation methods included initial observations and interviews to identify the partner’s needs, training on the utilization of Google Business as a location-based promotional platform, assistance in creating promotional content through Instagram, and training on digital bookkeeping using Microsoft Excel-based applications. The results of the PKM implementation indicate a significant improvement in business performance, with an increase in monthly revenue of approximately 30%, a growth in regular customers from 8–10 to 15–20 individuals, and the development of a digital business portfolio that supports sustainable promotion. Furthermore, the partner demonstrated improved knowledge and skills in managing the business more professionally and systematically. Therefore, this community service activity confirms that the application of simple digital technologies can generate substantial positive impacts on the development, professionalism, and competitiveness of rural micro-enterprises.
Pendekatan Machine Learning Berbasis Fitur Geospasial Imputasi Nilai AADT yang Hilang: Studi Kasus Texas Afridayani; Afrisawati; Rizky Maulidya Afifa; Cut Try Utari
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9703

Abstract

Annual Average Daily Traffic (AADT) is an important indicator in transportation planning and road network performance evaluation. However, missing values ​​due to sensor interference or unrecorded data can reduce the quality of the analysis. This study aims to estimate missing AADT values ​​using a geospatial feature-based machine learning approach with a case study in Texas, United States. Automatic Traffic Recorder (ATR) data is integrated with road network attributes from OpenStreetMap (OSM) through a spatial join process to produce features such as road classification, number of lanes, and speed limits. A Random Forest model is used to build an estimation model based on valid data (AADT > 0). The evaluation results show a coefficient of determination (R²) of 0.548, indicating that geospatial features can significantly explain variations in AADT. The imputation process successfully produced a dataset with a 100% convenience level and a spatial distribution pattern consistent with the road network hierarchy and metropolitan area. This approach demonstrates that the integration of spatial data and machine learning is effective in improving the integrity of traffic data to support data-driven decision making.
Co-Authors Afridayani Afrisawati Aulia Rahman Dalimunthe Donny Sanjaya Ismael Ismael Nugroho Syahputra Rizky Maulidya Afifa