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All Journal Jurnal Sains Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Engagement: Jurnal Pengabdian Kepada Masyarakat
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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.
Pemanfaatan Teknologi IoT dan Aplikasi Android untuk Pengendalian Kadar Amonia pada Peternakan Unggas Aditya Kristianto; Kevin Sirait; Sinaga, Triandes
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 4 (2025): November 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i4.6910

Abstract

This community service activity aims to implement an Internet of Things (IoT)–based system integrated with an Android application to monitor ammonia gas levels in poultry farms. The activity took place at Ziven Chicken Farm in Stabat, North Sumatra, which faces challenges related to ammonia accumulation from livestock waste. The methodology follows four phases: (1) initiation, involving initial observation and interviews with farm’s owner and workers concerning problems identifications and needs; (2) planning, including the design of the IoT solution and mobile application; (3) implementation, consisting of assembling the IoT device using the MQ-137 and DHT-22 sensors, integrating them with the ESP32 microcontroller, and developing the application connected to a cloud server; and (4) monitoring and control, which includes system performance monitoring, data validation, and evaluation of system use by the partner. The system provides real-time visualization of ammonia, temperature, and humidity levels through tables and graphs. Results show an upward trend of ammonia concentration, reaching 21 ppm on the third day, with humidity positively affecting ammonia levels. A notable spike occurred between 20:00 and 21:00 WIB, indicating the need for improved ventilation and cleaning routines. Overall, the system assists farmers in determining optimal maintenance schedules and supports the adoption of digital technologies in poultry farm management.
Segmentasi Investor Cryptocurrency Menggunakan Metode K-Means: Studi terhadap Faktor-Faktor yang Mendorong Keputusan Investasi Arosochi Yosua Daeli; Vicky Darmana; Sirait, Kevin Bastian; Sinaga, Triandes
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 5 No. 2 (2025): Oktober 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v5i2.7405

Abstract

The growth of cryptocurrency investors is very rapid with diverse characteristics and various investment driving factors. This study aims to analyze and form investor segmentation in cryptocurrency based on investment driving factors using the K-Means Clustering algorithm. A quantitative approach was applied through online questionnaires to 300 respondents who are cryptocurrency investors, with 289 valid data meeting the research criteria. The variables studied include four driving factors: Fear of Missing Out (FOMO), social media influence, high profit potential, and interest in the investment world. Data were processed through Min-Max normalization, Principal Component Analysis (PCA), and K-Means clustering using Orange Data Mining. The optimal number of clusters was determined using the Silhouette Score, while cluster validation used K-Nearest Neighbors (KNN). ANOVA and Games-Howell tests confirmed significant differences between clusters. The results identified four clusters: Cluster 1 (Emotional Investors, n=37), Cluster 2 (Ambitious Investors, n=156), Cluster 3 (Rational Investors, n=50), and Cluster 4 (Passive Investors, n=46). Cluster 3 is the most optimal in investment decision-making with a profit rate of 90% and zero loss (0%). These findings confirm that optimal investment decisions are driven by rational analysis and logical consideration without excessive emotional influence.
Co-Authors Ade Maulana Aditya Kristianto Arosochi Yosua Daeli Barus, Okky Putra Kevin Sirait Sirait, Kevin Bastian Vicky Darmana