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All Journal Journal of Telematics and Informatics ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Hilirisasi IPTEKS (JHI) Jurnal Teknik Elektro dan Komputasi (ELKOM) BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Electrical Engineering and Computer (JEECOM) Jurnal IPTEK Bagi Masyarakat Newton-Maxwell Journal of Physics Jurnal Pengabdian Masyarakat Nusantara SNTE Jurnal Teknik Elektro
Septia Refly
Universitas Maritim Raja Ali Haji
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Dissemination of Seagrass Density Information from Object-Based Image Analysis (OBIA) Mapping to Pengudang Coastal Community Esty Kurniawati; Indah Kartika; Try Febrianto; Septia Refly; Mario Putra Suhana; Asep Ma’mun; Muhammad Fajar Fajri Fardillah; Muhammad Johar Rudin
Jurnal IPTEK Bagi Masyarakat Vol 5 No 3 (2026)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/j-ibm.v5i3.1590

Abstract

Pengudang Village, Bintan Regency, Riau Islands, is a coastal area that hosts seagrass ecosystems with high biodiversity. However, this area is increasingly experiencing degradation pressures due to climate change and anthropogenic activities. The limited public knowledge regarding the condition of seagrass ecosystems has become a major obstacle to community-based conservation efforts in the region. This community service activity aimed to disseminate information on seagrass density mapping results using the Object-Based Image Analysis (OBIA) technology to the coastal community of Pengudang. The mapping was conducted at 59 survey points distributed across the shallow waters of Pengudang Village in October 2025. The results identified seven seagrass species with a total coverage area of 344.26 hectares, of which approximately 92% fell into the high-density category. The dissemination of the mapping results was carried out in December 2025 using an interactive outreach method supported by visual media and infographics, and was evaluated through questionnaires distributed to 10 respondents. The evaluation results showed that all assessment indicators were within a score range of 3.7–4.0, categorized as Very Good, with no respondents expressing disagreement regarding either the material or the implementation of the activity. This activity demonstrates that a dissemination approach based on modern mapping technology, delivered through participatory and contextual methods, is effective in improving public understanding, awareness, and conservation motivation among coastal communities toward seagrass ecosystems.
Multi-Channel Power Data Acquisition System for Solar Panel Monitoring Septia Refly; Adam BimaJaya; Basyaruddin Ismail Harahap
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.14224

Abstract

This study presents a low-cost and scalable multi-channel power data acquisition system for real-time solar photovoltaic (PV) panel monitoring, addressing the limitations of conventional single-channel approaches that provide only aggregate system measurements. The proposed system enables simultaneous per-panel measurement to support detailed performance analysis and improved fault localization. The system is implemented using an ESP32 microcontroller integrated with multiple calibrated INA219 sensors, which are connected via the I²C protocol to measure voltage, current, and electric power. A modular hardware design supports three independent PV channels, while data handling is achieved through dual-mode operation, consisting of local microSD card storage and wireless data transmission to the ThingSpeak IoT platform for real-time visualization. Calibration results demonstrate high measurement accuracy, with average errors below 1%, a voltage root mean square error (RMSE) of less than 0.07 V, and a current RMSE of less than 7 mA. Field testing conducted over two consecutive days confirms stable and uninterrupted operation, achieving 100% data acquisition reliability. The recorded data clearly reveal per-panel performance differences under real operating conditions, enabling effective identification of mismatch behavior among panels. The proposed system provides an affordable, reliable, and scalable solution for distributed PV monitoring, making it suitable for multi-panel and remote photovoltaic installations. Future improvements will involve temperature-based efficiency analysis and the integration of thermal management strategies to enhance photovoltaic performance.
Co-Authors Adam BimaJaya Ahmad Syafiq Anggarudin, Anggarudin Anton Hekso Yunianto Asep Ma’mun Basyaruddin Ismail Harahap Bavitra, Bavitra Bimbi Aditya Wanatirta BIMBI ADYTIA WANATIRTA Doli Bonardo Esty Kurniawati Guntara, Aldo Hollanda Arief Kusuma Indah Kartika Leo Anaris Sakti Lucky Pradana Mario Putra Suhana Muhammad Abiyyu Alharits Muhammad Fajar Fajri Fardillah Muhammad Johar Rudin Nurfalinda, Nurfalinda Pardede, Masta Angel Valentina Putra, Gandhi Rizky Mahendro Rahmat Kholis Harahap Rhamadita Oktavia Rifki Ariandhi Rozeff Pramana Rusfa Sapta Nugraha Simanullang, Andreas M Tonny Suhendra Try Febrianto