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OPTIMALISASI MANAJEMEN KEHADIRAN DENGAN SISTEM ABSENSI IOT BERBASIS RFID DAN ANALISIS AKTUARIA Utomo, Andri Dwi; Akbar, Andi Taufiqurrahman; Syafaat, Muhammad; Jeffry, Jeffry; A Suyuti, Muh Zulfadli; Adriani, Ika Reskiana
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i2.29748

Abstract

Abstrak: Di era digital, salah satu tantangan organisasi masyarakat adalah pengelolaan data kehadiran yang masih dilakukan secara manual, sehingga kurang mendukung analisis berbasis data. Untuk mengatasi masalah ini, program pengabdian kepada masyarakat ini bertujuan memberikan pelatihan mengenai sistem absensi berbasis Internet of Things (IoT) dengan data logger, serta analisis data menggunakan pendekatan aktuaria. Pelatihan ini bertujuan untuk meningkatkan hard-skills peserta dalam hal pemahaman dan penerapan teknologi IoT, konfigurasi perangkat, serta analisis data. Selain itu, pelatihan juga berfokus pada peningkatan soft-skills peserta dalam hal pemecahan masalah, kolaborasi tim, dan pengambilan keputusan berbasis data, yang akan berguna dalam implementasi sistem absensi secara mandiri. Kegiatan ini melibatkan Study Club Informatika Parepare sebagai mitra, dengan 22 peserta. Metode yang digunakan mencakup pengenalan teknologi IoT, praktik langsung, dan analisis data. Pelatihan terdiri dari pemahaman dasar IoT, konfigurasi perangkat, serta integrasi sistem dengan Google Spreadsheet untuk pencatatan data absensi secara otomatis. Hasil evaluasi menunjukkan peningkatan signifikan dalam pemahaman peserta, dengan rata-rata nilai pretest 61,52% meningkat menjadi 92,61% pada posttest. Implementasi sistem ini membantu organisasi dalam digitalisasi proses absensi, meningkatkan efisiensi administrasi, dan membuka peluang penerapan lebih luas di komunitas lainnya.Abstract: In the digital era, one of the challenges faced by community organizations is attendance data management, which is still done manually and does not adequately support data-driven analysis. To address this issue, this community service program aims to provide training on Internet of Things (IoT)-based attendance systems using data loggers, along with data analysis using an actuarial approach. This training aims to enhance the participants' Hard-Skills in understanding and applying IoT technology, device configuration, and data analysis. Additionally, the training focuses on improving the participants' soft skills in problem-solving, teamwork, and data-driven decision-making, which will be useful in the independent implementation of the attendance system. This program involves Study Club Informatika Parepare as a partner, with 22 participants. The methods used include IoT technology introduction, hands-on practice, and data analysis. The training covers basic IoT concepts, device configuration, and system integration with Google Spreadsheet for automated attendance recording. Evaluation results indicate a significant improvement in participants' understanding, with an average pretest score of 61.52% increasing to 92.61% in the posttest. The implementation of this system helps organizations digitize attendance processes, improve administrative efficiency, and expand its potential applications to other communities. 
Energy Efficient IoT-Based Forest Fire Detection Using LoRaWAN and AI Syafaat, Muhammad; A Suyuti, Muh Zulfadli; Alfiansyah, A
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2381

Abstract

Forest fires remain a global problem that has a major impact on the economy and health. Indonesia suffered losses of up to Rp. 72.95 trillion due to forest fires in 2019. Internet of Things (IoT) technology can be used for early detection of forest fires, but is constrained by limited network infrastructure and high energy consumption. This study aims to design a smart mitigation device and application for early detection of forest fires using LoRaWAN technology, which does not require an internet connection from the node to the gateway. In addition, an Artificial Intelligence method with adaptive sampling is applied, namely adaptive sampling threshold modeling and reinforcement Q-learning on the gateway to optimize energy use. The method used is Research and Development (R&D), with testing of the effectiveness of the design and descriptive statistical analysis to compare the energy efficiency between LoRaWAN devices with AI and conventional smart mitigation devices. The results of the study show that LoRa-based mitigation devices can cover the entire Jompie Botanical Garden area with a transmission distance of up to 3 kilometers and are 105% more energy efficient than conventional mitigation devices.