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All Journal Jurnal Media Infotama SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat
Denis Prayogi
STMIK PPKIA Tarakanita Rahmawati
Humanities Computer Science & IT Control & Systems Engineering Education Engineering Languange, Linguistic, Communication & Media Public Health Other

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Pengabdian Sebagai Dewan Juri Bidang Teknik Desain Laman Lomba Kompetensi Siswa (LKS) SMK Tingkat Provinsi Kalimantan Utara Tahun 2025 Novita Ranti Muntiari; Denis Prayogi
Jurnal Pengabdian Masyarakat - PIMAS Vol. 4 No. 4 (2025): November
Publisher : LPPM Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/pimas.v4i4.2186

Abstract

Student Competency Competition is an annual competition between students at the vocational high school level according to the areas of expertise taught at participating vocational schools. This LKS is equivalent to the OSN (National Science Olympiad) held in junior high schools/high schools. This activity is one part of a series of selections to get the best students from all over Indonesia who will be further guided by their respective competition field teams and will be included in international level expertise competitions. The purpose of the community service activities carried out through this LKS competition activity is for the Community Service Team to contribute as a jury in the Page Design Technique competition. The role of the jury is very important as the determinant of the final results of the provincial level LKS, so that the best students can represent North Kalimantan Province to compete at the national level. The final result of this community service activity is an official decision from the jury on the competition assessment process in determining the winner of the 2025 North Kalimantan Provincial Level SMK LKS.
Penerapan IoT pada Keamanan Lingkungan Berbasis Android Hadriansa Hadriansa; Denis Prayogi
Jurnal Media Infotama Vol 21 No 1 (2025): April 2025
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v21i1.7142

Abstract

The development of information and communication technology (ICT), especially the Internet of Things (IoT), has changed human interaction with the surrounding environment. IoT allows physical devices to connect and communicate over the internet, improving efficiency and security in various areas, including public safety. This research focuses on the development of an IoT-based Citizen Security System, which integrates the active participation of citizens in the environmental security system through the Environmental Security and Order System (Siskamling). Prototypes developed using the ESP32 Module are connected to a database to detect potential hazards, such as theft or fire. This tool allows homeowners to send emergency signals through a panic button that is directly connected to the kamling post. In addition, the system provides information about the schedule of the kamling post to residents. This study shows that this device functions manually and provides notifications to residents and kamling post officers through an Android application. With additional features such as RTC for time counting and buzzer as an alarm, the system is expected to increase awareness and response to emergency situations in the environment. The conclusion shows the significant potential of IoT technology in strengthening community security.
EVALUASI KINERJA KOMPUTER MIKRO RASPBERRY PI DENGAN PEMBELAJARAN MESIN UNTUK PENGENALAN WAJAH Denis Prayogi
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3649

Abstract

Limited computing resources and high hardware costs often limit the implementation of facial recognition systems on embedded devices. This study aims to evaluate and compare the performance of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms on a Raspberry Pi 4B microcomputer. The research method involves testing the CNN architecture and SVM kernel on a Kaggle dataset consisting of 2,000 facial images from five identity classes. The evaluation parameters used include accuracy, precision, recall, F-Score, resource usage (CPU/RAM), and inference speed. The test results show that the CNN algorithm achieves 93% accuracy but takes longer inference time, averaging 268.52 ms per image. On the other hand, SVM achieves 87% accuracy with much faster inference time, averaging 8.02 ms per image. Based on the test results, this study concludes that although CNN is superior in accuracy, SVM is more recommended for real-time biometric system applications on microcomputers due to its computational time efficiency and lower resource usage.
Co-Authors Hadriansa Hadriansa Muntiari, Novita Ranti