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All Journal Jurnal Media Infotama International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Journal of Innovation and Future Technology (IFTECH) Journal of Applied Microcontroller and Autonomous System SKANIKA: Sistem Komputer dan Teknik Informatika IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Masyarakat
Prayogi, Denis
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Implementation of Data Mining Using K-medoids Clustering Method for Determining Social Assistance Recipients Nurzainun, Ratnasari; Sinawati, S; Prayogi, Denis
IJISTECH (International Journal of Information System and Technology) Vol 8, No 5 (2025): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i5.372

Abstract

Regarding social assistance, the village head, “Lurah” of Selumit, is currently reviewing data on residents based on government-provided statistics on the capabilities of low-income families and the need for social assistance.  Therefore, this study proposes K-medoid clustering to ensure that the assistance provided is appropriate. The study collected 62 data on social assistance recipients consisting of 6 criteria, namely employment, assets, income, jak (who are still dependents), home status, and home conditions. K-Medoids analysis using the Euclidean distance function with K=3 produces cluster 1 with 12 data, cluster 2 with 31 data, and cluster 3 with 19 data. The recipients prioritized to receive social assistance are the data in cluster 2 by calculating the average of the most considerable maximum weight value.
Implementation of an Integrated Information System for Construction Services Development to Support Infrastructure Projects at the Public Works Department of Tarakan City Hadriansa, H; Prayogi, Denis; Fadlan, Muhammad; Fandariansyah, F
IJISTECH (International Journal of Information System and Technology) Vol 8, No 5 (2025): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i5.373

Abstract

Efficient infrastructure development requires effective management, particularly in developing construction services. The Public Works (PU) Department of Tarakan City faces challenges in overseeing and guiding construction service providers to ensure smooth execution and project quality. This study aims to design and implement an integrated information system using the Scrum method, which supports construction service development, project progress monitoring, contractor data management, and budget supervision. Scrum was chosen to enhance flexibility and collaboration among development teams by allowing the system to be built iteratively and incrementally. The system is designed to improve transparency, efficiency, and accuracy in managing construction projects. The implementation results show that applying the Scrum method in developing the integrated information system has successfully accelerated the verification process, reduced administrative errors, and improved budget control. Additionally, the system has increased project management efficiency, expedited decision-making, and facilitated monitoring and evaluation processes. This study recommends further development and expansion of the system to support more effective construction service development in the future
Penerapan IoT pada Keamanan Lingkungan Berbasis Android Hadriansa, Hadriansa; Prayogi, Denis
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.
Face Recognition Using Machine Learning Algorithm Based on Raspberry Pi 4b Sunardi, Sunardi; Fadlil, Abdul; Prayogi, Denis
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.574 KB) | DOI: 10.29099/ijair.v7i1.321

Abstract

Machine learning is one of artificial intelligence that is used to solve various problems, one of which is classification. Classification can separate a set of objects based on certain characteristics. This study discusses the classification of objects in the form of facial images with the aim of the system being able to recognize a person's face to access a room for security reasons. The application of machine learning using the support vector machine algorithm with the support vector classifier technique is implemented on a raspberry pi-based security device.  The results of training using this algorithm produce a model with 99% accuracy in 0.10 seconds based on testing data of 525 face images. The model evaluation got 99% precision, 99% recall, and 99% f1-score. Testing the model made from the training process using the raspberry pi model 4b is can recognize facial images in real-time.  If the security device detects someone at the door and then recognizes the face image then room access will be granted and an alarm is activated indicating the door is open.
EVALUASI KINERJA KOMPUTER MIKRO RASPBERRY PI DENGAN PEMBELAJARAN MESIN UNTUK PENGENALAN WAJAH Prayogi, Denis
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.
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.
Co-Authors Abdul Fadlil Achmad Dahlan Aidil Aidil Basuki Rahmat Endyk Noviyantono Fadlan, Muhammad Fandariansyah, F Hadriansa, H Hadriansa, Hadriansa Harianto, Kandi Haryansyah Haryansyah Heri Heri Irawansyah Irawansyah MARIA BINTANG MUHAMMAD FADLAN Muhammad Muhammad Muntiari, Novita Ranti Nurhamida Nurhamida Nurzainun, Ratnasari S Sunardi Sinawati, S Sunardi Sunardi Suryansyah Suryansyah Victor Christalomegatli Yuda Agus Saputra Zulhilmi, Hafizhan