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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 IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika)
Prayogi, Denis
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Social Sciences

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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.
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 Nurhamida Nurhamida Nurzainun, Ratnasari S Sunardi Sinawati, S Sunardi Sunardi Suryansyah Suryansyah Victor Christalomegatli Yuda Agus Saputra Zulhilmi, Hafizhan