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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Journal of Social Research
Eliezer Mangoting Rongre
Politeknik Negeri Manado
Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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RANCANG BANGUN SISTEM LOG AKSES KE RUANG SERVER BERBASIS IoT MENGGUNAKAN PENGENALAN WAJAH Janto Junior Rembet; Graciella Regita Maharani Kaligis; Folansky Vladimir Bambulu; Eliezer Mangoting Rongre; Yonatan Parassa
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 Nomor 02, Juni 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.48752

Abstract

This study aims to design and implement an Internet of Things (IoT)-based server room access logging system using facial recognition technology. Conventional security systems such as mechanical locks and access cards have several limitations, including vulnerability to loss, duplication, and unauthorized access, as well as the absence of automatic activity logging. Therefore, this research proposes an integrated system that combines facial recognition for authentication with real-time access logging and monitoring. The system utilizes ESP32-CAM as an image acquisition device, while face detection is performed using the Haar Cascade method and identification is conducted using cosine similarity. Data collection was carried out by capturing 50 facial images per registered user at a distance of approximately 30 cm with variations in facial angles. The system was tested under real operational conditions, showing an average accuracy of 91% with a response time of approximately 1–2 seconds. In addition, the system successfully records access activities, including access status and user data, which can be monitored through a web-based interface. The results indicate that the proposed system can improve server room security by providing automated authentication, access control, and real-time monitoring in an integrated manner.
A Web-Based Vector Reporting Information System Using Decision Trees for Risk Classification (Case Study: Manado Class 1 Health Quarantine Office, Manado Seaport Working Area) Fernanda Grety Panese; Eliezer Mangoting Rongre; Doostenreyk Niala Kantohe
Journal of Social Research Vol. 5 No. 7 (2026): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v5i7.3241

Abstract

Vector-borne diseases remain a critical public health challenge, particularly in tropical port cities where international maritime traffic increases the risk of introducing infected vectors. At the Class 1 Health Quarantine Center of Manado (BKKK Manado), traditional paper-based vector reporting workflows have caused delays, transcription errors, and inconsistent risk assessments, hindering timely and evidence-based decision-making. This study aims to develop and evaluate a web-based vector reporting information system integrated with a C4.5 Decision Tree classifier to automate risk classification and improve operational efficiency. An applied research approach using a research-and-development (R&D) methodology was employed, involving system design, implementation, and empirical evaluation at the Manado seaport. Data were collected from 312 historical vector surveillance records, field observations, and officer interviews. System performance was assessed through classification accuracy, functional testing, usability evaluation (System Usability Scale), and a time-efficiency comparison with paper-based reporting. The resulting system achieved 92.1% classification accuracy, a macro-averaged F1-score of 0.91, a 100% functional test pass rate, and an 80.7% reduction in reporting time, while usability was rated “Excellent” by officers. The study concludes that the web-based system effectively enhances vector surveillance and decision-making. Future research should focus on expanding datasets, integrating with national health platforms, and exploring alternative classifiers to improve scalability and robustness for broader vector-borne disease monitoring.
Co-Authors Doostenreyk Niala Kantohe Fernanda Grety Panese Folansky Vladimir Bambulu Graciella Regita Maharani Kaligis Janto Junior Rembet Yonatan Parassa