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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Journal Technology Information and Data Analytic
Timor Setiyaningsih
Darma Persada University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Social Sciences Other

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Journal : Journal Technology Information and Data Analytic

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Perancangan Aplikasi Absensi dan Pengawasan Ruangan dengan Pengenalan Wajah menggunakan metode Convolutional Neural Network Rizki Nurpadilah; Timor Setiyaningsih
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 1 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i1.81

Abstract

The development of technology in the field of facial recognition provides a great opportunity to improve efficiency and security in various aspects, one of which is the attendance and room surveillance system. This study aims to design an attendance and room surveillance application based on facial recognition using the Convolutional Neural Network (CNN) method in a private company engaged in the property sector. This application is designed to simplify the employee attendance process and improve room surveillance by automatically recognizing employee faces, thereby reducing the risk of attendance fraud and ensuring more accurate attendance. The CNN method was chosen because of its ability to process images and recognize facial patterns with high accuracy. This system consists of several main features, namely employee face registration, automatic face-based attendance, and monitoring employee presence in the office space. The test results show that this application is able to identify faces with a good level of accuracy, as well as provide convenience and comfort for users.
Decision Tree Regression Approach to Modeling Dengue, Tuberculosis, and Diarrhea Case Numbers Muhammad Dzaki Zahirsyah; Timor Setiyaningsih
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.121

Abstract

The increasing incidence of Dengue Hemorrhagic Fever (DHF), Tuberculosis (TB), and Diarrhea in a district area highlights the urgent need for a data-driven prediction system to support public health policy. This study develops a predictive model of case numbers at the sub-district level using the Decision Tree Regression algorithm within the CRISP-DM methodology. Secondary data from 2020-2023 were utilized, including disease case records (Health Office), demographic data (BPS), and environmental data (BMKG). The system was implemented as a web-based application built with PHP and Python/Flask, enabling dataset management, model retraining, and interactive visualization of predictions, complemented by risk classification and recommended interventions. Experimental results demonstrate high predictive accuracy, with R² values of 0.9130 for TB, 0.8805 for DHF, and 0.8228 for Diarrhea. Overall, the proposed system serves as an objective and measurable decision-support tool, assisting the District Health Office in formulating preventive policies more rapidly and effectively.
Co-Authors Andi Susilo Budi Sumartono Eva Novianti Herianto S.Pd., MT Linda Nur Afifah Muhammad Dzaki Zahirsyah Nur Hasanah Reza Istoni Rizki Nurpadilah Sarah Isniah Suzuki Syofian Yahya yefri chan