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All Journal JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Agrium Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Scine and Information Technology Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Jurnal Teknik dan teknologi tepat guna
Rianti, Eva
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Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Social Sciences

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EXPLANATION OF FEATURE EXTRACTION IN FACE RECOGNITION USING VIOLA JONES ALGORITHM Devita, Retno; Rianti, Eva; Yuhandri, Muhammad Habib; Putra, Ondra Eka
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3844

Abstract

Face recognition has become a common thing used in the field of surveillance and security in computer technology and image devices. This study aims to identify the usefulness of a person's face on 3 test images. This study examines the methods of cropping techniques, image enhancement through intensity measurement, and histogram analysis to improve the contrast and distribution of image intensity. In addition, the Viola-Jones algorithm is used to detect key facial features such as eyes, nose, and mouth. The results of the analysis are then applied in the feature evaluation stage, where usually between facial features are applied to measure the ratio of facial proportions. Furthermore, the comparison of proportional ratios of several images was analyzed using bar graphs and line graphs to evaluate the trend and stability of facial proportions. The results showed the best ratio stability with a smaller variation of the on-off ratio of image 2 which is 0.4762 pixels to 0.4983 pixels. Image 2 is the most ideal for face measurement systems based on geometric ratios because it provides more consistent and visible results.
Analisis Kepuasan Pelayanan Publik Menggunakan Metode Naïve Bayes Pada Dinas Kependudukan Dan Pencatatan Sipil Kabupaten Agam Wisky, Irzal Arief; Rianti, Eva; Syahira, Afika
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i3.784

Abstract

This study aims to analyze public satisfaction with services provided by the Department of Population and Civil Registration (Disdukcapil) of Agam Regency using the Naïve Bayes method. The research data were collected from 252 respondents through a public satisfaction survey covering nine service indicators based on the Ministry of Administrative Reform Regulation No.16 of 2014. The Naïve Bayes algorithm was applied to classify satisfaction levels into four categories: very dissatisfied, dissatisfied, satisfied, and very satisfied. The results indicate that the developed web-based system can accurately predict public satisfaction levels, with the highest probability value of 0.1127 falling under the “very satisfied” category. These findings demonstrate that the service quality at Disdukcapil Agam Regency has been well implemented, and the application of the Naïve Bayes method is effective in supporting the evaluation and continuous improvement of public service performance.
Implementasi Computer Vision Dalam Deteksi Dan Klasifikasi Sampah Otomatis Pada Sistem Pengolahan Limbah Perkotaan Lusman, Akbar; Devita, Retno; Putra, Ondra Eka; Rianti, Eva; Islami, Fajrul
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

Waste is a very serious environmental problem commonly faced by Indonesians. According to data from the National Waste Management Information System (SIPSN), Indonesia's waste volume reached 20.02 million tons in 2022. In Indonesia, the amount of waste generated reached 65 million tons per day in 2016 and increased to 66.5 million tons in 2018. The amount of waste in Indonesia continues to increase annually. In large cities, waste management is an increasingly pressing challenge, given the negative impacts caused by improper management, such as waste accumulation in landfills (TPA), water and air pollution, and public health issues. This study aims to design and implement an automatic waste classification system based on Computer Vision technologies as a solution for urban waste management. The system utilizes an Arduino Mega 2560, camera, ultrasonic sensor, servo motor, and conveyor to detect and classify five main types of waste: plastic, paper, glass, metal, and organic materials in real time. The camera captures images of waste, which are then analyzed using a Computer Vision model, while sensors and actuators control the flow and physical sorting process. This research seeks to improve waste processing efficiency by reducing human involvement in hazardous tasks and to promote the application of intelligent technologies in supporting sustainable recycling systems and reducing the burden on final disposal sites (landfills). The system created can detect and classify waste types well.
Co-Authors Bhaidawi, Bhaidawi Devita, Retno Enggari, Sofika Fajrul Islami Graceae, Marsaa Noverdi Gusmita, Devi Halifia Hendri Irzal Arief Wisky J, Shafa Adillah Khairul, Khairul Zaman Kurnia, Hezy Lusman, Akbar melanie, Vanesaputri Nitami, Dian Pradana, Taufan Pratiwi, Mutiana Purwoko, Agus Putra, Ondra Eka Ramadhanu, Agung - Retno Devita, Retno Devita Silfia Andini, Silfia Syahira, Afika Yenila, Firna Yuhandri, Muhammad Habib