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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT JTT (Jurnal Teknologi Terpadu) Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Mantik Penusa JIPN (Journal of Informatics Pelita Nusantara) Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) JATI (Jurnal Mahasiswa Teknik Informatika) INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) MEANS (Media Informasi Analisa dan Sistem) Journal of Soft Computing Exploration INCODING: Journal of Informatics and Computer Science Engineering Bulletin of Information Technology (BIT) Kode : Jurnal Bahasa Archive: Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Innovative: Journal Of Social Science Research Sasambo: Jurnal Abdimas (Journal of Community Service)
Insan Taufik
Universitas Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Face Recognition Motorcycle Rider Registration System for Rider Data Management Kana Saputra S; Insan Taufik; Irham Ramadhani; Putri Sasalia S; Yusfi Syawali; Dede Yusuf; Rezkya Nadilla Putri; Najwa Latifah Hasibuan; Fauzan Hafiz Harahap
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2157

Abstract

This research aims to develop a motorcycle rider registration system using facial recognition technology that can improve the efficiency of rider data management. This system is designed to identify and authenticate riders with high accuracy, thereby simplifying the registration and monitoring process. The methods used in this research include collecting rider facial data through cameras, image processing for feature extraction, and implementing a facial recognition algorithm. Testing was conducted in several locations with varying lighting conditions and viewing angles to ensure the system's robustness. The results show that the developed system is capable of achieving facial recognition accuracy of up to 95%. In addition, this system provides an intuitive user interface to facilitate the registration and data management process. With the implementation of this system, it is expected to reduce the time and costs required in managing motorcycle rider data, as well as improve safety and comfort while riding.
Pengembangan Bahan Ajar Drama Berbantuan Media Virtual Reality Mahasiswa Pendidikan Bahasa dan Sastra Indonesia Trisnawati Hutagalung; Lili Tansliova; Abdurrahman Adisaputera; Insan Taufik
Kode : Jurnal Bahasa Vol. 14 No. 4 (2025): Kode: Edisi Desember 2025
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tujuan penelitian ini adalah untuk mengembangkan bahan ajar berbantuan media Virtual Reality pada mata kuliah Drama di Program Studi Pendidikan Bahasa dan Sastra Indonesia FBS Unimed. Penelitian ini menggunakan metode Research and Development (R&D), penelitian ini dilakukan sampai revisi produk setelah dilakukan validasi. Populasi penelitian ini adalah seluruh mahasiswa semester VI Program Studi Pendidikan Bahasa dan Sastra Indonesia. Sampel dalam penelitian ini adalah mahasiswa reguler E Program Studi Pendidikan Bahasa dan Sastra Indonesia. Hasil penelitian ini memperoleh persentase validasi ahli materi untuk aspek isi dengan kualifikasi sangat baik (85,7%) hasil penelitian ahli materi untuk aspek penyajian berada pada kualifikasi sangat baik (85,5%), hasil validasi ahli materi aspek kebahasaan berada pada kategori kualifikasi sangat baik (85,8%) dan hasil validasi ahli desain berada pada kualifikasi sangat baik (86,6%). Kata Kunci: Bahan Ajar Virtual Reality, Pembelajaran Drama, Pendidikan Bahasa dan Sastra Indonesia.
Classification of Purple Passion Fruit Ripeness Levels Using Convolutional Neural Network (CNN) Mochammad Gani Alfa Alkhoiri Siregar; Said Iskandar Al Idrus; Hermawan Syahputra; Insan Taufik; Kana Saputra S
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1787

Abstract

Passiflora edulis Sims (purple passion fruit) is a fruit that offers numerous health benefits and possesses high economic value. However, the manual assessment of ripeness by traders tends to be subjective and inconsistent, leading to post-harvest losses of up to 50%. This study developed a classification model for determining the ripeness level of purple passion fruit using a Convolutional Neural Network (CNN) and implemented it in a web-based application. The CNN model was designed to classify four ripeness stages (unripe, half-ripe, ripe, and rotten) with the addition of a non-passion-fruit class to enhance the system’s robustness. The dataset consisted of 2,000 images divided into five classes: four ripeness levels of purple passion fruit (unripe, half-ripe, ripe, and rotten) and one non-passion-fruit class as a comparator. All images were in JPG and PNG formats. The CNN architecture comprised four convolutional layers with 16, 32, 64, and 128 filters, respectively. Evaluation of various data-splitting ratios (80:20, 70:30, 60:40) and learning rates (0.001, 0.0001, 0.01) showed that the optimal configuration was achieved at a ratio of 80:20 with a learning rate of 0.001, resulting in a training accuracy of 96.72% and a testing accuracy of 95.76%, with a loss value of 0.1811. Validation using 5-Fold Cross Validation produced an average accuracy of 95.40%. The model was integrated into a web application developed using Flask and JavaScript, deployed on the PythonAnywhere cloud platform, enabling users to upload images and automatically obtain ripeness predictions to assist traders in sorting fruits more quickly and accurately.
Co-Authors Abdul Ghofur Abdul Ghofur Abdurrahman Adisaputera Ahmad Landong Alfattah Atalarais Amran Sitohang Andriyani S, Rahayu Annisa Aulia Antonius Purba Arjon Samuel Sitio Arnita Arnita Arnita Arnita Ary Prandika Siregar Chairunisah Chairunisah, Chairunisah Dede Yusuf Dinda Farahdilla Dharma Dinda Kartika Farhan Ramadhan, Haikal Fauzan Hafiz Harahap Fevi Rahmawati Suwanto Fitra, Awaludin Fristy Riandari Hasugian , Paska Marto Hermawan Syahputra Ihsan Zulfahmi Irham Ramadhani Isda Pramuniati Izwita Dewi Kana Saputra S Kuraini, Atifa Nuzulul Lili Tansliova Lubis, Afiq Alghazali M. Revano Ananda Lubis MANSUR AS Mhd Hidayat Mhd Hidayat Mochammad Gani Alfa Alkhoiri Siregar Mohammed Hafizh Al-Areef Muhammad Noer Fadlan Muhammad, Fachrezzy Muthmainnah, Inna Najwa Latifah Hasibuan Nasution, Hamidah . Niska, Debi Yandra Nurliani Manurung Pane, M Iqbal Anata Pane, Yeremia Yosefan Parapak, R Putri Angela Pinem, Josua Purba, Boy Hendrawan Putri Sasalia S Putri, Rezkya Nadilla Raiyan Fairozi Rezkya Nadilla Putri Said . Iskandar Samosir, Wahyu Ardiantito Saragih, Vinny Ramayani Simamora, Elmanani Sinaga, Marlina Setia Siregar, Angginy Akhirunnisa Sri Mulyana SUSIANA Syifa Cendikia, Yolanda Tarigan, Yosua Yosephine Tengku Ratna Soraya TONNI LIMBONG Trisnawati Hutagalung Wahabi Hasibuan, Rahman Warjaya, Angga Wete Polili, Andi Wete Polili, Andi Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yusfi Syawali Zulfahmi Indra, Zulfahmi