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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of The Civil Engineering Student
Muzammil, Rivaul
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Transportation

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Pemenuhan Jaringan Air Minum IPA Glee Dagang Untuk Sistem Air Minum di Kecamatan Muara Batu dan Kecamatan Dewantara Kabupaten Aceh Utara Muzammil, Rivaul; Fauzi, Amir; Ziana, Ziana
Journal of The Civil Engineering Student Vol 3, No 1 (2021): Volume 3, Nomor 1, April 2021
Publisher : Jurusan Teknik Sipil, Fakultas Teknik, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/journalces.v3i1.12279

Abstract

Air minum merupakan penentu peningkatan kesejahteraan masyarakat, dengan Sistem Penyediaan Air Minum yang sudah beroperasi di Kecamatan Muara Batu dan Kecamatan Dewantara Kabupaten Aceh Utara belum bekerja optimal. Sistem ini diolah oleh IPA Glee yang pada saat ini kapasitas intake yang tersedia 90 l/d namun hanya sekitar 40 liter/detik yang mengalir. Tujuannya untuk mengevaluasi jaringan distribusi dengan memperoleh diameter pipa, debit aliran, kecepatan aliran, dan tekanan aliran yang baru. Data yang digunakan dalam studi ini berupa data pelanggan aktif, data penduduk, serta perhitungan jaringan eksisting. Simulasi pengembangan jaringan baru menggunakan software EPANET 2.0. Untuk masing-masing Kecamatan hasil yang didapatkan adalah jumlah penduduk yang diproyeksikan sampai tahun 2035 dengan diameter pipa sebesar 75 mm sampai 500 mm dengan debit aliran terbesar 47,62 l/d dan 21,23 l/d. Tekanan aliran terbesar 95,92 m dan 87,65 m. Kesimpulanya kebutuhan telah dioptimalkan lebih dari 40 l/d serta kecepatan dan tekanan yang meningkat.
Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System Muzammil, Rivaul; Oktiana, Maulisa; Roslidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2320

Abstract

The increasing number of vehicles in Indonesia has posed significant challenges in the management of parking facilities. This study proposes the development and implementation of an intelligent parking system based on automatic vehicle license plate character recognition. The proposed system employs the You Only Look Once version 8 (YOLOv8) model to detect the license plate region, and a Convolutional Recurrent Neural Network (CRNN) to recognize the alphanumeric characters contained within the plate. The system architecture integrates a Raspberry Pi, a camera module, and a servo motor to facilitate the automatic detection and recognition of license plates as vehicles enter and exit parking areas. The YOLOv8 model is responsible for identifying the license plate region by generating a bounding box through a convolutional layer, which is then used to isolate the license plate area from the original image. This cropped image undergoes a pre-processing stage to conform with the input specifications of the CRNN model. Subsequently, the CRNN model extracts visual features through convolutional layers and leverages recurrent layers to capture the sequential relationship among the characters on the license plate. The entire processing pipeline is deployed on the Raspberry Pi using TensorFlow Lite, ensuring efficient operation of both the YOLOv8 and CRNN models in a resource-constrained environment. Experimental results demonstrate that the YOLOv8 model achieved a detection accuracy of 94.69% for license plate localization, with a precision of 98.32%, recall of 96.25%, and an F1-score of 97.27%. In parallel, the CRNN model attained a character recognition accuracy of 93.8% across a test set comprising 30 license plates. Nevertheless, the system encountered some recognition errors, such as misclassification of the character 'G' as 'C', 'W' as 'H', and 'Q' as 'O'.
Co-Authors Amir Fauzi Oktiana, Maulisa Roslidar Ziana, Ziana