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All Journal Telematika Jurnal Informatika dan Teknik Elektro Terapan CogITo Smart Journal Jurnal Teknologi Sistem Informasi dan Aplikasi JURTEKSI Jurnal Teknologi Informasi dan Pendidikan Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Akrab Juara : Jurnal Ilmu-ilmu Sosial Nusantara Hasana Journal Prosiding Konferensi Nasional PKM-CSR Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) Journal of Multimedia Trend and Technology Prosiding Seminar Nasional Pemberdayaan Masyarakat (SENDAMAS)
Aulia Hamdi
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Sistem Deteksi Plat Nomor Kendaraan Pada Aplikasi Mobile Tiket Pariwisata Ilham Albana; Ecky Efansyah Sukoco; Akbar Fitrian; Anugerah Bagus Wijaya; Aulia Hamdi; Fiby Nur Afiana; Zanuar Rifai
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 8 No. 1 (2025): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v8i1.39259

Abstract

Vehicle license plate recognition plays a vital role in intelligent transportation systems, parking management, and digital ticketing. However, conventional license plate recognition systems often face challenges related to lighting variations, viewing angles, and character distortions, which can decrease accuracy and efficiency, especially on mobile devices. This study aims to implement an Optical Character Recognition (OCR) framework, specifically EasyOCR, in a mobile application designed to read vehicle license plates in real time. The system was developed using the Software Development Life Cycle (SDLC) methodology, which includes the stages of planning, analysis, design, implementation, and testing. The mobile application was built using Flutter, integrated with a smartphone camera for data input and EasyOCR for text extraction. The testing results demonstrate that the proposed system can accurately detect and recognize license plate characters with an average accuracy rate of 100%. The contribution of this study lies in the application of EasyOCR on mobile platforms through a structured SDLC approach, enhancing the practicality of OCR-based vehicle identification systems for real-world transportation and parking management applications.
Analisis Pola Konsumsi Air Menggunakan Algoritma Random Forest Classifier Pada Distribusi Air Bersih Desa Rempoah Baturraden Ngarifatul Khofiyah, Salma; Hamdi, Aulia; Nur Isnaini, Khairunnisak
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3001

Abstract

Clean water is a basic necessity for communities that must be managed efficiently to ensure its distribution remains equitable and sustainable. In Rempoah Village, water usage is still recorded manually, making it difficult to analyze consumption patterns and detect irregular usage. This study aims to analyze water consumption patterns using the Random Forest Classifier algorithm as an effort to support clean water distribution management by the Berkah Maju Bersama Village-Owned Enterprise (BUMDes). The research data was obtained through observation and interviews with the management, followed by a data preprocessing stage that included data cleaning, missing value handling, data exploration, label encoding, and data division into training and test data. The Random Forest model was used to classify water consumption patterns into three categories, namely economical, normal, and wasteful. The results showed that the model was able to classify the data with an accuracy rate of 100%, where all test data was correctly identified. Based on the analysis results, most customers were in the wasteful category at 56.2%, indicating the need for an evaluation of the efficiency of household water use. These findings prove that the application of machine learning methods can be an effective solution in supporting decision-making and clean water management at the village level in a sustainable manner.
Co-Authors Adam Prayogo Kuncoro Afiana, Fiby Nur Akbar Fitrian Al Afghani, Muhammad Akbar Andriyan Widiyanto Anugerah Bagus Wijaya Arief Feisal Basri Desty Rakhmawati Ecky Efansyah Sukoco Ferlin Christiana Fiby Nur Afiana Hardian, Riski Huberta, Bima Ilham Albana ilham albana Irfan Santiko Luky Rafi Anuggilarso Luzi Dwi Oktaviana Melia Dianingrum Ndari, Arum Vika Ngarifatul Khofiyah, Salma Novia Asri Romadhoni Nur Isnaini, Khairunnisak Nuraziz, Reza Eka Nurcandrani, Prita Suci Octavia, Restiana Tri Oktaviana, Luzi Dwi Prasetyo, Agung Prayoga Pribadi Ranggi Praharaningtyas Aji Ratna Suminar Restiana Tri Octavia Rifai, Zanuar Riski Hardian Rujianto Eko Saputro Safitri, Dela Salsa Billa Zulmi Zettira Septi Fajarwati Shandy Putra Gautama Sigit Priyanto Suliswaningsih Suliswaningsih, Suliswaningsih Tanzilla, Armeyta Putri Toni Anwar Wijaya, Anugerah Bagus Wijaya, Anugerah Bagus Zanuar Rifai Zanuar Rifai