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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Sinergi Sistemasi: Jurnal Sistem Informasi Journal of Embedded Systems, Security and Intelligent Systems
Rian Fahrizal
Jurusan Teknik Elektro Universitas Sultan Ageng Tirtayasa Cilegon - Banten Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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DETEKSI TINGKAT KEMATANGAN BUAH MELINJO MENGGUNAKAN METODE ALGORITMA SELF ORGANIZING MAP Lufianawati, Dina Estining Tyas; Baharani, Siti Nurfia; Fahrizal, Rian
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 26, No 2 April (2024): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.26.2.70-76

Abstract

Melinjo tersebar luas di Indonesia. Buah melinjo mengalami perubahan warna dari mentah hingga sangat matang. Proses pemilihan hasil panen baik oleh petani, masyarakat, dan pelaku usaha UMKM buah melinjo pada umumnya memilih melinjo sebagai bahan dasar suatu produk masih dilakukan secara manual menggunakan penglihatan manusia normal sehingga memiliki kelemahan antara lain waktu yang dibutuhkan relatif lama dan menghasilkan produk beragam karena tingkat kelelahan manusia. Perkembangan teknologi ilmu pengetahuan dan teknologi pengolahan citra digital bisa diterapkan untuk memilih buah dari hasil panen secara otomatis dengan bantuan aplikasi pengolah citra. Tujuan penelitian ini adalah membangun sebuah sistem untuk mengklasifikasi tingkat kematangan buah melinjo dengan menggunakan metode Algoritma Self Organizing Map dan ekstraksi fitur RGB (Red, Green, Blue) dan HSV (Hue, Saturation, Value) dengan bantuan aplikasi pengolah citra MATLAB. Deteksi tingkat kematangan buah melinjo memiliki 4 tingkat kematangan yaitu mentah, setengah matang, matang, dan sangat matang. Penelitian ini menggunakan 200 citra, 80% data training dan 20% data testing. Berdasarkan hasil pengujian diperoleh tingkat accuracy 97,5% kemudian specificity 99,16% dan sensitifity 97,5%.
Implementation of wavelet method and backpropagation neural network on road crack detection based on image processing Alfanz, Rocky; Fahrizal, Rian; Utomo, Tegar Priyo; Firmansyah, Teguh; Muhammad, Fadil; Muztahidul, Islam Md
SINERGI Vol 28, No 3 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.3.005

Abstract

Road crack detection is critical to road infrastructure maintenance, requiring sophisticated and accurate approaches. This research explores the utilization of a combination of Wavelet and Convolutional Neural Network (CNN) methods to improve efficiency and accuracy in detecting cracks in road images. The wavelet method was chosen for its capability to capture information at different scales, enabling improved feature extraction. Meanwhile, CNN was utilized to comprehend the spatial context and tackle image complexity. The research involves several stages, including data collection, pre-processing, decomposition using the Wavelet method, forming of the CNN architecture model, training, testing, and evaluating the result. The tested images involve three main types of cracks: alligator, linear, and images without cracks. The testing results show that the developed model is capable of classifying cracks with an F1-score of 0.96, recall of 0.96, and precision of 0.96. In real-time detection of road cracks, the testing obtained an F1-score of 0.84, recall of 0.92, and precision of 0.77. This research contributes to the advancement of road crack detection technology by leveraging the capabilities of Wavelet and CNN, enhancing the accuracy and efficiency of crack detection in road maintenance.
Vehicle Detection Counting using YOLO and DeepSORT on Edge Device Rafli; Wardoyo, Siswo; Alfanz, Rocky; Fahrizal, Rian; Muhammad, Fadil; Muttakin, Imamul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9482

Abstract

Vehicle counting is a crucial method used in traffic management. Computer vision can be employed for efficient detection and classification techniques for vehicle objects. This paper reports on a simultaneous process of vehicle classification and counting implemented on NVIDIA Jetson Nano. The use of YOLOv5 overcomes computational load issues in edge computing deployments, whereas its combination with the DeepSORT tracker algorithm enhances the accuracy of vehicle detection and counting in various directions. A total of 18200 images are used to train the detectors that are designed to target local vehicles. The average accuracy of the model for detecting cars, motorcycles, buses, and trucks is 72.1%, 21.56%, 70%, and 25.63%, respectively. Real-time tests obtained an overall average vehicle counting accuracy of 49.95%.
Classification of Beef, Goat, and Pork using GLCM Texture-Based Backpropagation Neural Network Saraswati, Irma; Fahrizal, Rian; Fauzan, Anugrah Nuur; Yudono, Muchtar Ali Setyo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4715

Abstract

Identifying different types of meat is crucial for preventing fraudulent activities and improving food safety. This research aims to create a classification system for various meat types (beef, goat, and pork) using the Gray Level Co-occurrence Matrix (GLCM) for extracting texture features, followed by classification through a Backpropagation Neural Network (BPNN). The methodology utilizes 60 images of beef, goat, and pork, achieving a remarkable accuracy of 100% in the training phase, which highlights the model's capability to effectively recognize patterns. However, when tested with new data, the system exhibits a sensitivity of 90% and a specificity of 95%, with some misclassifications occurring between goat and beef due to their similar textures. The findings of this study suggest that GLCM is an effective tool for deriving relevant statistical parameters necessary for classification. This research makes a significant contribution to developing a meat identification system that safeguards consumers and promotes awareness of food safety issues. The results are anticipated to provide a solid foundation for advancing meat type recognition and practical applications in the marketplace, ultimately boosting public trust in the meat products they purchase.
Co-Authors Baharani, Siti Nurfia Fauzan, Anugrah Nuur Imamul Muttakin Irma Saraswati Istiyah, Umi Lufianawati, Dina Estining Tyas Muchtar Ali Setyo Yudono Muhammad, Fadil Muztahidul, Islam Md Priswanto Priswanto, Priswanto Rafli Rocky Alfanz, Rocky romi wiryadinata Siswo Wardoyo Teguh Firmansyah Utomo, Tegar Priyo