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All Journal Jurnal Ilmiah Teknologi Informasi Asia KOMPUTIKA - Jurnal Sistem Komputer Jurnal Informatika Polinema (JIP)
Zilvanhisna Emka Fitri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science

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Ensiklopedia Digital Berdasarkan Klasifikasi Varietas Buah Mangga (Mangifera spp.) Menggunakan Algoritma Backpropagation Zilvanhisna Emka Fitri; Riska Aprilia; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Komputika : Jurnal Sistem Komputer Vol 11 No 2 (2022): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v11i2.5513

Abstract

Mango is a leading fruit commodity that is able to increase industrial development and exports in Indonesia. There are 33 species of mangoes spread throughout the territory of the Republic of Indonesia and have many variations of fruit shapes in each type. However, the problem that occurs is the difficulty of information related to data on mango varieties, so to help these problems, the researchers created a digital encyclopedia system that is able to provide information related to the diversity of mango varieties. This encyclopedia can classify and identify 5 types of mangoes, namely Apple Mango, Gedong Gincu Mango, Golek Mango, Manalagi Mango and Gadung Mango. Parameters used to distinguish mango varieties are area, perimeter, eccentricity, major axis length and diameter. The classification method used, namely backpropagation, is able to classify the five mango varieties with a training accuracy of 99.6% and a testing accuracy of 96%. Keywords - digital encyclopedia; mango varieties; computer vision; shape parameters; backpropagation.
Penerapan Fitur Warna dan Tekstur untuk Identifikasi Kerusakan Mutu Biji Kopi Arabika (Coffea Arabica) di Kabupaten Bondowoso Zilvanhisna Emka Fitri; Brilyan Andi Syahbana; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Ilmiah Teknologi Informasi Asia Vol 15 No 2 (2021): Volume 15 Nomor 2 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v15i2.593

Abstract

Plantation crops are also a source of foreign exchange Indonesia is coffee. There are only two types of coffee that have economic value for cultivation, namely Arabica coffee and Robusta coffee. Bondowoso is a district in East Java that develops Arabica coffee. The problem is that farmers still use direct observation (manual) on each coffee bean to determine the quality of coffee beans so that this research is expected to be able to assist farmers in sorting the damage to the quality of coffee beans based on color and texture. The features used are color features and GLCM texture features at 0̊ and 45̊ angles. The total number of data is 198. The Backpropagation method is able to classify quality damage to Arabica coffee beans with a training accuracy rate of 100% and a testing accuracy rate of 97.5% at a learning rate variation of 0.5.
Deteksi Kendaraan Truk pada Video Menggunakan Metode Tiny-YOLO v4 Primasdika Yunia Putra; Aji Seto Arifianto; Zilvanhisna Emka Fitri; Trismayanti Dwi Puspitasari
Jurnal Informatika Polinema Vol. 9 No. 2 (2023): Vol 9 No 2 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i2.1243

Abstract

Computer vision mampu meniru kemampuan manusia dalam hal mengenali sesuatu dengan ciri visual, dikarenakan bersifat konsisten. Deteksi kendaraan secara real-time dalam video pengamatan di jalan raya menjadi salah satu topik yang menarik diangkat dalam riset. Mendeteksi truk memiliki tantangan tersendiri karena memiliki kesamaan dimensi dengan bus dan struktur mekanis yang menyerupai pikap. Mendeteksi objek dengan metode YOLO (You Only Look Once) sangat populer. Langkah awal dilakukan percobaan dengan bobot dan model asli YOLO, namun hanya mampu mendeteksi bagian kepala dan bak truk saja, jika ada truk bermuatan maka bounding box tidak berhasil menandainya. Sehingga dilakukan custom dataset dengan cara membuat bounding box untuk seluruh bagian kepala, bak serta truk yang bermuatan. Tiny-YOLO sebagai varian dari YOLO dipilih karena strukturnya lebih sederhana dan kompatibel dengan perangkat low-end hingga high-end. Tiny-YOLO v4diimplementasikan pada 3 perangkat dengan spesifikasi berbeda, menghasilkan akurasi pengujian 98,2% (13FPS) pada perangkat A, 98% (28FPS) pada perangakt B dan 97,5% (38FPS) pada perangkat C. Pengaruh spesifikasi perangkat keras terhadap perolehan FPS yang didapat dan akurasi pendeteksian ditandai dengan perbedaan hasil yang signifikan pada saat pengujian. Semakin tinggi FPS maka semakin menurun akurasi pendeteksian. Untuk perangkat B dan C dengan spesifikasi CPU diatas 3.0 GHz, RAM minimum 16 GB, GPU lebih dari 4GB, dapat menjalankan pendeteksian truk menggunakan Tiny-Yolo v4 secara real-time dengan baik.
Co-Authors Abdul Madjid Aji Seto Arifianto Brilyan Andi Syahbana Imron, Arizal Mujibtamala Nanda Primasdika Yunia Putra Riska Aprilia Trismayanti Dwi Puspitasari