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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi INTEGER: Journal of Information Technology Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal Of Artificial Intelligence And Software Engineering
Ardhon Rakhmadi, Ardhon
Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember (ITS)
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Engineering Library & Information Science

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FITUR BERBASIS FRAKTAL DARI KOEFISIEN WAVELET UNTUK KLASIFIKASI CITRA DAUN Rakhmadi, Ardhon; Suciati, Nanik; Navastara, Dini Adni
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 15, No. 2, Juli 2017
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v15i2.a672

Abstract

Semakin banyak dan beragamnya jenis tanaman di dunia mengakibatkan semakin sulit untuk mengidentifikasi dan mengklasifikasi tanaman secara manual.  Daun merupakan bagian dari tanaman yang sering dipakai untuk identifikasi dan klasifikasi tanaman. Metode klasifikasi daun secara automatis telah banyak dikembangkan oleh para peneliti. Pada penelitian sebelumnya sistem klasifikasi daun otomatis dibangun menggunakan fitur berbasis fraktal yaitu dimensi fraktal dan lacunarity. Sistem klasifikasi daun otomatis berbasis dimensi fraktal dan lacunarity dapat mengklasifikasi daun dengan akurasi tinggi namun memerlukan banyak langkah preprocessing sehingga mengakibatkan komputasi sistem meningkat. Pada penelitian ini diusulkan penggunaan metode praproses dan ekstraksi wavelet pada ekstraksi fitur citra daun. Ekstraksi fitur menggunakan teknik perhitungan statistika sederhana pada koefisien wavelet sehingga komputasi menjadi lebih ringan. Hasil ekstraksi fitur citra daun akan menjadi data masukan untuk sistem klasifikasi Support Vector Machine (SVM). Hasil eksperimen menunjukkan bahwa metode ekstraksi fitur statistik pada dekomposisi wavelet lebih unggul dibandingkan dengan metode ekstraksi fitur berbasis fraktal (dimensi fraktal dan lacunarity) dari penelitian sebelumnya dengan akurasi 96.66% dan waktu komputasi 329.33 detik.
Ekstraksi Fitur Kupu-Kupu Menggunakan GLCM, Lacunarity, HSV, dan MLP Rahayu, Putri Nur; Annisa, Aulia Rahma; Ardiana, Mirza; Andika, Yudi; Rakhmadi, Ardhon
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7533

Abstract

Extraction feature in butterflies are using GLCM, Lacunarity, and HSV. The combination of extraction feature is to improve accuration of butterflies. In this research , there are three steps for extraction. First step is extraction with GLCM and lacunarity for extraction texture, and HSV for extraction color, the second step is classification with MLP.
Butterfly Feature Extraction Using HSV, Lacunarity, and CNN Rahayu, Putri Nur; Sukarno, Friska Intan; Augustino, Immanuel Freddy; Yuniati, R. A. Norromadani; Rakhmadi, Ardhon
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6876

Abstract

This study aims to extract the morphological features of butterflies using the HSV (Hue, Saturation, Value) and lacunarity. The HSV method is used to obtain color information from butterfly images. lacunarity is used to extract texture characteristic to enhance the visual representation of the object. These extracted features are used as input for the processing of classification using algorithm of Convolution Neural Network (CNN). Based on the experimental result, the classification has accuracy 70%. This accuracy indicates that the combination of HSV and lacunarity methods is sufficiently effective in describing of the visual butterflies features for automatic classification.
Rupiah Classification System using Segmented Fractal Texture Analysis and HSV Color Features Rakhmadi, Ardhon; Rahayu, Putri Nur; Thooriqoh, Hazna At; Mulyo, Budi Mukhamad
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.560

Abstract

The crime of forgery of rupiah currency can be anticipated by examining the rupiah banknotes based on traits or features contained on the original paper money. Features that are not owned by the rupiah banknote counterfeit is an ultraviolet sign that are owned by the original paper money. Rupiah banknotes feature extraction consists of a combination of color and texture feature extraction. The proposed method is the HSV color histogram for color feature extraction and Segmented Fractal Texture Analysis (SFTA) for texture feature extraction. The combination of HSV and SFTA is expected to improve the performance of rupiah banknotes feature extraction. Moreover this paper will analyze feature redundancy in Two Threshold Decomposition Algorithm in SFTA Algorithm. Experimental results show the proposed method can reach 100% accuracy. Experiment results also show that redundant features can be removed without affecting the accuracy of of the system so that it can reduce the computational cost.
Co-Authors Ardiana, Mirza Augustino, Immanuel Freddy Aulia Rahma Annisa Dini Adni Navastara, Dini Adni Mulyo, Budi Mukhamad Nanik Suciati Rahayu, Putri Nur Sukarno, Friska Intan Thooriqoh, Hazna At Yudi Andika Yuniati, R. A. Norromadani