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All Journal INTEGER: Journal of Information Technology ILKOMNIKA: Journal of Computer Science and Applied Informatics 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 Education Engineering

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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 (2025): April
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.
A Comparative Analysis of Resource Utilization using ISO/IEC 25010 in REST API File Upload Testing: Postman (GUI-Based) and Cucumber (Code-Based) Thooriqoh, Hazna At; Mulyo, Budi Mukhamad; Rakhmadi, Ardhon
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.773

Abstract

To guarantee quality and dependability, testing of REST Application Programming Interfaces (APIs) is an essential part of contemporary software development cycles. However, with a multitude of available testing frameworks, selecting the most efficient tool for specific tasks, such as file uploads, remains a challenge. The decision often overlooks the critical factor of resource utilization, which can significantly impact system performance and cost, particularly in continuous integration environments. This study addresses this problem by providing a comparative analysis of resource utilization between two popular REST API testing frameworks, Postman (GUI-based) and Cucumber (code-based), specifically for a file upload transaction. The research utilizes relevant metrics from the ISO/IEC 25010 standard, which cover processor, memory, I/O, storage, and bandwidth. The findings reveal that while Cucumber exhibits superior bandwidth efficiency, Postman demonstrates more significant efficiency in all other key resource metrics. Quantitatively, Postman's processor utilization was found to be approximately 74% lower, and its memory usage around 80% lower than that of Cucumber. These findings provide crucial empirical evidence for software developers and testers, enabling them to make informed decisions on tool selection based on specific resource efficiency priorities for file upload transactions.
Ekstraksi Fitur Kupu-Kupu dengan Lacunarity, Statistik, HSV dan Random Forest Rakhmadi, Ardhon; Rahayu, Putri Nur; Pamuji, Feby Agung; Thooriqoh, Hazna At; Mulyo, Budi Mukhamad
ILKOMNIKA Vol 7 No 2 (2025): Volume 7, Number 2, August 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i2.780

Abstract

Tujuan penelitian ini adalah mengekstraksi ciri morfologi kupu-kupu menggunakan Random Forest, fitur statistik, lakunaritas, dan HSV. Algoritma Random Forest menggunakan fitur ekstraksi ini sebagai input untuk proses klasifikasi. Fungsi HSV digunakan untuk mengekstrak informasi warna dari citra kupu-kupu, dan fungsi Lakunaritas digunakan untuk mengekstrak tekstur dan meningkatkan representasi visual suatu objek. Persentase akurasi, menurut hasil pengujian adalah 74%. Akurasi ini menunjukkan bahwa pendekatan Random Forest, karakteristik statistik, lakunaritas, dan HSV bekerja sama dengan baik untuk mendeskripsikan citra kupu-kupu untuk kategorisasi kupu-kupu otomatis.
Co-Authors Ardiana, Mirza Augustino, Immanuel Freddy Aulia Rahma Annisa Feby Agung Pamuji Mulyo, Budi Mukhamad Rahayu, Putri Nur Sukarno, Friska Intan Thooriqoh, Hazna At Yudi Andika Yuniati, R. A. Norromadani