Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal IJoICT (International Journal on Information and Communication Technology) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Farhan Bin Mohamed
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Image Color Enhancement Methods: An Experiment-Based Review younis, zainab; Mohd Shafry Mohd Rahim; Farhan Bin Mohamed
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1044

Abstract

Image color enhancement is a vital aspect in the field of image processing. It is a technique to enhance and improve the image's visual quality. Color enhancement is applied in different applications such as photography, medicine, and computer vision. This research reviews eight methods-based color enhancement methods according to their methodology, complexity, pros, and cons. Then, three evaluation metrics used Colorfulness (CF), average saturation measure (ASM), and average chroma measure (ACM) to assess each method. The result showed that fuzzy enhancement (FE) exceeded other methods and scored the highest records. This study provides a beneficial resource for researchers involved in image enhancement, as it presents a complete review and detailed analysis of various academic studies published in reputable journals. The study evaluates each research work's findings, proposed algorithm, and accuracy using many assessment metrics. Furthermore, it emphasizes the strengths and limitations of each method, giving a performance analysis. Additionally, the study discusses future recommendations for improving the effectiveness of these algorithms. Finally, this research is a rich and reliable reference for scholars aiming to develop novel algorithms in this domain.
Cosine Similarity as a Distance Metric for Javanese Script Image Recognition Classification Priyambodo, Aji Priyambodo; Prihati, Prihati; Danang, Danang; Farhan bin Mohamed
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.4123

Abstract

Javanese character (Hanacaraka) recognition presents significant challenges due to the intricate patterns and variations in character features. Addressing these issues is crucial for digitizing cultural heritage and supporting educational applications. This study aims to evaluate the effectiveness of cosine similarity as a distance metric for classifying Javanese characters, comparing its performance against traditional Euclidean and Manhattan distance metrics. The research used a feature-extraction technique based on the histogram of oriented gradients and evaluated cosine similarity across different classification models. Model performance was assessed using precision, recall, F1-score, and accuracy metrics. The results showed that cosine similarity, when combined with a support vector machine, achieved an accuracy of 99.84%, significantly outperforming other distance metrics. When applied to another classification model, cosine similarity improved accuracy to 90%, demonstrating its robustness in handling complex patterns. Parameter optimization was performed using a grid-based search, and model reliability was assessed through cross-validation. Compared with previous studies that primarily relied on deep learning, this research offers an alternative method that balances efficiency and accuracy while maintaining high interpretability. The findings establish a new benchmark for Javanese character recognition and highlight the potential of cosine similarity in broader applications. Future research can expand this study by incorporating more diverse feature extraction techniques, larger datasets, and hybrid approaches to further enhance recognition performance.
Co-Authors Danang, Danang Mohd Shafry Mohd Rahim Prihati Prihati Priyambodo, Aji Priyambodo Younis, Zainab