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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi
Muhammad Suzuri Hitam
Universiti Malaysia Terengganu
Computer Science & IT Dentistry Education Electrical & Electronics Engineering Library & Information Science

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Modified JSEG algorithm for reducing over-segmentation problems in underwater coral reef images Mohammad Sameer Aloun; Muhammad Suzuri Hitam; Wan NuralJawahir Hj Wan Yussof; Abdul Aziz K Abdul Hamid; Zainuddin Bachok
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (852.927 KB) | DOI: 10.11591/ijece.v9i6.pp5244-5252

Abstract

The original JSEG algorithm has proved to be very useful and robust in variety of image segmentation case studies.However, when it is applied into the underwater coral reef images, the original JSEG algorithm produces over-segementation problem, thus making this algorithm futile in such a situation. In this paper, an approach to reduce the over-segmentation problem occurred in the underwater coral reef image segmentation is presented. The approach works by replacing the color histogram computation in region merge stage of the original JSEG algorithm with the new computation of color and texture features in the similarity measurement. Based on the perceptual observation results of the test images, the proposed modified JSEG algorithm could automatically segment the regions better than the original JSEG algorithm.
Machine learning with multistage classifiers for identification of of ectoparasite infected mud crab genus Scylla Rozniza Ali; Muhamad Munawarar Yusro; Muhammad Suzuri Hitam; Mhd Ikhwanuddin Abdullah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 2: April 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i2.16724

Abstract

Recently, the mud-crab farming can help the rural population economically. However, the existing parasite in the mud-crabs could interfere the long live of the mud-crabs. Unfortunately, the parasite has been identified to live in hundreds of mud-crabs, particularly it happened in Terengganu Coastal Water, Malaysia. This study investigates the initial identification of the parasite features based on their classes by using machine learning techniques. In this case, we employed five classifiers i.e logistic regression (LR), k-nearest neighbors (kNN), Gaussian Naive Bayes (GNB), support vector machine (SVM), and linear discriminant analysis (LDA). We compared these five classfiers to best performance of classification of the parasites. The classification process involving three stages. First, classify the parasites into two classes (normal and abnormal) regardless of their ventral types. Second, classified sexuality (female or male) and maturity (mature or immature). Finally, we compared the five classifiers to identify the species of the parasite. The experimental results showed that GNB and LDA are the most effective classifiers for carrying out the initial classification of the rhizocephalan parasite within the mud crab genus Scylla.
Enhancing the Quality of Merged Process Models by Addressing Invisible Task Kelly Rossa Sungkono; Riyanarto Sarno; I Gusti Agung Chintya Prema Dewi; Muhammad Suzuri Hitam
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1381

Abstract

Model merging is a key approach for integrating multiple process model variants into a unified representation. Existing automated merging methods face challenges in handling invisible tasks, which are intentionally inserted in the process model to depict certain conditions, including stacked branching relationships. The inability to handle invisible tasks reduces the quality of the merged process models. A proposed graph-merging method explicitly addresses sequence, branching relationships, and invisible tasks. The proposed method first identifies common activities across model variants. Furthermore, the method applies the proposed graph rules grounded in behavioral and structural aspects to combine those common activities as well as their related relationships and generate the graph-based merged process model. Behavioral rules govern the integration of sequence and branching relationships, while structural rules handle branching and invisible tasks. An evaluation against two existing approaches by Derguech and Yohanes demonstrates that the proposed graph-merging method achieves higher precision. The graph-merging method substantially improves the quality of merged process models.
Co-Authors Abdul Aziz K Abdul Hamid I Gusti Agung Chintya Prema Dewi Mhd Ikhwanuddin Abdullah Mohammad Sameer Aloun Muhamad Munawarar Yusro Riyanarto Sarno Rozniza Ali Sungkono, B.J. Santosa Wan NuralJawahir Hj Wan Yussof Zainuddin Bachok