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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Shahreen Kasim
Universiti Tun Hussein Onn Malaysia
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Comparison of feature selection techniques in classifying stroke documents Nur Syaza Izzati Mohd Rafei; Rohayanti Hassan; RD Rohmat Saedudin; Anis Farihan Mat Raffei; Zalmiyah Zakaria; Shahreen Kasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1244-1250

Abstract

The amount of digital biomedical literature grows that make most of the researchers facing the difficulties to manage and retrieve the required information from the Internet because this task is very challenging. The application of text classification on biomedical literature is one of the solutions in order to solve problem that have been faced by researchers but managing the high dimensionality of data being a common issue on text classification. Therefore, the aim of this research is to compare the techniques that could be used to select the relevant features for classifying biomedical text abstracts. This research focus on Pearson’s Correlation and Information Gain as feature selection techniques for reducing the high dimensionality of data. Towards this effort, we conduct and evaluate several experiments using 100 abstract of stroke documents that retrieved from PubMed database as datasets. This dataset underwent the text pre-processing that is crucial before proceed to feature selection phase. Features selection phase is involving Information Gain and Pearson Correlation technique. Support Vector Machine classifier is used in order to evaluate and compare the effectiveness of two feature selection techniques. For this dataset, Information Gain has outperformed Pearson’s Correlation by 3.3%. This research tends to extract the meaningful features from a subset of stroke documents that can be used for various application especially in diagnose the stroke disease.
Contact Lens Classification by Using Segmented Lens Boundary Features Nur Ariffin Mohd Zin; Hishammuddin Asmuni; Haza Nuzly Abdul Hamed; Razib M. Othman; Shahreen Kasim; Rohayanti Hassan; Zalmiyah Zakaria; Rosfuzah Roslan
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i3.pp1129-1135

Abstract

Recent studies have shown that the wearing of soft lens may lead to performance degradation with the increase of false reject rate. However, detecting the presence of soft lens is a non-trivial task as its texture that almost indiscernible. In this work, we proposed a classification method to identify the existence of soft lens in iris image. Our proposed method starts with segmenting the lens boundary on top of the sclera region. Then, the segmented boundary is used as features and extracted by local descriptors. These features are then trained and classified using Support Vector Machines. This method was tested on Notre Dame Cosmetic Contact Lens 2013 database. Experiment showed that the proposed method performed better than state of the art methods.
Predicting child alimony under Islamic shariah law using hybrid fuzzy inference system Nur Aqilah Khadijah Rosili; Rohayanti Hassan; Noor Hidayah Binti Zakaria; Farid Zamani Che Rose; Shahreen Kasim; Tole Sutikno
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1367-1375

Abstract

This paper proposes a novel approach to predicting child alimony under Islamic Shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1 score metrics. The hybrid fuzzy system achieved promising results with 88% accuracy, 84% precision, 89% recall, and an 86% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decision-making tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic Shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.
Co-Authors Ahmad Firdaus Ahmad Firdaus Zainal Abidin Ahmad Heryanto Ahmad Hoirul Basori Ahmed Hussein Ali Anis Farihan Mat Raffei Anis Farihan Mat Raffei Arda Yunianta Azizul Azhar Ramli Choon Sen Seah Danakorn Nincarean Eh Phon Danakorn Nincarean Eh Phon Deris Stiawan Farid Zamani Che Rose Farizuwana Akma Zulkifle Haza Nuzly Abdul Hamed Hind Raad Ibraheem Hishammuddin Asmuni Ichwanul Muslim Karo Karo John Arthur Jupin Lina Handayani Mohammad Alsajri Mohammad Nazir Ahmad Mohammed K. Ali Mohd Arfian Ismail Mohd Arfian Ismail Mohd Faaizie Darmawan Mohd Faizal Ab Razak Mohd Faizal Ab Razak Mohd Farhan Md Fudzee Mohd Saberi Mohamad Mohd Shahizan Othman Mohd Zamri Osman Muhammed E. Abd Alkhalec Tharwat Ngahzaifa Ab. Ghani Nithya Dewi Subramaniam Chetty Noor Azida Binti Sahabudin Noor Hidayah Binti Zakaria Norhasyimah Hamzah Nur Aqilah Khadijah Rosili Nur Ariffin Mohd Zin Nur Shamsiah Abdul Rahman Nur Sholihah Zaini Nur Syaza Izzati Mohd Rafei Nurul Najihah Che Razali Nuryono Satya Widodo Ong Vienna Lee Razib M. Othman Rd. Rohmat Saedudin Rodziah Atan Rohayanti Hassan Rosfuzah Roslan Shahid Anwar Shahliza Abd Halim Syifak Izhar Hisham Tole Sutikno Waleed Mugahed Al-Rahmi Wan Isni Sofiah Wan Din Yundari, Yundari Zahraa Faiz Hussain Zalmiyah Zakaria Zuraina Ali