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All Journal Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics
Rahmalan, Hidayah
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Computer Science & IT Electrical & Electronics Engineering

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A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification Sari, Christy Atika; Sari, Wellia Shinta; Rahmalan, Hidayah
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29357

Abstract

Keywords are the labels of your manuscript and critical to correct indexing and searching. MRI or Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. The method often used in the previous method is segmentation, but the process is quite heavy and the results that are less accurate are still the main obstacles. This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. The accuracy obtained is 91.78%.
A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification Sari, Christy Atika; Sari, Wellia Shinta; Rahmalan, Hidayah
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29357

Abstract

Purpose: Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. Methods: This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. Result: The accuracy obtained is 91.78%. Novelty: K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI.
Conceptual design model of engaging gamification mechanic for online courses Mohd Yusoff, Azizul; Salam, Sazilah; Mohamad, Siti Nurul Mahfuzah; Lip, Rashidah; Pudjoatmodjo, Bambang; Rahmalan, Hidayah; Mazlan, Azlimi
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7261

Abstract

Online learning, or e-learning, delivers educational content and teaching through various formats, ranging from self-paced courses to synchronous virtual classrooms. Gamification, the incorporation of game-like elements into non-game contexts, enhances engagement through rewards, reputation points, and goal setting. In higher education, researchers seek effective methods to stimulate learning and boost learner engagement. This study employs the analytic hierarchy process (AHP) to identify suitable gamification elements for three types of learner interaction, breaking down the decision-making problem into a hierarchy. Through a pairwise comparison matrix, priorities among hierarchy elements are established. The research involves 36 learners from a technical and vocational education and training (TVET) Public University, selecting the top best six gamification mechanics for each construct: virtual goods, wally’s game, rewards, trophies-badges, skill points, and peer grading. The proposed conceptual design will be implemented in online courses to assess learning engagement in cognitive, behavioural, and affective domains in higher education.
Co-Authors Bambang Pudjoatmodjo Christy Atika Sari Lip, Rashidah Mazlan, Azlimi Mohamad, Siti Nurul Mahfuzah Mohd Yusoff, Azizul Salam, Sazilah Wellia Shinta Sari