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All Journal International Journal of Electrical and Computer Engineering HAYATI Journal of Biosciences ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Prosiding SNATIKA Vol 01 (2011) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics ILKOM Jurnal Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Aina Musdholifah
Department Of Computer Science And Electronics, Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Fine tuning attribute weighted naïve Bayes model for detecting anxiety disorder levels of online gamers Latubessy, Anastasya; Wardoyo, Retantyo; Musdholifah, Aina; Kusrohmaniah, Sri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3277-3286

Abstract

This research applies the fine tuning attribute weighted naïve Bayes (FTAWNB) model using ordinal data. It is known that in previous research, the FTAWNB model outperformed its competitors on the dataset used. However, the FTAWNB model has not been applied in the mental health domain that uses ordinal data. Therefore, this research used the anxiety gamers dataset to test the fine-tuning attribute weighted Naïve Bayes (FTAWNB) model. Anxiety disorders are mental health disorders that can indicate the emergence of a gaming disorder. Gamers can experience anxiety disorders classified into four classes, namely minimal, mild, moderate, and severe anxiety. Then compare the results by FTAWNB obtained with three other naïve Bayes algorithms, namely Gaussian naïve Bayes, multinomial naïve Bayes, and categorical naïve Bayes, using the same dataset. Model performance is measured based on accuracy, precision, recall, and processing time. The test results show that the FTAWNB outperforms the other three models' accuracy, precision, and recall, with an accuracy value of 99.22%. While the accuracy of Gaussian NB is 91.132%, Categorical is 91.592%, and multinomial naïve Bayes is 61.104%. However, the FTAWNB takes slightly longer than the other three models' processing time. The FTAWNB takes 0.07 seconds to build the model and 0.05 seconds to test the model on training data.
Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network Budiarsa, Rahmat; Wardoyo, Retantyo; Musdholifah, Aina
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3256-3265

Abstract

Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.
Co-Authors Abednego Dwi Septiadi Aditya Permana Anastasya Latubessy Anjani, Sarah Anny Kartika Sari Arda Yunianta Br Bangun, Elsi Titasari Ekki Rinaldi Erick Alfons Lisangan Faisal Ramadhan Hamda, Hizbullah Hananto, Andhika Rafi Hani Febri Mustika Hani Febri Mustika Haviluddin Haviluddin Herdiesel Santoso Herlina Jayadiyanti Jayadiyanti, Herlina Lilik Sumaryanti Luthfiansyah Ilhamnanda Yusuf Mirwan Ushada Mohd Shahizan Othman Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Rahman Indra Kesuma Rahmat Budiarsa Retantyo Wardoyo Rinaldi, Ekki Rosihan Rosihan, Rosihan Sapuletea, Chrissandy Sri Hartati Sri Kusrohmaniah, Sri Sri Mulyana Tempola, Firman Timothy K. Shih Tsuyoshi Okayama Wahyu Srimulyani Yudha Islami Sulistya Yunianta, Arda Yusof, Omar Obarukab Norazah