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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Scientific Journal of Informatics IJoICT (International Journal on Information and Communication Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Sistem Komputer dan Informatika (JSON) Sriwijaya Journal of Informatics and Applications Jurnal Generic
Dian Palupi Rini
Sriwijaya University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model Rini, Dian Palupi; Afandi, Defrian; Rodiah, Desty
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

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Abstract

The Fuzzy Hierarchical Model method can be used to predict the stage of heart disease. The use of the Fuzzy Hierarchical Model on complex problems is still not optimal because it is difficult to find a fuzzy set that provides a more optimal solution. This method can be improved by changing the membership function constraints using Genetic Algorithm to get better predictions. Tests carried out using 282 heart disease patient data resulted in a Root Mean Squared Error (RMSE) value of 0.55 using the best Genetic Algorithm parameters, including population size of 140, number of generations of 125, and a combination of cross-over rate and mutation rate of 0.4 and 0.6 whereas the RMSE value generated by the Fuzzy Hierarchical Model before being optimized by the Genetic Algorithm was 0.89. These results indicate an increase in the predictive value of the Fuzzy Hierarchical Model after being optimized using the Genetic Algorithm.
Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network Sari, Dwi Mei Rita; Nurmaini, Siti; Rini, Dian Palupi; Sapitri, Ade Iriani
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 1 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Skin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298.
Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method Sari, Tri Kurnia; Rini, Dian Palupi; Samsuryadi
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 2 (2023)
Publisher : Universitas Sriwijaya

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Abstract

The brain is one of the most important organs in the human body as a central nervous system which functions as a controlling center, intelligence, creativity, emotions, memories, and body movements. Epileptic seizure is one of the disorder of the brain central nervous system which has many symptoms, such as loss of awareness, unusual behavior and confusion. These symptoms lead in many cases to injuries due to falls, biting one’s tongue. Detecting a possible seizure beforehand is not an easy task. Most of the seizures occur unexpectedly, and finding ways to detect a possible seizure before it happens has been a challenging task for many researchers. Analyzing EEG signals can help us obtain information that can be used to diagnose normal brain activity or epilepsy. CNN has been demonstrated high performance on detection and classification epileptic seizure. This research uses CNN to classify the epilepsy EEG signal dataset. AlexNet and LeNet-5 are applied in CNN architecture. The result of this research is that the AlexNet architecture provides better precision, recall, and f1- score values on the epilepsy signal EEG data than the LeNet-5 architecture.
Optimization of Deep Neural Networks with Particle Swarm Optimization Algorithm for Liver Disease Classification Sidqi, Muhammad Nejatullah; Rini, Dian Palupi; Samsuryadi
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 1 (2023)
Publisher : Universitas Sriwijaya

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Liver disease has affected more than one million new patients in the world. which is where the liver organ has an important role function for the body's metabolism in channeling several vital functions. Liver disease has symptoms including jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, enlarged spleen and gallbladder and has abnormalities that are very difficult to detect because the liver works as usual even though some liver functions have been damaged. Diagnosis of liver disease through Deep Neural Network classification, optimizing the weight value of neural networks with the Particle Swarm Optimization algorithm. The results of optimizing the PSO weight value get the best accuracy of 92.97% of the Hepatitis dataset, 79.21%, Hepatitis 91.89%, and Hepatocellular 92.97% which is greater than just using a Deep Neural Network.
Co-Authors Abdiansah Abdiansah, Abdiansah Afandi, Defrian Alvi Syahrini Utami Arnelawati Arrizal, Affandi Arum, Akhiar Wista Astuti, Dwi Lydia Zuharah Cahyadi, Gabriel Ekoputra Hartono Cindo, Mona Darmawahyuni, Annisa Desty Rodiah Dzaky, Dewa Sheva Ermatita - Firdaus - Firmansyah Fitriati, Sri Hidayatullah, Altundri Wahyu Idawati Idawati Kanda Januar Miraswan Kurnia Sari, Winda M. Nejatullah Sidqi Mastura Diana Marieska Muhammad Naufal Rachmatullah Muhammad Taufik Roseno, Muhammad Taufik Nanda, Dika Kurnia Novi Yusliani Osvari Arsalan Passa, Rahma Satila Primanita, Anggina Putri Mirani Rosemari, Pita Samsuryadi Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Dwi Mei Rita Sari, Tri Kurnia Siti Annisa, Siti Siti Nurmaini Sukemi Syamsidi, Iman Carrazzi Syaputra, Hadi Utari, Aspirani Wahyu Nugraha Willy, Willy Winda Kurnia Sari YUNITA Zikri, Anharul