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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Annual Research Seminar IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Matrik Jiko (Jurnal Informatika dan komputer) Jurnal Generic

Dian Palupi Rini

Universitas Sriwijaya

Author-ID : 2406578

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 19 Documents Claim Missing Document

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Articles

Title

Segmentation of Squamous Columnar Junction on VIA Images using U-Net Architecture Akhiar Wista Arum; Siti Nurmaini; Dian Palupi Rini; Patiyus Agustiansyah; Muhammad Naufal Rachmatullah
Computer Engineering and Applications Journal Vol 10 No 3 (2021)
Publisher : Universitas Sriwijaya

Cervical cancer is the second most common cancer that affects women, especially in developing countries including Indonesia. Cervical cancer is a type of cancer found in the cervix, precisely in the squamous columnar junction (SCJ). Early screening for cervical cancer can be reduce the risk of cervical cancer. One of the popular screening tool methods for the detection of cervical pre-cancer is the Visual Inspection with Acetic Acid (VIA) method. This is due to the level of effectiveness, convenience and low cost. This paper proposes a method for the detection and segmentation of the SCJ region on VIA images using U-Net. This study is the first research conducted using the CNN method to perform segmentation tasks in the SCJ region. The best performance results are shown from the Pixel Accuracy, Mean IoU, Mean Accuracy, Dice coefficient, Precision and Sensitivity values, namely 90.86%, 56.5%, 75.69%, 34.09%, 41.24%, and 56.91%. Keywords: Cervical Pre-cancer, Screening VIA, SCJ, U-Net.

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

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.

Optimization of Deep Neural Networks with Particle Swarm Optimization Algorithm for Liver Disease Classification Muhammad Nejatullah Sidqi; Dian Palupi Rini; Samsuryadi Samsuryadi
Computer Engineering and Applications Journal Vol 12 No 1 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i1.432

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.

Classification of Epilepsy Diagnostic Results through EEG Signals Using the Convolutional Neural Network Method Tri Kurnia Sari; Dian Palupi Rini; Samsuryadi Samsuryadi
Computer Engineering and Applications Journal Vol 12 No 2 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i2.429

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.

Co-Authors Akhiar Wista Arum andre hardoni Darmawahyuni, Annisa Defrian Afandi Deris Stiawan Desty Rodiah Dwi Meylitasari Tarigan Dwi Meylitasari Tarigan Endang Lestari Ruskan Ermatita - Evi Purnamasari Ika Oktavianti Iman Saladin B. Azhar M. Nejatullah Sidqi Mona Cindo Muhammad Naufal Rachmatullah Patiyus Agustiansyah Qurrota ‘Aini Muthmainnah Rahma Satila Passa Renny Amalia Pratiwi Reza Firsandaya Malik Reza Firsandaya Malik Samsuryadi Samsuryadi Samsuryadi Siti Nurmaini Sukemi Sukemi Tri Kurnia Sari Verlly Puspita Winda Kurnia Sari

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