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All Journal Jurnal Ilmu Komputer dan Informasi Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi dan Informasi TEKNOLOGIA Building of Informatics, Technology and Science Journal of Data Science and Its Applications eProceedings of Engineering
Untari Novia Wisesty
Telkom University
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics

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Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data Wisesty, Untari Novia; Sthevanie, Febryanti; Rismala, Rita
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.127 KB) | DOI: 10.29099/ijair.v4i2.188

Abstract

Early detection of cancer can increase the success of treatment in patients with cancer. In the latest research, cancer can be detected through DNA Microarrays. Someone who suffers from cancer will experience changes in the value of certain gene expression.  In previous studies, the Genetic Algorithm as a feature selection method and the Momentum Backpropagation algorithm as a classification method provide a fairly high classification performance, but the Momentum Backpropagation algorithm still has a low convergence rate because the learning rate used is still static. The low convergence rate makes the training process need more time to converge. Therefore, in this research an optimization of the Momentum Backpropagation algorithm is done by adding an adaptive learning rate scheme. The proposed scheme is proven to reduce the number of epochs needed in the training process from 390 epochs to 76 epochs compared to the Momentum Backpropagation algorithm. The proposed scheme can gain high accuracy of 90.51% for Colon Tumor data, and 100% for Leukemia, Lung Cancer, and Ovarian Cancer data.
Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model Wisesty, Untari Novia; Wirayuda, Tjokorda Agung Budi; Sthevanie, Febryanti; Rismala, Rita
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1249

Abstract

Stroke is a disease which cause the death of brain cells, so that the part of the body controlled by the brain loses its function. If not treated immediately, this disease can cause long-term disability, brain damage, and death. In this research, stroke prediction was carried out on the Stroke dataset acquired from the Kaggle dataset using various machine learning models. Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. Pearson Correlation and Principal Component Analysis are also used for dimensional reduction and analyzing the important features that are most influential in predicting stroke. Pearson Correlation produces five attributes that have the highest Pearson coefficient, namely age, hypertension, heart disease, blood sugar level, and marital status. Experimental results have demonstrated that the utilization of RUS, ROS, and SMOTE sampling techniques can significantly boost the F1-Score testing by an impressive 43.44%, 34.44%, and 35.55% respectively, as compared to experiments conducted without implementing any data sampling techniques. The highest F1-Score testing was achieved using the Support Vector Machine and Gaussian Naïve Bayes models, namely 0.83.
Sentiment Analysis on a Large Indonesian Product Review Dataset Romadhony, Ade; Al Faraby, Said; Rismala, Rita; Wisesty, Untari Novia; Arifianto, Anditya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.167-178

Abstract

Background: The publicly available large dataset plays an important role in the development of the natural language processing/computational linguistic research field. However, up to now, there are only a few large Indonesian language datasets accessible for research purposes, including sentiment analysis datasets, where sentiment analysis is considered the most popular task. Objective: The objective of this work is to present sentiment analysis on a large Indonesian product review dataset, employing various features and methods. Two tasks have been implemented: classifying reviews into three classes (positive, negative, neutral), and predicting ratings. Methods: Sentiment analysis was conducted on the FDReview dataset, comprising over 700,000 reviews. The analysis treated sentiment as a classification problem, employing the following methods: Multinomial Naí¯ve Bayes (MNB), Support Vector Machine (SVM), LSTM, and BiLSTM. Result: The experimental results indicate that in the comparison of performance using conventional methods, MNB outperformed SVM in rating prediction, whereas SVM exhibited better performance in the review classification task. Additionally, the results demonstrate that the BiLSTM method outperformed all other methods in both tasks. Furthermore, this study includes experiments conducted on balanced and unbalanced small-sized sample datasets. Conclusion: Analysis of the experimental results revealed that the deep learning-based method performed better only in the large dataset setting. Results from the small balanced dataset indicate that conventional machine learning methods exhibit competitive performance compared to deep learning approaches.   Keywords: Indonesian review dataset, Large dataset, Rating prediction, Sentiment analysis
Detecting Type and Index Mutation in Cancer DNA Sequence Based on Needleman–Wunsch Algorithm Wisesty, Untari Novia; Mengko, Tati Rajab; Purwarianti, Ayu; Pancoro, Adi
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v17i2.1273

Abstract

Detecting DNA sequence mutations in cancer patients contributes to early identification and treatment of the disease, which ultimately enhances the effectiveness of treatment. Bioinformatics utilizes sequence alignment as a powerful tool for identifying mutations in DNA sequences. We used the Needleman-Wunsch algorithm to identify mutations in DNA sequence data from cancer patients. The cancer sequence dataset used includes breast, cervix uteri, lung, colon, liver and prostate cancer. Various types of mutations were identified, such as Single Nucleotide Variant (SNV)/substitution, insertion, and deletion, locate by the nucleotide index. The Needleman Wunch algorithm can detect type and index mutation with the average F1-scores 0.9507 for all types of mutations, 0.9919 for SNV, 0.7554 for insertion, and 0.8658 for deletion with a tolerance of 5 bp. The F1-scores obtained are not correlated with gene length. The time required ranges from 1.03 seconds for a 290 base pair gene to 3211.45 seconds for a gene with 16613 base pairs.
Swin Transformer V2 for Invasive Ductal Carcinoma Classification in Histopathological Imaging Ariyanto, Puguh Aiman; Wisesty, Untari Novia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38606

Abstract

Breast cancer is the second leading cause of mortality in women globally, with Invasive Ductal Carcinoma being the most dominant subtype that requires accurate diagnosis to increase patient life expectancy. Conventional diagnosis based on manual histopathological examinations is time-consuming, prone to misinterpretation, and exhibits significant inter-observer variability. This study implemented the Swin Transformer V2 architecture for the automatic classification of Invasive Ductal Carcinoma on 277,524 histopathological images, each measuring 50×50 pixels, which were resized to 256×256 pixels with geometric augmentation. The model was trained using AdamW optimization with a learning rate of 1 × 10⁻⁴, weight decay of 1 × 10⁻⁴, a batch size of 16, and mixed precision (FP16) for five epochs at a 70:20:10 data sharing ratio. The data augmentation includes a 50% probability of a random horizontal flip and a maximum of 10 degrees of random rotation to improve the model's generalization capabilities. Evaluation of 27,754 independent test samples resulted in an accuracy of 92.82%, an accuracy of 88.48%, a recall of 86.05%, an F1-score of 87.25%, and an AUC of 0.91. A hierarchical window attention-shifted mechanism with residual post-normalization has been shown to be effective in extracting local and global features from complex microscopic images. The results show that Swin Transformer V2 has significant potential as a diagnostic aid system to enhance the efficiency and accuracy of early breast cancer detection in clinical pathology practice.
Co-Authors Ade Romadhony Adi Pancoro Aditya Kusuma Setyanegara Adiwijaya Agung Toto Wibowo Agus Nursikuwagus Al Azhar Al Azhar Al Faraby, Said Ali Zainal Abidin Assajjad Anditya Arifianto Andrian Fakhri Annisa Aditsania Ariyanto, Puguh Aiman Ayu Purwarianti Brama Hendra Mahendra Devy Yendriani Dieka Nugraha Karyana Dimitri Mahayana Echa Pangersa Sugianto Oeoen Eko Darwiyanto Ergandara Purba Setra Ersa Christian Prakoso Fathurrohman Elkusnandi Febryanthi Sthevanie Febryanti Sthevanie Hadli Fadli Santoso Henri Tantyoko Hifzi Priabdi Hikmawati, Erna Huda Sepriandi Ibrahim Intan Raharni Wijaya Iqbal Hapid Sukana Putra Jondri Jondri Lalu Gias Irham Mahmud Dwi Sulistiyo Mengko, Tati Rajab Munggana, Wira Putri Tsatsabila Ramadhani Renette Ersti Riko Bintang Purnomoputra Rita Rismala Stefanus Christian Adi Pradhana Sugondo Hadiyoso Tiara Laksmi Basuki Tjokorda Agung Budi Wirayuda Wahyu Hauzan Rafi Wisnu Adhi Pradana Yosua Nababan