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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Tirana Noor Fatyanosa, Tirana Noor
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : journal of information technology and computer science

 1 
Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization Fatyanosa, Tirana Noor; Sihananto, Andreas Nugroho; Alfarisy, Gusti Ahmad Fanshuri; Burhan, M Shochibul; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 1 No. 2: November 2016
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.719 KB) | DOI: 10.25126/jitecs.20161215

Abstract

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result
Comparative Analysis of Machine Learning Algorithms for Optimal Decision-Making in Data-Driven Applications Fatyanosa, Tirana Noor; Brata, Gede Indra Adi; Reansyah, Javier Aahmes; Athaya, Haikal Thoriq; Adam, Muhammad Herdi; Aranda, Achmad Fauzi
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101921

Abstract

This research examines the effectiveness of machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine, Decision Tree, Logistic Regression, and Gradient Boosting, applied to Kaggle competition datasets. It also investigates essential data preprocessing techniques, such as Standard Scaler, Power Transform, and Simple Imputer, to enhance model performance. The paper provides a comprehensive analysis of best practices in model selection and data preparation to achieve optimal results across different domains. From our experimental results on different datasets, we found that combining Logistic Regression with XGBoost and using ensemble methods like XGBoost and CatBoost achieved the highest scores in several Kaggle competitions. Moreover, using preprocessing such as missing value imputation, data normalization, and feature engineering significantly improved the performance of all models. Our findings suggest that the selection of appropriate machine learning algorithms and data preprocessing techniques is crucial for achieving optimal results in data-driven decision-making. By employing these methodologies, we achieved commendable results across the utilized datasets. For instance, the Predict Failure Keep It Dry dataset yielded a Kaggle score of 0.5901, while the Smoker Status using Bio Signals dataset achieved a score of 0.8675. The Bank Churn Dataset resulted in a Kaggle score of 0.89046, the Spaceship Titanic dataset scored 0.80617, and the health of horses dataset attained a score of 0.76212. These results highlight the effectiveness of the proposed combination of machine learning algorithms and preprocessing techniques in enhancing model performance on real-world datasets.
A Comparative Study: Can Deep Learning Outperform Tree-Based Models in Tabular Data Classification? Fatyanosa, Tirana Noor; Hilmi, Fadhilah; Taqiyassar, Kenzie; Pratama, Naufal Romero Putra; Satrio Condro Kusuma; Hafiz Rizky Nurwachid
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101922

Abstract

The rapid growth of data in various domains has heightened the need for accurate and efficient predictive models, particularly for tabular data. While deep learning has revolutionized fields like computer vision and natural language processing, its effectiveness in tabular data classification remains a topic of debate. This study conducts a comprehensive comparison between deep learning models (NODE, SAINT, TabNet, Tab Transformers) and tree-based models (Random Forest, XGBoost, LightGBM, CatBoost) to determine whether deep learning can outperform traditional methods in this context. The results indicate that tree-based models, particularly LightGBM and CatBoost, consistently achieve the highest accuracy and F1 scores, coupled with efficient execution times, making them more suitable for real-world applications that require quick and accurate predictions. In contrast, deep learning models show varied performance. Although SAINT sometimes achieves comparable accuracy, its processing time makes it less practical. The findings suggest that despite the potential of deep learning, tree-based models remain superior for tabular data classification tasks, particularly when considering a balance of accuracy, speed, and robustness. This study contributes to the ongoing discussion on the role of deep learning in tabular data and highlights the conditions under which traditional models may still be preferred.
Heart Disease Prediction System Employing Machine Learning Rian Nopiardi; Raka Dimas Saputra; Niken Fitria Apriani; Al Hafiz Akbar Maulana Siagian; Shidiq Al Hakim; Fatyanosa, Tirana Noor
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102926

Abstract

Heart disease places a significant strain on healthcare systems. Moreover, it kills millions of people each year, which is a leading cause of death worldwide. Hypertension, diabetes, unhealthy lifestyles, and genetic predispositions are all risk factors for heart disease. However, it is not easy to identify a heart disease. For this reason, helping in identifying the heart disease is important to prevent a death, e.g., caused by a heart attack. In this study, we aim to develop a heart disease prediction system. The system is developed according to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework by employing machine learning algorithms. In this work, Logistic Regression (LR) and Random Forest (RF) are utilized as our machine learning algorithms for classifying heart disease using a heart disease dataset from Kaggle. Our results show LR has an AUC value of 0.921 and F1-Score 0.89 that outperforms RF with an AUC value of 0.920 and F1-Score 0.84 in this work. Then, we select LR to be applied to the developed heart disease prediction system.
Leveraging Stacked Vessel Segment and Channels of Fundus Image for Eye Disease Detection using Hybrid U-Net-Residual Convolutional Candra Dewi; Novanto Yudistira; Daffa Izzuddin; Nazura Wirayuda Tama; Fatyanosa, Tirana Noor
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102928

Abstract

Fundus images can be used to identify symptoms of various eye diseases. However, a limitation of using fundus images for classification is the generalization of information from the entire image, which can reduce recognition accuracy. To address this, this study proposes a combination of RGB channels from fundus images and segmented images for the identification process. Segmentation is performed using U-Net, which produces a segmentation of the blood vessels from the retinal image. The combined image is then used as input for the identification process with different ResNet models, including ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152. Three model tuning scenarios are explored to obtain an optimal hybrid segmentation and classification model: learning using ResNet without U-Net (nounet), learning using a ResNet model with a frozen U-Net model (frozenunet), and learning using both the U-Net and ResNet models (hotunet). Testing is carried out to recognize normal, cataract, and glaucoma classes. The results show that the highest accuracy of 0.82 is achieved with the hybrid U-Net and ResNet152 model using frozenunet learning. This indicates that the addition of segmented images can improve identification results, with the best performance for glaucoma having a precision of 0.90, recall of 0.86, and F1-score of 0.88.
Co-Authors Achmad Ridok Adam, Muhammad Herdi Agus Wahyu Widodo, Agus Wahyu Aji, Alham Fikri Al Hafiz Akbar Maulana Siagian Al Kautsar, Prima Daffa Amadeo, Yoel Ananta, Hizkia Jeremmy Krisna Andreas Nugroho Sihananto Aqil Aldiyan, Rizky Aranda, Achmad Fauzi Athaya, Haikal Thoriq Azhari, Muhammad Rizqi Azzam, Ja'far Shidqul Brata, Gede Indra Adi Candra Dewi Candra Dewi Daffa Izzuddin Edy Santoso Eman, Eleazar Tadeo Fidian, Diagne Alya Fitra Abdurrachman Bachtiar Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Hafiz Rizky Nurwachid Hakim, Gibran Hakim, Muhammad Yasin Hanggara, Buce Trias Hilmi, Fadhilah Idma, Faiz Alvino Indriati Indriati Kusuma, Satrio Condro Lailil Muflikhah Lazuardi, Muhammad Dzakwan Bintang Lina, Femi Novia M Shochibul Burhan, M Shochibul Mahendra Data Marchelo, Nalendra Marji Maulana, Muhammad Taufik Murnastiti, Nurul Annisa Nazura Wirayuda Tama Niken Fitria Apriani Novanto Yudistira Nurwachid, Hafiz Rizky Pasya, Muhammad Rizky Pratama, Naufal Romero Putra Prima Zulvarina, Prima Putra Pandu Adikara Putra, Hanif Fermanda Raditya, Gavin Rafsandjani, Moch. Gustav Ali Raisaputri, Dayang Alyssa Raka Dimas Saputra Ravindra Rahman, Azka Reansyah, Javier Aahmes Rian Nopiardi Rizal Setya Perdana Sagala, Revaldo Gemino Kantana Saladin, Abid Faiz Sari, Irawati Nurmala Satrio Condro Kusuma Shidiq Al Hakim SIAGIAN, AL HAFIZ AKBAR MAULANA Tanaya, I Putu Paramaananda Taqiyassar, Kenzie Wayan Firdaus Mahmudy Winata, Jaya Yashifa, Muhammad Arsya Zain Zenatmaja, I Wayan Ivan