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All Journal Jurnal Simetris JOIV : International Journal on Informatics Visualization Emerging Science Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Information Systems and Informatics APTIKOM Journal on Computer Science and Information Technologies (CSIT) IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Jurnal Teknologi Informasi Mura International Journal Research on Metaverse
Kurniawan, Tri Basuki
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Other

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Incorporate Transformer-Based Models for Anomaly Detection Dewi, Deshinta Arrova; Singh, Harprith Kaur Rajinder; Periasamy, Jeyarani; Kurniawan, Tri Basuki; Henderi, Henderi; Hasibuan, M. Said; Nathan, Yogeswaran
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.762

Abstract

This paper explores the effectiveness of Transformer-based models, specifically the Time-Series Transformer (TST) and Temporal Fusion Transformer (TFT), for anomaly detection in streaming data. We review related work on anomaly detection models, highlighting traditional methods' limitations in speed, accuracy, and scalability. While LSTM Autoencoders are known for their ability to capture temporal patterns, they suffer from high memory consumption and slower inference times. Though efficient in terms of memory usage, the Matrix Profile provides lower performance in detecting anomalies. To address these challenges, we propose using Transformer-based models, which leverage the self-attention mechanism to capture long-range dependencies in data, process sequences in parallel, and achieve superior performance in both accuracy and efficiency. Our experiments show that TFT outperforms the other models with an F1-score of 0.92 and a Precision-Recall AUC of 0.71, demonstrating significant improvements in anomaly detection. The TST model also shows competitive performance with an F1-score of 0.88 and Precision-Recall AUC of 0.68, offering a more efficient alternative to LSTMs. The results underscore that Transformer models, particularly TST and TFT, provide a robust solution for anomaly detection in real-time applications, offering improved performance, faster inference times, and lower memory usage than traditional models. In conclusion, Transformer-based models stand out as the most effective and scalable solution for large-scale, real-time anomaly detection in streaming time-series data, paving the way for their broader application across various industries. Future work will further focus on optimizing these models and exploring hybrid approaches to enhance detection capabilities and real-time performance.
Detecting Gender-Based Violence Discourse Using Deep Learning: A CNN-LSTM Hybrid Model Approach Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Henderi, Henderi; Hasibuan, M. Said; Zakaria, Mohd Zaki; Ismail, Abdul Azim Bin
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.761

Abstract

Gender-Based Violence (GBV) is a critical social issue impacting millions worldwide. Social media discussions offer valuable insights into public awareness, sentiment, and advocacy, yet manually analyzing such vast textual data is highly challenging. Traditional text classification methods often struggle with contextual understanding and multi-class categorization, making it difficult to accurately identify discussions on Sexual Violence, Physical Violence, and other topics. To address this, the present study proposes a hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is utilized for extracting key linguistic features, while LSTM enhances the classification process by maintaining sequential dependencies. This hybrid CNN+LSTM model is evaluated against standalone CNN and LSTM models to assess its performance in classifying GBV-related tweets. The dataset was sourced from Kaggle, containing real-world Twitter discussions on GBV. Experimental results demonstrate that the hybrid model surpasses both CNN and LSTM models, achieving an accuracy of 89.6%, precision of 88.4%, recall of 89.1%, and F1-score of 88.7%. Confusion matrix and ROC curve analyses further confirm the hybrid model’s superior performance, correctly identifying Sexual Violence (82%), Physical Violence (15%), and Other (3%) cases with reduced misclassification rates. These results suggest that combining CNN’s feature extraction with LSTM’s contextual learning provides a more balanced and effective classification model for GBV-related text. This work supports the development of AI-based tools for social media monitoring, policy-making, and advocacy, helping stakeholders better understand and respond to GBV discussions. Future research could explore transformer-based models like BERT and real-time classification applications to further improve performance.
Navigating Heart Stroke Terrain: A Cutting-Edge Feed-Forward Neural Network Expedition Praveen, S Phani; Mantena, Jeevana Sujitha; Sirisha, Uddagiri; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Onn, Choo Wou; Yorman, Yorman
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.763

Abstract

Heart stroke remains one of the leading causes of death worldwide, necessitating early and accurate prediction systems to enable timely medical intervention. While a variety of machine learning approaches have been employed to address this issue, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and K-Nearest Neighbors, these models often suffer from limitations such as overfitting, insufficient generalization, poor performance on imbalanced datasets, and inability to capture complex nonlinear patterns in clinical data. Additionally, many existing works do not comprehensively integrate both clinical and demographic features or lack rigorous evaluation metrics beyond accuracy alone. This study proposes a novel Feed-Forward Neural Network (FFNN) model for heart stroke prediction, designed to overcome the shortcomings of conventional models. Unlike shallow classifiers, the FFNN architecture employed here leverages multiple hidden layers and nonlinear activation functions to learn intricate relationships within the dataset. The dataset used comprises various attributes such as age, hypertension, heart disease, BMI, and smoking status, which were preprocessed through normalization, one-hot encoding, and imputation techniques to ensure data quality and model performance. Experiments were conducted using a stratified train-test split, and the model was trained using the Adam optimizer with carefully tuned hyperparameters. Comparative evaluations against baseline models (Logistic Regression, Random Forest, and SVM) were carried out using precision, recall, F1-score, and ROC-AUC as performance metrics. The proposed FFNN achieved the highest accuracy of 96.47%, along with substantial improvements in recall and F1-score, highlighting its superior capability in identifying potential stroke cases even in imbalanced datasets. This work bridges a significant gap in heart stroke prediction by demonstrating the effectiveness of deep learning models—specifically FFNNs—in extracting complex patterns from diverse patient data. It also sets the stage for further exploration of deep learning-based clinical decision support systems.
Progressive Massive Fibrosis Detection Using Generative Adversarial Networks and Long Short-Term Memory Irianto, Suhendro Y.; Karnila, Sri; Hasibuan, M.S.; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Kurniawan, Hendra
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.707

Abstract

Contribution: Progressive Massive Fibrosis (PMF) is a severe form of pneumoconiosis, affecting individuals exposed to mineral dust, such as coal miners and workers in the artificial stone industry. This condition causes significant pulmonary impairment and increased mortality. Early and accurate detection is vital for effective management, yet traditional diagnostic methods face challenges in differentiating PMF from other pulmonary diseases due to variability in clinical presentations and limitations in imaging techniques. Idea: The study introduces a novel diagnostic framework that integrates Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks to enhance the detection and monitoring of PMF. The GAN generates high-fidelity synthetic imaging data to address the issue of limited datasets, while the LSTM network captures temporal patterns in patient data, enabling real-time monitoring of disease progression. Objective: The primary objective of this research is to develop an AI-driven model that improves the accuracy and efficiency of PMF detection and monitoring, facilitating early diagnosis and better treatment planning. Findings: The integrated GAN-LSTM model significantly outperformed traditional diagnostic methods. It proved high accuracy, a Dice coefficient of 0.85, and an Area Under the Curve (AUC) of 0.92, showing precise differentiation of PMF from other pulmonary conditions, such as lung cancer and tuberculosis. Results: The GAN-LSTM framework achieved an accuracy of 91.3%, suggesting that the fusion of GAN and LSTM technologies can effectively address the challenges of limited datasets and heterogeneous disease progression. The model showed promise in enhancing the non-invasive detection and ongoing monitoring of PMF. Novelty: This research stands for a significant advancement in PMF diagnostics by combining GAN and LSTM technologies in a single framework. This approach improves diagnostic accuracy and eases continuous disease monitoring, offering a non-invasive and highly precise solution for PMF detection.
A Gaussian Naive Bayes and SMOTE-Based Approach for Predicting Breast Cancer Aggressiveness in Imbalanced Datasets Dewi, Deshinta Arrowa; Kurniawan, Tri Basuki
International Journal of Informatics and Information Systems Vol 8, No 1: January 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i1.250

Abstract

Breast cancer remains one of the leading causes of death among women worldwide, making early and accurate detection essential to improving patient outcomes. This study aims to develop a predictive model for breast cancer aggressiveness using the Gaussian Naive Bayes algorithm on the Breast Cancer Wisconsin Diagnostic Dataset. The dataset contains 569 instances with 30 numerical features representing various cell characteristics. Preprocessing steps included data cleaning, label encoding, and Min-Max normalization. The model was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. Initially, the model achieved an accuracy of 78.88%; however, the recall for malignant cases was relatively low at 45.5%, highlighting a critical limitation in detecting aggressive cancer. To address class imbalance and improve model sensitivity, the Synthetic Minority Oversampling Technique (SMOTE) was applied. While detailed post-SMOTE metrics were not reported in this version, the approach is expected to enhance recall and F1-score for the malignant class. This research demonstrates the potential of Gaussian Naive Bayes, combined with data balancing techniques, as a fast and interpretable tool for early breast cancer diagnosis. Future work will focus on model comparison, cross-validation, and statistical evaluation to improve robustness and reliability.
pengenlan angka tulis tangan mengunakan metode backpropagation studi kasus lembar c1 pada rekapitulasi pemilihan umum Yusuf, Abi daud; Kurniawan, Tri Basuki; Surya Negara, Edi
Jurnal Locus Penelitian dan Pengabdian Vol. 1 No. 6 (2022): Jurnal Locus Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v1i3.25

Abstract

 Tujuan dari penelitian ini adalah untuk menganalisis dan menerapkan machine learning. Penulisan penelitian ini bertujuan untuk mengenali angka yang berbeda beda pada hasil rekapitulasi c1 yang sering terjadi perbedaan selisih dalam penghitungan pada saat  pemilu untuk karna itu penulis  menggunakan metode backprogration yang sudah teruji valid dalam pengenalan angka dan di sertakan metode machine learning dan deep learning sebagai pembanding metode yang akan di gunakan untuk penghitungan akurasi nya. Pengumpulan data pada penelitian ini menggunakan teknik Scraping untuk mengambil data dari situs kpu  yang berupa image atau angka hasil tulis tangan yang bisa kelola dan di analisis menjadi suatu data yang bagus dan bisa dijadikan sebagai acuan data yang tersusun rapi.  Metode yang digunakan dalam penilitian ini adalah algoritma Backprogation  dengan melakukan fase pengumpulan data, pengolahan data, model atau metode yang diusulkan, pengujian dan evaluasi. Dengan dilakukannya penelitian ini diharapkan dapat menemukan model yang paling tepat untuk diterapkan pada klasifikasi data tulisan tangan  c1.
Komparasi Metode Klasifikasi terhadap Data Penderita Penyakit Diabetes Menggunakan Python 3 Pratiwi, Ayu Okta; Kurniawan, Tri Basuki; Negara, Edi Surya; Kunang, Yesi Novaria
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetes is a serious challenge in the world of health, with broad impacts. In an effort to overcome this problem, it is important to analyze the classification of diabetes data to provide valuable insights. This study focuses on the comparison of the two main classification methods, namely Naive Bayes and Support Vector Machine (SVM), in analyzing diabetes data. We use the Python 3 programming language for implementation. The initial study involved the characterization of the dataset, including parameters such as blood pressure and blood glucose levels, which were important factors in the analysis. The preprocessing process is carried out to ensure data quality by overcoming missing or invalid values. After that, the dataset is divided into training and testing subsets. The Naive Bayes and SVM methods are implemented using the scikit-learn library in Python 3. Both models are trained using a training subset and tested on a test subset. The test results show that both methods have good performance in classifying diabetes data, but SVM stands out with higher accuracy. SVM has the ability to handle complex data and find optimal decision boundaries. The Naive Bayes model achieves the highest accuracy of 78.13% on 70% training data and 30% testing data, while the SVM model achieves 79.63% on 90% training data and 10% testing data. Overall, this study provides an in-depth understanding of the effectiveness of both methods in the context of classifying data on diabetics.
Analyzing the Impact of Social Media and Influencer Endorsements on Game Revenue using Multiple Linear Regression in the Metaverse Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
International Journal Research on Metaverse Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i2.29

Abstract

The gaming industry, particularly within the metaverse, has seen significant transformations driven by the integration of social media, influencer marketing, and player engagement metrics. These elements are crucial in shaping the success and revenue generation of games. This study explores the role of social media mentions and influencer endorsements in influencing game revenue, applying DBSCAN clustering to segment player engagement into distinct groups. By analyzing the "Gaming Trend 2024" dataset, which includes key metrics such as social media mentions, influencer endorsements, in-game purchases, and game revenue, we identify patterns in player behavior that directly impact revenue generation. The DBSCAN clustering method was employed to group players based on their social media interactions and influencer influence, identifying key segments that contribute to game success. The results reveal that certain clusters, characterized by higher social media engagement and influencer endorsements, are associated with increased game revenue. In contrast, other segments showed lower engagement and contributed less to overall revenue. The clustering analysis highlights the power of social media and influencers in driving player behavior, which in turn drives financial outcomes for game developers. This research provides insights into how targeted marketing strategies, personalized influencer campaigns, and tailored engagement efforts can enhance game revenue. This study offers practical applications for game developers and marketers in the metaverse, emphasizing the need to leverage clustering insights to optimize marketing strategies and increase revenue. Future research could expand on these findings by integrating sentiment analysis of social media mentions, exploring alternative clustering methods like hierarchical clustering, and developing hybrid models that combine clustering with predictive analytics to forecast game revenue trends.
Co-Authors - Kurniawan, - Adi Wijaya Alde Alanda, Alde Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Antoni, Darius Armoogum, Sheeba Armoogum, Vinaye Asro Asro Astried, Astried Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bujang, Nurul Shaira Binti Chandra, Anurag Dedy Syamsuar Devi Udariansyah Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Diana Diana Edi Surya Negara FADLY FADLY Fatoni, Fatoni Febriyanti Panjaitan Firosha, Ardian Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hadi Syahputra Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Henderi . Hendra Kurniawan Herdiansyah, M. Izman Hidayani, Nieta Hisham, Putri Aisha Athira binti Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Joan Angelina Widians, Joan Angelina Kijsomporn, Jureerat Kurniawan, Dendi Lexianingrum, Siti Rahayu Pratami M Said Hasibuan Madjid, Fadel Muhammad Maizary, Ary Mantena, Jeevana Sujitha Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhamad Akbar Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Nathan, Yogeswaran Nazmi, Che Mohd Alif Oktariansyah Oktariansyah, Oktariansyah Onn, Choo Wou Periasamy, Jeyarani Prahartiningsyah, Anggari Ayu Pratiwi, Ayu Okta Praveen, S Phani Puspitasari, Novianti Qisthiano, M Riski R Rizal Isnanto Rahmi Rahmi RR. Ella Evrita Hestiandari Saksono, Prihambodo Hendro Saringat, Zainuri Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Sri Karnila Sulaiman, Agus Sunda Ariana, Sunda Suriani, Uci Syaputra, Hadi Taqwa, Dwi Muhammad Thinakaran, Rajermani Triloka, Joko Usman Ependi Wibaselppa, Anggawidia Yeh, Ming-Lang Yesi Novaria Kunang Yorman Yusuf, Abi daud Zakari, Mohd Zaki Zakaria, Mohd Zaki