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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) INOVTEK Polbeng - Seri Informatika Journal of Community Based Environmental Engineering and Management Indonesian Journal of Computing, Engineering, and Design Journal of Applied Data Sciences Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Luhur Bayuaji, Luhur
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Other

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Optimasi Algoritma Support Vector Machine (SVM) Dengan Menggunakan Feature Selection Gain Ratio Untuk Analisis Sentimen Yamin, Mochamad Amzah; Kusnadi, Kusnadi; Bayuaji, Luhur
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4197

Abstract

The ease of internet access has had a positive impact on the increase in the number of social media users in Indonesia. One of the most widely used applications is X or Twitter. Users often upload posts that contain opinions or sentiments, which trigger debates and discussions. This is interesting to analyze as a study of sentiments or opinions that are trending in society. For this analysis, algorithms such as Support Vector Machine (SVM) are required, which are often used for sentiment analysis. However, SVM lacks in accuracy due to the large number of similar words in the dataset. Words related to sentiment analysis usually have large dimensions, so feature selection is needed to improve SVM performance. This research aims to optimize SVM accuracy by using Feature Selection Gain Ratio. The object of research is a dataset related to the 2017 DKI elections from GitHub. The results showed an increase in SVM accuracy with Feature Selection Gain Ratio. With threshold weight gain ratio 0.0001 (1732 features), accuracy increases from 61.63% to 71.51%. For threshold weights 0.002 (518 features), the accuracy increased from 61.63% to 62.79%. Feature selection with Feature Selection Gain Ratio gain ratio produces better accuracy than gain ratio, namely 56.40% with gain ratio and 71.51% with gain ratio for weights 0.0001. The implications of these findings show that the use of Feature Selection Gain Ratio can improve the accuracy of SVM in sentiment analysis. Social media practitioners can utilize this technique to gain more accurate insights from user data. Further research can focus on developing sentiment analysis algorithms with more sophisticated feature selection techniques for various applications on social media platforms.
Optimizing Malware Detection and Prevention on Proxy Servers Through Random Forest and Lexical Feature Analysis Andalas Saputra, Meitro Hartanto; Pebrianti, Dwi; Bayuaji, Luhur; Rusdah
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 7 No. 1 (2025): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v7i1.485

Abstract

Malware has become a significant concern due to the increase in malicious websites hosting spam, phishing, malware, and other threats. This research aims to predict malware URLs using lexical features for feature extraction and random forest for classification. The dataset, sourced from kaggle.com, includes benign, phishing, spam, malware, and defacement URLs. To address data imbalance, random oversampling was applied for balanced training. Recursive feature elimination was used to optimize lexical features, testing various sets of features (10, 15, 19, 23, 29, 35) for classification accuracy, achieving 98% accuracy using 23 features. Validation tests with actual university network data confirmed this model’s effectiveness, classifying malicious URLs in 9 minutes using 11,566 samples. URL filtering involved log analyzer tools capturing internet traffic during working hours over one month. Results suggest that this approach can efficiently classify malicious URLs and could be implemented for real-time detection in proxy server logs, aiding IT departments in preventing malware spread via web traffic.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Sukmana, Husni Teja; Hasibuan, Zainal Arifin; Rahman, Abdul Wahab Abdul; Bayuaji, Luhur; Masruroh, Siti Ummi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Privacy-Preserving Healthcare Analytics in Indonesia Using Lightweight Blockchain and Federated Learning: Current Landscape and Open Challenges Mardiansyah, Viddi; Bayuaji, Luhur; Herlistiono, Iwa Ovyawan; Violina, Sriyani; Purnama, Adi; Prasetyo, Bagus Alit; Huynh, Phuoc-Hai
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.63

Abstract

Healthcare data are invaluable assets in today’s digital age; however, they are also highly vulnerable to misuse, breaches, and unauthorized access. The global healthcare sector faces a significant dilemma: To leverage exceptionally enormous and heterogeneous datasets, the protection of patient privacy must be ensured while simultaneously improving medical services and public health understanding. In recent years, blockchain technology has emerged as a promising solution to manage healthcare data in a decentralized, transparent, tamperproof, as well as secure way. However, several natural limitations often obstruct many conventional blockchain systems. These limitations include scalability issues, high energy consumption, in addition to increased latency, and they can greatly impede practical adoption in resource-limited settings, particularly in developing countries such as Indonesia. These many limitations considerably spurred developers to create lightweight blockchain frameworks. These frameworks aim to retain all of the core benefits of blockchain, such as its immutability in addition to traceability, and optimize both performance and efficiency. In the event that an individual integrates the proposed system by means of federated learning, which allows training of machine learning models across distributed data sources without data privacy being compromised, the system subsequently offers a compelling solution for healthcare analytics that preserves privacy in its entirety. This paper explores integrated technologies in Indonesian healthcare and highlights their potential and limitations. This study discusses how data can improve services while protecting patient confidentiality despite increasing cyber threats. It also considers regional policies like the Personal Data Protection Law and the BPJS health insurance. Identified are certain open challenges, in addition to particular future research directions, for the purpose of addressing the practical, technical, and regulatory hurdles that must be overcome to realize secure and privacy-aware healthcare analytics in Indonesia.
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting Pebrianti, Dwi; Kurniawan, Haris; Bayuaji, Luhur; Rusdah, Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27712

Abstract

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method.
Identification of Factors Influencing Gentrification: A Case Study in Solo, Indonesia Satriyono, Raden Danang Aryo Putro; Bayuaji, Luhur
Journal of Community Based Environmental Engineering and Management Vol. 8 No. 2 (2024): September 2024
Publisher : Department of Environmental Engineering - Universitas Pasundan - Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jcbeem.v8i2.12701

Abstract

Indonesia is a country with the highest social gap in Asia. This significant social gap drives the phenomenon of gentrification in several cities in Indonesia. Despite the rise in gentrification occurrences, there is very little research concerning the factors contributing to gentrification behaviors in Indonesia. This study investigates the factors contributing to gentrification in the Kerten Area, Solo, Indonesia, and its social effects. Solo area was selected because many regions in Solo are rapidly developing and attracting residential newcomers from various regions in Indonesia. This study delves into the factors influencing residents' intentions and behavior related to gentrification. The study incorporates variables from the extended Theory of Planned Behavior (TPB) associated with human behavior and perception, such as subjective norms, perceived behavioral control, economic factors, social factors, and social awareness. The survey method was employed with 320 respondents, and the analysis utilized Structural Equation Modeling (SEM). The findings indicate that five variables—subjective norms, social awareness, and economic factors—have a substantial effect on gentrification. This research also discovered that attitude and individual concern factors had no significant effect. These results highlight the importance of managing gentrification to minimize social and community impacts.
Using Content-Based Filtering and Apriori for Recommendation Systems in a Smart Shopping System Pebrianti, Dwi; Ahmad, Denis; Bayuaji, Luhur; Wijayanti, Linda; Mulyadi, Melisa
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 6 No. 1 (2024): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v6i1.393

Abstract

This research is motivated by the increasing significance of online shopping platforms and the challenges faced by users in locating products that align with their preferences and requirements, which can significantly influence the sales performance of online retailers. Consequently, the primary objective of this study is to design and implement a recommendation system capable of identifying suitable products and forecasting the purchase frequency for various product combinations, while also integrating this recommendation system with a smart shopping platform. To achieve this objective, the research employs machine learning techniques, specifically content-based filtering and the Apriori algorithm. Content-based filtering is utilized to analyze user preferences and behavioral patterns related to visited products, while the Apriori algorithm is employed to evaluate support and confidence values for item set combinations, thereby generating frequency values for future transactions involving product combinations. Additionally, a smart shopping system is developed and integrated, enhancing the shopping experience through smartphone applications and streamlining the payment process to facilitate seamless product purchases. The research methodology involves data collection pertaining to products and user preferences, followed by several testing involving a sample group of user respondents. The results demonstrate that the developed recommendation system effectively delivers relevant product recommendations based on user preferences, achieving a confidence value up to 98%. Furthermore, the smart shopping system proves capable of independently assisting users throughout the transaction process, thereby enhancing overall user experience and convenience.
Co-Authors Adi Purnama Ahmad, Denis Andalas Saputra, Meitro Hartanto Bagus Alit Prasetyo Haris Kurniawan, Haris Herlistiono, Iwa Ovyawan Husni Teja Sukmana Huynh, Phuoc-Hai Melisa Mulyadi Pebrianti, Dwi Raden Danang Aryo Putro Satriyono Rahman, Abdul Wahab Abdul Rusdah Rusdah Rusdah Siti Ummi Masruroh Sriyani Violina Tengku Riza Zarzani N Viddi Mardiansyah Wijayanti, Linda Yamin, Mochamad Amzah Zainal Arifin Hasibuan