Garuda - Garba Rujukan Digital

Wicaksana, Hilman Singgih

Universitas Karya Husada, Semarang

Author-ID : 7497543

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Determining community happiness index with transformers and attention-based deep learning Wicaksana, Hilman Singgih; Kusumaningrum, Retno; Gernowo, Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1753-1761

In the current digital era, evaluating the quality of people's lives and their happiness index is closely related to their expressions and opinions on Twitter social media. Measuring population welfare goes beyond monetary aspects, focusing more on subjective well-being, and sentiment analysis helps evaluate people's perceptions of happiness aspects. Aspect-based sentiment analysis (ABSA) effectively identifies sentiments on predetermined aspects. The previous study has used Word-to-Vector (Word2Vec) and long short-term memory (LSTM) methods with or without attention mechanism (AM) to solve ABSA cases. However, the problem with the previous study is that Word2Vec has the disadvantage of being unable to handle the context of words in a sentence. Therefore, this study will address the problem with bidirectional encoder representations from transformers (BERT), which has the advantage of performing bidirectional training. Bayesian optimization as a hyperparameter tuning technique is used to find the best combination of parameters during the training process. Here we show that BERT-LSTM-AM outperforms the Word2Vec-LSTM-AM model in predicting aspect and sentiment. Furthermore, we found that BERT is the best state-of-the-art embedding technique for representing words in a sentence. Our results demonstrate how BERT as an embedding technique can significantly improve the model performance over Word2Vec.

Penerapan Word2Vec dan SVM dengan Hyperparameter Tuning untuk Deteksi Phishing Wicaksana, Hilman Singgih; Huda, Khairul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8729

The advancement of information technology in today’s digital age takes place very rapidly from one time to another. This phenomenon is accompanied by increasing cybersecurity threats like phishing. Phishing links are often designed with uniform resource locator (URL) structures that appear convincing and are difficult to distinguish from genuine links. This research proposes a word-to-vector (Word2Vec) and Support Vector Machine (SVM) approach with hyperparameter tuning where Word2Vec is a word embedding technique used to create a word vector representation of a particular URL, SVM is used as a machine learning (ML) approach used in this research, and hyperparameter tuning is used as a technique to find the best combination of parameters to produce an optimal SVM model in detecting phishing. The purpose of this research is to compare the performance between SVM and XGBoost models that have been optimized and deploy ML models into a prediction system using the Streamlit framework to detect phishing based on input made by users in the form of certain URLs. The findings of this study indicated that the SVM model performed very well compared to the XGBoost model, with precision, recall, f1-score, and accuracy values of about 99.84% for SVM. On the other hand, the XGBoost model recorded precision, recall, f1-score, and accuracy values of about 99.70% each. Thus, the SVM model is the optimal model to detect phishing precisely and accurately.

Optimized Machine Learning Approach for Malware Detection using Bayesian Optimization Wicaksana, Hilman Singgih; Huda, Khairul
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2547

Information technology has facilitated innovation in numerous sectors and enhanced operational efficiency as a result of its rapid expansion. However, these technical innovations have also resulted in an increased likelihood of cyberattacks, particularly those that are initiated by malware. Sophisticated evasion techniques, such as polymorphic and metamorphic transformations, are frequently implemented by contemporary malware, which significantly reduces the reliability of conventional detection methods. This investigation endeavors to evaluate and contrast the efficacy of a variety of machine learning algorithms that have been enhanced by Bayesian Optimization, a probabilistic method for hyperparameter tuning that effectively identifies the optimal model configurations. The study investigates five algorithms: Multilayer Perceptron, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Hist Gradient Boosting, using a supervised learning methodology with labeled data. The performance of each model was evaluated based on its accuracy, precision, recall, and F1-score, while its optimal parameters were meticulously fine-tuned. Additionally, experiments were implemented on a dataset consisting of 58,596 records that underwent rigorous cleansing and preprocessing. The Multilayer Perceptron demonstrated the highest performance, obtaining 99.97% across all evaluation measures, according to the results. These discoveries underscore the efficiency, precision, and adaptability of refined machine learning models in detecting malware in response to the evolution of cyber threats.

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