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All Journal International Journal of Electrical and Computer Engineering International Journal of Engineering, Science and Information Technology
Rina Refianti
Gunadarma University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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A Preference Model on Adaptive Affinity Propagation Rina Refianti; Achmad Benny Mutiara; Asep Juarna; Adang Suhendra
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1767.959 KB) | DOI: 10.11591/ijece.v8i3.pp1805-1813

Abstract

In recent years, two new data clustering algorithms have been proposed. One of them isAffinity Propagation (AP). AP is a new data clustering technique that use iterative message passing and consider all data points as potential exemplars. Two important inputs of AP are a similarity matrix (SM) of the data and the parameter ”preference” p. Although the original AP algorithm has shown much success in data clustering, it still suffer from one limitation: it is not easy to determine the value of the parameter ”preference” p which can result an optimal clustering solution. To resolve this limitation, we propose a new model of the parameter ”preference” p, i.e. it is modeled based on the similarity distribution. Having the SM and p, Modified Adaptive AP (MAAP) procedure is running. MAAP procedure means that we omit the adaptive p-scanning algorithm as in original Adaptive-AP (AAP) procedure. Experimental results on random non-partition and partition data sets show that (i) the proposed algorithm, MAAP-DDP, is slower than original AP for random non-partition dataset, (ii) for random 4-partition dataset and real datasets the proposed algorithm has succeeded to identify clusters according to the number of dataset’s true labels with the execution times that are comparable with those original AP. Beside that the MAAP-DDP algorithm demonstrates more feasible and effective than original AAP procedure.
Comparison of Music Genre Classification Results Using Multilayer Perceptron With Chroma Feature and Mel Frequency Cepstral Coefficients Extraction Features Rina Refianti; Faradilla Mahardi
International Journal of Engineering, Science and Information Technology Vol 3, No 2 (2023)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v3i2.444

Abstract

The development of digital music, especially in genre classification has helped in the ease of studying and searching for a song. There are many ways that can be used to classify the songs/music into genres. Deep Learning is one of the Machine Learning implementation methods that can be used to classify the genre of music. The author managed to create a deep learning-based program using the MLP model with two extraction features, Chroma Feature and MFCC which can classify song/ music genres. Pre-processing of the song is done to take the features of the existing value then the value will be incorporated into the model to be trained and tested. The model was trained and tested with data of 3000 songs which were divided into 10 genres. The model was also tested using the Confusion Matrix with 600 songs of the total available data. The models with Chroma Features as extraction features have an accuracy rate of 53 %, while the MFCC extraction features have an accuracy rate of 80.2 %.
Comparison of Support Vector Machine and Naïve Bayes Algorithms Based on TF-IDF in Online Gambling Website Detection Rina Refianti; Husein Alhafiz
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1794

Abstract

The rapid growth of digital technology has significantly accelerated the spread of illegal online content, particularly gambling websites, which threaten social stability and regulatory enforcement. To address this issue, this study develops an automated detection system for online gambling sites using text classification with the Term Frequency–Inverse Document Frequency (TF-IDF) approach. A total of 1,225 website URLs were collected through web scraping, and after preprocessing, 1,166 valid entries were manually labeled into two classes: gambling and normal. The preprocessing steps included cleaning, tokenizing, stopword removal, stemming, and domain parsing, followed by feature extraction using TF-IDF, which generated 2,426 numerical features. To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training dataset. Two machine learning algorithms were implemented and compared: Support Vector Machine (SVM) with multiple kernels (Linear, RBF, Polynomial, and Sigmoid) and Multinomial Naïve Bayes (MNB). Experimental evaluation was conducted using accuracy, precision, recall, specificity, and F1-score metrics. Results demonstrate that SVM with the RBF kernel achieved the best performance, with an accuracy of 91.88% and an F1-score of 93.70%, while MNB obtained an accuracy of 88.46% and an F1-score of 91.00%. These findings confirm that SVM, particularly with the RBF kernel, delivers more stable and accurate performance in distinguishing gambling websites from normal ones. The proposed system offers a reliable foundation for the development of automated tools to monitor, detect, and block illegal online gambling content, thereby supporting regulatory enforcement and reducing the negative societal impacts of online gambling.
Co-Authors Achmad Benny Mutiara Adang Suhendra Asep Juarna Faradilla Mahardi Husein Alhafiz