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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Gibran Satya Nugraha
Universitas Mataram, Mataram, Indonesia
Computer Science & IT

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Feature Selection on Grouping Students Into Lab Specializations for the Final Project Using Fuzzy C-Means Indradi Rahmatullah; Gibran Satya Nugraha; Arik Aranta
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3341

Abstract

The student’s Final Project is critical as a requirement to graduate from the University. In the PSTI at Mataram University, each student is required to choose a specialization lab to focus on the final project topic that they will work on. From the questionnaire, 57.7% of students answered that it is difficult to select a lab, and others answered that they prefer to determine the labs based on the grades of the courses that represent each lab. This research aimed to group and analyze students in the final project specialization lab by using the main method, namely Fuzzy C-Means (FCM). The methods used were FCM for clustering, Silhouette Coefficient for analysis of cluster quality results, Pearson Correlation, and Principal Component Analysis for the feature selection processing. The results of this study showed that the FCM method followed by a method for feature selection has better results than previous studies that used the K-Means method without feature selection; with this research result using 131 data, the cluster validation result is 0.501, after feature selection using Pearson correlation is 0.534. Thus, Fuzzy C-Means followed by the right feature selection method can group students into specialization laboratories with good results and can be further developed.
Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation Nurun Latifah; Ramaditia Dwiyansaputra; Gibran Satya Nugraha
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3835

Abstract

The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.
Multiclass Text Classification of Indonesian Short Message Service (SMS) Spam using Deep Learning Method and Easy Data Augmentation Nurun Latifah; Ramaditia Dwiyansaputra; Gibran Satya Nugraha
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3835

Abstract

The ease of using Short Message Service (SMS) has brought the issue of SMS spam, characterized by unsolicited and unwanted. Many studies have been conducted utilizing machine learning methods to build models capable of classifying SMS Spam to overcome this problem. However, most of these studies still rely on traditional methods, with limited exploration of deep learning-based approaches. Whereas traditional methods have a limitation compared to deep learning, which performs manual feature extraction. Moreover, many of these studies only focus on binary classification rather than multiclass SMS classification which can provide more detailed classification results. The aim of this research is to analyze deep learning model for multiclass Indonesian SMS spam classification with six categories and to assess the effectiveness of the text augmentation method in addressing data imbalace issues arising from the increased number of SMS categories. The research method used were Indonesian version of Bidirectional Encoder Representations from Transformers (IndoBERT) model and exploratory data analysis (EDA) augmentation technique to address imbalance dataset issue. The evaluation is conducted by comparing the performance of the IndoBERT model on the dataset and applying EDA techniques to enhance the representation of minority classes. The result of this research shows that IndoBERT achieves 91% accuracy rate in classifying SMS spam. Furthermore, the use of EDA technique results in significant improvement in f1-score, with an average 12% increase in minority classes. Overall model accuracy also improves to 93% after EDA implementation. This research concludes that IndoBERT is effective for multiclass SMS spam classification, and the EDA is beneficial in handling imbalanced data, contributing to the enhancement of model performances.
Co-Authors Arik Aranta Indradi Rahmatullah Nurun Latifah Ramaditia Dwiyansaputra