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All Journal JIEET (Journal of Information Engineering and Educational Technology) KOMPUTEK Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika Terpadu Journal of Artificial Intelligence and Digital Business SCIENTIA JOURNAL : Jurnal Ilmiah Mahasiswa
Rudiman, Rudiman
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mechanical Engineering

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Journal : Building of Informatics, Technology and Science

 1 
Penerapan Metode GA-TL Pada Algoritma Naive Bayes Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Widyastuti, Dessy; Siswa, Taghfirul Azhima Yoga; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6737

Abstract

The Indonesia Smart Card (KIP) Scholarship Program aims to support students from underprivileged families in pursuing higher education, yet the distribution of recipient data often experiences class imbalance, leading to inaccuracies in scholarship allocation. This imbalance, characterized by disproportionate data between recipient and non-recipient groups, affects classification model performance, causing models to favor the majority class and overlook the minority class, potentially excluding eligible recipients. To address this issue, this study combines the Genetic Algorithm for feature selection and optimization with Tomek Links-Random Undersampling for data balancing. The research process includes data preprocessing, 10-fold cross-validation, and performance evaluation using a confusion matrix. Results indicate that without Tomek Links-Random Undersampling, Naïve Bayes accuracy increased from 65.2% to 66.0% after feature selection and optimization using the Genetic Algorithm, while applying Tomek Links-Random Undersampling improved accuracy from 56% to 63%. This method also enhanced fairness in recipient classification, promoting a more equitable distribution of benefits. The improved model accuracy significantly aids future scholarship selection processes, demonstrating that integrating efficient machine learning approaches optimizes the KIP Scholarship Program by ensuring beneficiaries are appropriately targeted based on predetermined criteria.
Penerapan Metode GA-CBU Pada Algoritma Logistic Regression Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Poernamawan, Ahmad Nugraha; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6747

Abstract

The issue of class imbalance often poses a challenge in data analysis, where the number of instances in the majority class is significantly higher than that in the minority class. This can lead classification models to be biased towards predicting the majority class, resulting in low accuracy in identifying the minority class. This research aims to implement the Logistic Regression (LR) algorithm combined with the Clustering Based Undersampling (CBU) method as an undersampling technique, feature selection, and optimization using Genetic Algorithm (GA) in classifying KIP-College scholarship data at Muhammadiyah University of East Kalimantan. In addition, this research also evaluates the performance of the model with 10-Fold Cross Validation and Confusion Matrix techniques as accuracy metrics and aims to overcome the problem of class imbalance in the data of scholarship recipients (KIP) at Muhammadiyah University of East Kalimantan. The data used consists of 1075 records with 37 features related to the socio-economic factors of scholarship recipients. The results from the application of the CBU method indicate an increase in the accuracy of the Logistic Regression model from 62.51% to 67.68%. Furthermore, the combination of GA and CBU has providing more stable results in classifying minority classes. It is hoped that this research can make a significant contribution to the development of a more accurate and efficient scholarship recipient selection system, as well as serve as a reference for future studies in the fields of data mining and machine learning.
Penerapan Metode GA-NM Pada Algoritma SVM Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Abror, Irfan Fiqry; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6756

Abstract

Class imbalance is a common challenge in data analysis, especially when the number of instances in the majority class significantly exceeds that in the minority class. This imbalance can cause classification models to favor the majority class, resulting in low accuracy in identifying the minority class. In this study, the Support Vector Machine (SVM) method combined with Near Miss and Genetic Algorithm (GA) is used to address the class imbalance problem in the scholarship recipient data of the Kartu Indonesia Pintar (KIP) program at Universitas Muhammadiyah Kalimantan Timur. The dataset consists of 1,075 records with 27 features representing the socio-economic factors of the scholarship recipients. Near Miss was applied to undersample the majority class, producing a more balanced data distribution. Subsequently, the SVM algorithm was utilized as the primary classification model, with feature selection and parameter optimization conducted using GA. The results indicate that the combination of SVM, Near Miss, and GA improved classification performance in identifying the minority class. The initial accuracy obtained without the method was 60.55% and after implementation it increased to 76.88%. This approach not only enhances the overall accuracy of the model but also ensures more stable performance, particularly for the minority class. Therefore, this study is expected to provide a significant contribution to the development of a more accurate and efficient scholarship selection system, as well as serve as a reference for future research in data mining and machine learning.
Penerapan Metode GA-RU Pada Algoritma Random Forest Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Rahman, Febrian Nor; Siswa, Taghfirul Azhima Yoga; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6757

Abstract

Class imbalance is a common challenge in data analysis, where the majority class significantly outnumbers the minority class. This condition causes classification models to lean toward predicting the majority class, resulting in low accuracy in identifying the minority class. This study proposes the application of Genetic Algorithm (GA) combined with Random Undersampling (RU) on the Random Forest algorithm to address class imbalance issues in the dataset of Indonesia Smart Card (KIP) scholarship recipients at Universitas Muhammadiyah Kalimantan Timur. The dataset comprises 1,080 records with 37 features related to the socio-economic factors of the scholarship recipients. After data cleaning, 1,075 records were retained. The results indicate that the Random Undersampling method improved the accuracy of the Random Forest model from 84.27% to 85.06%. Although this improvement appears modest, it is significant as it demonstrates increased model stability in classifying the minority class, which previously had low accuracy. The combination of GA and RU proved effective in enhancing model performance, resulting in more stable classification for the minority class. This study is expected to contribute to the development of more accurate and efficient scholarship selection systems and serve as a reference for research in data mining and machine learning.
Analisis Model Klasifikasi Sentimen Publik Terhadap Kebijakan Keberlanjutan IKN Menggunakan BERT Sebagai Feature Extractor dan K-Nearest Neighbor (KNN) Fiqri, Mohammad Hiqmal; Rudiman, Rudiman; Verdikha, Naufal Azmi
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8168

Abstract

This study aims to evaluate the performance of sentiment classification models for public opinions regarding the relocation of Indonesia’s new capital (IKN) using a combination of IndoBERT as a feature extractor and K-Nearest Neighbor (KNN) as a classifier. The dataset consisted of 1,274 YouTube comments related to IKN, which were annotated by an expert in sociology and text analysis. The preprocessing stage involved cleaning numbers, URLs, emojis, and punctuation, as well as removing stopwords using the Sastrawi library. IndoBERT produced 768-dimensional vector representations, which were then classified using KNN with k=5 and Euclidean distance. Evaluation with 5-fold cross validation achieved an accuracy of 73.31%. However, the recall for the positive class was relatively low (0.49), indicating challenges in detecting positive comments due to class imbalance (831 negative, 294 positive, 149 neutral). These findings suggest that the IndoBERT+KNN model performs well on majority classes but struggles with minority classes. The contribution of this research is to provide a critical analysis of the limitations of IndoBERT-based models in Indonesian sentiment classification and to recommend future directions, including data balancing and fine-tuning approaches.
Co-Authors Abror, Irfan Fiqry Ali, Sultan Anggi, Saputra Elfansyah, Muhammad Rayhan Fiqri, Mohammad Hiqmal Halim, Andi Nur Hidayat, Muhammad Rahman Ihram Nabawi, Ikhsan Nuttakwa Takbirata Munawaroh, Al Naufal Azmi Verdikha Nursyarif, Muhammad Khumaidi Ompusunggu, Hermaya Perdana, Muhammad Reifin Poernamawan, Ahmad Nugraha Pranata, Aditya Rahman, Febrian Nor Ridhoi, Reno Sidiq, Rezki Subhan Insani Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tirta, Muhamad Wahyu Verdikha, Nauval Azmi Widyastuti, Dessy