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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 1,045 Documents
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Perbandingan Kinerja XGBoost dan Naive Bayes dalam Analisis Sentimen Komentar TikTok Terhadap Ibu Kota Nusantara (IKN) pada Data Tidak Seimbang Novi Purnamasari; Nirwana Hendrastuty
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9488

Abstract

The growth of social media has generated diverse public responses regarding the development of Indonesia’s new capital city, Ibu Kota Nusantara (IKN), particularly on TikTok, a platform with high user interaction. This study aims to compare the performance of Naive Bayes and eXtreme Gradient Boosting (XGBoost) algorithms in sentiment analysis of TikTok comments related to IKN development under imbalanced data conditions. The dataset consists of 1,132 comments that underwent preprocessing, including case folding, text cleaning, tokenization, normalization, and stemming. Feature extraction was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, generating 1,926 features to represent word importance. The classification process used an 80:20 split for training and testing data. The results show that Naive Bayes achieved an accuracy of 61.23%, while XGBoost obtained a slightly higher accuracy of 62.11%. XGBoost improved recall in the negative class (from 0.21 to 0.40) and neutral class (from 0.11 to 0.26), although the improvement remains limited. The difference in accuracy between the models is relatively small and does not indicate a significant overall performance improvement. This study is limited by the relatively small dataset size and imbalanced class distribution, which may affect data representativeness and model generalization. Therefore, the results are not yet optimal for broader real-world applications.
Predictive Modeling of National University Rankings Using Ensemble Machine Learning and Multi-Dimensional Institutional Performance Indicators: Evidence from Japan Bernadus Gunawan Sudarsono; Raditya Galih Whendasmoro
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9525

Abstract

The global higher education landscape is becoming increasingly competitive in attracting outstanding students, qualified faculty, and international research collaborations. University ranking systems serve as strategic instruments for assessing institutional performance and as a basis for public policy. However, traditional ranking approaches employing linear aggregate scores often oversimplify the complex relationships among indicators such as research, internationalization, and graduate outcomes. This study develops a data-driven predictive model to map the non-linear relationships among university performance indicators. The research employs a quantitative predictive analytics approach using a dataset of 52 Japanese universities from the 2024–2026 period, encompassing the variables Research_Impact_Score, Employment_Rate, Intl_Student_Ratio, Institution_Age, Institution_Type, and Region, with National_Rank as the target variable. The research stages include data preprocessing (handling missing values, encoding, scaling), feature engineering (including Institutional Age), regression model development (Linear, Ridge, Lasso, SVR) as well as ensemble models (Random Forest and Gradient Boosting), evaluation using RMSE, MAE, and R², and explainable analysis based on feature importance. The results indicate that the Gradient Boosting model delivers the best performance with an RMSE of 1.175117, MAE of 1.087856, and R² of 0.994988, followed by Random Forest with an RMSE of 1.436536 and R² of 0.992510. Traditional linear regression models demonstrate significantly lower performance (R² 0.657519), confirming the superiority of non-linear approaches in modeling complex relationships among indicators. Stability testing using K-Fold Cross Validation yields an average RMSE of 1.1045 with a difference of 0.4493 between folds, indicating model consistency. Feature contribution analysis reveals that Research_Impact_Score is the dominant factor with a contribution of 97.94%, followed by Employment_Rate at 1.81%, while internationalization indicators and geographical factors contribute minimally. These findings confirm that research performance constitutes the primary determinant of university rankings, whereas employability and internationalization serve as supporting factors. This study demonstrates that ensemble-based machine learning models are effective in predicting national rankings accurately and interpretably. This approach offers a multidimensional evaluation framework that is more representative than linear aggregate scores, and provides policy implications for enhancing research quality, curriculum relevance, and internationalization strategies of higher education institutions.
Evaluasi KNN dan Logistic Regression untuk Klasifikasi Diabetes dengan Preprocessing Terstandarisasi: Trade-off Kinerja dan Interpretabilitas Alif Zayyin Kamandani; Egia Rosi Subhiyakto
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9534

Abstract

Although K-Nearest Neighbors (KNN) and Logistic Regression have been widely used in diabetes classification, studies that systematically combine a standardized preprocessing pipeline—including median imputation, feature standardization, and stratified data splitting—and evaluate the trade-off between predictive performance and model interpretability remain limited. This study aims to compare the performance of both algorithms in classifying diabetes status using the Pima Indians Diabetes dataset, which consists of 768 samples with eight numerical attributes. The research stages include data exploration, handling missing values using median imputation, feature standardization using StandardScaler, and stratified data splitting with a ratio of 80:20. Model evaluation is conducted using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC metrics. The experimental results show that KNN with an optimal parameter of K=21 achieves an accuracy of 75.97%, an F1-score of 61.86%, and a ROC-AUC of 0.8120, while Logistic Regression achieves an accuracy of 70.78%, an F1-score of 54.55%, and a ROC-AUC of 0.8130. Although KNN demonstrates higher predictive performance, Logistic Regression provides advantages in interpretability through model coefficients, where the variables Glucose (β=1.1825) and BMI (β=0.6887) are identified as the main predictors of diabetes risk. These findings indicate a clear trade-off between accuracy and interpretability, suggesting that KNN is more suitable for high-accuracy prediction tasks, while Logistic Regression is more appropriate in clinical contexts requiring transparency and model accountability.
Hybrid Feature Selection with Metaheuristics for Improving the Accuracy of Diabetes Disease Prediction Ida Maratul Khamidah; Suci Ramadhani; Aulia Khoirunnita
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9541

Abstract

Early diagnosis of diabetes mellitus is crucial to prevent severe complications and reduce long-term healthcare costs, making accurate and efficient predictive models an important research focus in medical data analytics. However, one of the main challenges in diabetes prediction lies in the presence of irrelevant and redundant features within medical datasets, which can degrade classification accuracy, increase computational complexity, and reduce model generalizability. To address this issue, this study proposes a Hybrid Feature Selection (HFS) approach that integrates filter-based methods and meta-heuristic optimization to identify an optimal subset of features for diabetes prediction. In the proposed framework, statistical filter techniques combining Chi-square and Mutual Information are first employed to rank and reduce feature dimensionality by selecting the most relevant attributes. Subsequently, a Genetic Algorithm (GA) is applied to further optimize the feature subset by maximizing classification accuracy while minimizing the number of selected features. The effectiveness of the proposed HFS approach is evaluated using the Pima Indian Diabetes Dataset, consisting of 768 instances and 8 clinical features, and tested across multiple machine learning classifiers, including Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Experimental results demonstrate that the proposed HFS significantly improves predictive performance compared to baseline models without feature selection. Specifically, the Random Forest classifier achieved the highest accuracy of 79.22%, compared to 74.03% in the baseline model, representing an improvement of approximately 5.2%. Additionally, notable improvements were observed in F1-score and AUC, with AUC increasing from 0.8336 to 0.8403. Beyond accuracy gains, the proposed method reduced feature dimensionality from 8 to 5 features, resulting in lower computational cost and faster model training time. These findings indicate that the hybrid integration of filter-based selection and meta-heuristic optimization provides a robust and efficient solution for feature selection in medical prediction tasks. Overall, the proposed HFS framework offers a promising approach for developing accurate, efficient, and reliable decision-support systems for early diabetes diagnosis.
Sentiment Analysis on the Allocation of the MBG Program Budget Using Support Vector Machine Aulia Kartika Dewi; Raissa Amanda Putri
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9502

Abstract

Sentiment analysis is one of the applications of artificial intelligence and machine learning used to automatically identify and classify public opinions, particularly those expressed on social media. This approach is important for understanding public perceptions of a policy, as it provides a systematic, fast, and data-driven overview. With the increasing use of social media, sentiment analysis can be utilized as an evaluation tool to support transparency and more objective decision-making. One issue that has attracted public attention is the MBG (Free Nutritious Food) Program, a government initiative aimed at improving community nutrition. The budget allocation for this program has generated various responses from the public, including both support and criticism regarding its implementation and policy priorities. Therefore, an analysis that can comprehensively capture these diverse perspectives is necessary. This study aims to analyze public sentiment toward the MBG Program budget using data from the social media platform X (Twitter), which is known for its ability to represent real-time and dynamic public opinion. The dataset collected through crawling consists of 2,487 entries, and after preprocessing, 1,686 valid data points were obtained for analysis. Feature extraction was performed using the TF-IDF method, while sentiment classification was conducted using the Support Vector Machine (SVM) algorithm. Model evaluation was carried out using 5-Fold Cross Validation and Confusion Matrix. The results show that the developed model achieved an accuracy of 81.17%, indicating good performance in sentiment classification. For the negative class, the precision reached 85.48% and recall 98.76%. For the neutral class, the precision was 57.58%, recall 44.19%, and F1-score 49.98%. For the positive class, the precision was 75.00%, recall 15.79%, and F1-score 26.09%. These findings indicate that a machine learning-based approach can contribute to understanding public opinion and support more effective, data-driven government policy evaluation. This study contributes by demonstrating the effectiveness of the SVM algorithm in classifying public sentiment on policy-related issues, as well as by applying k-fold cross-validation and confusion matrix to provide a more comprehensive and reliable evaluation. The findings are expected to support data-driven policy evaluation and enhance understanding of public opinion toward government programs.
Klasifikasi Opini Pengguna TikTok terhadap Keamanan dan Efektivitas Produk Skincare Lokal menggunakan Metode Naïve Bayes, Decision Tree, dan Random Fores Sintia Ariyani; Styawati Styawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9537

Abstract

This study aims to analyze and compare the performance of Naïve Bayes, Decision Tree, and Random Forest algorithms in classifying TikTok users’ opinions regarding the safety and effectiveness of local skincare products. The results show that these algorithms exhibit significant differences in performance for sentiment classification tasks. Before applying SMOTE, Random Forest achieved the highest accuracy of 87%, followed by Decision Tree at 79% and Naïve Bayes at 65%. The main weakness was observed in minority classes such as Safe and Unsafe, which had low recall values. After applying SMOTE, all models showed improved performance, particularly in recognizing minority classes, resulting in more balanced accuracy, precision, recall, and F1-score across all sentiment categories. The TF-IDF analysis revealed that the extracted features were still dominated by common words and brand names, indicating that they did not fully represent the specific aspects of safety and effectiveness. This suggests that the preprocessing and feature selection stages can be further improved to generate more relevant feature representations. The classification visualization showed that most comments were categorized as Effective and Ineffective, while the Neutral category contained fewer instances. The implementation of SMOTE improved model performance in handling imbalanced data; however, it must be applied carefully only to the training data to avoid evaluation bias. Overall, Random Forest demonstrated the best performance among the evaluated algorithms. This study contributes to the development of a multi-class sentiment analysis model capable of distinguishing between safety and effectiveness aspects of skincare products, and demonstrates that the application of SMOTE effectively improves classification performance on imbalanced datasets. Future research is recommended to enhance sentiment labeling methods, improve feature quality, and explore more advanced approaches such as deep learning to achieve more accurate and robust classification results.
Penerapan Regresi Logistik, K-NN, dan Naïve Bayes Berbasis Pendekatan CRISP-DM dalam Memprediksi Penyakit Jantung Rayna Shera Chang; Natalie Grace Widjaja Kuswanto; Jessica Laurentia Tedja; Christopher Andreas
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8518

Abstract

Heart disease remains the leading cause of mortality globally, despite having significant potential to be controlled through early detection and effective risk-factor management. To improve the accuracy and efficiency of early detection, machine learning technology is employed to develop predictive models for heart disease risk. The research aims to compare the performance of three classification algorithms in predicting heart disease risk to identify the most optimal model. This research applies the CRISP-DM methodology to build and compare predictive models for heart disease risk using three supervised learning algorithms: K-Nearest Neighbors (K-NN), Naïve Bayes, and Logistic Regression. The dataset used is a heart disease dataset obtained from the Kaggle platform, consisting of 10,000 records with variables such as Age, Blood Pressure, Smoking, Diabetes, Cholesterol, Triglyceride Level, Fasting Blood Sugar, and CRP Level. For the K-NN model, experiments were conducted using three values of k (k = 5, k = 10, and k = 20) to examine the effect of the number of neighbors on model performance. Meanwhile, the Naïve Bayes and Logistic Regression models were implemented using default parameters without additional tuning to ensure a consistent performance comparison. Model performance was evaluated using Accuracy and F1-Score metrics. The evaluation results indicate that the K-NN model with k = 5 achieved the best performance, with an accuracy of 0.7203 and an F1-Score of 0.7598, outperforming the Naïve Bayes and Logistic Regression models.
Analysis of Air Pollution Standard Index Using Support Vector Machine Algorithm Fitra Hidayat Lubis; Raissa Amanda Putri; M Fakhriza
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9506

Abstract

Air pollution is one of the major environmental problems in urban areas, including Medan City, Indonesia. The Air Pollution Standard Index (Indeks Standar Pencemar Udara / ISPU) data provided by the Environmental Agency is often difficult for the public to interpret due to its numerical format. This study aims to analyze and classify air quality using the Support Vector Machine (SVM) algorithm and present the results through data visualization. The dataset used in this research is secondary data obtained from the Environmental Agency of Medan City, including pollutant parameters such as PM10, PM2.5, SO₂, NO₂, CO, O₃, and HC. The research method follows a quantitative descriptive approach, including data preprocessing, ISPU calculation based on government regulations, classification using SVM, and visualization using graphical methods such as line charts, bar charts, and heatmaps. The results indicate that SVM is effective in classifying air quality categories into Good, Moderate, Unhealthy, Very Unhealthy, and Hazardous. Additionally, visualization techniques improve the interpretability of air quality data, making it easier for stakeholders and the public to understand environmental conditions. This study contributes to decision support systems for environmental monitoring and public awareness.
Analisis Segmentasi Pelanggan Menggunakan RFM dan K-Means Clustering sebagai Dasar Penyusunan Aturan Pendukung Keputusan Meisya Dwi Andini; Rafa Nadira Catra; Weli Ratri Homausyah; Haaniyah Aurelia; Allsela Meiriza; Ken Ditha Tania; Zaqqi Yamani
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9511

Abstract

One of the important methods in supporting data-driven Customer Relationship Management (CRM) initiatives is customer segmentation. However, in practice, segmentation results are often limited to descriptive analysis and are not further utilized in decision-support processes. This study aims to utilize customer segmentation results based on the Recency, Frequency, Monetary (RFM) approach and the K-Means algorithm as a basis for developing decision-support recommendations. The research stages include data preprocessing, RFM value calculation, normalization using the Min-Max Scaling method, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The evaluation results indicate that the optimal number of clusters is four, with a Silhouette Score of 0.61, which reflects a moderately good level of cluster separation. The segmentation results classify customers into four categories: High Value/VIP Customers, Loyal Customers, Potential Customers, and Low Value/Dormant Customers, each exhibiting distinct transactional behavior characteristics. These characteristics are then interpreted into decision rules using IF–THEN logic; for example, customers with low Recency, high Frequency, and high Monetary values are recommended strategies such as loyalty rewards and upselling. The findings suggest that customer segmentation can be extended beyond descriptive analysis and utilized as a practical basis for marketing decision-making, although the approach remains relatively simple and heuristic-based. The contribution of this study is to integrate RFM-KMeans segmentation results with IF–THEN decision rules to generate more applicable marketing strategy recommendations in supporting data-driven decision making.
Studi Komparasi Kinerja Algoritma AdaBoost dan CatBoost dalam Prediksi Perilaku Pembelian Pelanggan Princess Iqlima Kafilla; Fandy Setyo Utomo; Giat Karyono
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.7947

Abstract

Customer purchase behavior is a crucial factor in the development of effective marketing strategies. By leveraging predictive analytics, businesses can personalize recommendations, optimize marketing campaigns and improve user experience, ultimately contributing to increased conversion rates and customer retention. This research compares the performance of AdaBoost and CatBoost algorithms in predicting customer purchase behavior. The dataset used includes demographic attributes and customer behavior history, allowing for comprehensive analysis. The results showed that CatBoost performed better overall with an accuracy of 94%, while AdaBoost showed higher recall and F1-score values in the positive class. This study concludes that both algorithms have reliability in predicting customer behavior, where CatBoost is superior in handling categorical features, while AdaBoost offers good adaptability on simpler datasets. As a next step, future research can explore the implementation of these models in real-time scenarios.

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