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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 926 Documents
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Analisis Sentimen: Perbandingan Performa Algoritma Naive Bayes, Support Vector Machine, Random Forest, dan K-Nearest Neighbor Dalam Pemecatan Shin Tae Yong pada Media X Prasatya, Agung; Hendrastuty, Nirwana
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.v6i4.6987

Abstract

The dismissal of Shin Tae Yong as the coach of the Indonesian national team has triggered a variety of reactions, ranging from disappointment to relief, among Indonesian football fans. Factors such as unsatisfactory match results and internal conflicts within the team, as well as pressure from fans and the media, were the main reasons for this decision. Although this change opens up opportunities for a new coach to improve the performance of the Indonesian national team, it also raises controversy and debate. This study aims to compare the performance of Naïve Bayes, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms in analyzing sentiment related to this dismissal. The research data were obtained from the Twitter platform with a total of 4,345 tweets collected using crawling techniques. The data then underwent pre-processing stages to produce clean data. Testing was conducted to evaluate the accuracy of each model in predicting public sentiment. The test results showed that the SVM algorithm performed best with an accuracy of 78%, followed by Random Forest with an accuracy of 77%, and Naïve Bayes with an accuracy of 63% and KNN 74% before the application of Synthetic Minority Oversampling Technique (SMOTE). After optimization using SMOTE, the SVM algorithm still showed the best performance with an accuracy of 80%, followed by Random Forest with an accuracy of 79%, and Naïve Bayes and KNN with an accuracy of 72%. Based on these results, SVM proved to be the most effective algorithm in classifying sentiment related to the dismissal of Shin Tae Yong. It is hoped that the results of this study can contribute to understanding public opinion regarding the decision to dismiss Shin Tae Yong as coach of the Indonesian national team.
Deteksi Dini Risiko Penyakit Jantung Koroner Menggunakan Algoritma Decision Tree dan Random Forest Nurrohman, Slamet Hudha; Kurniawan, Defri
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.v6i4.7029

Abstract

Coronary heart disease is the leading cause of global mortality, accounting for 17.9 million deaths annually. Early detection is crucial in mitigating risks and preventing further complications. However, conventional diagnostic methods, such as traditional medical evaluations, often struggle to efficiently process large volumes of medical data, necessitating a more optimal approach. To enhance efficiency, this study employs machine learning to develop a classification model for coronary heart disease risk using Decision Tree and Random Forest algorithms. These models are then compared to determine the most optimal approach. The model is built using the Framingham Heart Study Dataset, consisting of 4,240 records with 15 relevant features. Due to class imbalance in the target variable, the Random Over-Sampling method is applied to improve classification performance. Model evaluation is conducted using a confusion matrix to compare the performance of both algorithms. The results indicate that Random Forest outperforms Decision Tree, achieving an accuracy of 97.64%, precision of 96.02%, recall of 99.29%, and F1-score of 97.63%. In contrast, Decision Tree yields an accuracy of 91.04%, precision of 84.76%, recall of 99.57%, and F1-score of 91.57%. This study suggests that Random Forest is more effective for early detection of coronary heart disease. Therefore, Random Forest-based models hold potential for clinical prediction systems, though further optimization is needed to enhance accuracy and reliability.
Klasifikasi Kelayakan Air Minum Mengkombinasikan Algoritma Random Forest dengan Teknik Optimasi Bayesian Darmawan, Aditya Aqil; D, Ishak Bintang; Astuti, Yani Parti; Winarno, Agus
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.v6i4.7055

Abstract

The quality of clean and safe drinking water is crucial for public health; however, environmental pollution from industrial waste, domestic waste, and urbanization has significantly deteriorated water quality. Manual methods for water quality analysis, such as the Water Quality Index (WQI) and STORET, have limitations in efficiency and accuracy. Therefore, this study proposes a machine learning-based classification system to determine the potability of drinking water more accurately and efficiently. The Water Potability dataset from Kaggle, consisting of 3,276 samples with nine key parameters, was used in this research. Initial analysis showed that most features had a nearly normal distribution, although some variables, such as Solids and Conductivity, exhibited right-skewness due to extreme values. Correlation analysis revealed no significant linear relationships between water quality parameters. The preprocessing stage included missing data imputation using the mean method, normalization, feature engineering, and oversampling with SMOTE to address class imbalance. The machine learning models used in this study include LightGBM, Random Forest, XGBoost, and CatBoost, with model optimization performed using Bayesian Search CV, which improved performance, particularly for Random Forest. Experimental results showed that the optimized Random Forest model achieved the best performance with an accuracy of 85.38%, precision of 85.86%, recall of 85.38%, and an F1-score of 85.37%. However, some misclassifications remained, especially in detecting potable water samples, indicating that ensemble learning methods can be effectively used to evaluate drinking water potability.
Perbandingan Algoritma Naive Bayes dan SVM dalam Analisis Sentimen Pengguna AI di Platform X Firdaus, Noval Dinda; Suryono, Ryan Randy
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.v6i4.7081

Abstract

The rapid development of artificial intelligence (AI) has also had a significant impact on various aspects of life, including interactions on social media platforms such as Platform X. On this platform, users actively discuss various topics related to AI, from the benefits to the challenges it poses. Understanding how the public responds to AI technology is important for developers, researchers, and policy makers in order to design strategies that are more in line with the needs and expectations of the community. This study aims to evaluate and compare the performance of two algorithms commonly used in sentiment analysis, namely Naïve Bayes and Support Vector Machine (SVM). Data were collected through crawling techniques using Google Colab, which resulted in 9,183 entries. Before the analysis was carried out, the data went through a series of initial processing stages, including text cleaning, letter normalization, tokenization, removing frequently used words (stopword removal), and stemming to simplify words. The results of the analysis show that SVM has advantages in terms of accuracy and capability, namely 96% accuracy in handling complex data, while Naïve Bayes is faster in the computational process and efficient for large datasets, resulting in an accuracy of 84% smaller than SVM accuracy. The assessment is carried out using accuracy, precision, recall, and F1-score metrics based on the confusion matrix.
Optimisasi Fungsi Aktivasi pada Arsitektur LeNet untuk Meningkatkan Akurasi Klasifikasi Citra Tumor Otak Harliana, Harliana; Rahadjeng, Indra Riyana; Winanjaya, Riki
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.v6i4.7108

Abstract

Brain hemorrhage is a critical medical condition that requires early and accurate detection to improve patient recovery outcomes. However, conventional image classification methods for brain hemorrhage still face limitations in terms of accuracy and efficiency. To address this issue, this study proposes optimizing the LeNet model using various activation functions—ReLU, Sigmoid, Tanh, and Swish—to enhance classification performance. Several optimization strategies were applied, including data augmentation techniques (flipping, rotation, shearing, rescaling) and fine-tuning of hyperparameters, to improve model generalization. Experimental results indicate that the model utilizing the Swish activation function achieves the most stable overall performance, with an accuracy of 55%, recall of 54%, precision of 54%, F1-score of 54%, and a ROC AUC value of 0.45. Although this performance is still below clinical application standards, the findings serve as an initial step toward exploring activation function optimization in CNN architectures. Further research is needed to significantly enhance classification accuracy and enable clinical viability.
Optimasi Model Particle Swarm Optimization (PSO) Menggunakan SMOTE Untuk Menentukan Penyakit Diabetes Mellitus Putro Utomo, Satrio Allam; Kurniawan, Defri
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.v6i4.7111

Abstract

Diabetes mellitus is a chronic disease that continues to increase globally and can affect various age groups. If not properly managed, this disease can lead to serious complications. In recent years, technological advancements, particularly in the field of machine learning, have significantly contributed to improving the accuracy of diabetes diagnosis and prediction. This study utilizes the Decision Tree algorithm, enhanced by two optimization methods: the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance and Particle Swarm Optimization (PSO) to optimize the model's hyperparameters, thereby improving classification accuracy. The dataset used in this study is the Diabetes Prediction Dataset available on Kaggle, consisting of 100,000 entries. Based on the analysis results, the implementation of data preprocessing and hyperparameter optimization has proven to increase the model's accuracy from 95.21% to 96.52%. Additionally, an evaluation using the confusion matrix shows an improvement in precision from 70.82% to 86.19% and an increase in the F1-score from 72.49% to 78.52%, although there is a slight decrease in recall from 74.24% to 72.11%. These findings demonstrate that a combination of data preprocessing, data balancing, and hyperparameter optimization can significantly enhance the performance of a classification model in detecting diabetes. For future development, it is recommended that the model be tested on other datasets to improve generalizability. Furthermore, exploring additional algorithms such as Random Forest or XGBoost could be beneficial in obtaining more optimal results.
Comparative Study of Machine Learning Models for Temperature Prediction: Analyzing Accuracy, Stability, and Generalization Airlangga, Gregorius
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.v6i4.7114

Abstract

Accurate temperature prediction is crucial for climate monitoring, energy management, and disaster preparedness. This study provides a comparative analysis of various machine learning models, including Random Forest, Gradient Boosting, Histogram-Based Gradient Boosting, XGBoost, Support Vector Regression (SVR), Ridge Regression, and Lasso Regression, to evaluate their predictive accuracy, stability, and generalization capability. The models are assessed using five-fold cross-validation, with the R² metric as the primary evaluation criterion. The results indicate that Random Forest achieves the highest accuracy, with an R² mean of 0.999994, demonstrating its strong ability to model temperature variations. Ridge Regression unexpectedly performs at a similar level, suggesting that the dataset contains strong linear dependencies. Gradient Boosting, Histogram-Based Gradient Boosting, and XGBoost also achieve high accuracy, confirming their effectiveness in capturing complex relationships between meteorological parameters. SVR, while effective, exhibits higher variance, indicating that it may require further tuning for improved consistency. Lasso Regression, with an R² mean of 0.9783, shows the lowest accuracy, confirming that linear models are less suitable for complex meteorological predictions. These findings highlight the superiority of ensemble-based methods in temperature forecasting, reinforcing their stability and adaptability. Future research should explore hybrid models that integrate ensemble techniques with feature engineering optimizations to further enhance predictive performance. This study contributes to the ongoing development of machine learning applications in meteorology, offering insights into model selection for climate-related forecasting tasks.
Hybrid Machine Learning Approaches for Atmospheric CO₂ Prediction: Evaluating Regression and Ensemble Models with Advanced Feature Engineering Airlangga, Gregorius
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.v6i4.7121

Abstract

The accurate prediction of atmospheric CO₂ concentrations is essential for understanding climate change dynamics and developing effective environmental policies. This study evaluates the predictive capabilities of various machine learning models, including ensemble-based regressors such as Random Forest, Gradient Boosting, and XGBoost, alongside traditional regression models such as Support Vector Regression (SVR), Ridge, and Lasso regression. The dataset, derived from meteorological observations, was preprocessed using multiple feature scaling techniques, including StandardScaler, MinMaxScaler, and RobustScaler, followed by feature engineering techniques such as polynomial transformation and Principal Component Analysis (PCA) to enhance predictive accuracy. Model performance was assessed using the coefficient of determination (R²) and cross-validation techniques. The results indicate that tree-based models, including Random Forest and XGBoost, struggled to generalize well, exhibiting negative R² values due to overfitting and an inability to capture the temporal dependencies in CO₂ variations. SVR emerged as the best-performing model, though its predictive power remained limited. Computational complexity analysis revealed that tree-based methods incurred high processing costs, while linear models such as Ridge and Lasso demonstrated lower complexity but failed to capture non-linear dependencies. The study highlights the challenges of CO₂ prediction using conventional machine learning techniques and underscores the need for advanced deep learning approaches, such as hybrid Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, to better capture spatial and temporal dependencies. Future research should explore integrating external environmental factors and leveraging deep learning architectures to improve predictive performance.
Kombinasi Metode Rank Order Centroid dan Additive Ratio Assessment Untuk Pemilihan Aplikasi Manajemen Inventaris Tanniewa, Adam M; Sah, Andrian; Kurniawan, Robi; Prayogo, M Ari
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.6347

Abstract

Selecting an appropriate inventory management application is a challenge for business actors, especially SMEs, due to the variety of features, costs, and complexities offered. Manual selection is often carried out without a clear systematic approach and tends to be influenced by bias, resulting in suboptimal decisions. This study aims to integrate the Rank Order Centroid (ROC) and Additive Ratio Assessment (ARAS) approaches in developing a Decision Support System (DSS) to determine the best inventory management application. ROC is used to assign proportional weights to criteria based on priority ranking, while ARAS evaluates alternatives using these weights and relative utility values against the ideal solution. The developed system includes key features such as data management for criteria, alternatives, and values, as well as the ability to generate recommendations through alternative ranking. Based on a case study, the best alternative identified is Sortly: Inventory Simplified, with the highest utility score of 0.8627, followed by Housebook - Home Inventory (0.8528), inFlow Inventory (0.8336), and Inventory Stock Tracker (0.7056). Usability testing showed an average user acceptance rate of 91%, categorized as "Excellent". The main contribution of this research is the implementation of a practical and efficient combination of ROC and ARAS for selecting inventory management applications. The findings can be adopted by businesses to support more accurate and efficient decision-making.
Comparative Analysis of CNN and SVM Algorithms for Pneumonia Classification from Chest X-Ray Images Bela, Ar Ainun; Lhaksmana, Kemas M
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7335

Abstract

Pneumonia significantly threatens human health, especially in children and the elderly. Diagnosing pneumonia using chest radiographs is time consuming and requires expert interpretation. This study proposes a comparative analysis of two algorithm models, namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN), in CNN algorithm, it specifically uses DenseNet121 and InterceptionV3 architectures for the classification of chest X-ray images in pneumonia and normal categories. The methods used include data preprocessing with normalization and augmentation. The dataset is split into training and testing subsets, and implementation of SVM and CNN algorithms for classification. Kaggle provided the dataset for this study, comprising 5,863 chest X-ray images. Metrics such as accuracy, precision, recall, and F1-score calculated from the confusion matrix were used to evaluate the model’s effectiveness. The test findings show that the DenseNet121 model has the best performance among the three models, with an accuracy, recall, and F1-score of 94%. The InceptionV3 model achieved 89% in accuracy, recall, and F1-score, which is higher than DenseNet121. Meanwhile, the SVM model showed the lowest performance with an accuracy of 81%, precision of 85%, recall of 81%, and F1-score of 79%. These outcomes signifies that Convolutional Neural Network (CNN) architectures, particularly DenseNet121, have superior capabilities in extracting complex features from chest X-ray images and show great potential to be applied in automatic and accurate pneumonia detection systems.

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