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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 889 Documents
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Analisis Perbandingan Algoritma Naïve Bayes dan Random Forest Dalam Klasifikasi Penyakit Stroke Pada Puskesmas Virgiawan, Iwan; Erizal, Erizal
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.v6i4.6771

Abstract

One of the main reasons people become disabled or die is because of a stroke. The key to swift and effective therapy is an early diagnosis. This research examines the relative performance of the Naïve Bayes and Random Forest algorithms in identifying stroke cases using data collected from patients at the Cipayung Health Center. Age, gender, BMI, smoking status, hypertension, and other physical and mental health issues are some of the characteristics represented in the 644 samples used in the study. Collecting data, cleaning it up, and then evaluating the model using metrics like recall, precision, and accuracy are all part of the research process. With a 92% accuracy rate, the Random Forest algorithm outperformed Naïve Bayes (87% accuracy rate), according to the data. Medical professionals may use these results as a guide to improve stroke detection, which in turn accelerates treatment and lessens the likelihood of consequences. The findings of this study also pave the way for future research into machine learning algorithms.
Prediksi Harga Saham Menggunakan Model Multivariate Long Short-Term Memories Mahulete, Ebenhaezer Yohanes Abdeel; Hendry, Hendry
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.v7i2.7978

Abstract

This study aims to develop and evaluate a stock price prediction model for Bank Central Asia (BBCA) using a multivariate Long Short Term Memory (LSTM) approach. The model utilizes four key variables from historical stock data open, high, low, and close prices and is compared to a univariate model that uses only the closing price as input. The research process includes data preprocessing, LSTM architecture design, model training over 200 epochs, and performance evaluation using MAE, RMSE, and MAPE metrics. The results demonstrate that the multivariate LSTM model provides higher predictive accuracy, achieving a MAPE of 2.41%, outperforming the univariate model which recorded 2.71%. Moreover, the multivariate model shows better stability across validation and test data, and greater adaptability in capturing market dynamics. Prediction result visualizations support these findings, with the multivariate model producing more consistent forecasts that closely follow actual data. These results suggest that integrating OHLC variables enhances prediction accuracy and model reliability. This study contributes to the advancement of stock price prediction systems based on deep learning and serves as a valuable reference for investors and decision-makers in designing more data-driven investment strategies.
Perbandingan Metode Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM) untuk Prediksi Curah Hujan Hermawan, Taufan; Zuliarso, Eri
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.v7i2.8099

Abstract

The increase in extreme rainfall intensity due to climate change has caused Batang Regency to become a hydrometeorological disaster-prone area. This research aims to build an day rainfall prediction model using Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) based on BMKG historical data. The model is evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The results show that LSTM has higher accuracy than RNN, with an RMSE: 0.1036 | MAE: 0.0730. Meanwhile, RNN obtained an RMSE: 0.1035 | MAE: 0.0763. LSTM is also more stable in predicting temperature, direction, and wind speed variables. These findings show that LSTM is more effective for weather time series data and can be used as a basis for developing data-based disaster early warning systems in local areas.
Optimasi Algoritma SVM dengan Teknik SMOTE dan Tuning Parameter pada Klasifikasi Balita Stunting Muttaqin, Muhammad Al Ghorizmi; Trisnapradika, Gustina Alfa
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8330

Abstract

Stunting in toddlers is a chronic nutritional problem that has long-term impacts on human resource quality, including cognitive development and vulnerability to diseases. Brebes Regency is one of the priority areas for stunting management in Indonesia. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm in classifying stunting status among toddlers by addressing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and parameter tuning. A total of 9,598 anthropometric samples collected from several community health center in Brebes were processed through stages of data cleaning, label encoding, outlier handling, standardization, and class splitting, and then divided into training data (80%) and testing data (20%). Two models were compared: the baseline SVM model and the optimized SVM model, which integrates SMOTE and parameter tuning through GridSearchCV. The results showed that the baseline model achieved an accuracy of 98.31%, but the recall for the stunting class was only 89.19%. After applying SMOTE and parameter tuning, the model’s performance improved, achieving an accuracy of 99.78% and a recall for the stunting class of 98.46%. This improvement demonstrates that the use of SMOTE and parameter tuning is highly effective in enhancing the model’s sensitivity toward the minority class. Therefore, this study shows that a comprehensive optimization approach can effectively support early detection of stunting, making it a valuable tool for more targeted health intervention planning.
Klasifikasi Tingkat Kemiskinan Kabupaten/Kota Di Indonesia Tahun 2023 Menggunakan Logistic Regression Hafizhah, Hafizhah; Yudhistira, Aditia
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.v7i2.8343

Abstract

Poverty remains a major challenge in Indonesia, with a national rate reaching 9.36 percent in 2023, despite significant disparities between rural (12.22 percent) and urban (7.29 percent) areas, as well as the influence of outlier that can distort classification analysis at the district/city level. This study aims to classify poverty levels in 514 districts/cities into high (above 9.36 percent) and low (below or equal to 9.36 percent) categories using logistic regression, and to compare the model performance on original data with outlier-adjusted data through Z-score and interquartile range (IQR) methods. The methods applied include the collection of secondary data from the Central Statistics Agency and the Ministry of Home Affairs, exploratory data analysis to identify patterns and correlations (such as the negative correlation between per capita expenditure and poverty), and pre-processing by capping outlier. logistic regression training with hyperparameter tuning through grid search and cross-validation, as well as evaluation using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC) metrics. The predictor variables include gross domestic product (GDP), life expectancy, average length of schooling, and per capita expenditure. The results show consistent performance across techniques, with test accuracy reaching 77.67 percent, ROC-AUC of 0.8566, macro precision of 77.90 percent, macro recall of 77.79 percent, and macro F1-score of 77.66 percent. Outlier handling reduced the poverty rate standard deviation from 6.45 to 5.99 (Z-score) and 5.57 (IQR), without changing the distribution of binary labels (266 low, 248 high). The model coefficients confirm the dominant negative influence of per capita expenditure (-1.067), supporting targeted policies to reduce regional disparities.
Decision Tree Classification for Reducing Alert Fatigue in Patient Monitoring Systems Herfiani, Kheisya Talitha; Nurhindarto, Aris; Alzami, Farrikh; Budi, Setyo; Megantara, Rama Aria; Soeleman, M Arief; Handoko, L Budi; Rofiani, Rofiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8414

Abstract

The development of information technology in healthcare opens new opportunities to improve continuous patient monitoring. A major challenge is alert fatigue, where medical personnel are overwhelmed by excessive notifications, reducing concentration, work efficiency, and potentially compromising patient safety. This study presents a proof-of-concept application of the Decision Tree algorithm to analyze alert triggering factors in patient monitoring systems. The dataset is a synthetic health monitoring dataset from Kaggle, containing 10,000 entries with vital parameters including blood pressure, heart rate, oxygen saturation, and glucose levels, designed with deterministic logical relationships between threshold indicators and alert outcomes. The imbalanced dataset (73.67% alert triggered, 26.33% no alert) was intentionally not processed using imbalanced learning techniques to demonstrate Decision Tree's capability in processing structured health data and producing interpretable classifications. The research methodology included data preprocessing, exploratory data analysis, data splitting (90% training, 10% testing), GridSearchCV optimization, and performance evaluation. Results showed perfect metrics (100% accuracy, precision, recall, F1-score), reflecting the deterministic nature of the synthetic dataset rather than real-world clinical complexity. Feature importance analysis identified blood pressure as the most dominant variable, followed by heart rate and glucose levels. This study demonstrates Decision Tree's interpretability and feature importance analysis capabilities in health data contexts, establishing a methodological framework that requires validation on real clinical Electronic Health Record (EHR) data for practical application in reducing alert fatigue and supporting informed clinical decisions.
Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women Nazella, Desvita Dian; Hadi, Heru Pramono; Al Zami, Farrikh; Ashari, Ayu; Kusumawati, Yupie; Suharnawi, Suharnawi; Megantara, Rama Aria; Naufal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8415

Abstract

Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values ​​indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
Deteksi Komentar dan Analisis Sentimen Promosi Judi Online pada Youtube Menggunakan IndoBERT dan XGBoost Putri, Naila Raihana; Kurniawan, Dedy; Tania, Ken Ditha
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8421

Abstract

YouTube, as a highly interactive platform, has become a medium for online gambling promotions, raising legal issues under the Electronic Information and Transactions (ITE) Law and social risks, particularly for adolescents. This study aims to analyse public responses to gambling-related comments and to develop an automatic detection system using Natural Language Processing (NLP). The research follows the Knowledge Discovery in Databases (KDD) stages, including web scraping, preprocessing, text transformation, model training, and evaluation. Sentiment analysis was performed on 999 comments labelled positive, negative, and neutral. Detection of promotional content was tested using IndoBERT and TF-IDF-based XGBoost, with 587 training samples and 885 external testing samples at an 80:20 ratio. The results show that the majority of comments (52.65%) are positive with a fairly high average confidence score (0.914), indicating public support for the eradication of online gambling. Meanwhile, negative comments (24.72%) with a confidence score of 0.888 generally contained criticism of the rampant practice of gambling promotion or YouTube's weak moderation system. For automatic detection, IndoBERT achieved superior performance with 0.94 accuracy and F1-score and only 10 misclassifications, significantly outperforming XGBoost, which reached 0.73 accuracy with 47 errors. This study highlights the effectiveness of transformer-based models in detecting gambling promotions while also indicating strong public support for eradication efforts. These findings provide an empirical foundation for advancing research on adaptive automated moderation systems capable of identifying concealed patterns of illicit content in digital platforms, particularly in the detection of online gambling promotional comments within the YouTube ecosystem.
Model Prediksi Multikomoditas (Padi, Jagung, dan Umbi-Umbian) Berbasis Faktor Cuaca Menggunakan Algoritma Naïve Bayes Nisa, Aurellia Ainun; Prasetya, M Riko Anshori; Nurhaeni, Nurhaeni; Subandi, Subandi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8423

Abstract

The agricultural sector plays a strategic role in supporting Indonesia’s food security and national economy. Agricultural productivity in South Kalimantan Province, particularly in Barito Kuala Regency, is strongly influenced by climatic dynamics such as temperature variation, rainfall, and humidity. This study develops a crop yield prediction model for major food commodities (rice, corn, and tubers) based on weather factors using the Naïve Bayes algorithm as a Decision Support System (DSS) to mitigate crop failure risks. The research data were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) and the Central Bureau of Statistics (BPS) of Barito Kuala Regency for the period 2018–2023, covering temperature, rainfall, humidity, sunlight duration, and yield production. The research stages include data preprocessing (cleaning, missing value imputation, augmentation, and labeling), machine learning modeling, and performance evaluation using accuracy and weighted F1-score metrics. The experimental results show that the Naïve Bayes model can classify crop yield categories (high, medium, low) with an accuracy of 90% and a weighted F1-score of 0.89. These results demonstrate stable and consistent performance across various climatic conditions. The main advantage of this study lies in the integration of local weather data with a lightweight machine learning model that is computationally efficient and easily implemented in regional agricultural prediction systems. This research provides a tangible contribution to strengthening food security and data-driven agricultural risk management in tropical humid regions such as South Kalimantan.
Komparasi Klasterisasi Data Historis Gempa Bumi Menggunakan DBSCAN, K-Means, dan Agglomerative Clustering Lakeisyah, Eka Therina; Tania, Ken Ditha; Afrina, Mira
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8426

Abstract

Earthquakes are one of the natural disasters that are prone to occur on the island of Sumatera and pose a serious challenge because they can have a devastating impact on human life, such as loss of life, material losses, and environmental damage. Therefore, earthquake hazard zone mapping is needed to provide information about the potential and history of disasters and is an important tool for disaster mitigation efforts. This study aims to map earthquake vulnerability in Sumatra by comparing three clustering algorithms, namely DBSCAN, K-Means, and Agglomerative Clustering, based on earthquake data in Sumatra from 1973 to 2023. This is to find the best algorithm so that it can provide recommendations for appropriate earthquake risk mitigation strategies. The results show that the K-Means algorithm is the best because it obtained the highest Silhouette Coefficient value, namely 0.3948 with a total of 3 clusters. It is hoped that this research can improve understanding of earthquake hazard zones on the island of Sumatra and provide practical contributions in the form of mitigation strategy recommendations tailored to the characteristics of each cluster to support the application of this research for the government and local communities.

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