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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.
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Articles 62 Documents
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Search results for , issue "Vol 7 No 3 (2025): December 2025" : 62 Documents clear
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.
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.
Implementasi Arsitektur MobileNetV2 dengan Metode Transfer Learning untuk Identifikasi Objek Wisata Religi Wijaya, Nabilah Putri; Christioko, Bernadus Very
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.v7i3.8447

Abstract

Semarang has five religious tourism icons that represent pluralism, but their promotion is still conventional and not yet optimal in the digital era. This problem hinders its tourism potential in reaching a wider audience. This study aims to develop an accurate and efficient automatic image identification model as a modern solution to these promotional challenges. The method implemented is deep learning using the MobileNetV2 CNN architecture through a transfer learning approach. MobileNetV2 was chosen because it is superior in computational efficiency on resource-constrained devices compared to other models like EfficientNet. The model was trained and validated using a dataset consisting of a total of 7,500 images comprising five classes of religious tourist attractions, namely Grand Mosque of Central Java, Blenduk Church, Buddhagaya Watugong Temple, Pura Agung Giri Natha Temple, and Sam Poo Kong Temple. The dataset was divided into 70% training data, 15% validation data, and 15% test data. The evaluation results on the test data showed satisfactory performance, where the developed model achieved an overall accuracy of 98%, with a macro average F1-Score of 0.98. This figure indicates high and balanced performance across all classes. Individual testing also proved the model's ability to recognize relevant images with high confidence and reject images outside the class. This success shows that the implementation of MobileNetV2 is effective and can be basic technology for development of innovative digital tourism applications in Semarang.
Perbandingan Random Forest dan XGBoost Untuk Prediksi Penjualan Produk E-Commerce Rumah Madu Hayatunnisa, Destaria; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
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.v7i3.8491

Abstract

Inventory management is one of the main challenges for small and medium enterprises (SMEs), including Rumah Madu in Bandar Lampung, where honey stock levels are often determined based on estimation rather than precise calculation. This study aims to analyze and compare the performance of the Random Forest and XGBoost algorithms in predicting honey sales to achieve more measurable stock management. The dataset consists of 1,699 honey sales transactions that have undergone cleaning, feature transformation, and standardization processes. The variables used include honey type, unit price, day, month, holiday status, and promotion indicators. Modeling was conducted using a time-series split approach, where historical data served as the training set and recent data as the testing set. The evaluation results show that Random Forest achieved an MAE of 24.35, RMSE of 29.04, and R² of -0.9685, while XGBoost achieved an MAE of 25.50, RMSE of 30.58, and R² of -1.1825. The negative R² values indicate that both models were unable to explain data variation optimally, with performance falling below a simple baseline. Nevertheless, the feature importance analysis revealed that unit price and honey type were the dominant factors influencing sales. This study highlights the need for further model development through parameter optimization and improved data quality to enhance prediction accuracy.
Optimasi Hyperparameter Gaussian Naive Bayes Untuk Prediksi Risiko Stroke Pada Data Tidak Seimbang Nida, Khoirun; Mahenra, Ridwan; Susanto, Erliyan Redi
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.v7i3.8497

Abstract

Stroke is a serious disease with global impact that requires high-accuracy early detection. Significant difficulties in designing machine learning-based predictive models arise due to disproportionate data conditions (imbalanced datasets). This occurs because the number of stroke cases (minority class) is very small compared to non-stroke cases. This imbalanced data situation often causes models to become biased and potentially produce high false negative rates, which is very risky in a clinical setting. This study focuses on improving the sensitivity of the Gaussian Naive Bayes (GNB) model through hyperparameter optimization and classification threshold adjustment. The research process included data preprocessing, stratified dataset division (70% training and 30% testing), feature scaling, var_smoothing parameter optimization using GridSearchCV, and threshold adjustment to maximize the Recall value. The results showed that the standard GNB model only achieved a Recall value of 0.4400. However, after var_smoothing optimization (1.00×10⁻¹⁰) and threshold adjustment to 0.0100, the Recall value increased significantly to 0.8000. This increase was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). This improvement was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). The high Recall (0.8000) indicates that the model is better for mass screening (early detection phase), although it must be balanced with further diagnostic processes due to low precision. This high Recall value confirms the model's success in minimizing False Negatives, which is a top priority in stroke risk prediction cases.
Analisis Respon Publik Terhadap Tren Penggabungan Foto Gemini AI Menggunakan Naive Bayes Afiani, Nanda; Mahenra, Ridwan
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.v7i3.8504

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has brought numerous innovations to the digital world, one of which is Gemini AI — an application capable of automatically merging photos based on user instructions. This phenomenon has gone viral on the TikTok platform and has sparked diverse public reactions, ranging from admiration for its visual results to concerns about ethical issues and the potential misuse of deepfake technology. This study aims to analyze public sentiment toward the trend of Gemini AI photo merging on TikTok using a sentiment analysis method based on the Naïve Bayes algorithm. Data were collected through a web scraping technique using the Apify platform, resulting in 5,061 user comments. The data processing stages included text preprocessing, TF-IDF transformation, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that neutral sentiment dominates (4,059 comments), followed by positive (745 comments) and negative (257 comments). The dominance of neutral sentiment occurs because most user comments are informative or descriptive, expressing ordinary responses without strong emotional tones, rather than showing indifference to ethical concerns. The Naïve Bayes model demonstrated good performance with an accuracy of 85.72%, precision of 87.84%, recall of 85.72%, and F1-score of 81.95% through 5-fold cross-validation. These findings confirm that the Naïve Bayes algorithm is effective for classifying public opinion toward generative AI technologies. Overall, this study contributes to a deeper understanding of public perception of AI innovations in the creative digital domain and their social implications on social media platforms.

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