cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
-
Journal Mail Official
jurnal.bits@gmail.com
Editorial Address
-
Location
Kota medan,
Sumatera utara
INDONESIA
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 1,083 Documents
 103   104   105   106   107 
Analisis Sentimen dan Evolusi Topik terhadap Program Makan Bergizi Gratis Menggunakan IndoBERT dan cDTM Muhammad Hamzah Fauzi; Ronsen Purba
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10085

Abstract

This study aims to analyze public sentiment and the development of discussion topics related to the MBG program. Sentiment analysis was conducted using the IndoBERT model, while evolution topic analysis used the Continuous-Time Dynamic Topic Model (cDTM). The evaluation results showed that the IndoBERT model was able to classify sentiment with an accuracy value of 92.5% and an F1-score of 0.924. Integration between IndoBERT and cDTM showed a dominance of negative sentiment, especially in topics related to program implementation, while positive sentiment appeared more often in topics related to health and nutrition. The integration of sentiment and temporal topic analysis provides a more comprehensive understanding of the dynamics of public opinion regarding the MBG program.
Optimisasi Menu Balita Berdasarkan Gizi Harian Menggunakan XGBoost, Genetic Algorithm dan Particle Swarm Optimization Evyra Rizki Safitri; Ahmad Muklason
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10150

Abstract

Toddler menu planning requires a systematic approach, as menu recommendations must account for nutritional adequacy, the child’s nutritional status, allergy restrictions, and feasible menu combinations. This study develops an allergy-aware decision-support workflow by integrating XGBoost as a menu-class classifier and Genetic Algorithm and Particle Swarm Optimization for daily toddler menu optimization. This approach is chosen because conventional methods that directly apply population-level dietary references values do not adequately capture individual differences in nutritional status and usually do not treat food allergy as a hard constraint during menu generation. Combined of XGBoost and metaheuristics have instantly map non-linear patterns of a toddler’s physical condition while efficiently exploring millions of complex food combinations a task computationally unachievable by conventional manual methods. In the proposed workflow, the XGBoost output in the form of menu class (catch-up nutrition, balanced nutrition, or energy control) is mapped into personalized nutrient targets for energy, protein, fat, and carbohydrates. These targets become the main inputs to the fitness function and guide the allergy-safe candidate filtering step before optimization. The XGBoost model achieved an average accuracy of 96,4% and a weighted F1-score of 96,38% in cross-validation testing, along with an 87,18% accuracy in the hold-out test. Across six experimental categories, both GA and PSO successfully generated menus closely matching the nutritional targets, with RDI scores generally ranging from 94,6% to 99,7%. GA achieved lower nutritional deviation in the catch-up and balanced nutrition scenarios, whereas PSO performed better in energy-control scenarios and consistently required significantly less computation time (averaging 17,74 seconds compared to 79,78 seconds for GA). These findings indicate that GA is preferable when nutritional precision is prioritized, whereas PSO is more suitable for real-time, time-constrained system deployments. The main contributions of this study are the creation of an end-to-end computational framework that adapts to clinical conditions and toddler allergy safe, and the provision of empirical evidence for selecting metaheuristic algorithm for personalized nutrition recommendation systems.
Evaluasi Komparatif Algoritma Decision Tree, Random Forest, dan XGBoost untuk Software Defect Prediction Menggunakan Dataset NASA Software Metrics Okky Prasetia; Syaeful Machfud
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10268

Abstract

Software Defect Prediction (SDP) has become an important approach for identifying software modules that are likely to contain defects during the early stages of software development. However, the performance of prediction algorithms remains highly dependent on dataset characteristics, and no single algorithm has consistently demonstrated superior performance across different datasets. In addition, the use of synthetic datasets in SDP research still requires empirical validation to ensure that their characteristics remain representative of benchmark datasets such as the NASA Metric Data Program (NASA MDP). Therefore, this study aims to compare the performance of Decision Tree, Random Forest, and XGBoost using the Playground Series Season 3 Episode 23 dataset, a synthetic dataset developed based on the characteristics of the NASA MDP dataset. Prior to model training, the dataset underwent preprocessing, including missing value imputation, label encoding, and feature standardization. Model performance was evaluated using 10-fold stratified cross-validation with Accuracy, F1-Score, and AUC-ROC as the primary evaluation metrics. The experimental results indicate that ensemble learning methods achieved competitive performance compared with the single-classifier approach. Random Forest achieved the highest Accuracy of 0.8144, while XGBoost obtained the highest AUC-ROC score of 0.7929, indicating strong capability in distinguishing between defective and non-defective software modules on the evaluated dataset. Furthermore, feature importance analysis identified Lines of Code (LOC), Cyclomatic Complexity, Halstead Volume, IOCode, and branchCount as the most influential factors affecting software defect prediction. Based on the experimental results obtained from the selected dataset, the ensemble learning approach demonstrated competitive predictive performance and may be considered a promising alternative for developing Software Defect Prediction models. Nevertheless, the selection of the most appropriate algorithm should remain dependent on dataset characteristics and specific implementation requirements
Perbandingan Algoritma Random Forest, XGBoost dan SVM Pada Klasifikasi Penyakit Demam Berdarah Dengue (DBD) Vionando Wira Mada; Damayanti Damayanti
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.v7i3.8591

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of the Aedes aegypti mosquito. This disease remains a serious health problem in Indonesia because it can cause severe complications and even death if not treated immediately. This study aims to classify the severity of DHF based on patient clinical data using three machine learning algorithms, namely Random Forest, XGBoost, and Support Vector Machine (SVM). The dataset used consists of 5,000 patient data that includes various vital parameters and laboratory test results such as age, gender, hemoglobin level, white blood cell count (WBC), leukocyte count (Differential Count), red blood cell parameters (RBC Panel), platelet count, and Platelet Distribution Width (PDW). The research stages include data cleaning, handling missing values, coding categorical variables, data normalization, and dividing the dataset into 80% training data and 20% test data. Evaluation is carried out using accuracy, precision, sensitivity (recall), and F1 score metrics. The results showed that the XGBoost algorithm performed best with an accuracy of 87.72%, followed by Random Forest (86.21%) and SVM (84.78%). Based on these findings, XGBoost was deemed most effective in classifying dengue fever. Further research is recommended to use a larger dataset and perform hyperparameter optimization to improve the accuracy and reliability of the resulting model.
Prediksi Harga Ikan Koi Berbasis Analisis Morfometrik Menggunakan Algoritma Random Forest Regressor Sepyan Purnama Kristanto; Lutfi Hakim; Dianni Yusuf; Moh. Erdda Habiby
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.v7i3.8617

Abstract

The lack of clarity in koi fish (Cyprinus rubrofuscus) pricing within the Indonesian ornamental fish market, driven by subjective valuation practices and information asymmetry, remains a primary challenge creating significant price disparities. The primary objective of this research is to address this challenge by designing and evaluating an objective predictive model. As its main contribution, this study develops the first Random Forest Regressor (RFR) based price prediction model. This model is specifically designed to handle complex non-linear relationships by integrating three main feature groups: morphometric parameters (species, size) and phenotypic characteristics (color patterns). Using a dataset of 800 samples collected from koi breeding centers in East Java, the optimized model achieved solid predictive performance, indicated by a Coefficient of Determination (R²) of 0.85 and a Root Mean Squared Error (RMSE) of IDR 265,000. Feature importance analysis revealed the significant finding that fish variety (one of the three analyzed feature groups) is the most dominant price determinant (62% contribution). The model quantitatively validates that rare varieties (such as Tancho/Utsuri) are valued 3 to 5 times higher than common varieties of the same size. Comparative analysis with traditional linear regression models (R² 0.61) also demonstrated the RFR's superiority in capturing complex morphological feature interactions. A critical finding indicates that the model's accuracy, already strong in the non-premium segment, can be improved by up to 15% through the quantification of qualitative aesthetic attributes (such as kiwa or gradation) using computer vision. The implementation of this model has the potential to standardize koi valuation, reduce market information asymmetry by up to 40%, and serve as a foundation for the development of the first AI-based price recommendation system in Indonesia's aquaculture industry.
Klasifikasi Status Gizi Bayi sebagai Upaya Deteksi Dini Masalah Gizi Menggunakan Algoritma Naive Bayes Muhammad Rafif AR; Fathoni Fathoni; Ken Ditha Tania; Allsela Meiriza; Ali Ibrahim
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.9490

Abstract

The classification of infant nutritional status remains a significant challenge in the utilization of health data, particularly in developing predictive models that are accurate and reliable for supporting decision-making at the primary healthcare level. This study aims to implement the Naïve Bayes algorithm to classify infant nutritional status based on anthropometric indicators, namely weight-for-age (W/A) and height-for-age (H/A). The data used in this study were obtained from Posyandu activity reports at Citra Medika Public Health Center covering the period from 2023 to 2025, with a total of 10,654 infant records. The class distribution in the dataset includes normal, well-nourished, undernourished, severely undernourished, overnourished, and at risk of overnutrition categories. Since the class proportions are relatively balanced, no oversampling technique was applied. The research process involved several stages, including data cleaning, category normalization, class label transformation, and dataset splitting using stratified sampling with a composition of 80% training data and 20% testing data. The evaluation results indicate that the Naïve Bayes model achieved an accuracy of 82%. The precision values were 0.92 for the normal class, 0.39 for the malnutrition class, and 0.14 for the overweight class. The recall values were 0.89 for the normal class, 0.63 for the malnutrition class, and 0.08 for the overweight class. Meanwhile, the F1-scores were 0.83 for the normal class, 0.48 for the malnutrition class, and 0.11 for the overweight class. These findings suggest that the model demonstrates fairly good performance in classifying infant nutritional status based on anthropometric data. Therefore, the Naïve Bayes algorithm can be considered effective for classifying infant nutritional conditions using W/A and H/A indicators. This study is expected to contribute to the development of data-driven systems to enhance analytical quality and support decision-making in primary healthcare services.
Analisis Disparitas Data Kebencanaan Indonesia sebagai Dasar Mitigasi Berbasis K-Means Vanisa Amalia Putri; Fathoni Fathoni; Yadi Utama
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.9667

Abstract

Indonesia experiences a high frequency of disasters; however, differences in characteristics between national and global datasets create challenges for consistent disaster analysis. Previous studies generally rely on a single data source, limiting the ability to capture disparities in data representation comprehensively. This study aims to analyze disparities in disaster data representation and identify regional disaster patterns using a data mining approach. A Disaster Severity Index was constructed using Min-Max normalization, and the K-Means algorithm was applied to cluster regions based on disaster index and event frequency. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, while a global dataset was used for comparison. The results indicate that the optimal model is achieved at K=4 with a Silhouette Score of 0.610883, indicating good cluster separation. Most regions exhibit moderate disaster characteristics, while a small number show extreme patterns with disproportionate relationships between frequency and severity. Differences between national and global datasets suggest variations in reporting mechanisms and data coverage. These findings demonstrate that relying on a single data source may lead to biased interpretations, highlighting the importance of multi-source data integration to improve the accuracy of disaster analysis.
Komparasi Algoritma Extreme Gradient Boosting, Support Vector Machine, dan Random Forest Klasifikasi Penyakit Jantung Fadil Arrosyid Fayyadh; Damayanti Damayanti
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9833

Abstract

Heart disease is one of the leading causes of death worldwide and requires early detection to reduce the risk of fatality. This study aims to analyze and compare the performance of three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and XGBoost, in heart disease classification. The dataset was obtained from the Kaggle platform and consists of variables such as BMI, smoking habits, physical activity, general health conditions, and other health-related attributes, with HeartDisease as the target variable. The research stages include data preprocessing, categorical data encoding, data normalization, and data splitting using the 80:20 train-test split method. The results show that SVM and XGBoost achieved an accuracy of 0.90, while Random Forest achieved 0.89. Based on other evaluation metrics, XGBoost demonstrated the best performance with a precision of 0.88, recall of 0.90, and F1-score of 0.88. Feature importance analysis also revealed that several health factors significantly influence the risk of heart disease. The contribution of this study lies in comparing the performance of machine learning algorithms for heart disease classification and identifying influential health factors related to heart disease risk. The findings are expected to serve as a reference for developing decision support systems to assist early heart disease detection more accurately and efficiently.
Studi Komparasi IndoBERT dan IndoBERTweet dengan Full Fine Tuning dan LoRA untuk Klasifikasi Emosi pada Tweet Bertopik GERD Luthfia Jayyida Ainaya Fatiha; Junta Zeniarja
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10039

Abstract

This study compares IndoBERT and IndoBERTweet for emotion classification on Indonesian tweets related to Gastroesophageal Reflux Disease (GERD). The term GERD-related tweets is used because the data were collected from social media X, and the clinical status of each user cannot be verified. The initial dataset contained 1,389 records. After validation, text cleaning, empty-text removal, label filtering, and deduplication, 1,343 tweets remained and were manually assigned into five emotion classes: angry, fear, joy, sad, and neutral. The experiments were conducted in Python on Google Colab with a Tesla T4 GPU using Pandas, PyTorch, Hugging Face Transformers, PEFT, scikit-learn, Matplotlib, and Seaborn. The models were evaluated using Stratified 5-Fold Cross Validation, lexical substitution-based data augmentation on the training data of each fold, early stopping, and Focal Loss to address class imbalance. The results show that full fine-tuning achieved the best performance. IndoBERTweet obtained the highest accuracy of 0.5703 and macro F1-score of 0.5629, while IndoBERT achieved an accuracy of 0.5145 and a macro F1-score of 0.5034. In contrast, IndoBERT + LoRA only achieved a macro F1-score of 0.2412, and IndoBERTweet + LoRA achieved 0.2227. These findings indicate that LoRA was not effective for this small and imbalanced health-emotion dataset, although it trained less than 1% of the model parameters. Parameter efficiency therefore did not translate into better classification performance in informal and semantically overlapping social media text.
Performance Evaluation of Naive Bayes, SVM, LSTM, and IndoBERT for Fashion Review Sentiment Classification Muhammad Iqbal Ismail; Denies Susanto; Indar Riyanto; Melinne Maldini Rosady
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10357

Abstract

The rapid growth of e-commerce has increased the volume of customer reviews, making it difficult for fashion businesses to manually identify customer sentiment, product concerns, and satisfaction drivers. This study aims to evaluate the performance of Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and IndoBERT in classifying sentiment polarity in Indonesian fashion product reviews, while also identifying topic-level sentiment patterns using Latent Dirichlet Allocation (LDA). The dataset consisted of 23,487 Shopee Indonesia fashion reviews collected during the 2025 observation period. After data cleaning, duplicate removal, text normalization, tokenization, stopword removal, and lemmatization, 22,961 valid reviews were retained and categorized into positive, neutral, and negative classes using rating-based labeling. To reduce majority-class bias, stratified data splitting and class-weighted learning were applied, while five-fold cross-validation was used to evaluate model stability. The results show that IndoBERT achieved the highest performance with an accuracy of 93.41%, precision of 92.87%, recall of 92.15%, and F1-score of 92.51%, outperforming LSTM with 90.80% accuracy, SVM with 88.90%, and Naive Bayes with 84.60%. The findings indicate that transformer-based contextual representation is more effective in handling noisy Indonesian fashion review text, including informal expressions, abbreviations, and context-dependent sentiment. Topic-based analysis further revealed that positive sentiment was mainly associated with product quality, material comfort, design, and value, while negative sentiment was driven by sizing mismatch, product inconsistency, delivery delay, and customer service issues. This study contributes to sentiment classification research by providing a comparative evaluation of machine learning, deep learning, and transformer-based models, and offers practical insights for improving fashion e-commerce product communication, customer satisfaction, and data-driven digital marketing decisions.

Page 105 of 109 | Total Record : 1083

 103   104   105   106   107 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 8 No 2 (2026): September 2026 Vol 8 No 1 (2026): June 2026 Vol 7 No 4 (2026): March 2026 Vol 7 No 3 (2025): December 2025 Vol 7 No 2 (2025): September 2025 Vol 7 No 1 (2025): June (2025) Vol 6 No 4 (2025): March 2025 Vol 6 No 3 (2024): December 2024 Vol 6 No 2 (2024): September 2024 Vol 6 No 1 (2024): June 2024 Vol 5 No 4 (2024): March 2024 Vol 5 No 3 (2023): December 2023 Vol 5 No 2 (2023): September 2023 Vol 5 No 1 (2023): June 2023 Vol 4 No 4 (2023): March 2023 Vol 4 No 3 (2022): December 2022 Vol 4 No 2 (2022): September 2022 Vol 4 No 1 (2022): June 2022 Vol 3 No 4 (2022): March 2022 Vol 3 No 3 (2021): December 2021 Vol 3 No 2 (2021): September 2021 Vol 3 No 1 (2021): June 2021 Vol 2 No 2 (2020): December 2020 Vol 2 No 1 (2020): June 2020 Vol 1 No 2 (2019): December 2019 Vol 1 No 1 (2019): June 2019 Vol 1 No 1 (2019): Edisi Juni More Issue