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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 1,045 Documents
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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.

Page 105 of 105 | Total Record : 1045

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