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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 1,045 Documents
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Segmentasi Pelanggan E-Commerce Berbasis Integrasi Text Mining dan RFM untuk Deteksi Dini Churn Violin Juneyla Nandita; Juseia Wulandari; Apriyadi Apriyadi; Ali Ibrahim; Fathoni Fathoni
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.v8i1.9687

Abstract

The growth of transactions on e-commerce platforms generates a massive volume of unstructured customer review data. However, traditional Customer Relationship Management (CRM) models such as RFM often only focus on quantitative transaction data and ignore the emotional dimension contained in customer reviews. This study aims to analyze the relationship between purchase frequency and customer comment polarity through the integration of Text Mining and CRM Analytics approaches. The novelty offered is the development of a hybrid method that combines Lexicon Refinement-based sentiment extraction with the Random Forest algorithm to overcome rating bias in global e-commerce platform data (Kaggle). The proposed method includes the use of Natural Language Processing (NLP) techniques, topic modeling based on Latent Dirichlet Allocation (LDA), and sentiment analysis to extract polarity scores. The test results show that the initial lexicon model has limitations with an accuracy of 52.14% due to noise in neutral reviews (3-star rating). However, after optimization using the Random Forest algorithm and neutral data filtering, the classification accuracy increased significantly to 74.62%. These results prove that sentiment integration is able to provide more accurate loyalty mapping and help e-commerce management detect potential churn in the At-Risk customer segment.
Perbandingan XGBoost dan Random Forest Menggunakan Seleksi Fitur ANOVA-MI Dalam Klasifikasi Kesehatan Janin Cardiotocography Abednego Destyo Amanda; Angga Bayu Santoso
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.v8i1.9688

Abstract

This study compares the performance of Random Forest and XGBoost algorithms in classifying fetal health problems using Cardiotocography (CTG) data. The imbalance in the amount of data between classes, the presence of less relevant features, and the challenge in identifying the Suspect class, which has characteristics between the Normal and Pathological classes, are the main problems in the CTG dataset. This condition is important because the early stage of fetal health risk determines further medical treatment represented by the Suspect class. This study uses ANOVA and Mutual Information feature selection techniques, as well as the ADASYN oversampling method to balance the data to overcome these problems. In addition, Random Search is used to optimize model parameters to improve its performance. Unlike previous studies that generally focus on improving accuracy, this study also emphasizes the model's ability to detect minority classes, especially the Suspect class. Based on the results of the study, in almost every test scenario, XGBoost consistently outperforms Random Forest. The XGBoost model obtained optimal accuracy from the combination of ANOVA, ADASYN, and hyperparameter tuning with an accuracy of 95.51%. Meanwhile, the application of Mutual Information with ADASYN and tuning was quite effective in identifying the Suspect class with a higher recall value of 81%. However, because the Suspect class attribute lies between the Normal and Pathological class attributes, the model still faces challenges in optimally distinguishing them. Overall, this study shows that a combination of appropriate feature selection, handling data imbalance, and parameter optimization in a single pipeline can improve model performance more balanced. This research is expected to support more objective medical decision-making, especially in detecting fetal risk conditions from an early stage.
Evaluasi Kinerja Naïve Bayes, Decision Tree, Dan Random Forest Serta Voting Ensemble Pada Klasifikasi Multi-Kelas Penyakit Sapi Berbasis Gejala Nazwa Diajeng Istika Rahmadhani; Nurgiyatna Nurgiyatna
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.v8i1.9697

Abstract

Cattle are an important livestock commodity; however, farmers often face difficulties in disease diagnosis due to the similarity of clinical symptoms and limited access to veterinary experts. This study aims to compare the performance of three machine learning classification algorithms, namely Naïve Bayes, Decision Tree, and Random Forest, and to evaluate the effectiveness of an ensemble approach using a Voting Ensemble method for cattle disease diagnosis. The study adopts the CRISP-DM methodology, consisting of data preprocessing, modeling, and evaluation stages. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. The experimental results show that Naïve Bayes achieves the best performance with an accuracy of 0.951 and an F1-score of 0.920. Random Forest obtains an accuracy of 0.799, while Decision Tree performs the lowest with an accuracy of 0.265. Ensemble methods, including Voting NB+RF, Voting Weighted, Voting Soft, and Voting Hard, achieve accuracies of 0.912, 0.900, 0.853, and 0.792, respectively. These findings indicate that Naïve Bayes is more suitable for high-dimensional and sparse symptom-based data, providing the most stable performance among the evaluated models. The developed system is implemented as a web-based expert system. Usability evaluation using the System Usability Scale (SUS) yields a score of 77, categorized as “Good.” This study demonstrates that machine learning can support decision-making in cattle disease diagnosis.
Leakage-Aware Random Forest Regression for Predicting Job Automation Risk Using Structured Labor Market Data Alya Zalfa Chairunnisa; Nawirah Athqiyah; Vanisa Amalia Putri; Ken Dhita Tania; Allsela Meiriza
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.v8i1.9706

Abstract

This study aims to predict job automation risk in the era of artificial intelligence (AI) using a leakage-aware Random Forest Regression approach. The automation risk score, defined as a composite index derived from task exposure to AI, occupational routine intensity, and technological susceptibility indicators sourced from the AI Impact Jobs Dataset, serves as the target variable. The dataset comprises 5,000 job vacancy records from 44 countries across 9 industries spanning 2010 to 2025. A rigorous methodological framework is applied by systematically identifying and eliminating potential data leakage features, including ai_intensity_score, reskilling_required, and ai_mentioned, which were found to share mathematical or conceptual derivation paths with the target variable. The model is evaluated using R², RMSE, MAE, and MAPE with 5-fold cross-validation. The results show that the model achieves an R² score of 0.8087 on testing data, with RMSE of 0.1129 and MAE of 0.0893. Feature importance analysis reveals that salary_change_vs_prev_year_percent is the most influential predictor (55.85%), which, although indicative of dominance bias typical in synthetic datasets, aligns with economic theories linking wage dynamics to automation incentives. The findings demonstrate that leakage control significantly reduces inflated performance estimates (from R² = 0.8857 to 0.8087), and that Random Forest Regression provides a robust predictive framework for tabular socio-economic data when combined with rigorous preprocessing. This study contributes a methodological template for preventing data leakage in labor market prediction tasks.
Segmentation-Aware Recommendation with Cluster-Specific Item Graphs Using Pointwise Mutual Information for Market Basket Analysis Khalifatur Rauf; Arief Hermawan; Donny Avianto
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.v8i1.9707

Abstract

Traditional Association Rule-based recommendation methods often exhibit limited coverage and high redundancy when applied to sparse transactional data, thereby constraining their effectiveness for product discovery in e-commerce systems. This study proposes a hybrid recommendation framework that integrates customer behavioral segmentation with graph-based item representation learning to address these limitations. Customers are first grouped into behaviorally homogeneous clusters using historical transaction features. For each cluster, an item co-occurrence graph is constructed and weighted using pointwise mutual information to mitigate sparsity bias and emphasize informative associations. Graph-based representation learning is then applied using Node2Vec to generate low-dimensional product embeddings that capture both local structural proximity and higher-order relational patterns. The proposed framework explicitly restricts the candidate item space to the Top 100 most frequent products within each behavioral cluster, thereby focusing the recommendation task on improving localized discovery within high-frequency product segments rather than global catalog exploration. The objective of this research is to assess whether segmentation-aware graph embeddings can outperform traditional FP-Growth association rules under a strict temporal split between the Historical Training Set and the Hold-out Evaluation Set, ensuring realistic and leakage-free evaluation. Model performance is evaluated using precision, recall, normalized discounted cumulative gain, and intra-list diversity on the Hold-out Evaluation Set. Experimental results indicate that the proposed graph-based approach improves ranking quality and diversity within constrained high-frequency item spaces, demonstrating more effective localized discovery within Top 100 product segments compared to FP-Growth. These results demonstrate that graph-based embeddings are more robust to sparse behavioral patterns within high-frequency product segments and better suited for exploratory recommendation scenarios within dense product subsets. The proposed framework offers a scalable and temporally valid foundation for knowledge-driven recommender systems.
Comparing TF-IDF Based SVM and Logistic Regression for Imbalanced Pertamina Corruption Tweet Sentiment Classification Khahlil Gibran; Wenty Dwi Yuniarti; Khotibul Umam; Mokhamad Iklil Mustofa
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.v8i1.9709

Abstract

The corruption case involving PT Pertamina (Persero) in early 2025 generated widespread public reactions on social media, particularly on the X (Twitter) platform. The rapid dissemination of opinions in digital environments highlights the importance of analyzing public sentiment toward socio-political issues. This study aims to examine public sentiment regarding the Pertamina corruption case using a text classification approach based on Term Frequency–Inverse Document Frequency (TF-IDF). This study contributes a controlled comparison of TF-IDF-based Support Vector Machine (SVM) and Logistic Regression on imbalanced Indonesian-language tweets related to a nationally salient corruption issue, while also emphasizing the importance of evaluating performance beyond accuracy alone through macro-F1 and minority-class recall. Two classification algorithms, Support Vector Machine (SVM) and Logistic Regression, were employed to compare their performance in predicting lexicon-derived positive and negative sentiment labels.. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. A total of 3,058 Indonesian-language tweets collected between February 25 and March 10, 2025 underwent preprocessing and sentiment labeling using the INSET Lexicon. The results show that SVM achieved higher overall accuracy of 94.93% and a macro-F1 score of 0.80, while Logistic Regression achieved an accuracy of 90.52% and a macro-F1 score of 0.73. However, class-wise evaluation indicates that accuracy should not be interpreted independently because the dataset was dominated by negative sentiment. For the positive minority class, SVM obtained an F1-score of 0.64 and recall of 0.60, whereas Logistic Regression obtained a lower F1-score of 0.52 but a higher recall of 0.69. These findings indicate a trade-off between overall classification performance and minority-class sensitivity.
Optimasi Bayesian pada Gradient Boosting untuk Prediksi Niat Beli E-Commerce pada Dataset dengan Ketidakseimbangan Kelas Imam Bagus Setyawan; Heribertus Himawan
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.v8i1.9710

Abstract

Predicting consumer purchase intention in e-commerce is a crucial challenge due to the high rate of class imbalance, where the majority of visitors only browse without making a transaction. This study compares the performance of three Gradient Boosting family algorithms (XGBoost, LightGBM, and CatBoost) using the Online Shoppers Intention dataset, which has a class ratio of 84.5% to 15.5%. To overcome majority class bias, the Synthetic Minority Oversampling Technique (SMOTE) approach was implemented on the training data. This research focuses on hyperparameter optimization implementation using the Optuna framework based on the Tree-structured Parzen Estimator (TPE), which is statistically validated using the Friedman and Post-Hoc Nemenyi tests. Model evaluation using stratified 10-Fold Cross-Validation shows that all three models can handle class imbalance effectively. LightGBM achieved an accuracy of 88.36% with an ROC-AUC of 0.9138, XGBoost achieved an accuracy of 88.56% with an ROC-AUC of 0.9127, and CatBoost achieved an accuracy of 88.56% with an ROC-AUC of 0.9121. Feature importance analysis identifies ProductRelated_Duration and ExitRates as the main predictors of purchase intention. The Friedman statistical test detected global performance differences (p=0.0450), but the Nemenyi post-hoc test found insufficient empirical evidence to claim significant pairwise performance differences. This research provides a practical contribution to the e-commerce industry by demonstrating that the selection of ensemble algorithms no longer needs to rely absolutely on pseudo-accuracy margins, but can be objectively recommended based on computational latency efficiency, where the LightGBM architecture proves to be efficient.
Komparasi Random Forest dan Artificial Neural Network dalam Prediksi Dampak AI terhadap Pekerjaan 2030 Jefri Jaka Tirta; Heni Sulistiani
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.v8i1.9716

Abstract

The development of Artificial Intelligence (AI) is expected to affect the future employment structure, particularly regarding automation risks in 2030 as a period of accelerated AI adoption across various industrial sectors. This study aims to compare the performance of the Random Forest and Artificial Neural Network (ANN) algorithms in predicting the impact of AI on jobs. The study employed two modeling approaches, namely regression to predict job automation probability and classification to determine job risk categories into Low, Medium, and High classes through a discretization process. The dataset was obtained from Kaggle with a total of 3,000 records and processed through preprocessing, feature engineering, and train-test splitting with an 80:20 ratio. Regression evaluation was conducted using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²), while classification evaluation used accuracy and F1-score. The results showed that Random Forest achieved the best regression performance with an MAE of 0.0786, RMSE of 0.0932, and R² of 0.8640, outperforming ANN with an MAE of 0.0949, RMSE of 0.1137, and R² of 0.7973. In the classification task, both algorithms achieved an accuracy and F1-score of 99.33%. This study shows that Random Forest is more stable on tabular data and contributes to the comparative analysis of ensemble learning and neural network approaches for predicting the impact of AI on jobs.
Deteksi Penyakit Jantung Menggunakan SVM dan XGBoost dengan Interpretabilitas SHAP dan Integrasi LLM Raihan Al Aziz; Egia Rosi Subhiyakto
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.v8i1.9722

Abstract

Cardiovascular disease remains the leading cause of death globally, demanding accurate early detection, yet limited access to specialist medical personnel in developing countries often hinders timely diagnosis. This study aims to address the critical gap between the high accuracy of machine learning models in academic research and the minimal adoption of practical clinical applications by developing a safe and trustworthy hybrid artificial intelligence-based heart disease triage system. The proposed methodology integrates a dual-model architecture in which Support Vector Machine serves as the primary prediction model and Extreme Gradient Boosting as a second-opinion model, both optimized with SMOTE oversampling technique to handle class imbalance, and implements SHAP to provide transparency in black-box model decisions. The system is enriched with Dynamic Prompt Engineering innovation on the Mistral-7B Large Language Model to translate numerical probabilities into safe, personalized, and empathetic medical narratives. Experimental results show that the Support Vector Machine model with RBF kernel delivers superior performance with an accuracy of 90.22% and sensitivity of 94.12%, which is crucial for minimizing false negative cases in medical screening, outperforming the Extreme Gradient Boosting model which recorded 88.04% accuracy. Interpretability analysis identified chest pain type, cholesterol level, and maximum heart rate as the primary risk indicators, validating the model's alignment with standard cardiology guidelines. A dual safety validation mechanism through programmed risk thresholds and language generation temperature control ensures the system does not produce harmful diagnostic hallucinations. In conclusion, the system implemented as a FastAPI-based microservice is proven technically feasible with low latency, offering an accurate, transparent, and communicative early screening solution to support healthcare service efficiency.
Perbandingan Kinerja Naïve Bayes, SVM, dan Random Forest dalam Klasifikasi Risiko Kehamilan Reva Ekalia; Dyah Ayu Megawaty
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.v8i1.9724

Abstract

Classifying pregnancy risk levels is a crucial aspect in supporting early detection of potential complications in pregnant women. However, most previous studies have focused on a single algorithm and relied solely on accuracy metrics, thus failing to provide a comprehensive picture of model performance in multiclass classification. Furthermore, performance comparisons between algorithms using more comprehensive evaluation approaches are still limited. This study aims to analyze and compare the performance of the Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms in classifying pregnancy risk levels using the Maternal Health Risk Dataset from the UCI Machine Learning Repository, which consists of 1,014 data sets with six maternal health attributes. The methods used include data preprocessing, hyperparameter optimization using GridSearchCV, and model evaluation using Stratified K-Fold Cross Validation with k = 10. Model performance was measured using accuracy, precision, recall, and F1-score metrics to provide a more comprehensive evaluation. The results showed that the Random Forest algorithm had the best performance with an accuracy value of 0.8629, precision of 0.8704, recall of 0.8629, and F1-score of 0.8635, followed by SVM and Naïve Bayes. The superiority of Random Forest is due to its ability to combine several decision trees and capture non-linear relationships between features, resulting in more accurate and stable predictions. Thus, Random Forest is recommended as the most effective method in pregnancy risk classification based on maternal health data.

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