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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 1,045 Documents
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Analisis Sentimen Persepsi Publik Terhadap Program MBG Pada Komentar YouTube Menggunakan Naïve Bayes dan Resampling Lutfi Najib; Adzhal Arwani Mahfudh; Syaiful Bakhri
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9400

Abstract

The Free Nutritious Meal Program (MBG), launched by the Indonesian government in 2025, has generated diverse public responses on social media, particularly on YouTube as an open digital discussion space. This study aims to analyze public perception of the MBG program through sentiment classification of YouTube comments using the Multinomial Naïve Bayes algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The dataset consists of 1,082 comments categorized into three sentiment classes: negative, neutral, and positive. The data distribution reveals significant class imbalance, with negative sentiment dominating at 70.61%. The baseline model achieved an accuracy of 70.67% with a macro F1-score of 27.60%, indicating bias toward the majority class. To address this imbalance, Random Oversampling (ROS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied. Although overall accuracy decreased to approximately 51% after resampling, the macro F1-score improved to 36.24% (SMOTE) and 37.09% (ROS), indicating enhanced performance in detecting minority classes. In the context of public policy evaluation, improved sensitivity to minority sentiment is considered more representative than high but biased accuracy. These findings highlight the importance of handling class imbalance in social media–based sentiment analysis for public policy monitoring.
A Comparative Study of LSTM and BiLSTM Performance in Predicting XAU/USD Prices I Ketut Agung Enriko; Fikri Nizar Gustiyana
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9414

Abstract

Gold price forecasting in the XAU/USD market is challenging due to nonlinear dynamics, high volatility, and sensitivity to global macroeconomic factors. This study compares the performance of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures in forecasting XAU/USD closing prices using historical data from 2023–2026. Data preprocessing includes cleaning, chronological ordering, normalization, and transformation using a sliding window approach. A window size of 60 time steps is selected to represent approximately three months of daily trading activity, enabling the models to capture short- to medium-term temporal dependencies while limiting excessive noise and computational burden. The dataset is divided chronologically into training and out-of-sample testing sets to ensure proper generalization assessment. Both models employ identical architectures with two recurrent layers (50 hidden units each) and are trained using the Adam optimizer with epoch variations (20–100). Evaluation on unseen test data uses MAE, MSE, RMSE, MAPE, and R² metrics. LSTM achieves its lowest MAE of 21.26 at 40 epochs, while BiLSTM attains its best performance at 80 epochs with an MAE of 20.86 and R² of 0.9981. However, extending training to 100 epochs leads to performance degradation in BiLSTM, indicating sensitivity to overtraining. Overall, optimal performance is achieved through balanced training duration rather than increased architectural complexity.
Analisis Komparatif Arsitektur Convolutional Neural Network untuk Klasifikasi Kualitas Cabai dengan Implementasi Perangkat Mobile Nur Ikhsanudin; Muhamad Akrom
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9419

Abstract

Manual chili quality sorting is susceptible to subjectivity and inter-assessor inconsistency, which can reduce product market value. This study conducts a comparative analysis of three Convolutional Neural Network (CNN) architectures—Custom CNN, MobileNetV3-Small, and EfficientNetV2-B0 for binary chili quality classification (Good/Bad) using a primary dataset of 1,383 chili images (684 Good-class, 699 Bad-class) captured with a smartphone camera. The Good class includes chili with a smooth surface, fresh color, and no decay spots, while the Bad class includes chili showing signs of decay, physical defects, or deformation. Evaluation was conducted based on accuracy, precision, recall, F1-Score, AUC, inference time, and post-quantization model size. The results show that EfficientNetV2-B0 achieved the highest accuracy of 92.0% (precision 92.4%, recall 92.0%, F1-Score 92.0%, AUC 0.961), MobileNetV3-Small obtained an accuracy of 87.7% with the lowest server-side inference latency (2.39 ms), and Custom CNN achieved 87.3% accuracy with the most compact model size (118 KB post-quantization). All three models were integrated into a Flutter-based Android application prototype as a proof-of-concept, displaying the classification result (Good/Bad), confidence score, and inference latency, with end-to-end response times ranging from 80 to 120 ms on a Xiaomi 13T device. This study contributes empirical comparative data on three CNN architectures in the chili quality classification domain, accompanied by the construction of a local dataset and technical validation of model deployment on a mobile device. The results of this study are expected to serve as a reference in selecting CNN architecture for the development of a mobile-based chili quality classification system, particularly as a first step toward the implementation of simple small-scale sorting at the farmer level.
Komparasi Model Ensemble dan Algoritma Machine Learning Untuk Memprediksi Penyakit Jantung Muhammad Syarief Albani; Dedy Kurniawan; Ken Ditha Tania
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8346

Abstract

This study compared the performance of nine machine learning algorithms in predicting heart disease using a dataset dating back to 1988 and consisting of four databases: Cleveland, Hungary, Switzerland, and Long Beach totaling 1025 data. The dataset used includes medical features that reflect physiological states, clinical examination results, and cardiovascular risk factors, namely age, gender, type of chest pain, resting blood pressure, serum cholesterol levels, fasting blood sugar levels, resting electrocardiography results, maximum heart rate, chest pain during physical activity, ST segment depression, ST segment slope, number of major blood vessels visible by fluoroscopy, and thalassemia status. The stages of this study include data cleaning, data transformation, and evaluation carried out using the data splitting method for training and testing as well as K-fold cross-validation with metrics of accuracy, precision, recall, F1 score, and AUC-ROC. The algorithms used in this study are Decision Tree, Random Forest, Support Vector Machine, MLP Classifier, Bagging Classifier, Gradient Boosting, CatBoost, XGBoost, and LightGBM with ensemble-based models, such as CatBoost, Random Forest, XGBoost, and LightGBM, showing consistent performance on various evaluation metrics when compared to non-ensemble models. Among all models tested, CatBoost showed the best performance, with an accuracy reaching 98%, an F1-Score of 0.980, and a Recall of 0.9875 then followed by other ensemble algorithms such as Random Forest, XGBoost and LightGBM. The results of this study indicate that ensemble models are proven to be more effective in predicting heart disease. This study aims to present an in-depth comparative study of the performance of ensemble algorithms and modern machine learning in predicting heart disease, as well as enriching the literature related to the application of Knowledge Discovery in the health sector and providing a basis for selecting more reliable prediction algorithms to support clinical decision making and the development of machine learning-based heart disease diagnosis support systems.
Analisis Komparatif Kinerja Algoritma Support Vector Machine, Random Forest, dan Naive Bayes untuk Klasifikasi Sentimen pada Komentar YouTube Eustachius Dito Dewantoro; Sindhu Rakasiwi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8959

Abstract

The rise of social media platforms like YouTube has made them a primary medium for public discourse on socio-political issues, such as the "August 25th protests," which triggered massive polarization in the digital space. The vast volume of comments necessitates a computational approach for sentiment analysis. This study aims to classify public sentiment into positive and negative categories while comparing the performance of Naive Bayes, Random Forest, and Support Vector Machine (SVM). These algorithms were selected for their computational efficiency on high-dimensional text data compared to Deep Learning models. The methodology involved collecting 2,917 comments via the YouTube Data API v3, followed by text preprocessing, lexicon-based automated labeling, and TF-IDF feature weighting. To address the dataset's imbalance, where negative sentiment dominated at 78.8%, stratified sampling was applied to maintain class proportions. Results indicate that SVM achieved the highest accuracy at 88.2%, outperforming Random Forest (83.1%) and Naive Bayes (81.2%). SVM's superiority stems from its ability to find an optimal hyperplane that maximizes class margins, ensuring stability in imbalanced datasets. This research contributes a robust classification framework for understanding public opinion dynamics on specific political issues in Indonesia.
Hybrid Entropy and CRADIS Method Approach in Decision Support System for Selecting the Best Employees Junhai Wang; Setiawansyah Setiawansyah; Very Hendra Saputra
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8985

Abstract

Selecting the right employees is a key factor in improving organizational performance and productivity. However, in many organizations, the employee selection process is still conducted through manual assessments and subjective judgments, which may lead to bias and inconsistent decisions. Therefore, a systematic and objective approach is needed to support the evaluation process. This study integrates the Entropy method and the CRADIS method within a decision support system to determine the best employee candidates. The Entropy method is applied to calculate objective criteria weights based on the variation of information in the data, while the CRADIS method is used to rank candidates according to their proximity to the ideal solution and distance from the anti-ideal solution. The integration of these two methods provides a framework that reduces subjectivity in determining criterion importance and produces more discriminative ranking results. The findings indicate that candidate GF achieved the highest score of 0.6848, followed by EY with 0.6835 and AR with 0.6528, showing that these candidates have performance profiles closest to the defined criteria. In addition, sensitivity analysis using several scenarios of criteria weight changes demonstrates that the proposed model is relatively stable, with an overall ranking consistency of 81.8%, while alternatives AR, DI, and FR show 100% ranking stability. These results indicate that the Entropy–CRADIS approach can improve the accuracy, objectivity, and reliability of employee selection decisions in multi-criteria decision-making environments.
Hybrid Music Recommendation System Using K-Means Clustering and Neural Collaborative Filtering for Spotify Playlist Personalization Rastomi Pamungkas; Permata Permata; Rakhmat Dedi Gunawan; Adhie Thyo Priandika
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9181

Abstract

Personalizing music recommendations has become a significant challenge on music streaming platforms such as Spotify due to the vast number of available songs and the limitations of conventional recommendation systems in accurately capturing user preferences. In addition, traditional single-method recommendation approaches often face the cold start problem, which reduces the effectiveness of generated recommendations. Therefore, this study aims to develop and evaluate a hybrid recommendation system that integrates the K-Means Clustering algorithm and Deep Collaborative Filtering based on Neural Matrix Factorization to improve the relevance of music playlist recommendations. The dataset used in this study consists of more than 15,151 Spotify songs obtained from the Spotify dataset available on Kaggle. The dataset was processed through several stages including data inspection, data cleaning, feature selection, and standardization. Audio features used in the analysis include danceability, energy, acousticness, instrumentalness, valence, tempo, and duration. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, resulting in five clusters with a relatively balanced data distribution. The clustering results were then used as the basis for Cluster-Based Filtering to narrow the search space of candidate songs before being processed by the Neural Matrix Factorization model. Performance evaluation was conducted using Hit Ratio at rank 10 and Normalized Discounted Cumulative Gain at rank 10. The proposed model achieved values of 0.1110 and 0.0507, respectively, indicating that the integration of clustering and deep collaborative filtering can improve the effectiveness and personalization of music recommendation systems. This study contributes by proposing a hybrid recommendation framework that integrates clustering-based item grouping with deep collaborative filtering to improve recommendation efficiency and playlist personalization in large-scale music streaming platforms.
Comparison of Geothermal Well Productivity Using KNN, SVM and Gradient Boost Methods Mina Winawati Dwi Aryani; Indra Nugraha Abdullah
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9302

Abstract

Manual and conventional processing of geothermal well production data is computationally inefficient and requires several hours to days to generate productivity assessments, particularly when dealing with large-scale and non-linear operational datasets. The complexity of geothermal production parameters this paper such as wellhead pressure (WHP), enthalpy, steam flow, brine flow, total flow, and generated power this paper creates challenges for accurate and timely productivity classification at the well level. This study utilizes 74,912 daily production records collected from January 2018 to June 2024, comprising 13 operational and production-related attributes. The objective is to identify the most effective machine learning algorithm for classifying geothermal well productivity levels to support faster and more reliable operational decision-making. A comparative machine learning classification approach was conducted using K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), and Gradient Boosting. Model evaluation was performed using three train–test split ratios: 70:30, 80:20, and 90:10. Two modelling scenarios were implemented: with and without Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The results indicate that the K-NN model achieved the highest classification performance, reaching 94.22% accuracy using the 90:10 split ratio without SMOTE. Gradient Boosting demonstrated stable performance across all ratios, with its best accuracy of 91.39% at the 70:30 split without SMOTE. In contrast, SVM produced the lowest performance, with a maximum accuracy of 79.78% at the 90:10 ratio without SMOTE. The application of SMOTE improved minority class recall, particularly for SVM, but generally reduced overall model accuracy for K-NN and Gradient Boosting. These findings demonstrate that classical machine learning algorithms, particularly K-NN, provide an efficient and accurate solution for geothermal well productivity classification. The proposed approach significantly reduces processing time compared to conventional analytical methods and supports data-driven decision-making in geothermal production forecasting and development planning.
Pemodelan Pola Temporal Action Unit untuk Pengenalan Ekspresi Wajah Berbasis Bidirectional LSTM Muhammad Ghozali Sulton; Sugiyanto Sugiyanto
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9315

Abstract

This study develops a facial expression recognition system based on Facial Action Units (AU) data using a Bidirectional Long Short-Term Memory (BiLSTM) model. The dataset consists of AU data obtained from a supervisor, sourced from DCAP-SWOZ (USC Institute for Creative Technologies), a multimodal corpus containing AU values extracted from human interaction videos. A total of 188 AU files were used in this research. Initial labeling was performed using Facial Action Coding System (FACS)-based rules as pseudo-labels serving as a starting point for training the BiLSTM model. This approach was chosen because the dataset lacks inherent emotion labels, necessitating a label initialization mechanism. The BiLSTM model functions as a temporal smoother designed to reduce noise and label inconsistencies that commonly occur in frame-by-frame rule-based approaches. The trained model then performs inference on the same data to generate final labels with improved temporal stability. Evaluation was conducted by measuring model consistency against FACS rules and qualitative analysis of temporal stability in generated labels. Data were processed into 30-frame sequences with a 1-frame sliding window to effectively capture expression dynamics patterns. The BiLSTM model was trained using two hidden layers with dropout regularization. Evaluation results showed 96.61% consistency against FACS rules with high performance across all emotion classes, including anger (99.11%), disgust (97.98%), fear (94.08%), happiness (99.29%), neutral (96.42%), sadness (98.31%), and surprise (99.16%). Qualitative analysis demonstrated that the model successfully reduced frame-by-frame label fluctuations by 73% compared to pure rule-based approaches, producing more stable and realistic emotion segmentation. These results demonstrate that the combination of FACS-based labeling and the BiLSTM model can produce a temporally consistent automated labeling system capable of accelerating labeled dataset creation, although validation against human ground truth remains necessary as future research.
Sentimen Analisis Pengguna Jasa Layanan Kereta Api dengan Menggunakan Metode CNN (Convolutional Neural Network) Zidan Alfikri; Ari Muzakir; Susan Dian Purnamasari; Rahayu Amalia
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9423

Abstract

Train services are a popular mode of transportation in Indonesia, especially in the Greater Jakarta area. However, the quality of train services is often debated among users. This study aims to analyze the sentiment of train service users using the Convolutional Neural Network (CNN) method with a focus on the DAOP 1 Jakarta area. The data used are reviews or comments of train users taken from Indonesian Railways social media. The results of the study show that the CNN method can classify user sentiment analysis with accurate results or high accuracy. This sentiment analysis shows that train users in DAOP 1 Jakarta have positive sentiments towards aspects such as punctuality, service, comfort and safety. The results of this study can help the railway to understand user needs and complaints so that they can improve service quality with a final value of 89.29% accuracy, 88.73% precision, 90.00% recall, and 89.36% F1-score.

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