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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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Fusi Cross-Attention CNN–Transformer untuk Klasifikasi Multi-Kelas Acute Lymphoblastic Leukemia Wahyuni Fithratul Zalmi; Rahmi Putri Kurnia; Yulia Jihan Sy
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.v8i1.9831

Abstract

Acute Lymphoblastic Leukemia (ALL) is a hematological malignancy that requires a quick and accurate initial examination. Peripheral Blood Smear (PBS) imaging can be used as a source of cell morphology information, but image-based classification still faces challenges due to variations in the shape, color, and structure of blood cells. This study proposes an ALL multi-class classification model based on CNN–Transformer cross-attention fusion with two image inputs, namely the original PBS image and the segmented image that is already available in the dataset. The main contribution of this study lies in the integration of the local features of the CNN and the global features of the Transformer through the cross-attention mechanism, as well as the evaluation of the model components through baseline comparison and ablation studies. The dataset used consisted of 3,256 pairs of PBS images in four classes, namely Benign, Early, Pre, and Pro. The test results showed that the model obtained an accuracy of 0.9980 and a macro F1-score of 0.9975 on the test data. Nonetheless, this very high performance needs to be interpreted with caution as the research has not involved external validation based on different institutions or direct assessments by pathologists. Therefore, the proposed model is more appropriately positioned as a potential computational approach to the classification of PBS images, rather than as a final clinical diagnostic system. Advanced evaluation of external datasets, patient-based allocation schemes, and expert validation are required to assess the generalization and clinical relevance of the model.
Analisis Sentimen Twitter Terhadap Isu Royalti Lagu di Industri Musik Indonesia Menggunakan Naive Bayes dan Support Vector Machine Berbasis TF-IDF Alif Fadhil Wibowo; Ajib Susanto
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.v8i1.9842

Abstract

The development of digital platforms in Indonesia’s music industry has triggered various debates regarding the song royalty system, particularly those related to copyright and income distribution for songwriters. Public opinions on these issues are widely expressed through Twitter, making it a valuable data source for sentiment analysis. This study aims to analyze public sentiment toward song royalty issues in the Indonesian music industry and compare the performance of Multinomial Naive Bayes and Support Vector Machine (SVM) algorithms using TF-IDF weighting. This study contributes through the implementation of semi-manual labeling, the use of a stratified 5-fold cross-validation approach, and multi-metric evaluation to obtain more representative sentiment classification results on song royalty issues in Indonesian social media. The initial dataset was collected through Twitter scraping using keywords related to song royalties and music copyright. The data were then processed through preprocessing stages, including case folding, cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using a semi-manual approach, involving lexicon-based pre-labeling followed by manual verification into three sentiment categories: positive, negative, and neutral. Model evaluation was performed using stratified 5-fold cross-validation with accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM algorithm outperformed Multinomial Naive Bayes, achieving an accuracy of 93.21%, while Multinomial Naive Bayes obtained an accuracy of 82.53%. These findings demonstrate that SVM is more effective in handling high-dimensional textual data represented using TF-IDF for Indonesian sentiment analysis. This study is expected to provide insights into public perceptions regarding song royalty issues and serve as a reference for sentiment analysis applications on Indonesian social media data.
Analysis of Public Opinion on TikTok Regarding the MBG Program Controversy Using the Support Vector Machine Algorithm Fera Febrianti; Sahrul Ramadhan; Irfan Irfan
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.v8i1.9880

Abstract

This study examines the analysis of public opinion regarding the controversy surrounding the Free Nutritional Meal Program (MBG), a strategic policy of the Indonesian government aimed at reducing stunting rates and improving child nutrition. Despite its important social objectives, the program has sparked various public reactions concerning budget transparency, equitable distribution of aid, and food security. TikTok, as a social media platform with high levels of interaction, has become a primary platform for the public to express opinions on public policy. However, its use in sentiment analysis research remains relatively limited compared to other platforms such as Twitter and Instagram. This study aims to analyze public perceptions of the MBG program using a combination of the Support Vector Machine (SVM) algorithm and Word2vec. Research data was obtained through the collection of 2,381 TikTok comments, followed by preprocessing steps such as data cleaning, tokenization, slang normalization, stop-word removal, and stemming. After the data selection process, 2,376 comments were used in the lexicon-based sentiment labeling and classification process using SVM. The test results show that the SVM model achieved an accuracy of 80% before class imbalance handling, whereas after applying class imbalance handling techniques, the accuracy increased to 83%, with a weighted precision of 0.84, a recall of 0.83, and an F1 score of 0.83. This improvement indicates that processing the data in the database enhances the model’s ability to recognize all sentiment classes more evenly, particularly positive sentiment, which previously had a smaller dataset. The sentiment analysis results show that the majority of opinions are dominated by neutral and negative sentiments, reflecting public concerns regarding the program’s implementation effectiveness, budget management transparency, and equitable distribution. These findings suggest that public opinion on social media can be leveraged as a real-time source for evaluating government policies to help the government develop public communication strategies that are more transparent, responsive, and targeted toward the implementation of the MBG Program.
Classification of Swiftlet Nest Quality Based on SNI 8998:2021 Using Deep Learning Ilmiati Ilmiati; Siti Mutmainah; Khairunnas Khairunnas
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.v8i1.9901

Abstract

The quality of swiftlet nests is a key factor in determining the market value and quality standards of this commodity in both domestic and international markets. The quality classification process, which is currently dominated by manual methods, has fundamental weaknesses, namely high subjectivity and inconsistency in sorting results. This study aims to evaluate the performance of deep learning architectures in automatically classifying the quality of swiftlet nests based on visual characteristics. The main contribution of this study is to address the research gap in previous publications by strictly aligning quality class labels with the formal document of the Indonesian National Standard (SNI) 8998:2021, as well as presenting a cross-architecture comparative analysis to map model performance trade-offs. Evaluations were conducted on the MobileNetV2, and presents a cross-architecture comparative analysis to map model performance trade-offs. Evaluations were conducted on the MobileNetV2, ResNet50, and YOLOv8n-cls architectures using accuracy, precision, recall, and F1-score metrics. The research dataset includes visual images of swiftlet nests grouped into three quality classes (good, moderate, and poor) through self-documentation and augmentation techniques. Test results show that YOLOv8n-cls achieved the highest performance in this scenario with an accuracy of 99.5%, precision of 98.78%, recall of 98.72%, and an F1-score of 98.71%. Meanwhile, MobileNetV2 achieved a competitive accuracy of 98.37% with good computational efficiency, while ResNet50 demonstrated the lowest performance (66% accuracy) due to network complexity on the limited dataset. This research indicates that lightweight architectures exhibit good stability for limited-size visual datasets; however, external validation using larger datasets remains necessary to test the models’ generalization capabilities more broadly.
Analisis Spasio-Temporal Berbasis Data Video untuk Identifikasi Bangunan Melayu Menggunakan Metode Hybrid CNN-LSTM Ines Triseptiani; Sri Winiarti
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.v8i1.9917

Abstract

Malay buildings have distinctive architectural characteristics and require technology-based identification systems to support cultural documentation and preservation. This study aims to develop an identification system for Malay and non-Malay buildings using video data extracted into frame-by-frame images. The use of video data in this study is not intended to analyze the physical movement of buildings, but to utilize visual variations caused by changes in camera angle, recording distance, lighting, object composition, and visible building elements. The proposed method is CNN-LSTM, where CNN extracts visual features from each frame, while LSTM learns inter-frame feature relationships as a sequence of visual information. To reduce redundant information between adjacent frames and minimize the risk of excessive similarity between training and testing data, the number of frames was limited to a maximum of 15 frames per video folder, and data splitting was performed by considering video source groups. The dataset consists of Riau Malay buildings, Kalimantan Malay buildings, and non-Malay buildings. The research stages include frame extraction, image resizing to 224×224 pixels, normalization, data augmentation, class labeling, train-test splitting, modeling, evaluation, GroupKFold validation, and web-based system implementation. The best testing scenario was obtained using an 80:20 data split, 80 maximum epochs, and a batch size of 16, achieving an accuracy of 0.9916 and a test loss of 0.0852. GroupKFold validation produced an average accuracy of 99.1% with a standard deviation of 0.5%. These results indicate that the model can recognize architectural visual patterns, such as roofs, windows, doors, ornaments, and overall building appearance, while the performance should still be interpreted within the scope of the dataset and evaluation scenario used in this study.
Comparison of Random Forest and XGBoost Methods Based on Hyperparameter Tuning for Classification of Customer Churn Rate of Telecommunication Providers Abdul Karim; Muhammad Hidayatullah; Nora Dery Sofya; Erwin Mardinata; Shinta Esabella
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.v8i1.9918

Abstract

Customer churn represents one of the most critical challenges in the telecommunications industry, as the cost of acquiring new customers significantly outweighs the expense of retaining existing ones. High churn rates directly impact corporate revenue stability and market competitiveness, necessitating the development of precise predictive systems. This study presents a comprehensive comparative analysis of two prominent ensemble learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to establish a robust predictive framework for identifying potential churners using a large-scale Telco subscriber dataset. To ensure the reliability and scientific validity of the comparison, the research methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to rigorously address the inherent class imbalance within the dataset, ensuring that the minority churn class is adequately represented during the training phase to avoid model bias. Furthermore, a systematic hyperparameter tuning process was executed via GridSearchCV, exploring multiple combinations of estimators, depth, and learning rates to identify the optimal configurations for both algorithms. The experimental results reveal that while both models are highly effective, Random Forest slightly outperformed XGBoost, achieving an overall accuracy of 77.54% and a balanced F1-score of 0.616, compared to XGBoost’s accuracy of 76.54% and F1-score of 0.605. Notably, although both models demonstrated an identical recall rate of 67.64%, Random Forest exhibited superior precision (56.47% vs. 54.76%), which is vital for minimizing false positives and ensuring cost-effective retention campaigns. Feature importance analysis, conducted through Gini impurity and gain metrics, further identified tenure, total charges, and month-to-month contract types as the primary drivers of customer attrition. This study concludes that an optimized Random Forest model provides a more stable and accurate framework for telecommunication providers to proactively mitigate customer turnover. The findings offer valuable business intelligence, allowing stakeholders to transition from reactive measures to proactive, data-driven loyalty programs that enhance long-term business sustainability.
Evaluasi Kinerja Sensor FC-28 Untuk Monitoring Kelembapan Tanah Tanaman Indoor Menggunakan Confusion Matrix Herva Emilda Sari
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.v8i1.9932

Abstract

Technology plays an important role in transforming complex challenges into simpler and more practical processes, including soil moisture monitoring for indoor plants. Soil moisture is one of the most critical factors affecting the survival and growth of indoor plants. However, indoor plant owners often face difficulties in determining the moisture condition of the soil, creating the need for a simple and easy-to-use tool for monitoring soil moisture. The FC-28 soil moisture sensor is designed as a simple drought detection device equipped with an LED indicator to provide information regarding soil moisture conditions. The sensor operates by comparing the detected moisture level with a predefined comparator threshold. When the soil moisture falls below the threshold, the LED indicator turns on, indicating that the soil is dry. Conversely, when the moisture level remains above the threshold, the LED stays off, indicating that the soil is sufficiently moist. This study aims to evaluate the performance of the FC-28 sensor in detecting soil moisture conditions in indoor plants. An experimental approach was employed by conducting tests under dry and wet soil conditions. The sensor detection results were compared with the actual soil conditions (ground truth) using the Confusion Matrix method to obtain accuracy, sensitivity, specificity, and error rate values. The results indicate that the FC-28 sensor can consistently distinguish between dry and wet soil conditions in accordance with the actual conditions observed during the testing scenarios. This study contributes by providing a quantitative performance evaluation of the FC-28 sensor using the Confusion Matrix approach through accuracy, sensitivity, specificity, and error rate parameters for indoor soil moisture monitoring applications. The evaluation results demonstrate a high level of agreement between the sensor outputs and the actual soil conditions within the limited testing scope. These findings suggest that the FC-28 sensor can be utilized as a simple and cost-effective soil moisture monitoring tool for indoor plants under controlled environmental conditions.
Klasifikasi Pesan Penipuan pada Platform WhatsApp Menggunakan Metode Naïve Bayes Berbasis TF-IDF, N-Gram, dan Chi-Square Hardika Nur Saputra; Ardytha Luthfiarta
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.v8i1.9941

Abstract

The rapid development of digital communication has led to an increase in message exchanges across various platforms, accompanied by the widespread spread of fraudulent messages (scams). This situation demands an automated system capable of identifying and classifying messages quickly and accurately. This study aims to develop a text-based message classification system on the WhatsApp platform using the Naïve Bayes algorithm. The research stages include text preprocessing consisting of case folding, cleaning, normalization, stopword removal, and stemming to improve data quality. Next, feature extraction is carried out using Term Frequency-Inverse Document Frequency (TF-IDF) combined with the N-Gram (unigram) approach to represent each word in the text, and Chi-Square feature selection is applied to obtain the most relevant features in the classification process. The dataset used consists of three categories of WhatsApp messages: normal, promotional, and fraudulent. In addition, this study also applies a data balancing method using Random Oversampling to increase the number of minority class samples in the training data for optimal model performance. The main contribution of this research is the application of a combination of TF-IDF unigram, Chi-Square feature selection, and Random Oversampling in the Naïve Bayes algorithm to improve the classification performance of Indonesian WhatsApp messages, especially in conditions of unbalanced class distribution. Model evaluation is carried out using a Confusion Matrix with accuracy, precision, recall, and F1-score metrics. The test results show that the model built is able to achieve an accuracy level of 95.63%, so the method used is proven to be effective in classifying WhatsApp messages accurately and consistently.
Comparing TabNet and CatBoost Models for Explainable Student Depression Prediction Triana Dewi Salma; Rizqi Darmawan; Fauzan Natsir; Esa Kurniawan
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.v8i1.9964

Abstract

Early identification of depression risk among students is increasingly important for educational institutions because mental health problems may affect academic engagement, well-being, and learning continuity. This study aims to compare TabNet and CatBoost in predicting student depression risk and to examine their explainability in identifying influential predictors from structured tabular data. The dataset used in this study consists of student-related variables covering personal characteristics, academic conditions, psychological indicators, and lifestyle factors. The experimental procedure included data cleaning, missing value treatment, categorical feature transformation, feature scaling, model training, testing, and interpretation. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Meanwhile, model explainability was examined through attention-based feature importance for TabNet and SHAP-based interpretation for CatBoost. The experimental results indicate that CatBoost produced better overall classification performance, achieving 84.54% accuracy compared with 83.44% for TabNet. CatBoost also obtained higher precision, F1-score, and ROC-AUC values. In contrast, TabNet showed slightly better recall, suggesting stronger sensitivity in detecting students classified as at risk. The interpretation results show that suicidal thoughts, financial stress, academic pressure, sleep duration, study satisfaction, dietary habits, and workload-related variables were consistently relevant to the prediction process. These findings indicate that model selection for student depression prediction should consider not only accuracy, but also sensitivity and interpretability.
Komparasi Naïve Bayes, SVM, dan Decision Tree untuk Klasifikasi Komentar Provokatif pada Instagram Terkait Aksi Demonstrasi Agustus 2025 Muhamad Yusuf; Erizal Erizal
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.v8i1.9989

Abstract

The accelerating growth of social media has transformed digital platforms into spaces where people express opinions on social and political issues. One event that generated numerous public comments was the demonstration held on August 25–31, 2025. Many comments contained harsh language, provocation, insults, and calls for conflict that had the potential to trigger negative emotions in digital spaces. This study aims to classify provocative and non-provocative comments on Instagram using the Naïve Bayes algorithm and compare its performance with Support Vector Machine (SVM) and Decision Tree algorithms. The data were collected through a web scraping process, resulting in 3,396 comments. After the cleansing and preprocessing stages, the dataset was reduced to 2,490 comments. The preprocessing stages included transform case, tokenizing, stopwords removal, filter tokens, and stemming. Furthermore, word weighting was carried out using the TF-IDF method and implemented in RapidMiner with an 80:20 data split ratio. Based on manual labeling, 1,279 provocative comments and 1,211 non-provocative comments were obtained. The evaluation results showed that Naïve Bayes achieved an accuracy of 72.15%, SVM achieved 69.44%, and Decision Tree achieved 72.91%. Although Decision Tree produced a slightly higher accuracy, Naïve Bayes demonstrated a more balanced performance in detecting both comment classes, even though the accuracy value was still in the moderate category. The findings provide insights into the effectiveness of machine learning algorithms for identifying provocative comments and may support the development of automated content moderation on social media platforms.

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