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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 926 Documents
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Penerapan Support Vector Machine untuk Analisis Sentimen Pengguna X terhadap IndiHome, Biznet, dan Starlink Alfian, Zhevin; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7429

Abstract

This study aims to analyze user sentiment on the social media platform X toward three major internet service providers in Indonesia, IndiHome, Biznet, and Starlink. The analysis focuses on five key variables: internet speed, network stability, pricing and service packages, customer service quality, and coverage availability. A total of 4,500 data points were collected through data crawling, then processed using text mining techniques and the Support Vector Machine (SVM) algorithm, with data imbalance addressed through the Random Oversampling method. Evaluation results show that IndiHome consistently demonstrated the best performance, achieving an accuracy of up to 90% in the customer service quality variable, and an overall average accuracy above 85% across all variables. Biznet generally ranked second, with accuracy ranging from 63% to 80%. Starlink placed lowest overall, although it still recorded competitive results, such as 82% accuracy in the internet speed variable. The application of Random Oversampling improved the model’s classification accuracy by an average of 6–12% compared to the non-oversampling model. This study offers strategic insights into public perception of internet services and can serve as a reference for improving service quality based on data-driven user feedback.
Analisis Sentimen Masyarakat Terhadap Kebijakan Ekspor Pasir Laut Berdasarkan Ulasan Twitter Menggunakan Algoritma Naive Bayes dan Support Vector Machine Zarqani, Zarqani; Afdal, M; Novita, Rice; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7431

Abstract

The export of sea sand has been banned since 2003 through a Decree of the Minister of Industry and Trade. However, on May 15, 2023, President Joko Widodo once again allowed the export of sea sand through Government Regulation No. 26 of 2023. This policy sparked controversy and went viral on social media, including on Twitter. This study aims to analyze public sentiment toward the policy based on reviews on Twitter using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Data was collected through crawling techniques, then processed using text preprocessing methods, word weighting using TF-IDF, and random oversampling to balance the data. The data was then categorized into four thematic variables—economy, environment, social, and geological policy—to examine a more focused distribution of sentiment. Analysis of 2,765 data points revealed that the majority of sentiment was negative (55%), indicating public opposition to the sea sand export policy, followed by neutral sentiment (30%) and positive sentiment (15%). Performance evaluation shows that SVM excels in the Economy category with nearly 95% accuracy, while in other categories the difference with Naïve Bayes is relatively small. This study is expected to provide insights into the Indonesian public's perception of the sea sand export policy and its implications across various sectors.
Analisis Sentimen Terhadap Pemain Naturalisasi dan Lokal Tim Nasional Sepakbola Indonesia Menggunakan Support Vector Machine Arrazak, Fadlan; Afdal, M; Novita, Rice; Megawati, Megawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7471

Abstract

The inclusion of naturalized players in Indonesia's national football team has sparked diverse public reactions, particularly on social media platforms like Twitter. This study aims to compare public opinion toward naturalized and local players through sentiment analysis. A total of 2,342 tweets were categorized into three sentiment classes: positive, neutral, and negative. Naturalized players received a higher number of positive sentiments, totaling 809, compared to 333 negative and 231 neutral sentiments. In contrast, local players gained 465 positive sentiments, 317 negative, and 187 neutral, indicating a generally more favorable perception of naturalized players among the public. Further analysis was conducted using the Support Vector Machine (SVM) classification algorithm along with the SMOTE technique for data balancing, focusing on five key aspects: performance, experience, physical condition, adaptability, and communication. The classification results showed that naturalized players outperformed in physical condition with an accuracy of 96 percent, followed by performance and adaptability, each at 90 percent. On the other hand, local players showed superiority only in communication with an accuracy of 92 percent. In terms of precision and recall, naturalized players again led in physical condition, achieving 97 percent precision and 96 percent recall, while local players excelled in communication with both precision and recall at 92 percent. These findings offer valuable insights for policymakers and football organizations in formulating more effective naturalization strategies.
Analisis Sentimen Masyarakat Terhadap Kebocoran Pusat Data Nasional Sementara Menggunakan Algoritma Random Forest dan Support Vector Machine Basri, Faishal Khairi; Afdal, M; Angraini, Angraini; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7473

Abstract

A ransomware attack on Indonesia’s Temporary National Data Center (PDNS) in June 2024 triggered major public concern over data security and government preparedness. This study aims to analyze public sentiment toward the incident using an Aspect-Based Sentiment Analysis approach on 2,700 Indonesian-language tweets collected from the X platform. The research follows the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, involving text preprocessing, aspect extraction using part-of-speech tagging and named entity recognition, feature representation using Term Frequency-Inverse Document Frequency, and aspect refinement through semantic coherence. Extracted aspects are grouped into five categories: data security, institutions, infrastructure, politics and economy, and impact. Sentiment classification is carried out using the IndoBERTweet model. Results indicate a strong dominance of negative sentiment, particularly in the infrastructure and institutional categories, with no positive sentiment recorded in the political and economic aspect. To address class imbalance in sentiment distribution, the Synthetic Minority Oversampling Technique is applied during model training. Performance evaluation of two algorithms—Random Forest and Support Vector Machine—shows that Random Forest performs best, achieving 96% accuracy on a 70:30 data split and 99.05% average accuracy using 10-fold cross-validation. These findings highlight the effectiveness of aspect-based sentiment analysis and demonstrate Random Forest's superiority in handling imbalanced sentiment classification tasks.
Comparison of RoBERTa and IndoBERT on Multi-Aspect Sentiment Analysis of Indonesian Hotel Reviews with Tuning Optimization Syarif, Rizky Ahsan; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7523

Abstract

The hospitality industry heavily relies on online reviews as a crucial source of information that influences potential guests' decisions. However, conducting sentiment analysis on hotel reviews can be challenging due to the complexity of language and contextual diversity, especially in Indonesian. This study aims to develop and optimize a RoBERTa-based sentiment analysis model to improve the accuracy of sentiment classification in Indonesian hotel reviews, focusing on the aspects of facilities, cleanliness, location, price, and service. The methodology includes data collection through web scraping from the Traveloka platform, manual labeling, and text pre-processing. The RoBERTa model was trained and optimized using fine-tuning techniques and evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The results show that the optimized RoBERTa model achieves competitive performance, although the IndoBERT model with Bayesian Optimization demonstrates superior performance, particularly in terms of accuracy and efficiency in identifying positive and negative sentiments. This study is expected to contribute to the development of more effective and accurate aspect-based sentiment analysis (ABSA) for Indonesian-language hotel reviews. It also opens opportunities for applying NLP technology in the hospitality industry and across other review platforms, thereby improving sentiment analysis quality and assisting hotel managers in enhancing service and customer experience.
Milk Production Estimation Model for Cattle Based on Image Processing using Random Forest, XGBoost, and LightGBM Niswati, Za'imatun; Nurdiati, Sri; Buono, Agus; Sumantri, Cece
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7585

Abstract

Milk is a livestock product consumed by individuals of all ages. Therefore, it is essential to increase milk production in Indonesia to meet domestic demand. The growth of dairy cattle populations and milk production has not been able to keep up with rising consumption, resulting in a reliance on imports for most dairy products and their derivatives, with imports steadily increasing over the years. Therefore, alternative solutions are needed to enhance the milk production. One approach is to develop a milk production estimation model to determine the optimal number of dairy cattle to be cultivated by farmers and livestock companies to meet domestic demand. The objective of this study was to create a dairy milk production estimation model through image analysis using the Random Forest, XGBoost, and LightGBM algorithms. The milk production estimation model used in this study used CLAHE for contrast enhancement and VGG-16 for feature extraction. The results showed that XGBoost provided the best performance, explaining 74% of the data variation in the Y variable with a relatively small estimation error of 0.92. After parameter tuning using Grid Search, an improvement was observed, where XGBoost explained 86% of the data variation in the Y variable, and the estimation error decreased to 0.72. Image processing and machine learning technologies are part of precision agriculture that aims to improve the efficiency, productivity, and sustainability of livestock operations.
Analisis Perbandingan Algoritma Random Forest dan K-Nearest Neighbors pada Klasifikasi Tingkat Stres Pekerja Manurung, Syalom Kristian; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7589

Abstract

Work stress has become a prominent concern in the modern professional landscape, as it can lead to reduced productivity, diminished work quality, and decreased mental well-being among employees. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in classifying levels of work stress. The data were obtained through an online questionnaire completed by 212 respondents from various employment sectors in Indonesia. The responses were converted from Likert scale to numerical values, grouped using the K-Means clustering method, and categorized into five levels of stress, ranging from no stress to very high stress. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The modeling process was conducted using three different data split scenarios, namely 90:10, 80:20, and 70:30, and evaluated using metrics such as accuracy, precision, recall, f1-score, and cross-validation. The findings indicate that the Random Forest algorithm consistently outperformed KNN across all scenarios. After applying SMOTE, both algorithms showed improved performance, with the Balanced Random Forest model achieving the highest accuracy and f1-score of 92 percent in the 70:30 scenario. These results suggest that combining Random Forest with SMOTE offers an effective and reliable solution for classifying work stress levels and could be developed as an objective and efficient early detection system.
A Comparative Study of Machine Learning Classifiers with SMOTE for Predicting Purchase Intention Khairunnisa, Khairunnisa; Soim, Sopian; Lindawati, Lindawati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7615

Abstract

The rapid growth of e-commerce has made it increasingly important for online platforms to understand user behavior, particularly in predicting purchasing intention. This study examines the implementation of three machine learning models: Logistic Regression, Random Forest, and Gradient Boosting, to classify purchase intention using real transaction session data. One of the primary obstacles confronted in this investigation is the matter of class imbalance found in the dataset, where 10422 records indicate no purchase while only 1908 indicate a completed purchase. This disparity may result in a biased model performance that prioritizes the dominant class and limits the ability to accurately detect minority class behavior, which in this case is the actual purchase. To resolve this matter, During the data preprocessing phase, the Synthetic Minority Over-sampling Technique (SMOTE) was implemented. Accuracy, precision, recall, and F1-score metrics were implemented to assess each model's functionality. The results indicate that following the implementation of SMOTE, the Random Forest model attained the best accuracy of 93%, succeeded by Gradient Boosting at 90% and Logistic Regression with 84%. These findings demonstrate that the use of SMOTE significantly improves model sensitivity and balance. This study provides useful insights into designing fairer and more effective predictive systems in the field of e-commerce.
Implementation of an Artificial Neural Network in the Classification of Handwritten Javanese Script Images Rohim, Zainuri; Nasucha, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7625

Abstract

Javanese script is an Indonesian cultural heritage rich in historical, aesthetic, and spiritual values, but it is now becoming marginalized. To reintroduce its use, this research develops a Javanese script recognition application based on an Artificial Neural Network (ANN). In this study, the Javanese script was divided into 120 classes (ha, hi, hu, he, hee, ho, up to nga, ngi, ngu, nge, ngee, ngo). Each class was represented by 40 sample images of the script handwritten by 40 different respondents, resulting in 4800 samples. The research began with preprocessing, which included adding padding to the top, bottom, left, and right sides of the script; downsizing the image to a 33x33 resolution by applying average pooling; image segmentation to separate the script characters from the background; converting the color image to grayscale; and converting the grayscale image to a binary image with the help of thresholding. A number of images that had undergone preprocessing were then structured into a ready-to-use dataset of 4800 samples. This dataset was then divided with an 80:20 ratio, where 80% of the data was used to train the model and 20% was used to validate the model. An evaluation was conducted to measure the model's accuracy. Subsequently, the application was developed using PySide6 as the desktop interface. After the application development, the researchers provided an additional 600 images, where each class was represented by 5 samples, for real-world application testing. The evaluation results showed that the model achieved a validation accuracy of 70.21%. Meanwhile, testing with the application using the additional test images showed an accuracy of 73.83%.
Implementasi Model LSTM, CNN+LSTM Hybrid, dan Transformer untuk Prediksi Cuaca Harian Berbasis Data Multivariat Wulandari, Heptyana Sri; Aziz, RZ Abdul
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7655

Abstract

Global climate change and the increasing frequency of extreme weather events demand more accurate and adaptive weather prediction systems. This study aims to implement and compare three deep learning models, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)+LSTM Hybrid, and Transformer for predicting next-day weather events using daily multivariate meteorological data. The dataset was obtained from the Climatology Station Class IV Lampung and includes air temperature, rainfall, humidity, solar radiation, air pressure, wind direction, and wind speed, collected in CSV format from February 2000 to March 2025. The analysis results indicate that the CNN+LSTM Hybrid model achieved the best performance, with an RMSE of 1.158, MAE of 0.521, R² Score of 0.323, accuracy of 75%, and Macro F1 score of 0.75. The LSTM model demonstrated moderate performance, while the Transformer model yielded the lowest results among the three. These findings suggest that combining CNN's spatial feature extraction with LSTM's sequential processing enhances the prediction quality of short-term weather forecasts based on multivariate data. This study is expected to contribute to the development of AI-based weather forecasting systems in Indonesia, particularly for hydrometeorological disaster mitigation.

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