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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 777 Documents
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Stock Price Prediction Using LSTM and XGBoost with Social Media Sentiment Harani, Nisa Hanum; Marismati, Marismati
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.v7i2.8284

Abstract

The influence of social media on financial markets is growing and motivates research on the predictive role of sentiment in stock price movements. Bank Negara Indonesia (BBNI) is part of the Danantara holding company, and BBNI's strategic position is an important indicator for measuring the performance of the broader financial ecosystem in Indonesia. This study analyzes the influence of social media sentiment on the stock price prediction of Bank Negara Indonesia (BBNI), which is part of the state-owned holding company Danantara. Historical market data is combined with sentiment indicators obtained from public conversations on X/Twitter. Daily sentiment features are then integrated with market variables, including OHLCV data, to form a combined dataset. Two machine learning approaches were employed: Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). The results revealed contrasting patterns between the two models. The LSTM Baseline consistently produced RMSE around (≈46–65) across all scenarios. However, XGBoost-Extended is the best-performing and recommended model for sentiment-integrated prediction with RMSE (≈30–40).
Narasi Presiden Indonesia: Analisis Wacana Politik Menggunakan BERTopic dalam Mengungkap Pola Tematik Pidato Presiden Uliyatunisa, Uliyatunisa; Tukiyat, Tukiyat; Waskita, Arya Adhyaksa; Handayani, Murni; Zain, Rafi Mahmud
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.v7i2.8298

Abstract

The speeches of the President of Indonesia play an important role as a means of political communication, policy delivery, and leadership image building in front of the public. However, the increasing volume of speeches presents new challenges in the manual analysis process, as it is time-consuming and prone to researcher subjectivity. This study offers a solution by using BERTopic, a transformer-based topic modelling method that utilises semantic representations from modern embedding models. The research data consists of transcripts of President Joko Widodo's official speeches obtained from the Cabinet Secretariat portal. To improve the quality of semantic representations, this study compares several Indonesian language embedding models, namely DistilBERT, NusaBERT, IndoE5, and SBERT. The analysis process was carried out through the stages of data preprocessing, embedding formation, dimension reduction, clustering, and model evaluation using topic coherence metrics. The objectives of this study were to reveal the themes contained in the President's speeches and to evaluate the effectiveness of embedding models in producing more coherent topics. The results show twenty main themes that consistently appear, including infrastructure development, economic policy, health and the pandemic, digital transformation, international diplomacy, sports, nationalism issues, and regional development. In terms of performance, SBERT provides the best results with a coherence value of UMass = -2.036 and NPMI = 0.082, indicating a positive semantic relationship. A UMass value close to zero indicates greater coherence of words within a topic, while an NPMI value above zero indicates that the connections between words are more easily understood by humans. This research contributes to the development of NLP-based political discourse studies in Indonesia, providing an empirical overview of the selection of appropriate embedding models in topic modelling and opening up opportunities for the integration of similar methods in public policy analysis.
Analisis Sentimen Publik Terhadap Deepfake AI Menggunakan Aplikasi X Dengan Metode Support Vector Machine dan Naive Bayes Classifier Al Afif, Satria; Suryono, Ryan Randy
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.v7i2.8303

Abstract

The rapid development of artificial intelligence (AI) technology has driven increased public interaction with AI-based platforms, including Deepfake AI. One of the main challenges that arises is how to objectively assess public opinion, particularly on social media, which serves as a primary medium for expressing opinions. This study aims to compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB), in analyzing public sentiment toward Deepfake AI on the X social media platform. The research dataset consists of 7,774 tweets collected between October and November 2024. After preprocessing, 5,559 tweets were used, categorized into three sentiment classes: positive, negative, and neutral. Data imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), with 80% of the data allocated for training and 20% for testing. The results show that before applying SMOTE, the SVM algorithm achieved the highest accuracy at 71%, while Naïve Bayes only reached 62%. After the application of SMOTE, the performance of both algorithms improved, with SVM achieving 77% accuracy and Naïve Bayes reaching 68%. Thus, SVM proved to be the best-performing algorithm in this study, both before and after SMOTE application, delivering more balanced results across sentiment classes. This research demonstrates that sentiment analysis based on machine learning can be utilized to understand public opinion toward AI platforms, while also providing valuable insights for developers to improve service quality and strengthen public trust.
Integrasi K-Modes dalam Analisis Data Gizi Balita untuk Model Klasifikasi Risiko Stunting Desyanti, Desyanti; Renaldi, Reno; Mesran, Mesran
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.v7i2.8323

Abstract

The nutritional status of toddlers is an important indicator in assessing child growth and development and is closely related to the risk of stunting. However, the process of recording and classifying nutritional status at the Bukit Kapur Community Health Center is still done manually, making it prone to analysis delays and data processing errors. This study aims to implement the K-Modes algorithm in classifying toddler nutritional status based on categorical data, such as age, weight, and height. Toddler data were collected from the Bukit Kapur Community Health Center and underwent pre-processing, data transformation, and the application of the K-Modes algorithm to determine toddler nutritional groups. The results showed that the K-Modes algorithm was able to group toddler data into three main categories: well-nourished, at-risk of overnutrition, and overnourished. The majority of toddlers fell into the well-nourished category (98 toddlers), while only a small proportion fell into the at-risk of overnutrition and overnourished categories (1 toddler each). These findings indicate that the K-Modes method is effective in classifying toddler nutritional status based on categorical data and can assist health workers in monitoring child growth and preventing stunting.
Stacking Machine Learning Approach for Predicting Thermal Stability of Zinc–Metal Organic Frameworks (Zn-MOF) Pratama, Ananta Surya; Irnanda, Muhammad Diva; Umam, Taufiqul; Nugroho, Dandy Prasetyo; Azies, Harun Al
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.v7i2.8329

Abstract

Thermal stability is a fundamental parameter that determines the feasibility of Metal Organic Frameworks (MOF) for high-temperature industrial applications, including catalysis, gas purification, and energy storage. Experimental evaluation of thermal stability, while accurate, is often costly and time-consuming, highlighting the need for computational prediction models that are both efficient and dependable. This study develops a Quantitative Structure Property Relationship (QSPR) model using a stacking ensemble regression framework to predict the thermal stability of Zn-MOFs. The stacking approach combines Linear Regression, Lasso Regression, and Huber Regression as base learners, with Linear Regression serving as the meta-model, thereby leveraging the complementary strengths of individual algorithms. Results demonstrate that the stacking ensemble consistently outperformed all single models, delivering highly reliable predictions that remained stable across multiple validation scenarios. Furthermore, external validation with experimental data confirmed the model’s robustness and its ability to generalize beyond the training dataset. These findings underline the reliability of stacking as not only a tool for improving accuracy but also for ensuring predictive stability and reproducibility. The study highlights the potential of machine learning, particularly ensemble methods, as a powerful and trustworthy predictive framework for the rational design of thermally stable MOFs, offering both scientific and industrial significance in sustainable energy applications.
Comparison of Certainty Factor, Dempster Shafer, and Bayes' Theorem in Expert Systems for Diagnosing Female Reproductive System Diseases Mesran, Mesran; Rasli, Roznim Mohamad; Setiawansyah, Setiawansyah; Arshad, Muhammad Waqas
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.v7i2.8334

Abstract

Expert systems are one application of artificial intelligence used to mimic the ability of an expert in diagnosing a disease. This study aims to compare the performance of three inference methods Certainty Factor, Dempster-Shafer, and Bayes' Theorem in the diagnosis of female reproductive system diseases. Symptom data and expert knowledge values were obtained from medical experts to support the system's validity. Each method was implemented on the same symptom data, and the results were analyzed to assess the consistency of the diagnoses produced. The results show that the Certainty Factor method produced a diagnosis of Cervical Cancer with the highest confidence value of 0.9999, followed by the Dempster-Shafer method with the same diagnosis and a confidence value of 0.852. However, the Bayes Theorem method produced a different diagnosis, namely Ovarian Cyst, with a confidence value of 0.911. These differing results indicate that the characteristics and approaches of each method significantly influence the final diagnosis outcome. This study contributes insights to expert system developers regarding the strengths and weaknesses of each inference method. The selection of the appropriate method must be tailored to the system's requirements, data complexity, and the level of uncertainty in the medical information used.
Perbandingan Algoritma Naive Bayes, Random Forest, dan Support Vector Machine Terhadap Pandangan Masyarakat Mengenai Revisi Undang-Undang TNI di Instagram Nasrul, Royhan; Yudhistira, Aditia
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.v7i2.8164

Abstract

The revision of the Indonesian National Army Law (TNI Law), enacted in 2025, sparked widespread controversy within society, particularly concerning issues of civilian supremacy and potential military dominance. With the growing use of social media as a platform for public expression, platforms such as Instagram have become the primary medium for the public to voice their opinions regarding this issue. This study aims to analyze public sentiment toward the revision of the TNI Law by utilizing text classification algorithms, namely Naive Bayes, Random Forest, and Support Vector Machine (SVM). Data was collected from 28,669 Instagram comments and analyzed through stages of data crawling, preprocessing, and labeling. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Subsequently, classification was performed using the three algorithms, with evaluation metrics including accuracy, precision, recall, and F1-score. The results after SMOTE demonstrated that the SVM algorithm delivered the best performance with an accuracy of (92%), followed by Random Forest at (88%), and Naive Bayes at (76%). Consequently, SVM was deemed the most effective in capturing patterns of public sentiment objectively. This research is expected to contribute to the advancement of digital public opinion studies and support the evaluation process of national defense policies

Page 78 of 78 | Total Record : 777

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