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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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Perbandingan Algoritma NBC Dan SVM Untuk Melakukan Analisis Sentimen Terhadap PP NO.82 Tahun 2021 Rani, Arum Mustika; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6496

Abstract

Government Regulation (PP) No. 82/2021, which regulates the payment of pensions and allowances for Constitutional and Supreme Court Justices, has sparked public debate, especially after allegations of significant cuts to the Supreme Court's budget. This issue raises concerns regarding policy transparency, making it important to analyze public sentiment towards this PP. This study uses two sentiment analysis methods, namely Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM), to evaluate public opinion based on data from Twitter. The dataset consists of 2,719 tweets that have gone through preprocessing stages, such as cleansing, stemming, and using SMOTE techniques, with 70% data division for training and 30% for model testing. This study tests the performance of NBC and SVM through four scenarios: (1) without stemming and without SMOTE, (2) without stemming with SMOTE, (3) with stemming without SMOTE, and (4) with stemming and SMOTE. The results show that SVM has a more stable performance than NBC in all scenarios. In the scenario without stemming and without SMOTE, both models recorded 100% accuracy, but NBC failed to detect positive sentiment accurately. When SMOTE was applied without stemming, NBC's accuracy decreased to 97%, while SVM still achieved a perfect accuracy of 100%. In the scenario with stemming without SMOTE, NBC recorded 97% accuracy, while SVM reached 99%. With the application of SMOTE and stemming, NBC accuracy decreased to 95%, while SVM again recorded a perfect accuracy of 100%. This study concludes that SVM is the best method for sentiment analysis of PP No. 82 of 2021, especially in scenarios with stemming and SMOTE, providing important insights into public opinion and confirming the superiority of SVM in sentiment classification related to public policy.
Implementasi LDA, TF-IDF, dan BERT dalam Sistem Rekomendasi Dosen Pembimbing untuk Mahasiswa Syabilla, Mutiara; Zeniarja, Junta; Nabila, Qotrunnada
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6499

Abstract

The selection of thesis supervisors is often done manually, which tends to be time-consuming in matching students' research topics with the expertise of faculty members. This study develops a thesis supervisor recommendation system based on the title and abstract of students' final projects, integrating Latent Dirichlet Allocation (LDA), Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations from Transformers (BERT). The research dataset includes 1,096 records from 71 faculty members in the Informatics Engineering Department at Universitas Dian Nuswantoro, collected through Google Scholar. The analysis process begins with text preprocessing such as case folding, tokenization, and stemming, followed by topic analysis using LDA, term-specific weighting through TF-IDF, and context-rich vector representation using BERT. The model matches students' research topics with faculty expertise using Cosine Similarity. Evaluation results show an accuracy of 80%, precision of 66%, and recall of 19%, indicating that the model can provide accurate recommendations, though some relevant items are still missed. This model proves effective in facilitating the selection of thesis supervisors. This research is expected to assist students in finding suitable supervisors and help faculty members identify students with relevant research interests.
Comparison of Naive Bayes and SVM Methods for Identifying Anxiety Based on Social Media Nugraha, Endri Rizki; Maharani, Warih
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6506

Abstract

This research aims to detect anxiety patterns from social media posts using Naive Bayes (NB) and Support Vector Machine (SVM) algorithms. Tweets are extracted using Data Crawling techniques, then continued their way into labeling using Depression Anxiety Stress Scale (DASS-42) questionnaire along with Random Oversampler to balance out the unbalanced dataset and NB and SVM were chosen for their effectiveness in text sentiment classification. This study integrates textual features obtained from the Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) methods. The study compares the performance of these algorithms in detecting anxiety using datasets from the X platform. The comparison aims to identify the advantages and limitations of each method in handling textual sentiment data. This research aims to analyze sentiment data by calculating accuracy, recall, and F1-score to determine the most optimal performance outcome. The results indicate that the SVM with TF-IDF feature extraction achieved the highest accuracy of 72% and an average F1-Score of 61%, while the NB with BoW achieved 56% accuracy and an average F1-Score of 49%. These findings highlight the effectiveness of combining SVM and TF-IDF features which improve model effectiveness with SVM producing the best overall result in identifying anxiety from social media data.
Penerapan Naïve Bayes Untuk Analisis Sentimen Pada Ulasan Aplikasi Mobile Legends Perkasa, Attila Elang; Putri, Astrid Novita
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6507

Abstract

Indonesia has become one of the potential markets for the gaming industry, continually increasing the number of gamers. One of the most popular mobile games in Indonesia is Mobile Legends. Mobile Legends is an online multiplayer battle arena (MOBA) video game developed by Moonton, a game development company based in China. The influence of reviews on the reputation of an app also affects potential new users, whether the reviews are positive or negative. Research has shown that the Naïve Bayes model provides good accuracy for sentiment analysis. This study is expected to help understand the perceptions and experiences of players of the game. The study uses the KDD (Knowledge Discovery in Data) method due to its advantages in identifying organized patterns from a complex dataset, making the data easier to understand. During the research process, 320,513 positive reviews, 185,777 negative reviews, and 20,210 neutral reviews were obtained. The accuracy value remained constant at 87% for the 80:20 data split scenario. Performance on the negative class showed high precision at 88%. The negative recall was 92%, indicating that the model could accurately capture truly negative reviews. A stable F1-Score of 82% signifies a good balance between precision and recall for the negative class. Performance on the positive class showed 87% precision and 83% recall. The F1-Score between the two was nearly balanced, indicating that the model performed similarly for both labels overall.
Classification of Key and Time Signature in Western Musical Notation by using CRNN Algorithm with Bounding Box Soeroso, Dennis Adiwinata Irwan; Winarno, Sri; Luthfiarta, Ardytha; Aryanti, Firda Ayu Dwi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6510

Abstract

This research seeks to employ the Convolutional Recurrent Neural Network (CRNN) algorithm to develop a method for classifying key and time signatures from sheet music images. The research design involved compiling a dataset of 285 sheet music images, which includes 15 types of key signatures and 19 types of time signatures. The methodology encompasses annotation using the bounding box technique, image preprocessing, and applying the CRNN model for classification using K-Fold Cross Validation because of the limited dataset. Then, the model is evaluated using the Multi Class Confusion Matrix and performance metrics. The primary findings of this study reveal that the developed model achieves 96% accuracy in key signature classification and 95% in time signature classification when utilizing bounding boxes. Conversely, the absence of bounding boxes substantially negatively impacted the accuracy of key signature classification, resulting in only a 58% accuracy rate. Time signature classification performed even worse, with an accuracy of just 19%. This research highlights the substantial accuracy enhancements achievable by incorporating bounding boxes. Therefore, we anticipate that this research will help singers, especially those in choirs, to understand and express music better using existing technologies while enhancing the accuracy of optical music recognition using the CRNN model.
Klasifikasi Citra X-Ray Tuberkulosis Menggunakan Convolutional Neural Networks Mubarak, Haykal Alya; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6515

Abstract

Tuberculosis (TB) is a serious infectious disease that is still one of the main causes of death in the world, especially in developing countries. X-ray image analysis is an important step in controlling this disease. This research aims to classify X-ray images of tuberculosis using a deep learning approach with three Convolutional Neural Networks (CNN) architectures: DenseNet201, Xception, and MobileNetV2. The dataset used consists of 3,000 X-ray images, divided into two categories: normal and TBC, obtained from Kaggle, which are then processed through normalization, augmentation, and data division using the hold-out method with a ratio of 70:30, 80:20 , and 90:10. The research results show that DenseNet201 with the Nadam optimizer at 90:10 data division produces the highest accuracy of 100%, making it the best combination for TBC X-ray image classification. Xception achieved the best accuracy of 96.66% with the Nadam optimizer at a data split of 80:20. MobileNetV2 shows an optimal accuracy of 98.69% using the Adam optimizer at a 90:10 data split. This research proves that DenseNet201 with the Nadam optimizer is very effective in handling medical image classification, especially for tuberculosis. These results provide an important contribution to the development of deep learning-based technology to improve the accuracy of tuberculosis diagnosis.
Klasifikasi Citra CT Scan Kanker Paru-Paru Menggunakan Pendekatan Deep Learning Mulya, Anggi; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6528

Abstract

This research aims to develop a reliable deep learning model for classifying CT-scan images of lung cancer. This research has the advantage of evaluating the performance of several Convolutional Neural Networks (CNN) architectures including DenseNet121, InceptionResNetV2, InceptionV3 and ResNet152V2 to compare their performance in classification accuracy. The dataset consists of 1,561 CT scan images obtained from Kaggle and the dataset is categorized into malignant cancer, benign cancer and normal. Through a combination of innovative data pre-processing techniques, such as augmentation with random rotation and normalization, division of the dataset using the hold-out method with ratios of 70:30, 80:20, and 90:10, and model training using Adam's optimizer and SGDM, researchers achieved very high classification accuracy. The evaluation results showed that InceptionV3 with SGDM optimizer at 90:10 ratio achieved performed very well with an accuracy of 99.38% while InceptionResNetV2 with Adam optimizer at 80:20 hold-out the highest performance, with an accuracy of 99.40%. These promising results indicate great potential in supporting the early discovery of lung cancer, thereby improving the accuracy of diagnosis and the chances of patient recovery. This research opens up opportunities for further development, such as the application of fine-tuning, ensemble learning, or integration with clinical decision support systems for medical applications.
Optimizing Mental Health Classification on Reddit: A Comparative Study of Adam, RMSProp, and SGD with L2 Regularization Putra, Vander Mulya; Zeniarja, Junta
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6532

Abstract

The rising prevalence of mental health discussions on social media platforms has created new opportunities for understanding and supporting individuals facing psychological challenges. This study examines the automated classification of mental health content on Reddit, focusing on five clinically significant conditions (ADHD, anxiety, bipolar disorder, depression, and PTSD) and non-clinical discussions. Reddit was selected as the primary data source due to its unique subreddit structure and rich user-generated content in mental health communities, where individuals actively seek support and share experiences. Using a Multi-layer Perceptron (MLP) architecture, the study conducted a comprehensive evaluation of three optimization algorithms (Adam, RMSProp, and SGD) in conjunction with L2 regularization (λ=0.01) for mental health text classification. The study incorporated Easy Data Augmentation (EDA) techniques to enhance model robustness, implementing paraphrase-based augmentation methods that improved classification performance by 3%. Through systematic evaluation across multiple metrics, the study found that the RMSProp optimizer without L2 regularization achieved optimal performance, demonstrating 83% precision and 82% recall across all diagnostic categories. Notably, the application of L2 regularization consistently resulted in decreased model performance across all optimizers, with performance degradation ranging from 3% to 52%. These findings contribute to the development of more accurate automated mental health monitoring systems while highlighting the critical role of optimizer selection in mental health-related Natural Language Processing (NLP) tasks.
Klasifikasi Sentimen Pengguna X Terhadap Pemboikotan Produk Pro Israel Menggunakan Algoritma Machine Learning Susanti, Pingki Muliya; Afdal, M; Permana, Inggih; Marsal, Arif
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6533

Abstract

The campaign to boycott pro-Israel goods emerged as a result of the enduring conflict between Israel and Palestine. This boycott initiative led to a decline in sales, which adversely impacted the livelihoods of employees, manifesting in diminished bonuses, salary reductions, and job terminations. Such actions elicited a variety of reactions from the public on platform X. This study seeks to categorize the sentiments of X users regarding the boycott of pro-Israel products by comparing the efficacy of Machine Learning algorithms, namely Support Vector Machine and Random Forest. To address the class imbalance within the dataset, this research employs the synthetic minority over-sampling technique (SMOTE). The dataset comprised 2,275 entries, gathered through web scraping methods on the X platform. The findings indicate that a majority of X users in Indonesia endorse the boycott movement, exhibiting a positive sentiment of 58%. The SVM algorithm, when combined with SMOTE, demonstrated the highest performance in sentiment classification, achieving an accuracy of 90.54%, whereas Random Forest attained an accuracy of only 83.1%. This research offers insights into the views of the Indonesian populace regarding the boycott of pro-Israel products.
Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Algoritma Machine Learning Pada Sosial Media X Triningsih, Elsa; Afdal, M; Permana, Inggih; Rozanda, Nesdi Evrilyan
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6534

Abstract

The government has launched the Free Nutritious Meal Program as part of a strategic effort to reduce stunting in Indonesia. However, the program has generated a lot of controversy among the public, especially regarding the large budget allocation that is considered burdensome and its impact on the education sector and the country's financial stability. This study aims to analyze public sentiment towards the program by utilizing data from social media platform X (Twitter) as much as 2,400 data. Public sentiment is classified into three categories, namely positive, negative, and neutral, using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest. In addition, the SMOTE technique is used to handle data imbalance in the model training process. The analysis results showed that negative sentiments dominated at 46%, with the main issue highlighted being the high budget allocation and its impact on education. In terms of performance, the SVM algorithm with SMOTE produced the highest accuracy of 85.74%, outperforming the Random Forest algorithm which only achieved 81.53% accuracy.

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