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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Elin Haerani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : bulletin of computer science research

 1 
Klasifikasi Kondisi Janin Berdasarkan Data Kardiotogram Menggunakan Algoritma Naive Bayes Isruel Syah Utama; Elin Haerani; Fitri Wulandari; Siti Ramadhani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.584

Abstract

Fetal health during pregnancy is a crucial aspect that can be monitored through cardiotocography (CTG) data; however, manual interpretation of this data often encounters challenges due to class imbalance. This study aims to develop a fetal condition classification model using the Naive Bayes algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE) to address the disparity in class distribution. The CTG dataset, obtained from Kaggle, consists of 2,126 records categorized into three target classes: Normal, Suspect, and Pathological. Data processing followed the Knowledge Discovery in Databases (KDD) framework, including data selection, cleaning, normalization, splitting into four ratios (70:30, 80:20, 85:15, and 90:10), SMOTE application, and model evaluation using accuracy and F1-Macro metrics. The results showed that the 80:20 ratio yielded the highest accuracy at 79.81%, while the 90:10 ratio produced the highest F1-Macro score of 0.6788. These findings indicate that although accuracy remained relatively stable, the F1-Macro metric provided a better representation of performance across all classes, especially minority ones. The application of SMOTE proved effective in balancing class distribution and enhancing model sensitivity. This study serves as a foundational step in developing a more reliable and adaptive fetal condition classification system and highlights opportunities for further exploration of alternative algorithms and SMOTE parameter optimization.
Klasifikasi Kondisi Janin Menggunakan Algoritma K-Nearest Neighbors dan Teknik SMOTE Berdasarkan Data Kardiotogram Dede Fadillah; Elin Haerani; Fitri Wulandari; Fadhilah Syafria
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.585

Abstract

Fetal health is a crucial aspect in reducing infant mortality rates, where cardiotocography (CTG) is used to monitor fetal condition through recordings of fetal heart rate and uterine contractions. However, manual interpretation of CTG data still faces challenges, particularly due to imbalanced class distribution. This study aims to develop a classification model for fetal conditions using the K-Nearest Neighbors (K-NN) algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset used, sourced from Kaggle, consists of 2,126 CTG examinations categorized into three classes: Normal, Suspect, and Pathological. The data processing follows the Knowledge Discovery in Databases (KDD) process, including data selection, cleaning, normalization, splitting, balancing with SMOTE, and classification using K-NN. The model was evaluated using four training-testing split ratios (70:30, 80:20, 85:15, and 90:10) with accuracy and macro F1-score as metrics. The results indicate that the 85:15 split ratio achieved the highest accuracy of 89.7%, while the 90:10 ratio yielded the highest macro F1-score of 0.83. These findings suggest that the 85:15 ratio offers an optimal balance between model training and evaluation, whereas the highest F1-score at 90:10 reflects greater model sensitivity to minority classes. The combination of K-NN and SMOTE proved effective in addressing data imbalance and supports model stability in the overall classification process of fetal conditions.
Klasifikasi Sentimen Masyarakat Terhadap Revisi Undang-Undang Tentara Nasional Indonesia Menggunakan Naïve Bayes Classifier Abdul Haris Kurnia Sandi Harahap; Elin Haerani; Lola Oktavia; Okfalisa Okfalisa; Fitra Kurnia
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.615

Abstract

The revision of the Indonesian National Armed Forces Bill (RUU TNI) has become a hot topic in Indonesian public policy and has sparked controversy among the public due to its sudden emergence and lack of open planning process. This has raised concerns about the potential for military domination and the return of the dual function of the ABRI (Indonesian Armed Forces). The classification of public sentiment towards the RUU TNI is the focus of this study. Comments are categorized into two types of sentiment classes, namely positive and negative. The research stages include data collection, sentiment labeling, data cleaning, text normalization to lowercase letters, sentence or document segmentation into smaller parts, text data normalization, negation handling, stopword removal, and stemming, weighting using the TF-IDF technique, model classification development, and evaluation of the model's performance. The Naïve Bayes Classifier method classified 1,547 comment data points collected from two Instagram social media accounts. The Naïve Bayes Classifier model achieved an accuracy of 83.74%, precision of 81.17%, recall of 87.86%, and an F1-score of 84.38%. This study has limitations, including the limited amount of data collected. These include an imbalance in the amount of data between sentiment categories, data from only one social media platform, and the suboptimal identification of positive and negative sentiments. It is recommended that future research compare this method with other classification methods, expand the dataset, broaden the scope of data collection by involving various social media platforms over a wider time span, thereby providing a more comprehensive picture of public opinion, and test a wider range of algorithm combinations. This study can serve as an initial indicator for rapid policy evaluation, where positive or negative comments from the public on social media can provide important input in assessing the effectiveness of a policy.
Implementasi Fuzzy Sugeno Berbasis IoT untuk Peringatan Kualitas Air Akuarium Ikan Mas Koki Muhammad Taufikur Rahman; Febi Yanto; Elin Haerani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.627

Abstract

The manual monitoring of aquarium water quality is often ineffective due to time constraints and the potential delays in detecting critical parameter changes that can threaten fish health. This research develops a real-time water quality monitoring system for goldfish aquariums based on the Internet of Things (IoT) using the Sugeno fuzzy logic method. The system utilizes an Arduino Uno R4 WiFi microcontroller to process data from turbidity, Total Dissolved Solids (TDS), and water temperature sensors. The Sugeno fuzzy method is chosen for its ability to produce precise numerical outputs based on fuzzy rules. To assess water quality, the sensor data undergoes fuzzification, rule evaluation, implication/aggregation function application, and defuzzification stages. The measurement results are then processed in real-time and sent via WiFi connection to the Blynk application, which serves as a monitoring medium and sender of warning notifications to users when water quality falls outside safe limits, while information is also displayed on the OLED screen of the system. Water quality assessment is classified based on fuzzy output values into several condition categories: 0-20 (Very Good), 21-40 (Good), 41-60 (Fair), 61-80 (Poor), 81-100 (Very Poor). Based on the test results, the system has been proven to effectively detect and classify water quality conditions with high accuracy, as well as provide effective warning notifications. This system is expected to assist aquarium owners in maintaining optimal environmental conditions for the health of goldfish in an automatic, sustainable, and efficient manner.
Penerapan Support Vector Machine Dengan Smote Untuk Klasifikasi Sentimen Pada Data Ulasan Aplikasi Trading View Muhammad Badri; Elin Haerani; Fadhilah Syafria; Okfalisa Okfalisa; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.793

Abstract

In the digital era, user feedback on mobile applications serves as highly valuable information for developers to evaluate app performance. One popular application in the field of finance and investment is TradingView, widely used for technical analysis by traders. User feedback on this application reflects various user sentiments, including positive, negative, and neutral. However, the large volume of reviews and the unstructured nature of text data make manual analysis inefficient and prone to high subjective bias. Therefore, the use of automatic classification methods capable of processing text data with reasonable accuracy is required. This study aims to implement the “Support Vector Machine (SVM)” technique to classify user feedback on the TradingView application. To address the issue of imbalanced sentiment class distribution, the study also employs the “Synthetic Minority Over-sampling Technique (SMOTE)”. The study utilizes 10,000 reviews obtained via web scraping from the Google Play Store. The study workflow consists of text preprocessing, feature extraction using “Term Frequency-Inverse Document Frequency (TF-IDF)”, data balancing, SVM model training, and model evaluation. The evaluation results show that the application of SVM with SMOTE achieves an accuracy of approximately ±85.56% across data splits (70:30, 80:20, 90:10). In each scenario, the highest F1-score was achieved for the positive sentiment class, while the performance of minority classes (negative and neutral) improved after data balancing with SMOTE, with an average F1-score increase of 1.67% for the negative class and 10.67% for the neutral class. Without SMOTE, the average negative F1-score was ±57%, and the neutral class was undetected (0.00%). Furthermore, validation using K-Fold Cross Validation yielded an average accuracy of 89.20%, which increased to 95.10% after applying SMOTE. This improvement was consistent across all data proportions (70:30, 80:20, 90:10), with an average increase of 5.44%. These findings confirm that integrating SVM with SMOTE not only enhances classification performance on imbalanced data but also maintains model stability. Therefore, this study contributes to the advancement of automated sentiment classification systems, particularly for financial mobile app reviews, and can serve as a reference for future research in user review analysis on similar applications.
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pada Komentar Bitcoin Di Aplikasi X Yaskur Bearly Fernandes; Elin Haerani; Fadhilah Syafria; Muhammad Fikry; Lola Oktavia
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.928

Abstract

Social media has become a primary medium for users to express opinions, including those related to Bitcoin, whose fluctuating value often triggers diverse public responses. The large volume of unstructured comments makes manual sentiment analysis inefficient, thereby necessitating an automated approach based on machine learning. This study aims to classify positive and negative sentiments in Bitcoin-related comments on the X platform using the Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting. The dataset consists of 1,750 Indonesian-language comments labeled by three annotators. The data were processed through several preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, and stemming. Model evaluation was conducted using four data split ratios, namely 90:10, 80:20, 70:30, and 60:40. The experimental results indicate that the 90:10 ratio achieved the best performance, with an accuracy of 72.57%, precision of 0.75, recall of 0.73, and an F1-score of 0.67. The SVM model demonstrates strong performance in identifying positive sentiments; however, it is less effective in detecting negative sentiments due to class imbalance in the dataset. As an additional experiment, testing was performed using a balanced dataset obtained through an undersampling process and several SVM kernel types for comparison. The results show that using a balanced dataset leads to more evenly distributed classification performance across sentiment classes, while the linear kernel provides the most stable performance compared to other kernels. Overall, SVM with TF-IDF weighting proves to be an effective approach for sentiment analysis of Bitcoin-related comments on social media.
Klasifikasi Sentimen Bitcoin Terhadap Komentar Di Aplikasi X Menggunakan Metode Decision Tree C4.5 Habibi Putra Indrizal; Fadhilah Syafria; Elin Haerani; Yelvi Vitriani; Yusra Yusra
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.932

Abstract

Sentiment analysis is an important method for understanding user perceptions of cryptocurrency assets such as Bitcoin, whose price movements are strongly influenced by public opinion. This study aims to classify user sentiment from comments posted on the X platform into two classes, namely positive and negative, using the Decision Tree C4.5 algorithm. The dataset consists of 5,000 Indonesian-language comments collected through a web scraping process and processed through text preprocessing and TF-IDF–based feature extraction. The model was trained using a 70% training data and 30% testing data split. The evaluation results show that the C4.5 model achieved an accuracy of 78%. For the positive class, the model obtained a very high recall of 0.99 with an F1-score of 0.83, indicating strong performance in identifying positive comments. In contrast, the negative class achieved a recall of 0.51 with an F1-score of 0.67, despite having a high precision of 0.97. The disparity in performance between classes is influenced by the data distribution, which is not fully balanced, with positive comments being more dominant than negative ones, causing the model to be more sensitive to the majority class. Overall, the results indicate that the Decision Tree C4.5 algorithm is sufficiently effective for Indonesian-language Bitcoin sentiment classification, although it still has limitations in recognizing the minority class. Future research may explore the application of data imbalance handling techniques or more advanced algorithms to improve the balance of classification performance across classes.
Analisis Kinerja Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Penyakit Stroke pada Data Tidak Seimbang Faridatul Jannah; Siska Kurnia Gusti; Elin Haerani; Teddie Darmizal
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1147

Abstract

Stroke is a non-communicable disease with high mortality and disability rates, necessitating a classification approach that can facilitate more effective detection. Class imbalance in stroke datasets causes classification models to be biased toward the majority class, resulting in suboptimal classification performance. This study aims to analyze the performance of Recursive Feature Elimination (RFE) in a Support Vector Machine (SVM) model with data imbalance handling using Adaptive Synthetic Sampling (ADASYN) in stroke classification. The dataset used is a secondary dataset from Kaggle consisting of 5109 data points after the preprocessing stage. The modeling process was conducted by testing various data split ratios as well as combinations of kernels and SVM parameters using a 5-fold cross-validation approach. The results show that the best model was obtained with an 80:20 split ratio, a polynomial kernel, and a C parameter of 0.1, yielding an accuracy of 0.75, precision of 0.14, recall of 0.82, an F1-score of 0.24, and an AUC of 0.8245. The application of RFE resulted in improved model performance compared to without RFE, although the magnitude of the improvement was relatively small. The still low precision value indicates that the model still produces many false positives, so the classification challenge on the stroke dataset has not been fully resolved. On the other hand, an AUC value of 0.8245 indicates that the model performs reasonably well in distinguishing between the two classes overall, although its application in a clinical context still requires further refinement.
Co-Authors Abdul Haris Kurnia Sandi Harahap Alwis Nazir Amelia Irsyada Ayu Sri Wahyuni Dede Fadillah Elvia Budianita Fadhilah Syafria Faridatul Jannah Febby Kurniawan Febi Yanto Fitra Kurnia Fitri Wulandari Gusti, Siska Kurnia Habibi Putra Indrizal Hafsyah Hertati Iis Afrianty Isruel Syah Utama Iwan Iskandar Jasril Jasril Liza Afrianti Liza Afriyanti Lola Oktavia Muhammad Badri Muhammad Fikry Muhammad Irsyad Muhammad Taufikur Rahman Neni Sari Putri Juana Novi Lestari Novriyanto Novriyanto Nurainun Nurainun Nurkholimah Faridhotun Nurul Ramadiah Madjid Okfalisa Okfalisa Putri Azzahra Rahayu Anggraini Reski Mai Candra Rezky Abdillah Risky Silitonga Satria Bumartaduri Siti Ramadhani Sola Huddin Teddie Darmizal Tiara Ramayanti Wahyu Cahyadi Yaskur Bearly Fernandes Yelfi Vitriani Yelvi Vitriani Yusra Yusra