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All Journal JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer
Siti Ramadhani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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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.
Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra Yusra; Muhammad Fikry; Surya Agustian; 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.v5i4.626

Abstract

Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
Implementasi Regresi Linier Berganda Untuk Prediksi Harga Mobil Bekas Di Indonesia Berbasis Gradio M Ridho Alfani; Elvia Budianita; Lestari Handayani; Siti Ramadhani
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.v6i4.1097

Abstract

The price of a used vehicle depends on various aspects that cause changes in the selling value in the market, such as model, year, transmission, mileage, fuel, tax, mpg, and cc. A common problem in used car transactions is determining prices that are still not fully based on measurable data analysis. The purpose of this study is to design a model to estimate the price of a used car through the multiple linear regression method and implement it in the User Interface. The data used in this study is secondary data obtained from the Kaggle public repository, and collected from several used car buying and selling forums in Pekanbaru and social media platforms such as Facebook that contain vehicle price information. The dataset contains 400 rows of data with a range of car years from 2005 to 2025. The research stages include data preprocessing in the form of categorical variable encoding and data normalization. Data is divided into training data and testing data, followed by the process of model building and model performance assessment. Evaluation is carried out using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²) metrics. The model was built using several independent variables, namely model, year, transmission, kilometer, fuel, tax, mpg, and cc, with vehicle price as the dependent variable. Based on the test results, the multiple linear regression method shows the ability to produce used car price estimates and has potential for application in decision support systems. The test results show that the MSE value on the training data is 0.004 and the testing data is 0.010, MAE on the training data is 0.046 and the testing data is 0.071, and RMSE on the training data is 0.062 and 0.100 on the testing data, the coefficient of determination (R²) on the training data is 0.985 and on the testing data is 0.955. The next model is implemented using the Python Gradio library so that users can predict vehicle prices through the User Interface.
Prediksi Saham Berdasarkan Data Teknikal Serta Fundamental Menggunakan Algoritma XGBoost Yoga Nur Pradana; Fitri Insani; Jasril Jasril; Siti Ramadhani
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.v6i4.1126

Abstract

The capital market has an important role in the economy as a means of investment and fundraising, with banking sector stocks being one of the main contributors to market capitalization in Indonesia. However, the investment decision-making process often faces obstacles in the form of limited investors' ability to comprehensively analyze fundamental and technical data, as well as irrational behavior that causes decisions to be less than optimal. This conditions encourage the need for a more objective and data-driven approach to help predict stock price movements. The results of the model evaluation on the test data showed excellent performance: BCA obtained a MAPE of 2.8% and an R² of 0.9488; BNI with MAPE 3.06% and R² 0.8863; Bank Mandiri with a MAPE of 4.70% and R² 0.9114; and BRI with MAPE of 2.48% and R² 0.8872. Based on this model, the results of the share price prediction for 2026 show that BCA is predicted to experience a significant increase from IDR 7,756 (January) to IDR 7,846 (June), while Bank Mandiri is predicted to grow from IDR 5,211 (January) to IDR 5,930 (June). BNI and BRI are predicted to experience an increase in share prices, respectively from IDR 3,327 (January) to IDR 3,683 (June) and from IDR 4,124 (January) to IDR 4,541 (June). This research contributes by presenting a stock prediction model that combines technical and fundamental data at once, applied to four major Indonesian banks Bank Central Asia, Bank Rakyat Indonesia, Bank Mandiri, dan Bank Negara Indonesia in a single modeling framework. This approach has proven to produce good accuracy with an average MAPE of 3.13% and R² 0.919, as well as being a more objective alternative for investors in analyzing stock price movements. However, the prediction results obtained in this study are analytical tools and are not intended as direct investment recommendations.
Co-Authors Alfin Hernandes Alfitra Salam Elin Haerani Elvia Budianita Fadhilah Syafria Febi Yanto Fitri Insani Fitri Wulandari Gusti, Siska Kurnia Isruel Syah Utama Jasril Jasril Lestari Handayani M Ridho Alfani Muhammad Fikry Muhammad Iqbal Maulana Raja Indra Ramoza Risfi Ayu Sandika Satria Bumartaduri Surya Agustian Yoga Nur Pradana Yusra Yusra