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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Sains dan Informatika Jurnal Nasional Komputasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Computer Science Research
Fitri Wulandari
Fakultas Sains Dan Teknologi Universitas Islam Negeri (UIN) Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Neuroscience Transportation Other

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Journal : bulletin of computer science research

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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.
Co-Authors Adhitya Pramata Alwis Nazir Amany Akhyar Amelia Irsyada Dede Fadillah Dimas Rifqi Ekaryanto Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Fadhilah Syafria Farhan Adib Surya Febi Nur Salisah Fitri Insani Gusti, Siska Kurnia Isruel Syah Utama Liza Afriyanti Muhammad Affandes Muhammad Irsyad Muhammad Ridho Amrullah Nurul Ikhsan Reski Mai Candra Rice Novita Rinto Setiawan Siti Ramadhani Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Andika Yusra