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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) 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 Bulletin of Information Technology (BIT) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science
Gusti, Siska Kurnia
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : Bulletin of Computer Science Research

 1 
Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine Alwaliyanto; Siska Kurnia Gusti; Iis Afrianty; 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.v5i4.612

Abstract

Stroke is one of the leading causes of death and disability worldwide, making it essential to develop classification models that can assist in early and accurate diagnosis. This study aims to implement the Support Vector Machine (SVM) algorithm with three types of kernels linear, polynomial, and Radial Basis Function (RBF) to classify stroke disease data. The Adaptive Synthetic Sampling (ADASYN) method is employed to address the class imbalance problem, while model training and evaluation are carried out using 5-Fold Cross-Validation to ensure stable and reliable results. The findings indicate that ADASYN successfully improves the model’s sensitivity to stroke cases (the minority class), as reflected by an increase in recall and F1-score, despite a slight decrease in overall accuracy a common trade-off in handling imbalanced data. The linear kernel (after ADASYN) achieved the best performance after imbalance handling, with an average AUC-ROC of 0.8333, recall of 0.7827, and F1-score of 0.2181 for the stroke class. Although the F1-score remains relatively low, it improved compared to the pre-ADASYN results, indicating better detection of stroke cases. The implementation was conducted using Google Colab, which also contributed to efficient data processing and visualization. Overall, the results demonstrate that the combination of SVM and ADASYN is effective in enhancing the model’s sensitivity to minority classes and is well-suited for medical data classification tasks, particularly in the early diagnosis of stroke using machine learning approaches.
Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation Khair, Nada Tsawaabul; Afrianty, Iis; Syafria, Fadhilah; Budianita, Elvia; Gusti, Siska 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.v5i4.637

Abstract

Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
Perbandingan Teknik Penyeimbang Kelas Pada Multi-Layer Perceptron (MLP) Berbasis Backpropagation Untuk Klasifikasi Diabetes Mellitus Robby Azhar; Siska Kurnia Gusti; Iis Afrianty; Elvia Budianita
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.804

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected early; therefore, early diagnosis is highly important. One of the methods that can be applied for early diagnosis is the classification technique in data mining. However, the classification process often faces challenges due to class imbalance, which can reduce model performance. This study aims to analyze the effect of class balancing techniques on the performance of the Backpropagation Neural Network (BPNN) in classifying DM cases. BPNN is a form of Multi-Layer Perceptron (MLP) with a simple structure and the ability to solve complex problems with good accuracy. The dataset used in this study is the Pima Indians Diabetes Dataset, consisting of 768 instances, including 500 non-diabetic and 268 diabetic cases. The research was conducted using three scenarios: without balancing, Synthetic Minority Over-sampling Technique (SMOTE), and Random Under Sampling (RUS). The BPNN model was designed with two architectural variations (one hidden layer and two hidden layers), three learning rate values (0.1, 0.01, and 0.001), and a varying number of neurons. The dataset was divided using the 10-Fold Cross Validation technique. The results show that applying SMOTE achieved the best performance, with an average accuracy of 90.89%, precision of 91.22%, recall of 90.89%, and F1-score of 90.89% on the BPNN architecture with one hidden layer. Furthermore, the single hidden layer architecture proved more stable than the two hidden layers, especially when the dataset size decreased due to RUS. Therefore, the combination of SMOTE and BPNN with one hidden layer provides better performance in classifying Diabetes Mellitus cases.
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki Cut Lira Kabaatun Nisa Destri Putri Yani Devi Julisca Sari Dina Septiawati efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Erni Rouza, Erni Fadhilah Syafria Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Hamwar, Syahbudin Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Khair, Nada Tsawaabul Kurniansyah, Juliandi Lestari Handayani M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Fauzan Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Nazir, Alwis Norhiza, Fitra Lestari Novriyanto Novriyanto Nurul Ikhsan Okfalisa Okfalisa Pizaini Pizaini Prima Yohana Rahmah Miya Juwita Raja Indra Ramoza Ramadhani, Astrid Risfi Ayu Sandika Robbi Nanda Robby Azhar Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syaputra, Muhammad Dwiky Umam, Isnaini Hadiyul Vusuvangat, Imam Wulandari, Fitri Yayuk Wulandari Yelfi Yelfi Yola, Melfa Yusra Yusra, - Yusra, Yusra