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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Teknika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) jurnal Teknologi Informasi Magister Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) JMM (Jurnal Masyarakat Mandiri) Prosiding Seminar Nasional Darmajaya JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Mathvision : Jurnal Matematika Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Komunitas: Jurnal Pengabidian Kepada Masyarakat Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences Jurnal Abdimas Bina Bangsa JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Konferensi Nasional PKM-CSR Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Publika Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT SWAGATI: Journal of Community Service JIMAD : Jurnal Ilmiah Multidisiplin Journal of Data Science Methods and Applications
Hendra Kurniawan
Institute Of Informatics And Business Darmajaya
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Journal : journal of data science methods and applications

 1 
Prediksi Survivabilitas Pasien Kanker Payudara dengan Penanganan Imbalance Data Menggunakan Algoritma Machine Learning Fikri, Ruki Rizal Nul; Prasetyo, Indra; Soleh, Ary Sofyan; Pratama, Reza Lintang Hana; Kurniawan, Hendra
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Breast cancer is one of the leading causes of death among women worldwide. A major challenge in modeling patient survivability prediction is imbalanced data, where the number of surviving patients significantly outweighs the deceased ones. This study aims to compare the performance of three machine learning algorithms: Logistic Regression, Support Vector Classifier (SVC), and Gradient Boosting Classifier, in predicting patient survivability status. To address the class imbalance issue, Random Over Sampling (ROS) technique was applied during the data preprocessing stage. The methodology includes categorical data encoding, resampling, and model evaluation using accuracy, precision, recall, and F1-score metrics. Experimental results show that the application of ROS successfully balanced the class distribution. Among the three models tested, the Gradient Boosting algorithm demonstrated the best performance compared to linear and vector-based models. This study provides insights into the importance of handling imbalanced data to improve the accuracy of AI-based medical diagnoses.
DIAGNOSIS PCOS BERDASARKAN FAKTOR GAYA HIDUP DAN FAKTOR REPRODUKSI MENGGUNAKAN REGRESI LOGISTIK DAN RANDOM FOREST Kurniawan, Hendra; Kultsum, Rahil Urwa; Safitri, Egi; Antonio, Yandi Jaya; Andini, Rekha Aprilia; Syahputra, Lingga; Adytama, Muhammad Rezky
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder occurring in women of reproductive age, with a global prevalence ranging from 6% to 21%. Current management of PCOS remains limited to symptomatic treatment without addressing the root cause. This study aims to build an accurate predictive model for PCOS diagnosis in Indonesia by analyzing lifestyle and reproductive factors using machine learning algorithms, such as Logistic Regression and Random Forest.The research dataset consists of 541 patient records, which were divided into 80% for training and 20% for testing. The data was normalized using the Min-Max Scaler method, and class imbalance was handled using the SMOTE (Synthetic Minority Oversampling Technique) method. The models were validated using the K-Fold Cross-Validation method and evaluated based on accuracy, precision, recall, and F1-score.The results showed that Logistic Regression with SMOTE in predicting reproductive factors achieved the highest accuracy (82%), while Random Forest with SMOTE demonstrated more stable performance based on average accuracy, particularly for reproductive factors. ROC curve analysis also revealed that Logistic Regression with SMOTE in predicting reproductive factors achieved the highest AUC ($0.84$), making the Logistic Regression model superior in predicting the diagnosis compared to Random Forest. This study confirms that reproductive factors play a more dominant role in predicting PCOS compared to lifestyle factors. Utilizing machine learning algorithms can effectively predict PCOS to support management and prevention, as well as accelerate the early detection process of PCOS.
Co-Authors - Nurjoko Abdi Darmawan Abdullah Merjani, Abdullah Ade Moussadecq Adytama, Muhammad Rezky Agung Pradana Agus Rahardi Ahmad Nur Hakim Amrullah Ahnaf Ronaldo Nadhir Alda Caesar Valensia Alendra Natuah Maensya Andini, Rekha Aprilia Anggreiny, Cut Dini Anita Dewi Purwati Annisa Anggun P Annisa Latifa Antoni Suseno Antonio, Yandi Jaya Assatulaini Assatulaini Astuti, Miguna Azima, Muhammad Fauzan Azima, Muhammad Fauzan Bagus Prihadi Damayanti, Irah Danang Ade Muktiawan Dani Rofianto Denny Andreas Desi Ratna Sari Dewi, Deshinta Arrova Dina Warsahanda Dona Yuliawati Edi Edi Pranyoto Egi Safitri Elsa Agustin Marbun Fajri, Ika Nur Fikri, Ruki Rizal Nul Fitria - Gusnanda Oscar Halimah Halimah Harijanto Wijaya Hasibuan, M.S. Heni Nastiti Hermanto HERMANTO Herwanto, Riko Herwanto, Riko Hikmah, Nor Irawan Setyabudi Irianto, Suhendro Y. Kultsum, Rahil Urwa Kurniawan, Tri Basuki Lilik Joko Susanto M Yusendra M. Zaky Fanany Zaky Maria, Okta Melda Agarina Mochammad Imron Awalludin Muhamad Ariza Eka Yusendra Muhamad Iqbal Ardiansyah Muhammad Ariza Eka Yusendra Muhammad Redintan Justin Muhammad Rezky Adytama Muhammad Sahri Muji Lestari Neni Purwati Niken Larasati Novi Herawadi Sudibyo Nurjoko Nurjoko Nurjoko Nurjoko Nurlistiani, Rini Nursiyanto Pedliyansah, Yogi Prasetyo, Indra Pratama, Raynaldo Syah Pratama, Reza Lintang Hana Pratama, Wanda Andika Raden Abdurrahman Rafli Banu Satrio Raihan Hasbid Rifqatunnisak, Rifqatunnisak Rini Nurlistiani Rizal, Ruki Rizki Aditya Ramadhan Rizky Samjaya Putra Rohiman, Rohiman Rohmat Hidayat, Kardilah Romadona, Romadona Rosali, Rosali Rossa Wulandari Ruki Rizal Ruki Rizal Ruki Rizalnul Fikri Rumini Safitri, Egi Saputra, M Hardi Sasya Nadira Satrio, Rafli Banu Shofiyurrahman Shofiyurrahman Siswahyudianto Soleh, Ary Sofyan Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Sri Karnila Karnila Sri Lestari Sri Rahayu Stefanus Rumangkit Suhendro Yusuf Irianto Sumarya, Edi Supriyadi Susanti Susanti Susanti Susanti Susanto, Lilik Joko Sushanty Saleh Sutedi Sutedi Syahputra, Lingga Syidada, Amran Rahman Theresia, Sumini Tri Erri Astoeti Tri Melda Yama Triyasri, Novita Wicakso Bandung Bondowoso Widijanto Sudhana Wijaya, Nanda Y, M Ariza Eka Y. Suhendro Yan Aditiya Pratama Yogi Pedliyansah Yuda Septiawan Yuni Arkhiansyah Yusminar Yusminar Yusminar Yusminar Zahra Putri Assyfa Zahra, Amalia