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All Journal Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika CommIT (Communication & Information Technology) Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Buletin Poltanesa Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Algoritma Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Egia Rosi Subhiyakto, Egia Rosi
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Journal : Jurnal Algoritma

 1 
Analisis Komparatif Akurasi Prediksi Kanker Payudara Menggunakan Algoritma Random Forest dan Logistic Regression Hulaifah Al Abrori, Zahra Zul; Subhiyakto, Egia Rosi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2164

Abstract

This study analyzes the performance of Random Forest and Logistic Regression algorithms in detecting breast cancer using datasets from Kaggle. Evaluation was done based on metrics such as accuracy, precision, recall, and F1-score to classify benign and malignant cancers. Logistic Regression recorded 98% accuracy, with 99% precision for benign class and 98% for malignant class, and 99% recall for both classes. Meanwhile, Random Forest showed an accuracy of 96%, a precision of 96% for benign class and 98% for malignant class, and a recall of 99% for benign class and 93% for malignant class. This study contributes by highlighting the superiority of Logistic Regression in producing more accurate and consistent results on simple datasets, while Random Forest shows greater potential in handling data with more complex patterns. Different from previous studies, this research emphasizes the importance of matching dataset characteristics with the selected algorithm to improve the accuracy of early breast cancer detection. These results are expected to support evidence-based decision-making in the clinical field, especially in choosing the algorithm that best suits the needs and resource constraints.
Klasifikasi Penyakit Gagal Jantung Menggunakan Algoritma Naive Bayes Faradeya, Muhammad Az-Zauqy; Subhiyakto, Egia Rosi
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2178

Abstract

Heart failure disease shows an alarming increase in global prevalence with significant clinical impact complexity. This study implements the Naive Bayes algorithm to predict heart failure risk, presenting a solution that is more computationally and interpretationally efficient than the high computationally time-consuming Random Forest or SVM with 92% accuracy. The methodological approach includes structured data preprocessing, including missing value handling, feature development, scale normalization, and dataset balancing. The application of K-Fold Cross Validation with K variations (2, 4, 5, 10) achieved optimal performance at K=4 with an accuracy of 85.1%, which enabled a reduction in the misdiagnosis rate to 14.9%. Achieving a precision of 81.1%, recall of 86.1%, and AUC-ROC of 0.914 contributed to savings in treatment costs through early identification accuracy. The system can be integrated in automated screening for efficient allocation of medical resources, resulting in significant operational savings through prioritization of high-risk patients and timely preventive interventions. Performance stability with consistent AUC-ROC (0.91-0.92) makes it a reliable foundation for clinical decision support systems that improve overall patient outcomes.
Co-Authors Abiyyi, Ryandhika Bintang Abu Salam Adhitya Nugraha Andreas Wilson Setiawan Ardytha Luthfiarta Astuti, Yani Parti Astuti, Yani Parti Astuti, Yani Parti Bahres, Salim Bin Sulaiman Cahyati, Ade Puput Catur Supriyanto Dzaky, Azmi Abiyyu Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faradeya, Muhammad Az-Zauqy Faudyta, Haniifa Aliila Fauzi Adi Rafrastara Guruh Fajar Shidik Hafidhoh, Nisa'ul Hapsari, Swita Amalia Hulaifah Al Abrori, Zahra Zul Ika Novita Dewi Iskandar, Marcelino Junta Zeniarja Kurniawan, Defri Kusumasari, Dinita Liya Umaroh, Liya Manurung, Ayub Michaelangelo Miyanthi, Sheryn Aulia Mutia Rahmi Pratiwi Ningrum, Novita Kurnia Ningrum, Novita Kurnia Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Paramita, Cinantya Prajanto Wahyu Adi, Prajanto Wahyu Prihatmoko, Radig Gedhe Pulung Nurtantio Andono Puspitaningrum, Hapsari Retno Rahadian, Arief Ramadhani, Frida Rohman, Muhammad Syaifur Sabilillah, Ferris Tita Safina, Nabila Safina, Nabila Santoso, Ilham Savicevic, Anamarija Jurcev Septiani, Karlina Dwi Simbolon, Calvin Samuel Sinaga, Jesica Trivena Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Triono, Justin Matthew Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Widi Utomo, Emanuel Pinesthi Winarsih, Nurul Anisa Sri Yunianto, Amar Haris