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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi The Indonesian Journal of Public Health Jurnal Kesehatan Komunitas Indonesian Journal of Obstetrics and Gynecology (Majalah Obstetri dan Ginekologi Indonesia) JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal IDAMAN (Induk Pemberdayaan Masyarakat Pedesaan) Legalite: Jurnal Perundang Undangan dan Hukum Pidana Islam Jurnal MID-Z (Midwivery Zigot) Jurnal Ilmiah Kebidanan ARTERI : Jurnal Ilmu Kesehatan Journal of Intelligent Decision Support System (IDSS) Jurnal Kebidanan Malakbi SKANIKA: Sistem Komputer dan Teknik Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal Kesehatan Masyarakat Mulawarman (JKMM) Kreatif: Jurnal Pemikiran Pendidikan Agama Islam Jutsi: Jurnal Teknologi dan Sistem Informasi Jurnal ABDIMAS-HIP Pengabdian Kepada Masyarakat Jurnal Teknisi INDRA: Jurnal Pengabdian kepada Masyarakat Shihatuna : Jurnal Pengabdian Kesehatan Masyarakat Indonesian Journal of Community Dedication in Health (IJCDH) Scientific Journal Quality : Jurnal Kesehatan Makara Human Behavior Studies in Asia Jurnal Sistem Informasi Jurnal Pengabdian Kepada Masyarakat: Kesehatan (JPKMK) Jurnal Teknologi dan Mutu Pangan Surya Informatika Jurnal Kesehatan dr. Soebandi Jurnal Inovasi Hasil Pengabdian PANDAWA : Jurnal Pengabdian kepada Masyarakat Sadewa: Jurnal Pengabdian Masyarakat Jurnal Integrasi Sains dan Qur'an (JISQu) Jurnal Teknik Informatika dan Desain Komunikasi Visual
Handayani, Yuni
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Journal : JOURNAL OF SCIENCE AND SOCIAL RESEARCH

 1 
PERBANDINGAN ALGORITMA LOGISTIC REGRESSION DAN NAÏVE BAYES CLASSIFIER DALAM IDENTIFIKASI PENYAKIT LIVER Handayani, Yuni; Hidayat, Taufik; Novitaningrum, Dian; Ismail, Abdul Rahman
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2892

Abstract

Abstract: Liver disease is a condition caused by various factors that can damage liver function, such as viral infections and alcohol consumption. Additionally, obesity is closely associated with liver damage. Over time, liver damage can lead to serious consequences. The presence of experts in this field is crucial to addressing liver disease by identifying the symptoms experienced by patients, determining the type of liver disease affecting them, and providing appropriate treatment guidance. The severity of this disease in Indonesia is evident from various studies, research, and related observations. In this study, researchers utilized and compared two data mining classification methods, namely Logistic Regression and Naïve Bayes, to diagnose liver disease. The findings revealed that the Logistic Regression method achieved an accuracy rate of 84.62% with an area under the curve (AUC) value of 0.841, while the Naïve Bayes method achieved an accuracy rate of 83.71% with an AUC value of 0.816. Based on the t-test, it was found that there was no significant difference between the two methods, with a p-value of 0.821 > 0.05. This indicates that the performance of Logistic Regression is comparable to Naïve Bayes in diagnosing liver disease.Keyword: Liver Disease, Logistic Regression, Naïve Bayes, Confusion Matrix, ROC CurveAbstrak: Penyakit hati atau liver adalah kondisi yang disebabkan oleh berbagai faktor yang dapat merusak fungsi hati, seperti infeksi virus dan konsumsi alkohol. Selain itu, obesitas juga memiliki kaitan erat dengan kerusakan hati. Dalam jangka panjang, kerusakan hati dapat menimbulkan konsekuensi serius. Kehadiran ahli di bidang ini sangat diperlukan untuk membantu menangani masalah penyakit hati dengan mengidentifikasi gejala yang dialami pasien, menentukan jenis penyakit hati yang diderita, serta memberikan panduan penanganan yang sesuai. Skala permasalahan penyakit ini di Indonesia dapat diamati melalui berbagai studi, penelitian, dan pengamatan yang telah dilakukan. Dalam penelitian ini, peneliti menerapkan serta membandingkan dua metode klasifikasi data mining, yaitu Logistic Regression dan Naïve Bayes, untuk mendeteksi penyakit liver. Hasil penelitian menunjukkan bahwa Logistic Regression memiliki tingkat akurasi sebesar 84,62% dengan nilai area under the curve (AUC) sebesar 0,841, sementara Naïve Bayes mencapai akurasi 83,71% dengan AUC sebesar 0,816. Berdasarkan hasil uji-t, tidak ditemukan perbedaan signifikan antara kedua metode tersebut, dengan nilai p = 0,821 yang lebih besar dari 0,05. Ini menunjukkan bahwa performa Logistic Regression sebanding dengan Naïve Bayes dalam proses diagnosis penyakit liver.Kata kunci: Penyakit Liver, Logistic Regression, Naïve Bayes, Confusion Matrix, ROC Curve
Co-Authors Aeni, Robikhatul Ahmad Azis Rosyidi Ahmad Syaifudin Anggraeni , Ernawati anggraeni, Ernawati Arruhama, Hanif Ashan, Haves Bagus Prindo Sugihartono Putro Boinem, Boinem Damai, Silvia Darnis, Rahmi Dewi, Yashinta Kumala Fachrizal, Yohana Fadilah, Ainun Fatahillah, Ikhwan Aulia Fauzi , Fivie Faronita Fernandy, Handy Fitriani, Alifia Hafidz, Ananda Akhmad Hakim, Alvin Rainaldy Hakim, Anisa Handayani, Ririn Hendra Dwi Cahyono Heppy, Salma Thahira Adelia Hinayuan Narendra, Hastu Ismail, Abdul Rahman Jemkhairil Kumala D, Yashinta Kustin Kustin Kustin, Kustin Lidiastuti, Riska Maharani, Ridya Kartika Maula, Marwa Zaenil Mitra, Efriza Mufti, Muhammad Zainul Muhammad Khozin Noerfadilah, Siti Novitaningrum, Dian Nurul Arifin, Astri Siska Oktaviani, Rizki Pohan, Sry Dhina Pradana, Bagus Aji Puspita Sari, Melati Putri Maharani Putri, Meyla Eka Putro, Bagus Prindo Sugihartono Ririn Handayani Riskawati Riskawati, Riskawati sari, asri iman Sari, Melati Puspita Sugiono, Muhamad Ishak Tanggasari, Devi Taufik Hidayat Tiee Teduh, Ratu Ayu Yahya, Ali Yuningsih Yuningsih Yuningsih Yusri Yusri Zalika, Laila Karimatu