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All Journal Jurnal Simantec Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Conference on Innovation and Application of Science and Technology (CIASTECH) Applied Technology and Computing Science Journal Scientific Journal of Informatics E-Link: Jurnal Teknik Elektro dan Informatika G-Tech : Jurnal Teknologi Terapan Prosiding Conference on Research and Community Services Jurnal Teknik Informatika (JUTIF) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) As-Sidanah : Jurnal Pengabdian Masyarakat Jurnal Teknologi dan Manajemen Industri Terapan JUSTIFY : Jurnal Sistem Informasi Ibrahimy "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika International Journal of Health and Information System (IJHIS) Gudang Jurnal Multidisiplin Ilmu Nusantara Journal of Computers and its Applications INTER TECH Jurnal Riset Teknik Komputer Abhakte Journal Of Global Computer Science
Ahmad Homaidi
Universitas Ibrahimy
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Journal : Jurnal Teknik Informatika (JUTIF)

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IMPLEMENTATION OF THE K-NEAREST NEIGHBORS METHOD FOR DETERMINING FETAL HEALTH STATUS Mawaddah, Maulidatul; Homaidi, Ahmad; Lidimillah, Lukman Fakih
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2173

Abstract

Determining the health status of the fetus is a crucial aspect of pregnancy monitoring to reduce the risk of complications and increase the safety of the mother and baby. The K-Nearest Neighbors (KNN) method has been implemented as a classification technique in determining fetal health status based on cardiotocography (CTG) data. This study describes the use of the KNN algorithm to analyze various CTG parameters, including fetal heart rate and uterine contraction frequency, to classify fetal health status into three categories: normal, suspect, and pathologic. The implementation process involves collecting normalized data, selecting relevant features, and using the KNN algorithm with varying K values ​​to determine the most optimal value. The research results show that the KNN method with the right K value can achieve high accuracy in classifying fetal health status, with accuracy reaching up to 89%. These findings indicate that KNN is an effective and reliable method in supporting medical personnel to make decisions based on CTG, which can ultimately improve the quality of maternal and infant health care. In addition, the implementation of this method is relatively simple and can be integrated into existing health systems without requiring large computing resources. Further research is recommended to compare the performance of KNN with other machine learning methods such as Support Vector Machine(SVM) and Random Forest to identify the best method in this context. The use of larger and more diverse data is also expected to increase the accuracy and generalization of the model in various clinical conditions.
Co-Authors Abd Ghofur Abd. Ghofur Abdul Jalil ABDUS SAMAD Abu Dzarrin Al Ghifari Ach. Zubairi Ach. Zubairi Ach. Zubairi Ahmad Ambari Ahmad Jalaludin, Ahmad Ahmad Lutfi Ahmad Lutfi Ahmad Lutfi Ahmad Muflih Wafir S.A Ahmad Yogianto Akhlis Munazilin Ammar Farisi Atika Lina Bahtiarullah, Febri Basufi Baijuri, Achmad Damayanti, Alfina Damayanti Dwitya Sitaresmi Suharjo Edwin Wira Liyanto Efendi, Ahmad Fadil Dwi Eko Fendy Hermawan Fahreza Adams Lazuardy Fatah, Zaehol Fatah, Zaehol Fauzan Firdaus Firman Santoso Firmansyah Widiarto Prabowo Ganang Aji Pambudhi Hali Mukid Hari Santoso Hermanto Hermanto , Hermanto Hermanto Hermanto Hermanto Hermanto Hermanto IKA INDAH LESTARI Irma Yunita Irma Yunita Irma Yunita Irma Yunita irma yunita Jarot Dwi Jarot Dwi Prasetyo Jarot Dwi Prasetyo Jarot Dwi Prasetyo Jarot Dwi Prasetyo Jarot Dwi Prasetyo Khairil Anam, Khairil Lidimilah, Lukman Fakih Lidimillah, Lukman Fakih Lina, Atika Lukman Fakih Lukman Fakih Lidimilah Lukman Fakih Lidimilah Lukman Fakih Lidimillah Lutfi, Zainul Mawaddah, Maulidatul Medi Sugiarto Muhamad Ilhan mansiz Muhammad Dzikry Afandi Muhammad Ramadhani Muwasatil Muhtajin Nabila Nabila Nico Irawan Nori Nur Fasratul Aini Nur Azizah Nur Azizah Prasetyo, Jarot Dwi Ria Nufika Rofiatul Munawaroh Rohiqim Mahtum Saleh, Taufik Santoso, Firman Siti Nur Aizah Sobri, Miftahus Syahrul Ibad Taufik Saleh Taufik Saleh Yanto Yusfi Chusnul Raufah Zaehol Fatah Zahroh, Siti Zainal Arifin Zainur Rohman Zendy Robi Junianto Zubairi, Ach. Zulfa Faradila