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All Journal Jurnal Teknik Hidro PROtek : Jurnal Ilmiah Teknik Elektro ILKOM Jurnal Ilmiah Jurnal Teknologi Terpadu Building of Informatics, Technology and Science JIKA (Jurnal Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) Journal Peqguruang: Conference Series Indonesian Collaboration Journal of Community Services (ICJCS) Transformasi Ceddi Journal of Education CSRID Jurnal Pengabdian Masyarakat (JURDIMAS) DIPANEGARA KOMPUTER SISTEM INFORMASI (DIPAKOMSI)
Nurdiansah, Nurdiansah
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Model Klasifikasi Machine Learning Berbasis Multiple Measurement Distance Arwansyah, Arwansyah; Susanto, Cucut; Nurdiansah, Nurdiansah
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.256-269

Abstract

This study aims to explore and develop a K-Nearest Neighbors (KNN)-based classification model using various distance calculation methods, namely Euclidean, Manhattan, Minkowski, and Hamming Distance. To improve the model’s accuracy, the results from each distance method are combined using a weighted average technique. The datasets used are the Iris and Breast Cancer datasets obtained from the UCI Machine Learning Repository. Preprocessing is carried out using normalization with StandardScaler to ensure uniform feature scaling. The model is tested using cross-validation techniques and evaluated using accuracy metrics and a confusion matrix to assess classification performance. Based on the experimental results, the use of multiple distance methods combined with a weighted average approach yields improved accuracy compared to the conventional KNN method that relies on a single distance calculation. The findings of this study indicate that the combination of distance methods in KNN can enhance model performance in classification tasks. This study is expected to contribute to the development of a more adaptive KNN algorithm tailored to diverse data characteristics.
Co-Authors Abdul Rauf Afdillah Mappatunru, Andi Harun Ahmad Ahmad Ahyuna Ahyuna, Ahyuna Aini Nurul Akbar Bahtiar Akhmad Qashlim, Akhmad Akhriana, Asmah Annah Annah, Annah Ansarullah, Ansarullah Ardimansyah Ardimansyah Arwansyah Arwansyah Asran, Asran Bahtiar, Akbar Cucut Susanto, Cucut Dani Rachman, Suci Rahma Djamro, Risnayanti Andi Erfan Hasmin Faizal Faizal, Faizal Fithriyah Arief Wangsa Hardi Hardi, Hardi Haris, Adammas Harlina, Sitti Hasriani Hasriani, Hasriani Hasryif, Hasryif Hasyrif SY Heriadi Heriadi, Heriadi Heriadi, Herman Herlinda Herlinda, Herlinda Husain Husain Indo Intan Irmawati Irmawati Irmawati Irwan, Muhammad Ahlil Khairi Johanis, Andrew Ridow Karim, Nenny Kasmawaru, Kasmawaru KHADAFI, MUHAMMAD Kunda, Asri Marsa, Marsa Michael Octavianus Milletri Pangloli, Helga Chriesensia Mochtar Yunus, Farhan Mudarsep, Mudarsep Nenny, Nenny Nirwana, Nirwana Nurhudayah Manjani Pratama, Arkjun Yudistira Rahman, Baharuddin S. Kuba, Muhammad Syafa'at Santi Santi Sitti Aisa, Sitti Sopyan, Edy Sri Wahyuni Sulkifly, Muhammad Suryani Suryani Suryani Suryani Syafruddin Syafruddin Syamsudddin, Sadly T, Husain Tamsir, Nurlindasari Thabrani R, Thabrani R Tumiwa, Joseph Tutang, Risma Putri Willy Usman Usman Usman W Soetikno, Y Johny Wangsa, Fithriyah Arief WILDA, WILDA