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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal CoreIT JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Penelitian Tarbawi: Pendidikan Islam dan Isu-Isu Sosial Jurnal Teknologi Dan Sistem Informasi Bisnis Syntax: Journal of Software Engineering, Computer Science and Information Technology Instal : Jurnal Komputer Melek IT: Information Technology Journal Ilmu Komputer untuk Masyarakat Data Sciences Indonesia (DSI) Internet of Things and Artificial Intelligence Journal Journal of Artificial Intelligence and Digital Business JEECS (Journal of Electrical Engineering and Computer Sciences) Jurnal Ilmiah Sistem Informasi Indonesian Journal of Education and Development Research Cosmic Jurnal Teknik
Harahap, Lailan Sofinah
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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

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Comparison of the Performance of Random Forest and K-Nearest Neighbor in Classifying Leukemia Using Principal Component Analysis Sriani, Sriani; Ikhsan, Muhammad; harahap, lailan sofinah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2165

Abstract

Leukemia is the most common blood cancer in Asia, one of which is Indonesia. Leukemia can affect blood cells, bone marrow, lymph nodes and other parts of the lymphatic system. One way to detect leukemia is to use microarray technology by applying gene expression. Microarrays have a very large number of genes so it is necessary to reduce the number of genes in order to eliminate irrelevant features and increase the accuracy of the classification process. The leukemia feature/gene reduction process was carried out using PCA and the classification process was carried out using RF and KNN. The accuracy results from the RF classification method using 100 n_estimators were 78.57%, while using the KNN method the accuracy results with K=1 were 78.57%, K=3 and 5 were 85.71%, and K=7 and 9 were 71.42%. The best accuracy results use KNN with K=3 and 5.
Comparison of the Performance of Random Forest and K-Nearest Neighbor in Classifying Leukemia Using Principal Component Analysis Sriani, Sriani; Ikhsan, Muhammad; harahap, lailan sofinah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2165

Abstract

Leukemia is the most common blood cancer in Asia, one of which is Indonesia. Leukemia can affect blood cells, bone marrow, lymph nodes and other parts of the lymphatic system. One way to detect leukemia is to use microarray technology by applying gene expression. Microarrays have a very large number of genes so it is necessary to reduce the number of genes in order to eliminate irrelevant features and increase the accuracy of the classification process. The leukemia feature/gene reduction process was carried out using PCA and the classification process was carried out using RF and KNN. The accuracy results from the RF classification method using 100 n_estimators were 78.57%, while using the KNN method the accuracy results with K=1 were 78.57%, K=3 and 5 were 85.71%, and K=7 and 9 were 71.42%. The best accuracy results use KNN with K=3 and 5.
Co-Authors Abdillah, Rizky Alamsyah, Muhammad Fadlan Ananda, Surizky Armansyah Aziz, Fahar Abdul Aznawi, Nasrul Mahruf Fadillah, hasti Firjatullah, Maulana Furqon, Mhd Gimnastiar, Alfin Hsb, Adinda Tarisyah Irsyad, Raihan Maulana Januar, Bagus Kembaren, Muhammad Fajar Hidayah Maria Ulfa Muhammad Faruqi Muhammad Ikhsan Nasution, Najwa Fadiyah Pangestu, Aji Dewo Panjaitan, Rosita Prayoga, M Irsan Putri, Laila Ali Ramadhan, M. Wahyu Rifaldy, Faqih Salsabila, Awalia Silalahi, Yayi Naulia Sriani Suci Wulandari Widiya, Widiya