Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset
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Abstract
Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.
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References
A. Rahma, D. Claudia, F. A. Yulianto, and N. Romadhona, “Systematical Review : Pengaruh Olahraga Sepeda terhadap Penurunan Berat Badan Pada Dewasa Muda,” J. Integr. Kesehat. Sains, vol. 3, no. 1, pp. 117–123, Mar. 2021, doi: 10.29313/jiks.v3i1.7427.
C. J. L. Murray et al., “Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019,” Lancet, vol. 396, no. 10258, pp. 1223–1249, Oct. 2020, doi: 10.1016/S0140-6736(20)30752-2.
F. Khozeimeh et al., “Intralesional immunotherapy compared to cryotherapy in the treatment of warts,” Int. J. Dermatol., vol. 56, no. 4, pp. 474–478, Apr. 2017, doi: 10.1111/ijd.13535.
N. C. Clarissa and I. Defiana, “Rumah Singgah untuk Penderita Kanker dengan Konsep Healing Environment,” J. Sains dan Seni ITS, vol. 7, no. 2, Feb. 2019, doi: 10.12962/j23373520.v7i2.35295.
A. Dwiantoro, I. Maulana, N. P. Damayanti, and R. N. Al Zahra, “Artificial intelligence (AI) imaging for enhancement of parking security,” J. Student Res. Explor., vol. 1, no. 1, pp. 15–20, Dec. 2022, doi: 10.52465/josre.v1i1.110.
F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, A. Khosravi, P. Layegh, and S. Nahavandi, “An expert system for selecting wart treatment method,” Comput. Biol. Med., vol. 81, pp. 167–175, Feb. 2017, doi: 10.1016/j.compbiomed.2017.01.001.
A. Supriyatna and W. P. Mustika, “Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 2, no. 2, p. 152, Sep. 2018, doi: 10.30645/j-sakti.v2i2.78.
G. A. W. SARI, “Peningkatan Akurasi Algoritma C4.5 Menggunakan Diskritisasi Dan Pemilihan Fitur Berbasis Korelasi,” Universitas Gadjah Mada, 2020. Accessed at: https://etd.repository.ugm.ac.id/penelitian/detail/196384
Y. A. Pratama, “Analisis Metode Seleksi Fitur untuk Meningkatkan Akurasi pada Variant Metode Klasifikasi K-Nearest Neighbor (kNN),” Universitas Sumatera Utara, 2019. Accessed at: https://repositori.usu.ac.id/handle/123456789/22016
A. R. Safitri and M. A. Muslim, “Improved Accuracy of Naive Bayes Classifier for Determination of Customer Churn Uses SMOTE and Genetic Algorithms,” J. Soft Comput. Explor., vol. 1, no. 1, Sep. 2020, doi: 10.52465/joscex.v1i1.5.
R. Siringoringo, “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma Smote dan K-Nearest Neighbor,” J. Inf. Syst. Dev., vol. 3, no. 1, pp. 44–49, 2018. Accessed at: https://ejournal-medan.uph.edu/index.php/isd/article/view/177
M. H. Ramdani, I. G. P. S. Wijaya, and R. Dwiyansaputra, “Optimalisasi Pengenalan Wajah Berbasis Linear Discriminant Analysis Dan K-Nearest Neighbor Menggunakan Particle Swarm Optimization,” J. Teknol. Informasi, Komput. dan Apl., vol. 4, no. 1, pp. 40–51, 2022. Accessed at: https://jtika.if.unram.ac.id/index.php/JTIKA/article/view/167
L. I. Prahartiwi and W. Dari, “Komparasi Algoritma Naive Bayes, Decision Tree dan Support Vector Machine untuk Prediksi Penyakit Kanker Payudara,” J. Tek. Komput., vol. 7, no. 1, pp. 51–54, Feb. 2021, doi: 10.31294/jtk.v7i1.9191. Accessed at: https://ejournal.bsi.ac.id/ejurnal/index.php/jtk/article/view/9191
R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 646, Dec. 2021, doi: 10.32493/informatika.v5i4.7622.
R. Y. Hayuningtyas, “Penerapan Algoritma Naïve Bayes untuk Rekomendasi Pakaian Wanita,” J. Inform., vol. 6, no. 1, pp. 18–22, Apr. 2019, doi: 10.31294/ji.v6i1.4685.
U. Erdiansyah, A. Irmansyah Lubis, and K. Erwansyah, “Komparasi Metode K-Nearest Neighbor dan Random Forest Dalam Prediksi Akurasi Klasifikasi Pengobatan Penyakit Kutil,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 1, p. 208, Jan. 2022, doi: 10.30865/mib.v6i1.3373.
A. Nurdina and A. B. I. Puspita, “Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis,” J. Inf. Syst. Explor. Res., vol. 1, no. 2, pp. 83–92, 2023, doi: https://doi.org/10.52465/joiser.v1i2.167.
R. Harun, K. C. Pelangi, and Y. Lasena, “Penerapan Data Mining untuk Menentukan Potensi Hujan Harian dengan Menggunakan Algoritma K Nearest Neighbor (KNN),” J. Manaj. Inform. dan Sist. Inf., vol. 3, no. 1, pp. 8–15, 2020.
Y. I. Kurniawan and T. I. Barokah, “Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor,” J. Ilm. Matrik, vol. 22, no. 1, pp. 73–82, Mar. 2020, doi: 10.33557/jurnalmatrik.v22i1.843.
M. R. Handoko and N. Neneng, “Sistem Pakar Diagnosa Penyakit Selama Kehamilan Menggunakan Metode Naive Bayes Berbasis Web,” J. Teknol. dan Sist. Inf., vol. 2, no. 1, pp. 50–58, 2021. Accessed at: http://jim.teknokrat.ac.id/index.php/sisteminformasi/article/view/739
H. Putri, A. I. Purnamasari, A. R. Dikananda, O. Nurdiawan, and S. Anwar, “Penerima Manfaat Bantuan Non Tunai Kartu Keluarga Sejahtera Menggunakan Metode NAÏVE BAYES dan KNN,” Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 331–337, Dec. 2021, doi: 10.47065/bits.v3i3.1093.
L. Alfaris, R. C. Siagian, A. C. Muhammad, U. I. Nyuswantoro, N. Laeiq, and F. D. Mobo, “Classification of Spiral and Non-Spiral Galaxies using Decision Tree Analysis and Random Forest Model: A Study on the Zoo Galaxy Dataset,” Sci. J. Informatics, vol. 10, no. 2, pp. 139–150, May 2023, doi: 10.15294/sji.v10i2.44027.
V. Anestiviya and A. F. O. Pasaribu, “Analisis Pola Menggunakan Metode C4.5 untuk Peminatan Jurusan Siswa Berdasarkan Kurikulum (Studi Kasus : SMAN 1 Natar),” J. Teknol. dan Sist. Inf., vol. 2, no. 1, pp. 80–85, 2021. Accessed at: http://jim.teknokrat.ac.id/index.php/sisteminformasi/article/view/393
S. Febriani and H. Sulistiani, “Analisis Data Hasil Diagnoga Untuk Klasifikasi Gangguan Kepribadian Menggunakan Algoritma C4.5,” J. Teknol. dan Sist. Inf., vol. 2, no. 4, pp. 89–95, 2021. Accessed at: http://jim.teknokrat.ac.id/index.php/sisteminformasi/article/view/1373
F. Khozeimeh, R. Alizadehsani, M. Roshanzamir, and P. Layegh, “Immunotherapy Dataset,” UCI Mach. Learn. Repos., 2018, doi: https://doi.org/10.24432/C5DC72.
A. M. Siregar and A. Puspabhuana, DATA MINING: Pengolahan Data Menjadi Informasi dengan RapidMiner, Pertama. Surakarta: CV Kekata Group, 2017. Accessed at: https://books.google.co.id/books?id=rTlmDwAAQBAJ&printsec=frontcover&hl=id#v=onepage&q&f=false
A. Y. Rangan, M. I. Ukkas, and S. Qomariah, “Implementasi Metode Support Vector Mechine dan K-Means Clustering dengan Support Vector Mechine Tema Karya Ilmiah pada Stmik Widya Cipta Dharma,” vol. 3, pp. 274–279, 2018. https://jurnal.poltekba.ac.id/index.php/prosiding/article/view/605