ANALISIS PERFORMA METODE K-NEAREST NEIGHBOR UNTUK IDENTIFIKASI JENIS KACA


Mus Mulyadi Baharuddin(1); Huzain Azis(2*); Tasrif Hasanuddin(3);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(*) Corresponding Author

  

Abstract


Nowadays, the industry makes various types of goods that have glass-based materials, float car window panes, non-float building windows, lamps, jars, and tableware. These glasses have the same production material, the difference between one and the other is the composition of the production material. K-Nearest Neighbor (KNN) algorithm which is one of the classification methods in data mining and also a supervised learning algorithm in machine learning is a method for classifying objects based on learning data that is the closest distance to the object.. This study discusses the performance measurement (accuracy, precision, recall and f-measure) of the KNN method with a variety of values on 1000 glass type production data objects obtained from the central UCI Machine Learning Repository dataset. The conclusion of this research is the results of the value of K = 3 to K = 9, the best performance values obtained at K = 3, where the level of accuracy reaches 64%, 63% precision, 71% recall, and F-Measure of 67%.


Keywords


K-Nearest Neighbor; classification; supervised learning; data mining; machine learning

  
  

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doi  https://doi.org/10.33096/ilkom.v11i3.489.269-274 		  

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References


R. Adi, “Implementasi Algoritma K-Nearest Neighbor Untuk Identifikasi Implementasi Algoritma K-Nearest Neighbor Untuk Identifikasi Kualitas Air ( Studi Kasus : Pdam Kota Surakarta ),” no. April, 2018.

A. Apriansyah, Ilhamsyah, and T. Rismawan, “Prototype Kunci Otomatis Pada Pintu Berdasarkan Suara Pengguna Menggunakan Metode KNN (K-Nearest Neighbor),” J. Coding, Sist. Komput. Untan, vol. 04, no. 1, pp. 45–56, 2016.

I. A. A. Angreni, S. A. Adisasmita, and M. I. Ramli, “Pengaruh Nilai K Pada Metode K-NEAREST NEIGHBOR (KNN) Terhadap Tingkat Akurasi Identifikasi Kerusakan Jalan,” vol. 7, no. 2, pp. 63–70, 2018.

A. A. Karim, H. Azis, and Y. Salim, “Kinerja Metode C4.5 dalam Penyaluran Bantuan Dana Bencana 1,” vol. 3, no. 2, pp. 84–87, 2018.

M. Lestari, “Penerapan Algoritma Klasifikasi Nearest Neighbor (K-NN) Untuk Mendeteksi Penyakit Jantung,” Fakt. Exacta, vol. 7, no. September 2010, pp. 366–371, 2014.

H. Leidiyana, “Penerapan Algoritma K-Nearest Neighbor Untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bemotor,” J. Penelit. Ilmu Komputer, Syst. Embed. Log., vol. 1, no. 1, pp. 65–76, 2013.

N. Fadhillah, H. Azis, and D. Lantara, “Validasi Pencarian Kata Kunci Menggunakan Algoritma Levenshtein Distance Berdasarkan Metode Approximate String Matching,” vol. 3, no. 2, pp. 3–7, 2018.

Gavin Hackeling 2014, Mastering Machine Learning with scikit-learn. .

M. amayr. & Stephane.ploix, “Machine Learning with Python and Scikit-Learn,” 2015.

A. Fitria, Muslim, and H. Azis, “Analisis Kinerja Sistem Klasifikasi Skripsi menggunakan Metode Naïve Bayes Classifier,” vol. 3, no. 2, pp. 102–106, 2018.

B. Santoso, “Bahasa Pemrograman Python di Platform GNU/LINUX,” pp. 1–9, 2016.

K. R. R, A. Rahmansyah, W. Darwin, and A. R. Box, “Penggunaan Bahasa Pemrograman Python Sebagai Pusat Kendali Pada Robot 10-D,” pp. 23–26, 2017.

C. A. Ul Hassan, M. S. Khan, and M. A. Shah, “Comparison of Machine Learning Algorithms in Data classification,” 2018 24th Int. Conf. Autom. Comput., no. September, pp. 1–6, 2019.

L. Nurhayati and H. Azis, “Perancangan Sistem Pendukung Keputusan Untuk Proses Kenaikan Jabatan Struktural Pada Biro Kepegawaian,” pp. 6–7, 2016.

N. Puspitasari, V. N. Vadilla, U. Hairah, H. Azis, M. Wati, and E. Budiman, “Usability Study of Student Academic Portal from a User ’ s Perspective,” 2018 2nd East Indones. Conf. Comput. Inf. Technol., pp. 108–113, 2018.


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