Optimasi Algoritma K- Nearest Neighbor Berbasis Particle Swarm Optimization Untuk Meningkatkan Kebutuhan Barang

  • Taofik Safrudin * Mail Universitas Pelita Bangsa, Indonesia
  • Gatot Tri Pranoto Universitas Pelita Bangsa, Indonesia
  • Wahyu Hadikristanto Universitas Pelita Bangsa, Indonesia
DOI: https://doi.org/10.47065/bit.v4i3.724
Keywords: Data Mining, PSO, K-NN,Barang Pokok

Abstract

Abstract− The application of the K-Nearest Neighbor algorithm can be implemented where the results also show a new insight, namely predicting the level of need. With a ratio of 90%:10%, where there are 50 data objects tested to predict the level of needs in 2 groups, namely low needs or high needs. The results of the model scenario show that there are 2 objects in the Low needs group and 1 object in the High needs group. In evaluating this model, it was obtained from 10 fold Cross Validation that the Accuracy value was 82%, then the Precision value was 87.50%, and the Recall value was 80%. By measuring the performance of the model with Cross Validation, the resulting accuracy has a standard value or standard deviation, which aims to see the distance between the average accuracy and the accuracy of each experiment. While the Test Results using PSO In the evaluation of this model, it is obtained from 10 fold Cross Validation the Accuracy value is 100%, then the Precision value is 100%, and the Recall value is 100%, the test results have increased significantly

References

J. S. Informasi And F. Teknik, “Optimasi Teknik Klasifikasi Modified K Nearest Neighbor Menggunakan Algoritma Genetika,” No. September, Pp. 130–134, 2014.

A. Ristekdikti Et Al., “Optimasi Algoritma Svm Dan K-Nn Berbasis Particle Swarm Optimization Pada Analisis Sentimen Fenomena Tagar,” Vol. Vi, No. 1, 2020, Doi: 10.31294/Jtk.V4i2.

Y. Darmi And A. Setiawan, “Penerapan Metode Clustering K-Means Dalam Pengelompokan Penjualan Produk,” J. Media Infotama Univ. Muhammadiyah Bengkulu, Vol. 12, No. 2, Pp. 148–157, 2016.

T. Informatika, “Pengelompokkan Loyalitas Pelanggan Dengan Menggunakan Kombinasi Rfm Dan Algoritma K-Means,” Vol. 5, No. 1, Pp. 7–13, 2020.

A. F. Watratan, A. P. B, D. Moeis, S. Informasi, And S. P. Makassar, “Journal Of Applied Computer Science And Technology ( Jacost ) Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia,” Vol. 1, No. 1, Pp. 7–14, 2020.

D. N. Sulistyowati, N. Yunita, S. Fauziah, And R. L. Pratiwi, “Implementation Of Data Mining Algorithm For Predicting Popularity Of Playstore Games In The Pandemic Period Of,” Vol. 6, No. 1, Pp. 95–100, 2020, Doi: 10.33480/Jitk.V6i1.1425.

C. Medel-Ramírez, H. Medel-López, U. Veracruzana, I. De Investigaciones, And S. Económicos, “Data Article . Data Mining For The Study Of The Epidemic ( Sars- Cov-2 ) Covid-19 : Algorithm For The Authors Corresponding Author A Bstract Keywords,” Pp. 1–8.

M. Peta, P. Pasien, C.-D. Kombinasi, And M. U. Fahri, “Jurnal Teknologi Terpadu Journal Of Integrated Technology,” Vol. 6, No. 1, Pp. 25–30, 2020.

S. Hikmawan, A. Pardamean, And S. N. Khasanah, “Sentimen Analisis Publik Terhadap Joko Widodo Terhadap Wabah Covid-19 Menggunakan Metode Machine Learning,” Vol. 20, No. 2, Pp. 167–176, 2020.

N. Dwitri, J. A. Tampubolon, S. Prayoga, And P. P. P. A. N. W. F. I. R. H. Zer, “Penerapan Algoritma K-Means Dalam Menentukan Tingkat Penyebaran Pandemi Covid-19 Di Indonesia,” Vol. 4, No. 1, Pp. 128–132, 2020.

Retno Tri Vulandari, Data Mining. Yogyakarta: Gava Media, 2017.

Suyanto, Data Mining. Yogyakarta: Informatika, 2017.

G. Widi N. Dicky Nofriansyah, Algoritma Data Mining Dan Pengujian. Yogyakarta: Cv Budi Utama, 2015.

O. Villacampa, “(Weka - Thesis) Feature Selection And Classification Methods For Decision Making: A Comparative Analysis,” Proquest Diss. Theses, No. 63, P. 188, 2015.

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Published
2023-09-24
How to Cite
Taofik Safrudin, Tri Pranoto, G., & Hadikristanto, W. (2023). Optimasi Algoritma K- Nearest Neighbor Berbasis Particle Swarm Optimization Untuk Meningkatkan Kebutuhan Barang . Bulletin of Information Technology (BIT), 4(3), 281 - 286. https://doi.org/10.47065/bit.v4i3.724
Issue
Vol 4 No 3: September 2023
Section
Articles

Copyright (c) 2023 Taofik Safrudin, Gatot Tri Pranoto, Wahyu Hadikristanto

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