Analysis of Public Purchase Interest in Yamaha Motorcycles Using the K-Nearest Neighbor Method

Authors

  • Diana Juni Triani Universitas Labuhanbatu
  • Muhammad Halmi Dar Universitas Labuhanbatu
  • Gomal Juni Yanris Universitas Labuhanbatu

DOI:

10.33395/sinkron.v8i3.12433

Keywords:

Classification, Confusion Matrix, Data Mining, K-Nearest Neighbor (kNN), Motorcycle.

Abstract

This data mining will carry out a classification of people who are interested and not interested in buying Yamaha motorcycles. In the data mining process, a method is needed that can provide goals to the data mining process. That's because there are many data mining methods that can be used. In this study the method that will be used by the author is the K-Nearest Neighbor (kNN) method. This method will be used to classify people's buying interest in Yamaha motorbikes. This research was conducted because there are some people who say that Yamaha motorbikes are not good, use of wasteful fuel. Therefore this research was conducted to prove this statement. So a research was made about people's buying interest in Yamaha motorbikes. Classification results obtained from 100 community data. From the classification process that has been carried out, the results show that 41 community data (41% representation) are interested in buying Yamaha motorcycles and 59 community data (59% representation) are not interested in buying Yamaha motorbikes. The results obtained state that there are still many people who are interested in Yamaha motorbikes. But it can be used as a reference that people are interested in motorbikes that have a good appearance, use economical fuel and are affordable. These results were obtained from the community's answers in the questionnaire, they were interested in motorbikes that use little fuel, have good designs and are affordable.

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References

Achyunda Putra, F. A. I., Utaminingrum, F., & Mahmudy, W. F. (2020). HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(3), 231. https://doi.org/10.22146/ijccs.54050

Al-Rasheed, A. (2021). Identification of important features and data mining classification techniques in predicting employee absenteeism at work. International Journal of Electrical and Computer Engineering, 11(5), 4587–4596. https://doi.org/10.11591/ijece.v11i5.pp4587-4596

Dhina Nur Fitriana, & Yuliant Sibaroni. (2020). Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(5), 846–853. https://doi.org/10.29207/resti.v4i5.2231

Kurniadi, D., Mulyani, A., & Muliana, I. (2021). Prediction System for Problem Students using k-Nearest Neighbor and Strength and Difficulties Questionnaire. Jurnal Online Informatika, 6(1), 53. https://doi.org/10.15575/join.v6i1.701

Nugraha, K. A., & Herlina, H. (2021). Klasifikasi Pertanyaan Bidang Akademik Berdasarkan 5W1H menggunakan K-Nearest Neighbors. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 7(1), 44. https://doi.org/10.26418/jp.v7i1.45322

Patil, R., & Tamane, S. (2018). A comparative analysis on the evaluation of classification algorithms in the prediction of diabetes. International Journal of Electrical and Computer Engineering, 8(5), 3966–3975. https://doi.org/10.11591/ijece.v8i5.pp3966-3975

Pour, E. S., Esmaeili, M., & Romoozi, M. (2022). Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification. International Journal of Electrical and Computer Engineering, 12(6), 6397–6409. https://doi.org/10.11591/ijece.v12i6.pp6397-6409

Prasetio, R. T. (2020). Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification. Jurnal Online Informatika, 5(2), 153. https://doi.org/10.15575/join.v5i2.656

Sanjaya, R., & Fitriyani, F. (2019). Prediksi Bedah Toraks Menggunakan Seleksi Fitur Forward Selection dan K-Nearest Neighbor. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(3), 316. https://doi.org/10.26418/jp.v5i3.35324

Supriyadi, D., Safitri, S. T., Amriza, R. N. S., & Kristiyanto, D. Y. (2022). Klasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning. JEPIN (Jurnal Edukasi Dan Penelitian Informatika), 8(April), 38–43. https://doi.org/10.26418/jp.v8i1.49300

Uçar, T., & Karahoca, A. (2021). Benchmarking data mining approaches for traveler segmentation. International Journal of Electrical and Computer Engineering, 11(1), 409–415. https://doi.org/10.11591/ijece.v11i1.pp409-415

Waliyansyah, R. R., & Fitriyah, C. (2019). Perbandingan Akurasi Klasifikasi Citra Kayu Jati Menggunakan Metode Naive Bayes dan k-Nearest Neighbor (k-NN). Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(2), 157. https://doi.org/10.26418/jp.v5i2.32473

Watratan, A. F., B, A. P., Moeis, D., Informasi, S., & Makassar, S. P. (2020). Implementation of the Naive Bayes Algorithm to Predict the Spread of Covid-19 in Indonesia. Journal of Applied Computer Science and Technology, 1(1), 7–14.

Yassir, A. H., Mohammed, A. A., Alkhazraji, A. A. J., Hameed, M. E., Talib, M. S., & Ali, M. F. (2020). Sentimental classification analysis of polarity multi-view textual data using data mining techniques. International Journal of Electrical and Computer Engineering, 10(5), 5526–5534. https://doi.org/10.11591/IJECE.V10I5.PP5526-5534

YAVUZ, Ö. (2022). A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract. International Scientific and Vocational Studies Journal. https://doi.org/10.47897/bilmes.1029171

Yun, H. (2021). Prediction model of algal blooms using logistic regression and confusion matrix. International Journal of Electrical and Computer Engineering, 11(3), 2407–2413. https://doi.org/10.11591/ijece.v11i3.pp2407-2413

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How to Cite

Triani, D. J., Dar, M. H. ., & Yanris, G. J. . (2023). Analysis of Public Purchase Interest in Yamaha Motorcycles Using the K-Nearest Neighbor Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1238-1254. https://doi.org/10.33395/sinkron.v8i3.12433

Issue

Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023

Section

Articles

License

Copyright (c) 2023 Diana Juni Triani, Muhammad Halmi Dar, Gomal Juni Yanris

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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