Analisis KNN untuk Tempat Rekomendasi Tempat Wisata Sumba Barat
DOI:
https://doi.org/10.61132/neptunus.v2i4.408Keywords:
KNN, Tourist Attractions, RecommendationAbstract
The growth of the internet has influenced the tourism industry because the internet makes it easier for individuals to obtain reviews about places to visit and because the internet is a tool used by tourist site managers to assess the quality of their offerings. The increase in the number of tourists of almost two million in just three years in West Sumba is proof of this influence. Social media is a tool that people use to interact with each other online; some people have multiple accounts on platforms such as Instagram, WhatsApp, Facebook, Telegram, Twitter, and so on. Tourists can receive recommendations for tourist attractions based on price and type of trip desired through a tourist attraction recommendation system that uses the KNN algorithm. Three factors were used in this research: activity, type of tourism, and type of price. An accuracy of 63.16% is found in the test results using the KNN algorithm and the Rapid Miner application with a K value of 5. The analysis results show that the K-Nearest Neighbor (K-NN) approach can be used as a guideline for recommending tourist destinations to visitors in West Sumba.
References
Ainurrohmah. (2021). Akurasi algoritma klasifikasi pada software RapidMiner dan Weka. Prisma, 4, 493–499. Retrieved from https://journal.unnes.ac.id/sju/index.php/prisma/
Amanda, U. R., & Utomo, D. P. (2021). Penerapan data mining algoritma hash based pada data pemesanan buah impor CV. Green Uni Fruit. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 5(1).
Aulia, D., & Budayawan, K. (2023). Penerapan algoritma C4.5 untuk klasifikasi jurusan siswa SMA Negeri 6 Padang. Jurnal Vocational Teknik Elektronika dan Informatika, 11(4), 1–9. Retrieved from http://ejournal.unp.ac.id/index.php/voteknika/index
Ayudhitama, A. P., & Pujianto, U. (2020). Analisa 4 algoritma dalam klasifikasi liver menggunakan RapidMiner. Jurnal Informatika Polinema, 6(2), 1–9. https://doi.org/10.33795/jip.v6i2.274
Hijrah, M., & Adri. (2022). Analisis RapidMiner dan Weka dalam memprediksi kualitas kinerja karyawan menggunakan metode algoritma C4.5. Jurnal Teknik Informatika dan Sistem Informasi, 9(2), 1655–1665.
Indini, D. P., Mesran, & Utomo, D. P. (2023). Penerapan data mining dalam pengelompokan data reseller di Telkomsel Authorized Partner (TAP) Deli Tua dengan algoritma K-Means. Jurnal Ilmiah Media Sisfo, 17(2), 189–202. https://doi.org/10.33998/mediasisfo.17.2.1391
Indini, D. P., Siburian, S. R., Nurhasanah, & Utomo, D. P. (2022). Implementasi algoritma DBSCAN untuk clustering seleksi penentuan mahasiswa yang berhak menerima beasiswa yayasan. In Prosiding Seminar Nasional Sosial, Humaniora, dan Teknologi (pp. 325–331).
Jufani, M. N., Zahro', H. Z., & Achmadi, S. (2023). Pengembangan penentuan sistem pendukung keputusan penjurusan siswa di SMAN 1 Sanggar menggunakan metode Analytical Hierarchy Process (AHP) dan Technique for Order Preference By Similarity To Ideal Solution (TOPSIS). JATI (Jurnal Mahasiswa Teknik Informatika), 6(2), 945–952. https://doi.org/10.36040/jati.v6i2.5405
Mu'Alim, F., & Hiday, R. (2022). Implementasi metode Random Forest untuk penjurusan siswa di Madrasah Aliyah Negeri Sintang. Jupiter, 14(1), 116–125. Retrieved from https://www.neliti.com/publications/441871/implementasi-metode-random-forest-untuk-penjurusan-siswa-di-madrasah-aliyah-nege#cite
Nanang. (2022). Analisis perbandingan algoritma data mining metode decision tree C4.5 dengan Naïve Bayes dalam penjurusan siswa (studi kasus MAN 1 Kota Tangerang Selatan). Scientia Sacra: Jurnal Sains, Teknologi dan Masyarakat, 2(1), 44–61.
Nuraeni, S., Syam, S. P. A., Wajdi, M. F., Firmansyah, B., & Malkan, M. (2023). Implementasi metode K-NN untuk menentukan jurusan siswa di SMAN 02 Manokwari. G-Tech Jurnal Teknologi Terapan, 7(1), 89–95. https://doi.org/10.33379/gtech.v7i1
Paul, V. M., Indra, G. M., Damanik, B. E., Parlina, I., & Saputra, W. (2021). Dalam menentukan kelayakan penerimaan bantuan bedah rumah pada Desa Tiga Dolok. STIKOM Tunas Bangsa Pematangsiantar, 1, 396–409.
Roihan, A., Sunarya, P. A., & Rafika, A. S. (2020). Pemanfaatan machine learning dalam berbagai bidang: Review paper. Indonesian Journal of Computer and Information Technology, 5(1), 75–82. https://doi.org/10.31294/ijcit.v5i1.7951
Soleh, M., Andani, S. R., & Qurniawan, H. (2021). Algoritma C4.5 dalam mengklasifikasi tindak kejahatan yang sering terjadi di Kecamatan Siantar Barat Pematangsiantar. Jurnal Ilmiah, 2(5), 335–340.
Triyanto, A. (2022). Implementasi algoritma C4.5 dalam memprediksi. Jurnal Ilmiah, XI(1), 312–320.
Wanto, A., Siregar, M. N. H., et al. (2020). Data mining algoritma & implementasi. Bandung: Open Library Telkom University.
Witten, I. (2021). Practical machine learning tools and techniques. Burlington: Morgan Kaufmann.
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