OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES

Authors

  • I Made Satria Bimantara Universitas Udayana, Indonesia
  • I Made Widiartha Universitas Udayana, Indonesia

DOI:

https://doi.org/10.21107/kursor.v12i01.269

Keywords:

Clustering, K-Means, Optimization, Particle Swarm Optimization

Abstract

K-Means Algorithm can be used to group tourists based on reviews on tourist destination objects. This algorithm has a weakness that is sensitive to the determination of the initial centroid. The initial centroid that is determined at random will decreasing the level accuracy, often gets stuck at the local optimum, and gets a random solution. Optimization algorithms such as PSO can overcome this by determining the optimal initial centroid. The optimal number of clusters (K) will be determined using the Elbow method by calculating the SSE value of the resulting cluster. The average Silhouette Coefficient (SC) is used to measure the quality of the clusters produced by the K-Means Algorithm with and without the PSO Algorithm. This study uses secondary data obtained from the UCI Machine Learning Repository with the name Travel Reviews Data Set which consists of 980 records and 10 attributes. The test results show that K=2 is the optimal number of clusters. The K-Means and PSO Algorithm gives an average SC value of 0.300358 which is better than without the PSO Algorithm of 0.300076. The optimal PSO hyperparameter generated is the number of particles=30, \varphi_1=2.2, and {\ \varphi}_2=3 at maximum iteration of 100.

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References

[1] M. Nurjanah and T. Arifin, “PENERAPAN ALGORITMA K-MEANS UNTUK ANALISIS DATA ULASAN DI SITUS TRIPADVISOR,” JURNAL RESPONSIF, vol. 3, no. 1, pp. 75–82, 2021, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti
[2] A. Kustanto, “PARIWISATA : SEBAGAI SALAH SATU UPAYA MEMPEROLEH DEVISA BAGI PEMERINTAH INDONESIA,” 2019.
[3] S. A. Azzahra and A. Wibowo, “ANALISIS SENTIMEN MULTI-ASPEK BERBASIS KONVERSI IKON EMOSI DENGAN ALGORITME NAÏVE BAYES UNTUK ULASAN WISATA KULINER PADA WEB TRIPADVISOR,” vol. 7, no. 4, 2020, doi: 10.25126/jtiik.202071907.
[4] F. K. Wardhani, E. Suryani, and A. Mukhlason, “Penerapan metode GA-Kmeans untuk pengelompokan pengguna pada Bapersip Provinsi Jawa Timur,” JURNAL TEKNIK ITS, vol. 1, no. 1, pp. 545–550, 2012.[5]S. Rustam, H. A. Santoso, and C. Supriyanto, “OPTIMASI K-MEANS CLUSTERING UNTUK IDENTIFIKASI DAERAH ENDEMIK PENYAKIT MENULAR DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION DI KOTA SEMARANG,” 2018.
[6] B. Santoso, I. Cholissodin, and B. D. Setiawan, “Optimasi K-Means untuk Clustering Kinerja Akademik Dosen Menggunakan Algoritme Genetika,” 2017. [Online]. Available: http://j-ptiik.ub.ac.id
[7] D. A. Kuntjoro, B. Darma Setiawan, and R. S. Perdana, “Algoritme Genetika Untuk Optimasi K-Means Clustering Dalam Pengelompokan Data Tsunami,” 2018. [Online]. Available: http://j-ptiik.ub.ac.id
[8] Y. P. Anggodo, W. Cahyaningrum, A. N. Fauziyah, I. L. Khoiriyah, O. Kartikasari, and I. Cholissodin, “HYBRID K-MEANS DAN PARTICLE SWARM OPTIMIZATION UNTUK CLUSTERING NASABAH KREDIT,” vol. 4, no.2, pp. 2355–7699, 2017.
[9] Suyanto, Swarm Intelligence Komputasi Modern Untuk Optimasi dan Big Data Mining. Bandung: Informatika Bandung, 2017.
[10] Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data, Revisi. Bandung: Informatika Bandung, 2018.
[11] I. Hidayatin, S. Adinugroho, and C. Dewi, “Pengelompokan Wilayah berdasarkan Penyandang Masalah Kesejahteraan Sosial (PMKS) dengan Optimasi Algoritme K-Means menggunakan Self Organizing Map (SOM),” 2019. [Online]. Available: http://j-ptiik.ub.ac.id
[12] A. Primandana, S. Adinugroho, and C. Dewi, “Optimasi Penentuan Centroid pada Algoritme K-Means Menggunakan Algoritme Pillar (Studi Kasus: Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Timur),” 2019. [Online]. Available: http://j-ptiik.ub.ac.id
[13] H. Muhamad, C. A. Prasojo, N. A. Sugianto, L. Surtiningsih, and I. Cholissodin, “OPTIMASI NAÏVE BAYES CLASSIFIER DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION PADA DATA IRIS,” vol. 4, no. 3, pp. 180–184, 2017.
[14] N. F. Istighfarin, R. A. Rahmastati, and H. Nugroho, “PENERAPAN METODE PARTICLE SWARM OPTIMIZATION (PSO) DAN GENETIC ALGORITHM (GA) PADA SISTEM OPTIMASI VISIBLE LIGHT COMMUNICATION (VLC) UNTUK MENENTUKAN POSISI ROBOT,” Jurnal SIMETRIS, vol. 11, no. 1, pp. 279–286, Apr. 2020.
[15] M. Sharif, J. Amin, M. Raza, M. Yasmin, and S. C. Satapathy, “An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor,” Pattern Recognit Lett, vol. 129, pp. 150–157, Jan. 2020, doi: 10.1016/j.patrec.2019.11.017.
[16] T. M. Phan, P. T. Ha, T. L. Duong, and T. T. Nguyen, “Improved particle swarm optimization algorithms for economic load dispatch considering electric market,” International Journal of Electrical and Computer Engineering, 10Jurnal Ilmiah KURSOR Vol. 12, No. 1, Juli 2023, hal 1-10vol. 10, no. 4, pp. 3918–3926, 2020, doi: 10.11591/ijece.v10i4.pp3918-3926.
[17] A. Rahman and H. M. Asih, “Optimizing shipping routes to minimize cost using particle swarm optimization,” International Journal of Industrial Optimization, vol. 1, no. 1, pp. 53–60, 2020.
[18] R. Wati, “PENERAPAN ALGORITMA NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI BERITA HOAX PADA MEDIA SOSIAL,” Jurnal Ilmu Pengetahuan dan Teknologi Komputer, vol. 5, no. 2, pp. 9–14, 2020, [Online]. Available: www.bsi.ac.id
[19] T. Arifin and D. Ariesta, “PREDIKSI PENYAKIT GINJAL KRONIS MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER BERBASIS PARTICLE SWARM OPTIMIZATION,” Jurnal Tekno Insentif, vol. 13, no. 1, pp. 26–30, Apr. 2019, doi: 10.36787/jti.v13i1.97.
[20] K. S. Nugroho, I. Istiadi, and F. Marisa, “Naive Bayes classifier optimization for text classification on e-government using particle swarm optimization,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 1, pp. 21–26, Jan. 2020, doi: 10.14710/jtsiskom.8.1.2020.21-26.
[21] F. Yunita, Pranowo, and A. J. Santoso, “Hybrid model of particle swarm and ant colony optimization in lecture schedule preparation,” in AIP Conference Proceedings, American Institute of Physics Inc., Jun. 2018. doi: 10.1063/1.5042895.
[22] D. Novianti, D. Anggraini, and P. Hapsari, “Analisis Perbandingan Algoritma Particle Swarm Optimization Dan Firefly Algorithm Dalam Menentukan Minimum Spanning Tree,” Rang Teknik Journal, vol. I, no. 2, 2018, [Online]. Available: http://joernal.umsb.ac.id/index.php/RANGTEKNIKJOURNAL
[23] W. A. Setyowati and W. F. Mahmudy, “Optimasi Vektor Bobot Pada Learning Vector Quantization Menggunakan Particle Swarm Optimization Untuk Klasifikasi Jenis Attention Deficit Hyperactivity Disorder (ADHD) Pada Anak Usia Dini,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 11, pp. 4428–4437, Nov. 2018.
[24] I. Romadhona, I. Cholissodin, and Marji, “Penerapan Algoritme Particle Swarm Optimization-Learning Vector Quantization(PSO-LVQ) Pada Klasifikasi Data Iris,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 12, pp. 6418–6428, 2018, [Online]. Available: http://j-ptiik.ub.ac.id
[25] A. T. Rahman, “Coal Trade Data Clusterung Using K-Means (Case Study PT. Global Bangkit Utama).”
[26] B. Purnama, Pengantar Machine Learning Konsep dan Praktikum Dengan Contoh Latihan Berbasis R dan Python. Bandung: Informatika, 2019.
[27] Suyanto, Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika Bandung, 2018.
[28] E. Susilowati, A. T. Hapsari, M. Efendi, and P. E. Kresnha, “DIAGNOSA PENYAKIT KANKER PAYUDARA MENGGUNAKAN METODE K-MEANS CLUSTERING,” JUST IT: Jurnal Sistem Informasi, Teknologi Informasi dan Komputer, vol. 10, no. 1, pp. 27–32, 2019, [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it
[29] T. Z. Khalaf, H. Çağlar, A. Çağlar, and A. N. Hanoon, “Particle swarm optimization based approach for estimation of costs and duration of construction projects,” Civil Engineering Journal (Iran), vol. 6, no. 2, pp. 384–401, Feb. 2020, doi: 10.28991/cej-2020-03091478.
[30] Suyanto, A. Arifianto, R. Rita, and S. Andi, Evolutionary Machine Learning Pembelajaran Mesin Otonom Berbasis Komputasi Evolusioner. Bandung: Informatika Bandung, 2020

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Published

2023-06-30

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