PENERAPAN TEKNOLOGI MACHINE LEARNING DENGAN METODE VADER PADA APLIKASI SENTIMEN TAMU DI HOTEL DYMENS
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Abstract
Penelitian ini bertujuan untuk menerapkan teknologi Machine Learning dengan menggunakan metode VADER (Valence Aware Dictionary and sEntiment Reasoner) dalam menilai tingkat kepuasan tamu di Hotel DYMENS. Metode VADER digunakan untuk menganalisis data berupa ulasan dan komentar yang diberikan oleh tamu di platform daring, seperti situs ulasan hotel atau media sosial. Dalam penelitian ini, berfokus pada masalah penilaian kepuasan tamu secara akurat dan objektif. Data dikumpulkan dari 200 ulasan dan komentar tamu yang terdiri dari 60% ulasan positif, 30% ulasan netral, dan 10% ulasan negatif. Setiap ulasan diberi skor dari -1 hingga 1, dengan -1 untuk ulasan negatif, 0 untuk ulasan netral, dan 1 untuk ulasan positif. Menerapkan teknologi Machine Learning dengan metode VADER, kami mengolah data ulasan dan komentar untuk menentukan skor sentimen keseluruhan dari setiap ulasan. Dari hasil analisis, didapatkan bahwa 75% tamu merasa sangat puas (skor sentimen > 0.8), 20% merasa cukup puas (skor sentimen antara 0.4 hingga 0.8), dan 5% mengalami ketidakpuasan (skor sentimen < 0.4). Hasil penelitian ini membantu manajemen Hotel DYMENS dalam memahami kebutuhan dan preferensi tamu dengan lebih baik. Dengan menggunakan teknologi Machine Learning dan metode VADER, hotel dapat meningkatkan layanan dan pengalaman tamu dengan lebih tepat sasaran.
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References
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Achmad, W., & Yulianah, Y. (2022). Corporate Social Responsibility of the Hospitality Industry in Realizing Sustainable Tourism Development. Enrichment: Journal of Management, 12(2), 1610–1616.
Aditiya, D., & Latifa, U. (2023). Uji Efektivitas Penerapan Machine Learning Classification Untuk Survey Kepuasan Pelanggan Maskapai Penerbangan X. Barometer, 8(1), 9–18. https://doi.org/10.35261/barometer.v8i1.6566
Aichner, T., Grünfelder, M., Maurer, O., & Jegeni, D. (2020). Twenty-Five Years of Social Media: A Review of Social Media Applications and Definitions from 1994 to 2019. Cyberpsychology, Behavior, and Social Networking, 24(4), 215–222. https://doi.org/10.1089/cyber.2020.0134
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1
Ben Chaabene, W., Flah, M., & Nehdi, M. L. (2020). Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials, 260, 119889. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2020.119889
Diana, Y., & Hadi, F. (2023). Analisa Penjualan Menggunakan Algoritma K-Medoids Untuk. Joisie Journal Of Information System And Informatics Engineering, 7(1), 97–103.
Farki, A., Baihaqi, I., & Wibawa, M. (2016). Pengaruh online customer review rating terhadap kepercayaan place di indonesia. 5(2).
Fitri Ayu and Nia Permatasari. (2018). perancangan sistem informasi pengolahan data PKL pada divisi humas PT pegadaian. Jurnal Infra Tech, 2(2), 12–26.
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40–55. https://doi.org/10.1038/s41580-021-00407-0
Hutto, C.J. and Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for. Eighth International AAAI Conference on Weblogs and Social Media, 18.
Ikhsan. (2014). Penentuan Titik Pemasangan Akses Point Pada Gedung Dengan Memanfaatkan Aplikasi Wireless Wizard Dalam Mendukung Aktivitas Dan Kinerja Jaringan Internet (Studi Kasus STMIK-AMIK Jayanusa Padang). Jurnal TEKNOIF, 2(2), 21–26.
Kumar, V. (2021). There is No Substitute for Human Intelligence. In Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine (Vol. 25, Issue 5, pp. 486–488). https://doi.org/10.5005/jp-journals-10071-23832
M. Maatuk, A., & A. Abdelnabi, E. (2021). Generating UML Use Case and Activity Diagrams Using NLP Techniques and Heuristics Rules. International Conference on Data Science, E-Learning and Information Systems 2021, 271–277. https://doi.org/10.1145/3460620.3460768
Majeed, M., Asare, C., Fatawu, A., & Abubakari, A. (2022). An analysis of the effects of customer satisfaction and engagement on social media on repurchase intention in the hospitality industry. Cogent Business & Management, 9(1), 2028331. https://doi.org/10.1080/23311975.2022.2028331
Paramita, P., & Ibrahim, A. (2023). Analisis Sentimen Terhadap Pengguna Qris ( Quick Respond Code Indonesian Standart ) Pada Twitter Menggunakan Metode Naïve. JOISIE Journal Of Information System And Informatics Engineering Vol., 7(1), 1–6.
Perdana, R. W., & Meri, R. (2023). Implementasi Data Mining Pada Penjualan Seprai Menggunakan Abstrak. JOISIE Journal Of Information System And Informatics Engineering, 7(1), 144–154.
Php, M., Bekasi, K., Noviana, R., Margonda, J., No, R., & Cina, P. (2021). Perancangan Web Sistem Analisis Sentimen Media Sosial Twitter Dengan Metode Valence Aware Dictionary And Sentimen Reasoner (Vader) Menggunakan PHP & MysSQL pada Pemerintah Kota Bekasi. Jurnal Ilmiah Komputasi, 20(1), 1–14. https://doi.org/10.32409/jikstik.20.1.369
Staskal, O., Simac, J., Swayne, L., & Rozier, K. Y. (2022). Translating SysML Activity Diagrams for nuXmv Verification of an Autonomous Pancreas. 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 1637–1642. https://doi.org/10.1109/COMPSAC54236.2022.00260
Sunarya, A., Santoso, S., & Sentanu, W. (2015). Sistem Pakar Untuk Mendiagnosa Gangguan Jaringan Lan. Creative Communication and Innovative Technology Journal, 8(2), 1–11.
Syafitri, S. A., Pratama, A., & Ulva, A. F. (2020). Sistem Informasi Administrasi Persuratan (Paperless Office) Berbasis Web Pada Fakultas Teknik Universitas Malikussaleh. Sisfo: Jurnal Ilmiah Sistem Informasi, 4(1), 95–110. https://doi.org/10.29103/sisfo.v4i1.6278
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021). Machine learning algorithms for social media analysis: A survey. Computer Science Review, 40, 100395. https://doi.org/https://doi.org/10.1016/j.cosrev.2021.100395
Taufiq Anwar, M., Riandhita Arief Permana, D., STMI Jakarta, P., Sistem Informasi Industri Otomotif, P., Letjen Suprapto No, J., & Pusat, J. (2023). Analisis Sentimen Masyarakat Indonesia Terhadap Produk Kendaraan Listrik Menggunakan VADER. 10(1), 783–792. http://jurnal.mdp.ac.id
Thommandru, A., Espinoza-Maguiña, M., Ramirez-Asis, E., Ray, S., Naved, M., & Guzman-Avalos, M. (2023). Role of tourism and hospitality business in economic development. Materials Today: Proceedings, 80, 2901–2904. https://doi.org/https://doi.org/10.1016/j.matpr.2021.07.059
Yordanova, M., Evstatieva, Y., Chernev, G., Ilieva, S., Denkova, R., & Nikolova, D. (2013). Enhancement of xylanase production by sol-gel immobilization of Aspergillus awamori K-1. Bulgarian Journal of Agricultural Science, 19(SUPPL. 2), 117–119.
Published
2023-07-26
How to Cite
RAMADHANU, Agung et al.
PENERAPAN TEKNOLOGI MACHINE LEARNING DENGAN METODE VADER PADA APLIKASI SENTIMEN TAMU DI HOTEL DYMENS.
JOISIE (Journal Of Information Systems And Informatics Engineering), [S.l.], v. 7, n. 1, p. 165-173, july 2023.
ISSN 2527-3116.
Available at: <https://ejournal.pelitaindonesia.ac.id/ojs32/index.php/JOISIE/article/view/3118>. Date accessed: 05 oct. 2024.
doi: https://doi.org/10.35145/joisie.v7i1.3118.
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