Peningkatan Layanan Customer Service Melalui Chatbot Menerapkan Algoritma Text Mining dan TF-IDF
-
Fajar Surya Atmaja
*
Universitas Budi Darma, Medan,
Indonesia
(*) Corresponding Author
Abstract
Dr. Hospital Pirngadi is a regional general hospital owned by the government and is a type B hospital located in the Medan City area, North Sumatra. Apart from that, Dr. Pirngadi is also a referral hospital for the Medan and surrounding areas. As a regional general hospital, Pirngadi Regional Hospital also plays a role in providing health services for the people of Medan city and its surroundings, services provided by customer service at Dr. Pirngadi Medan City, such as registration and information on patients who wish to register for either inpatient or outpatient care, information regarding doctor's practice schedules, facility service information, patient guarantor cooperation, bad management, and visitor information. Customer service is not yet optimal for patients and visitors, such as limited information provided, lack of accessibility and clarity of information, lack of coordination between various hospital departments. To overcome this problem, customer service can utilize artificial intelligence technology to improve customer service. This research provides a solution by building a system in the form of a chatbot, this chatbot system will become an information medium for patients and visitors. The chatbot development process uses a text mining algorithm for text processing and TF-IDF to give weight to each document available in the database. The system provides responses based on the highest level of similarity, with text mining and TF-IDF algorithms, chatbots can provide precise and accurate information on questions asked by patients and visitors. The final result of this research is a chatbot that can be used by patients and visitors to find out available information. The existence of a chatbot can make it easier for patients and visitors to get information about the services available at Dr. RSUD. Pirngadi, Medan City.
Downloads
References
E. N. S. C. P and I. Afrianto, “Rancang Bangun Aplikasi Chatbot Informasi Objek Wisata Kota Bandung Dengan Pendekatan Natural Language Processing,” Komputa J. Ilm. Komput. dan Inform., vol. 4, no. 1, pp. 49–54, 2015, doi: 10.34010/komputa.v4i1.2410.
J. Wiratama, S. A. Sanjaya, and V. I. Sugara, “Rancang Bangun Fitur Chatbot Customer Service Menggunakan Dialogflow,” Komputasi J. Ilm. Ilmu Komput. dan Mat., vol. 19, no. 1, pp. 25–37, 2022, doi: 10.33751/komputasi.v19i1.4474.
H. fonda Fonda, B. Mustika, E. Sabna, and Y. Irawan, “Implementasi Text Mining Pada Twitter Dengan Algoritma K-Means Clustering Sebagai Dasar Kebijakan Marketing Biro Perjalanan Wisata,” J. Ilmu Komput., vol. 9, no. 2, pp. 134–147, 2020, doi: 10.33060/jik/2020/vol9.iss2.180.
N. Anggraini, E. S. N. Harahap, and T. B. Kurniawan, “Text mining - Analisis teks terkait isu vaksinasi COVID-19,” J. Ilmu Pengetah. dan Teknol. Komun., vol. 23, no. 2, pp. 141–153, 2021.
E. A. Lisangan, “Natural Language Processing Dalam Memperoleh Informasi Akademik Mahasiswa Universitas Atma Jaya Makassar,” J. Temat., vol. 1, no. 1, pp. 1–9, 2013.
S. Mulyatun, H. Utama, and A. Mustopa, “Pendekatan Natural Language Processing Pada Aplikasi Chatbot Sebagai Alat Bantu Customer Service,” J. Inf. Syst. Manag., vol. 3, no. 1, pp. 12–17, 2021, doi: 10.24076/joism.2021v3i1.404.
H. H. Herwin, “Super Agent Chatbot ‘3S’ Sebagai Media Informasi Menggunakan Metoda Natural Language Processing(NLP),” J. Teknol. Dan Open Source, vol. 2, no. 1, pp. 53–64, 2019, doi: 10.36378/jtos.v2i1.144.
Y. Aprilinda, T. Martavia, E. Erlangga, F. N. Afandi, and U. Rizal, “Chatbot Menggunakan Natural Language Processing untuk Pembelajaran Bahasa Inggris Berbasis Android,” Expert J. Manaj. Sist. Inf. dan Teknol., vol. 12, no. 1, p. 23, 2022, doi: 10.36448/expert.v12i1.2629.
A. S. Sri Widaningsih, “Klasifikasi Jurnal Ilmu Komputer Berdasarkan Pembagian,” Semin. Nas. Teknol. Inf. dan Komun. 2018 (SENTIKA 2018), vol. 2018, no. Sentika, pp. 320–328, 2018.
A. Fauzi, “Bulletin of Data Science Penerapan Algoritma Text Mining dan Lexrank dalam Meringkas Teks Secara Otomatis,” Media Online), vol. 1, no. 2, pp. 65–72, 2022.
A. Amrulloh and I. F. Adam, “Sistem Pencarian Similaritas Judul Tugas Akhir Menggunakan Metode TF-IDF,” J. CoreIT J. Has. Penelit. Ilmu Komput. dan Teknol. Inf., vol. 7, no. 2, p. 74, 2021, doi: 10.24014/coreit.v7i2.14917.
A. D. Sipayung, Fauziah, and Nurhayati, “Sistem Aplikasi Penilaian Jawaban Essay Test Calon Karyawan PT Siloam Hospitals TB Simatupang Menggunakan Algoritma Text Mining TF-IDF Berbasis Web,” J. Media Inform. Budidarma, vol. 4, no. 4, pp. 872–878, 2020, doi: 10.30865/mib.v4i4.2202.
P. H. Saputro, M. Aristin, and Dy. L. Tyas, “Klasifikasi Lagu Daerah Indonesia Berdasarkan Lirik Menggunakan Metode Tfidf Dan Naïve Bayes,” J. Teknol. Inform. dan Terap., vol. 4, no. 1, pp. 45–50, 2017.
I. P. G. Hendra Suputra, K. D. Prebiana, and F. O. Gorianto, “Perbandingan Jenis TF terhadap Hasil Evaluasi Information Retrieval,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 8, no. 2, p. 207, 2021, doi: 10.24843/jlk.2019.v08.i02.p13.
N. Silalahi and G. L. Ginting, “Analisa Sentimen Masyarakat Dalam Penggunaan Vaksin Sinovac Dengan Menerapkan Algoritma Term Frequence –Inverse Document Frequence (TF-IDF) dan Metode Deskripsi,” Josh, vol. 3, no. 3, p. 206−217, 2022, doi: 10.47065/josh.v3i3.1441.
S. Andayani and A. Ryansyah, “Implementasi Algoritma TF-IDF Pada Pengukuran Kesamaan Dokumen,” JuSiTik J. Sist. dan Teknol. Inf. Komun., vol. 1, no. 1, p. 53, 2017, doi: 10.32524/jusitik.v1i1.218.
A. Y. Chandra, D. Kurniawan, and R. Musa, “Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop),” J. Media Inform. Budidarma, vol. 4, no. 1, p. 208, 2020, doi: 10.30865/mib.v4i1.1505.
D. R. Rizkiyani, K. Sujatmoko, and F. Akhyar, “IMPLEMENTASI VIRTUAL COSTUMER SERVICE DENGAN ROBOTIC PROCESS AUTOMATION ( RPA ) DAN KECERDASAN BUATAN Implementation of Customer service Virtual with Robotic Process Automation ( RPA ) and Artificial Intelligence,” e-Proceeding Eng., vol. 8, no. 6, pp. 11582–11589, 2021.
E. Mahdiyah and Y. Andriyani, “Analisa Algoritma Pemahaman Kalimat Pada ALICE ChatBot Dengan Menggunakan Artificial Intelligence Markup Language (AIML),” Pros. SEMIRATA 2013, vol. 1, no. 1, pp. 193–201, 2013.
I. Setyaningsih, “Ananlisis Kualitas Pelayanan Rumah Sakit Terhadap Pasien Menggunakan Pendekatan Lean Servperf,” Spektrum Ind., vol. 9, no. 204, pp. 609–620, 2013.
H. Kusmanto, “Strukturasi : Jurnal Ilmiah Magister Administrasi Publik Analisis Kualitas Pelayanan Publik Rawat Inap Pada Badan Layanan Umum Rumah Sakit Umum Daerah Kota Subulussalam Analysis of Public Service Quality Institution in Public Service Agency Regional Gener,” J. Ilm. Magister Adm. Publik, vol. 1, no. 1, pp. 22–31, 2020.
“Rumah Sakit Umum Daerah Dr. Pirngadi Kota Medan,” 2023.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Peningkatan Layanan Customer Service Melalui Chatbot Menerapkan Algoritma Text Mining dan TF-IDF
Pages: 57-69
Copyright (c) 2025 Fajar Surya Atmaja
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).