Menentukan Dosen Pembimbing Secara Otomatis Dengan Algoritma Text Mining Dan TF-RF

Ryan Andika

doi:10.62866/bios.v2i1.89

Ryan Andika * Universitas Budi Darma, Medan, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.62866/bios.v2i1.89

Keywords: Supervisor; Text Mining; TF-RF

Abstract

The system for determining the supervisor automatically is the system used to determine the right supervisor in accordance with the thesis title and the method used by students to complete the final assignment or thesis. The purpose of this research is to develop an Information System for determining supervisors automatically so that it can collect, analyze, and present the information needed in the preparation of appropriate, time-efficient, and accurate data for supervisors. Therefore, for the sake of work efficiency and effectiveness, it is necessary to make the right decision based on the existing criteria and the weight of each criterion. Determining the Algorithm Text mining is the process of mining data in the form of text where the data source is usually obtained from documents and the goal is to find words that can represent the contents of the document so that an analysis of the connectivity between documents can be carried out. Processes in text mining include tokenization, stemming and filtering processes. Methods of data collection using the library method, the stages of application development include process design, table design, system implementation and testing. The work in this final project uses a case study of the Text Mining algorithm and the TF-RF algorithm in determining the supervisor of the final assignment or thesis for students. This research was conducted at Budi Darma University, the source of the data came from the student portal and the lecturer portal. Information system development is carried out by following the procedures for collecting and analyzing supporting data through direct observation, interviews and recording. From the results of weight calculations using the TF-IDF algorithm, it shows that the title of the 3rd thesis has the largest weight value, namely 5,919, based on this, the title of the thesis "Designing a Duplicate Image Scanner Application Using the SHA1 Method" in the category is Image processing and cryptography.

References

W. Gata, “Akurasi Text Mining Menggunakan Algoritma K-Nearest Neighbour pada Data Content Berita SMS,” vol. 6, pp. 1–13, 2017.

A. Deolika, K. Kusrini, and E. T. Luthfi, “Analisis Pembobotan Kata Pada Klasifikasi Text Mining,” J. Teknol. Inf., vol. 3, no. 2, p. 179, 2019, doi: 10.36294/jurti.v3i2.1077.

A. Januardi, “Analisa Perbandingan Algoritma Brute Force Dan Boyer Moore Dalam Pencarian Word Suggestion Eksponensial,” Pelita Inform. Budi Darma, vol. IV, pp. 18–24, 2013.

M. N. Fauzy and K. Kusrini, “Chatbot menggunakan metode fuzzy string matching sebagai virtual assistant pada pusat layanan informasi akademik,” J. Inf. J. Penelit. dan Pengabdi. Masy., vol. 5, no. 1, pp. 61–67, 2019.

E. Ernawati, A. Johar, and S. Setiawan, “Implementasi Metode String Matching Untuk Pencarian Berita Utama Pada Portal Berita Berbasis Android (Studi Kasus: Harian Rakyat Bengkulu),” Pseudocode, vol. 6, no. 1, pp. 77–82, 2019.

A. Priyanto and M. R. Ma’arif, “Implementasi web scrapping dan text mining untuk akuisisi dan kategorisasi informasi dari internet (studi kasus: Tutorial hidroponik),” Indones. J. Inf. Syst., vol. 1, no. 1, pp. 25–33, 2018.

K. Fitriani, I. Isbandi, and A. Amaliyah, “Perancangan Sistem Manajemen Dokumen Dengan Menggunakan Metode Text Mining Di Kantor Kelurahan Sekejati,” Telematika, vol. 3, no. 1, pp. 45–59, 2021.

F. Fathonah and A. Herliana, “Penerapan Text Mining Analisis Sentimen Mengenai Vaksin Covid-19 Menggunakan Metode Naïve Bayes,” J. Sains dan Inform., vol. 7, no. 2, pp. 155–164, 2021.

E. Yulian, “Text mining dengan k-means clustering pada tema LGBT dalam arsip tweet masyarakat Kota Bandung,” J. Mat. “MANTIK, vol. 4, no. 1, pp. 53–58, 2018.

A. F. Firdaus and W. I. Firdaus, “Text Mining Dan Pola Algoritma Dalam Penyelesaian Masalah Informasi:(Sebuah Ulasan),” JUPITER J. Penelit. Ilmu dan Teknol. Komput., vol. 13, no. 1, pp. 66–78, 2021.

A. V. Sudiantoro and E. Zuliarso, “Analisis sentimen twitter menggunakan text mining dengan algoritma Naïve Bayes Classifier,” 2018.

R. Ramadhan, Y. A. Sari, and P. P. Adikara, “Perbandingan Pembobotan Term Frequency-Inverse Document Frequency dan Term Frequency-Relevance Frequency terhadap Fitur N-Gram pada Analisis Sentimen,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 11, pp. 5075–5079, 2021.

F. I. N. Haq and E. B. Setiawan, “Implementasi Naive Bayes Classifier untuk Prediksi Kepribadian Big Five pada Twitter Menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) dan Term Frequency-Relevance Frequency (TF-RF),” eProceedings Eng., vol. 6, no. 2, 2019.

M. F. Luthfi and K. M. Lhaksamana, “Implementation of TF-IDF method and support vector machine algorithm for job applicants text classification,” J. Media Inform. Budidarma, vol. 4, no. 4, pp. 1181–1186, 2020.

D. Y. KHAIRUN and I. AL HAKIM, “Profil Layanan Dosen Pembimbing Akademik,” J. EDUKASI J. Bimbing. Konseling, vol. 4, no. 1, p. 31, 2018, doi: 10.22373/je.v4i1.3521.

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