Sistem Aplikasi Penilaian Jawaban Essay Test Calon Karyawan PT Siloam Hospitals TB Simatupang Menggunakan Algoritma Text Mining TF-IDF Berbasis Web

Authors

  • Agung Daniel Sipayung Universitas Nasional, Jakarta Selatan
  • Fauziah Fauziah Universitas Nasional, Jakarta Selatan
  • Nurhayati Nurhayati Universitas Nasional, Jakarta Selatan

DOI:

https://doi.org/10.30865/mib.v4i4.2202

Keywords:

Computers, Text mining, Tf-Idf, Algorithms, Application Systems

Abstract

The computer is a sophisticated technology that is widely used for now, a lot of work that used to use manual processes, has now been replaced by a computerized system, one example is the process of evaluating answers to essay tests online, the process of evaluating answers to essay tests that the authors currently use is by using the Tf-Idf text mining algorithm, which application system is a model for evaluating the essay test answers for prospective employees at PT. Siloam Hospitals TB Simatupang. The aim is to accelerate the process of evaluating essay test answers to prospective employees at PT. Siloam Hospitals TB Simatupang. In order to make the same frequency between several words in the sentence that can be used Term Frequency equation, Term Frequency is a factor that can determine word weight based on the number of word frequencies in each document, 4 sample essay questions are taken , the results can be if the prospective employee correctly solved the problem and the result or the value of the equation is 1, the value obtained by the prospective employee is 100: 4 = 25. From the results of the study that the essay test assessment system using text mining methods has worked well in the application Essay examination trials show the suitability of the assessment in the application with  the scores made by HRD with an average of 73.51%

Author Biographies

Agung Daniel Sipayung, Universitas Nasional, Jakarta Selatan

Fakultas Teknologi Komunikasi dan Informatika, Teknik Informatika

Fauziah Fauziah, Universitas Nasional, Jakarta Selatan

Fakultas Teknologi Komunikasi dan Informatika, Teknik Informatika

Nurhayati Nurhayati, Universitas Nasional, Jakarta Selatan

Fakultas Teknologi Komunikasi dan Informatika, Teknik Informatika

References

Syafrial Fahri Pane, Wahyu Kurnia Sari, Zanwar Arif Wicaksono. 2020. Membuat Aplikasi Pengolahan Data Administrasi Barang Menggunakan Aplikasi Apex Online. Bandung: Kreatif Industri Nusantara.

Putri Ratna, A.A., Budiardjo, B., & Hartanto, D. (2007). SIMPLE : Sistem Penilaian Esei Otomatis Untuk Menilai Ujian Dalam Bahasa Indonesia. Makara, Teknologi, Vol, 11, No.1.

Aditya, M., & Witanti, N. (2009). Penilaian Esai Jawaban Bahasa Indonesia Menggunakan Metode SVM - LSA. Journal of Information Systems, Volume 5, Issues 1 , p 33-41.

Jiawei, H., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques Third Edition.

Ilustrasi Algoritma TF-IDF (Harlian, 2006 dalam Muhajir 2012).

Fuat,R. (2010). Sistem Nilai Essay Otomatis Pada Elearning menggunakan Metode Cosine Similarity. Surabaya: Buku Tugas Akhir Mahasiswa Jurusan Sistem Informasi Fakultas Teknologi Informasi Institut Teknologi Sepuluh November .

Perkasa, D.A, Eki Saputra, Mona Fronita (2015) System ujian daring essai dengan penilaian menggunakan metode Latent Sematic Analysis (LSA), Jurnal Rekayasa dan Manajemen Sistem Informasi Vol. 1, No. 1, pp.1-9 ISSN 2460-8181

International Journal of Computer Applications (0975 – 8887) Volume 181 – No.1, July 2018.

C. C. Aggarwal and C. X. Zhai, Mining text data, vol. 9781461432. 2013.

Riffat Naz & M. N. A. Khan (2015) Rapid Applications Development Techniques: A Critical Review,International Journal of Software Engineering and Its Applications Vol. 9, No. 11 (2015), pp. 163-176

Safrian Aswati (2016:1) Rapid Aplication Development (RAD)

Fitri, Meisya. Perancangan Sistem Temu Balik Informasi dengan Metode Pembobotan Kombinasi TF-IDF untuk Pencarian Dokumen Berbahasa Indonesia. http://jurnal.untan.ac.id/index.php/justin/article/view/1319. 15 Agustus 2015(13.42)

Miner, G., Delen, D., Elder, J., Fast, A., Hill, T., & Nisbet, R. (2012). Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications. Oxford: Elsevier.

Downloads

Published

2020-10-20

Issue

Vol. 4 No. 4 (2020): Oktober 2020

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. 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.
  2. 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.
  3. 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).