DOI: https://doi.org/10.21456/vol10iss2pp228-234

Sistem Informasi Evaluasi Perkuliahan dengan Sentimen Analisis Menggunakan Naïve Bayes dan Smoothing Laplace

*Nilam Ramadhani  -  Universitas Madura, Indonesia
Novan Fajarianto  -  Universitas Madura, Indonesia
Open Access Copyright (c) 2020 JSINBIS (Jurnal Sistem Informasi Bisnis)

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Abstract
A good lecture is certainly a goal so that students achieve maximum learning outcomes. In order for good lecture quality, lecture evaluation needs to be done,beside lecturer professional competency training. In order to improve the quality of lectures, Departement Informatics of Madura University (UNIRA) evaluates lecturers' performance in each semester. Form of evaluation is a questionnaire that filled out by students.Results of the questionnaire, then it is analyzed to find out whether the comments are positive, negative, or neutral. The method that can be used to solve the problem of sentiment classification analysis is Naïve Bayes that combined with text processing techniques.The data comments that collected are 342. After grouping the comments by subject, there were 31 comments for subject Human and Computer Interaction (HCI). In this data comments then performed data cleaning, data transformation, text processing and labeling. Then classifying comments using Naïve Bayes with Smoothing Laplace. Results of accuration obtained an accuracy to 80%. The results of  implementation Naïve Bayes algorithm with Smoothing Laplace, it can be seen the sentiment analysis of the subjects that lectures taught.
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Keywords: Information System; Lecture Evaluation; Sentiment Analysis; Naïve Bayes; Laplacian Smoothing
Funding: Universitas Madura

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Section: Research Articles
Language : EN
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  1. Aggarwal, S., 2013. Naïve Bayes Classifier with Various Smoothing. 4 (April), 873–876
  2. Agusta, L., 2009. Perbandingan algoritma stemming porter dengan algoritma nazief & adriani untuk stemming dokumen teks Bahasa Indonesia. Konferensi Nasional Sistem Dan Informatika 2009, KNS&I09-036, 196–201
  3. Alwi, H., Lapoliwa, H. & Darmowidjojo, S., 1998. Tata Bahasa Baku Bahasa Indonesia (Edisi Ketiga)
  4. Asian, J., Williams, H.E. & Tahaghoghi, S.M.M., 2005. Stemming Indonesian. Conferences in Research and Practice in Information Technology Series, 38(4), 307–314. https://doi.org/10.1145/1316457.1316459
  5. Cherian, V. & Bindu, M.S., 2017. Heart Disease prediction using naïve bayes algorithm and laplace smoothing technique. International Journal of Computer Science Trends and Technology, 5 (2), 68–73
  6. Jiawei, H., Micheline, K., Jian, P., 2012. Data Mining: Concepts and Techniques. Morgan Kaufman Publisher
  7. Listiowarni, I., 2019. Implementasi Naïve Bayessian dengan Laplacian Smoothing untuk Peminatan dan Lintas Minat Siswa SMAN 5 Pamekasan. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 8(2), 124. https://doi.org/10.32736/sisfokom.v8i2.652
  8. Prasetyowati, E. & Ramadhani, N., 2018. Sistem evaluasi dan klasifikasi kinerja akademik mahasiswa Universitas Madura menggunakan Naive Bayes dengan Dirichlet Smoothing. JUTI: Jurnal Ilmiah Teknologi Informasi, 16(2), 192. https://doi.org/10.12962/j24068535.v16i2.a688
  9. Pratama, N.D., Sari, Y.A. & Adikara, P.P., 2018. Analisis sentimen pada review konsumen menggunakan metode Naive Bayes dengan Seleksi Fitur Chi Square untuk rekomendasi lokasi makanan tradisional. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(9), 2982–2988
  10. Said, B., Studi, P., Informatika, T., Teknik, F., Madura, U., Menengah, U. K., Sentimen, A., Daerah, P. & Pamekasan, K., 2016. Klasifikasi dan Analisis Sentimen Data SMS Center Bupati Pamekasan Menggunakan Naïve Bayes. 25(2), 1–5
  11. Wulan, S., Vitandy, U., Supianto, A.A. & Bachtiar, F.A., 2019. Analisis sentimen evaluasi kinerja dosen menggunakan Term Frequency- Inverse Document Frequency dan Naïve Bayes Classifier. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(6)
  12. Yunarso, E.W., 2011. Absensi Berbasis Electronics finger print Untuk Mengukur Tingkat Presensi Pegawai Studi Kasus : Unit SDM Politeknik Telkom

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