Published: 2025-01-01

Analisis Sentimen Terhadap Sistem Informasi Akademik Mahasiswa pada Aplikasi Edlink dengan Metode K-Nearest Neighbor

DOI: 10.35870/jtik.v9i1.3017

Citra Pricylia Ananda Mulya, Veri Arinal
  • Citra Pricylia Ananda Mulya: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Affiliation name not available , Indonesia
  • Veri Arinal: Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika , Affiliation name not available , Indonesia
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Abstract

The Sevima Edlink application is an academic information system that is widely used by educational institutions in Indonesia to manage student academic data and information. Although this application has various useful features, its successful implementation also depends greatly on user satisfaction and acceptance. Therefore, it is important to analyze user sentiment towards these applications to identify existing strengths and weaknesses. This research aims to analyze user sentiment towards the Sevima Edlink application using the K-Nearest Neighbor (K-NN) method. The K-NN method was chosen because of its simplicity and effective ability to classify sentiment data. The data used in this research are reviews from application users collected from various sources. The results of this research used the K-NN method, namely an accuracy value of 94.47%. So it can be said that the K-KNN algorithm can classify data well and correctly.

Keywords

Edlink ; Sentiment Analysis ; K-Nearest Neighbor ; Academic Information System

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Mulya, C. P. A., & Arinal, V. (2025). Analisis Sentimen Terhadap Sistem Informasi Akademik Mahasiswa pada Aplikasi Edlink dengan Metode K-Nearest Neighbor. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(1), 142-148. https://doi.org/10.35870/jtik.v9i1.3017
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 9 No. 1 (2025)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2025

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Author Biographies
Citra Pricylia Ananda Mulya
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Veri Arinal
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

References
  • Adiansyah, A. (2023). Analisis Sentimen Pada Ulasan Aplikasi Home Credit Dengan Metode SVM dan K-NN. Jurnal Komputer Antartika, 1(4), 174-181. DOI: https://doi.org/10.70052/jka.v1i4.50.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Alga, J., Wulandari, C., & Intan, B. (2024). Analisis Sentimen Aplikasi Youtube di Google Play Store Menggunakan Machine Learning. Resolusi: Rekayasa Teknik Informatika dan Informasi, 4(4), 408-416. DOI: https://doi.org/10.30865/resolusi.v4i4.1750.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Aziz, A., Widianto, F., & Purwanto, A. (2024). Analisis Pengunaan Learning Management System Sebagai Media Pembelajaran Pada Mahasiswa Tahun Pertama. Jurnal Studi Guru dan Pembelajaran, 7(1), 13-27. DOI: https://doi.org/10.30605/jsgp.7.1.2024.3354.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Baker, S. B., Xiang, W., & Atkinson, I. (2017). Internet of things for smart healthcare: Technologies, challenges, and opportunities. Ieee Access, 5, 26521-26544.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Brilianti, A. I. A., & Matondang, N. (2021). Perancangan Sistem Informasi Akademik Berbasis Web pada RA TK AL-Muttaqin. In Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya (Vol. 2, No. 2, pp. 472-483).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Bulu, A., Umar, E., & Ate, P. M. (2023). Analisis Sentimen Terhadap Sistem Informasi Akademik STIMIKOM Stella Maris Sumba Menggunakan Algoritma Naïve Bayes. Jurnal Sistem Informasi Dan Informatika, 1(2), 115-124. DOI: https://doi.org/10.47233/jiska.v1i2.1085.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Dharmawan, L. R., Arwani, I., & Ratnawati, D. E. (2020). Analisis Sentimen pada Sosial Media Twitter Terhadap Layanan Sistem Informasi Akademik Mahasiswa Universitas Brawijaya dengan Metode K-Nearest Neighbor. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 4(3), 959-965.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Guha, R., & Sutikno, T. (2022). Natural language understanding challenges for sentiment analysis tasks and deep learning solutions. Int J Inf & Commun Technol, 11(3), 247-256.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hafidhuddin, M. A., & Rahayu, T. (2021, July). Aplikasi Sistem Informasi Akademik pada Paud Al-Hafizh Haji Radun Kadir Berbasis Web. In Prosiding Seminar Nasional Mahasiswa Bidang Ilmu Komputer dan Aplikasinya (Vol. 2, No. 1, pp. 470-484).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Julianto, I. T., & Lindawati, L. (2022). Analisis Sentimen Terhadap Sistem Informasi Akademik Institut Teknologi Garut. Jurnal Algoritma, 19(1), 458-468. DOI: https://doi.org/10.33364/algoritma/v.19-1.1112.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kisma, A. J. N., & Widiawati, C. R. A. (2023). Analisis Aplikasi Di Playstore Berdasarkan Rating Dan Type Menggunakan Naive Bayes Dan Logistik Regresi. Tek. Inform. dan Sist. Inf, 10(2), 174-184.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kustiyahningsih, Y., & Permana, Y. (2024). Penggunaan Latent Dirichlet Allocation (LDA) dan Support-Vector Machine (SVM) Untuk Menganalisis Sentimen Berdasarkan Aspek Dalam Ulasan Aplikasi EdLink. Teknika, 13(1), 127-136. DOI: https://doi.org/10.34148/teknika.v13i1.746.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Putra, A. D. A., & Juanita, S. (2021). Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 8(2), 636-646. DOI: https://doi.org/10.35957/jatisi.v8i2.962.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Wang, H., Pei, P., Pan, R., Wu, K., Zhang, Y., Xiao, J., & Yang, J. (2022). A collision reduction adaptive data rate algorithm based on the fsvm for a low-cost lora gateway. Mathematics, 10(21), 3920. DOI: https://doi.org/10.3390/math10213920.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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