Analisis Sentimen Terhadap Sistem Informasi Akademik Mahasiswa pada Aplikasi Edlink dengan Metode K-Nearest Neighbor
DOI:
https://doi.org/10.35870/jtik.v9i1.3017Keywords:
Edlink, Sentiment Analysis, K-Nearest Neighbor, Academic Information SystemAbstract
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.
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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.
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.
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.
Baker, S. B., Xiang, W., & Atkinson, I. (2017). Internet of things for smart healthcare: Technologies, challenges, and opportunities. Ieee Access, 5, 26521-26544.
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).
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.
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.
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.
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).
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.
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.
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.
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.
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.
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