ANALYSIS OF STUDENT ACADEMIC ACHIEVEMENT LEVELS USING FUZZY LOGIC
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Jan 25, 2023
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The purpose of this study is to use fuzzy logic to analyze and retrieve data about student performance levels. This data will later be used to facilitate the analysis of the performance of SMP Negeri 5 Batusangkar students. Data for this study were obtained through observation and literature review from a variety of sources. The results of this survey analysis make it easier for teachers to analyze student performance without manual searches, and the ability to quickly process values using the provided media such as Microsoft Excel, Access, etc. We provide accurate and accurate data. When judging the characteristics of a student's level of academic performance, she complies with the Order of the Minister of Education, Culture, Research and Technology (Permendikbudristok) No. 21 of 2022 on Educational Evaluation Standards. Analyzing student performance levels is performed using a fuzzy logic database by mapping student results based on variables. Ethics of use, knowledge and aspects of information (attitudes). This technique is tested using Microsoft Excel to query the designed data.
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Nofri Yoliadi, D. (2023). ANALYSIS OF STUDENT ACADEMIC ACHIEVEMENT LEVELS USING FUZZY LOGIC. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(1), 373-390. https://doi.org/10.35870/jimik.v4i1.201
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References
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Londa, M.A., Radja, M. and Sara, K., 2020. Penerapan Metode Logika Fuzzy dalam Evaluasi Kinerja Dosen. Matrix: Jurnal Manajemen Teknologi dan Informatika, 10(2), pp.78-86. DOI: 10.31940/matrix.v10i2.1841.
Toibin, T.A.A. and Purnomo, A., 2018, October. Sistem Pakar Pengembangan Skala Minat Karir Mahasiswa Dengan Inferensi Fuzzy Tsukamoto. In Seminar Multimedia & Artificial Intelligence (Vol. 1, pp. 156-162).
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Apriliana, R., Damayanti, A. and Pratiwi, A.B., 2020. Sistem Pakar Diagnosa Hipertiroid Menggunakan Certainty Factor dan Logika Fuzzy. Contemporary Mathematics and Applications (ConMathA), 2(1), p.57. DOI: 10.20473/conmatha.v2i1.19302.
Samsinar, R. and Susanto, H., 2018. RANCANG BANGUN PENGATURAN TEMPERATUR UDARA PADA KONVEYOR INDUSTRI ELEKTRONIK MENGGUNAKAN KENDALI LOGIKA FUZZY. Prosiding Semnastek.
Wang, C., 2015. A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making. Lehigh University.
Martin, H., 2019. Measuring Qualitative Performance Criteria with Fuzzy Sets. In Business Information Systems Workshops: BIS 2019 International Workshops, Seville, Spain, June 26–28, 2019, Revised Papers 22 (pp. 417-423). Springer International Publishing. DOI: 10.1007/978-3-030-36691-9_35.
Papadimitriou, S., Chrysafiadi, K. and Virvou, M., 2019. FuzzEG: Fuzzy logic for adaptive scenarios in an educational adventure game. Multimedia Tools and Applications, 78, pp.32023-32053. DOI: https://doi.org/10.1007/s11042-019-07955-w.
Karthika, R., Jegatha Deborah, L. and Vijayakumar, P., 2020. Intelligent e-learning system based on fuzzy logic. Neural Computing and Applications, 32, pp.7661-7670. DOI: https://doi.org/10.1007/s00521-019-04087-y.
Chrysafiadi, K., Troussas, C. and Virvou, M., 2020. Combination of fuzzy and cognitive theories for adaptive e-assessment. Expert Systems with Applications, 161, p.113614. DOI: https://doi.org/10.1016/j.eswa.2020.113614.
Megahed, M. and Mohammed, A., 2020. Modeling adaptive E-learning environment using facial expressions and fuzzy logic. Expert Systems with Applications, 157, p.113460. DOI: https://doi.org/10.1016/j.eswa.2020.113460
Ismunu, R.S., Purnomo, A.S. and Subardjo, R.Y.S., 2020. Sistem Pakar Untuk Mengetahui Tingkat Kecemasan Mahasiswa Dalam Menyusun Skripsi Menggunakan Metode Multi Factor Evaluation Process Dan Inferensi Fuzzy Tsukamoto. Seminar Nasional Multi Disiplin Ilmu (SENDI) (pp.65-72). Semarang: Universitas Stikubank
Kurniati, N.I., Mubarok, H. and Reinaldi, A., 2017. Rancang Bangun Sistem Pakar Diagnosa tingkat Depresi Pada Mahasiswa Tingkat Akhir Menggunakan Metode Fuzzy Tsukamoto (Studi Kasus: Universitas Siliwangi). Jurnal Online Informatika, 2(1), pp.49-55. DOI: https://doi.org/10.15575/join.v2i1.87.
Prastianingrum, G. and Purnomo, A.S., 2019. Sistem Pakar Diagnosa Fobia Menggunakan Metode Certainty Factor. JMAI (Jurnal Multimedia & Artificial Intelligence), 3(2), pp.73-80. DOI: https://doi.org/10.26486/jmai.v3i2.90.
Londa, M.A., Radja, M. and Sara, K., 2020. Penerapan Metode Logika Fuzzy dalam Evaluasi Kinerja Dosen. Matrix: Jurnal Manajemen Teknologi dan Informatika, 10(2), pp.78-86. DOI: 10.31940/matrix.v10i2.1841.
Toibin, T.A.A. and Purnomo, A., 2018, October. Sistem Pakar Pengembangan Skala Minat Karir Mahasiswa Dengan Inferensi Fuzzy Tsukamoto. In Seminar Multimedia & Artificial Intelligence (Vol. 1, pp. 156-162).
Prastyo, R.D. and Pati, D.A.P.S.A., 2018. Sistem Informasi Pendeteksi Hama Penyakit Tanaman Padi Menggunakan Metode Fuzzy Tsukamoto Berbasis Android. Speed-Sentra Penelitian Engineering dan Edukasi, 10(2).
Saputri, A.D., Ramadhani, R.D. and Adhitama, R., 2019. Logika Fuzzy Sugeno untuk Pengambilan Keputusan dalam Penjadwalan dan Pengingat Service Sepeda Motor. Journal of Informatics Information System Software Engineering and Applications (INISTA), 2(1), pp.49-55. DOI: 10.20895/inista.v2i1.95.
Azizah, A., Waris, A. and Sapsal, M.T., 2019. Penerapan Sistem Fuzzy Logic pada Alat Ukur Kadar Nutrisi pada Sistem Hidroponik. Jurnal Agritechno, pp.85-93. DOI: 10.20956/at.v0i0.215.
Apriliana, R., Damayanti, A. and Pratiwi, A.B., 2020. Sistem Pakar Diagnosa Hipertiroid Menggunakan Certainty Factor dan Logika Fuzzy. Contemporary Mathematics and Applications (ConMathA), 2(1), p.57. DOI: 10.20473/conmatha.v2i1.19302.