Email this article 
Prediksi Prestasi Akademik Mahasiswa Bekerja Paruh Waktu Menggunakan Artificial Neural Network
Abstract
Intisari–Mahasiswa yang bekerja paruh waktu dituntut agar bisa membagi waktu mereka secara efektif dan efisien antara waktu untuk bekerja dan dan waktu untuk kuliah. Prediksi terhadap mereka yang kuliah sambil bekerja diharapkan dapat menjadi salah satu pertimbangan kebijakan bagi pihak akademik agar mahasiswa yang bekerja sambil bekerja dapat menyelesaikan masa studi mereka secara tepat waktu. Penelitian ini di mulai dengan tahapan mengumpulkan data mahasiswa yang kuliah sambil bekerja untuk selanjutnya dilakukan proses data cleaning. Data lalu dibagi atas dua kelompok data yaitu data training dan data testing yang dinormalisasi dengan metode min-max. Algoritma neural network digunakan untuk melakukan prediksi terhadap hasil studi bagi mereka yang kuliah sambil bekerja yang di kategorikan dalam 3 label. Optimasi dilakukan terhadap parameter dengan memamfaatkan perangkat optimize parameter. Pada pengujian model, parameter yang ditampilkan berupa training cycle, learning rate, momentum, akurasi dan nilai RMSE dengan rentang nilai learning rate dan momentum 0,1 sampai dengan 0,9, dengan fungsi aktivasi sigmoid. Validasi nilai terbaik didapat pada training cycle 201, learning rate 0,74, momentum 0,9 dengan nilai akurasi 89,62%, RMSE 0,263 dengan nilai k-fold=3.
Full Text:
PDF (Bahasa Indonesia)References
S. Saud, B. Jamil, Y. Upadhyay, and K. Irshad, “Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach,†Sustain. Energy Technol. Assessments, vol. 40, no. April, p. 100768, 2020, doi: 10.1016/j.seta.2020.100768.
F. Okubo, “A Neural Network Approach for Students ’ Performance Prediction,†no. March, pp. 5–7, 2017, doi: 10.1145/3027385.3029479.
L. Mahmoud and A. Zohair, “Prediction of Student ’ s performance by modelling small dataset size,†2019.
A. Olawoyin, Y. Chen, A. Olawoyin, and Y. Chen, “ScienceDirect ScienceDirect ScienceDirect Predicting the Future with Artificial Neural Network Predicting the Future with Artificial Neural Network,†Procedia Comput. Sci., vol. 140, pp. 383–392, 2018, doi: 10.1016/j.procs.2018.10.300.
A. A. Aryaguna and D. O. Anggriawan, “Identifikasi Jenis Gangguan Pada Jaringan Distribusi Menggunakan Metode Artificial Neural Network,†no. April, pp. 27–35, 2021.
A. Çetinkaya and Ö. K. Baykan, “Prediction of middle school students’ programming talent using artificial neural networks,†Eng. Sci. Technol. an Int. J., no. xxxx, 2020, doi: 10.1016/j.jestch.2020.07.005.
E. Bahadır, “Prediction of Prospective Mathematics Teachers’ Academic Success in Entering Graduate Education by Using Back-propagation Neural Network,†J. Educ. Train. Stud., vol. 4, no. 5, pp. 113–122, 2016, doi: 10.11114/jets.v4i5.1321.
Ş. Aydoğdu, “Predicting student final performance using artificial neural networks in online learning environments,†2019, doi: doi.org/10.1007/s10639-019-10053.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,†Appl. Soft Comput. J., p. 105524, 2019, doi: 10.1016/j.asoc.2019.105524.
S. Jain, S. Shukla, and R. Wadhvani, “Dynamic selection of normalization techniques using data complexity measures,†Expert Syst. Appl., vol. 106, pp. 252–262, 2018, doi: 10.1016/j.eswa.2018.04.008.
J. Walach, P. Filzmoser, and K. Hron, Data Normalization and Scaling : Consequences for the Analysis in Omics Sciences, 1st ed. Elsevier B.V., 2018.
A. Ali and N. Senan, “The Effect of Normalization in Violence Video Classification Performance,†IOP Conf. Ser. Mater. Sci. Eng., vol. 226, no. 1, 2017, doi: 10.1088/1757-899X/226/1/012082.
Y. Baashar et al., “applied sciences Toward Predicting Student ’ s Academic Performance Using Artificial Neural Networks ( ANNs ),†2022.
G. Jiang and W. Wang, “Error estimation based on variance analysis of k-fold cross-validation,†Pattern Recognit., vol. 69, pp. 94–106, 2017, doi: 10.1016/j.patcog.2017.03.025.
S. Eker, E. Rovenskaya, S. Langan, and M. Obersteiner, “Model validation: A bibliometric analysis of the literature,†Environ. Model. Softw., vol. 117, no. December 2018, pp. 43–54, 2019, doi: 10.1016/j.envsoft.2019.03.009.
M. W. Liemohn, A. D. Shane, A. R. Azari, A. K. Petersen, B. M. Swiger, and A. Mukhopadhyay, “Journal of Atmospheric and Solar-Terrestrial Physics RMSE is not enough : Guidelines to robust data-model comparisons for magnetospheric physics,†J. Atmos. Solar-Terrestrial Phys., vol. 218, no. March, p. 105624, 2021, doi: 10.1016/j.jastp.2021.105624.
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification : A measure driven view,†Inf. Sci. (Ny)., no. xxxx, 2019, doi: 10.1016/j.ins.2019.06.064.
M. Mikaela, M. Pekka, and M. Jaakko, “PT SC,†Behav. Processes, 2018, doi: 10.1016/j.beproc.2018.01.004.
DOI: https://doi.org/10.35314/isi.v7i1.2368
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This Journal has been listed and indexed in :
Copyright of Jurnal Inovtek Polbeng - Seri Informatika (ISSN: 2527-9866)
ISI: Inovtek Polbeng Seri Informatikan is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Editorial Office :
Pusat Penelitian dan Pengabdian kepada Masyarakat
 Politeknik Negeri BengkalisÂ
Jl. Bathin alam, Sungai Alam Bengkalis-Riau 28711Â
E-mail: jurnalinformatika@polbeng.ac.id
www.polbeng.ac.id
View My Stats