Linear Regression Analysis To Predict The Length Of Thesis Completion
Keywords:
big data, prediction, simple linear regression.
Abstract
Students who carry out knowledge in the undergraduate program will certainly be faced with the preparation of a thesis at the end of their study period. However, every year students still find it takes longer than the time specified in completing their thesis. This is caused by several things, such as students who are working, working hours that do not support the implementation of thesis preparation, students who already have families and other factors. This of course makes universities have to prepare special strategies in order to reduce the number of students who cannot complete their thesis on time in the future, one of which is with a decision support. This can be done by utilizing university big data. Prediction of the length of time for completion of college student thesis can be done by utilizing data mining and a simple linear regression approach. Using 1 independent variable, namely the average inhibiting factor (Working Status, Working Hours, Work Sip, Guidance Media, Status) (X1) and the number of days of thesis completion being the dependent variable (Y). After looking for the regression value of b and constant a, then the simple linear regression equation model is: Y = 280.450 + 1.650 X.
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References
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[2] A. Yang, Y. Han, C.-S. Liu, J.-H. Wu, and D.-B. Hua, “D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer,” IEEE Trans. Ind. Informatics, vol. 3203, no. c, pp. 1–1, 2020, doi: 10.1109/tii.2020.3011675.
[3] A. Dridi, M. M. Gaber, R. M. A. Azad, and J. Bhogal, “Scholarly data mining: A systematic review of its applications,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., no. October, pp. 1–23, 2020, doi: 10.1002/widm.1395.
[4] T. M. Song and J. Song, “Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data,” Telemat. Informatics, vol. 58, p. 101524, 2021, doi: 10.1016/j.tele.2020.101524.
[5] D. Muriyatmoko, “Analisa Volume Terhadap Sitasi Menggunakan Regresi Linier Pada Jurnal Bereputasi di Indonesia,” J. Ilm. Simantec, vol. 6, no. 3, pp. 129–134, 2018.
[6] J. Z. Wang and J. Zhang, “Predicting in BIM Labour Cost with a hybrid approach Simple Linear Regression and Random Forest,” IOP Conf. Ser. Earth Environ. Sci., vol. 565, no. 1, 2020, doi: 10.1088/1755-1315/565/1/012108.
[7] E. A. Mandhour, “Prediction of Compression Index of the Soil of Al-Nasiriya City Using Simple Linear Regression Model,” Geotech. Geol. Eng., vol. 38, no. 5, pp. 4969–4980, 2020, doi: 10.1007/s10706-020-01339-w.
[8] T. H. Ansari, M. Ahmed, S. Akter, M. S. Mian, M. A. Latif, and M. Tomita, “Estimation of Rice Yield Loss Using a Simple Linear Regression Model for Bacterial Blight Disease,” Bangladesh Rice J., vol. 23, no. 1, pp. 73–79, 2020, doi: 10.3329/brj.v23i1.46083.
[9] A. Hijriani, K. Muludi, and E. A. Andini, “Implementasi Metode Regresi Linier Sederhana Pada Penyajian Hasil Prediksi Pemakaian Air Bersih Pdam Way Rilau Kota Bandar Lampung Dengan Sistem Informasi Geofrafis,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 11, no. 2, p. 37, 2016, doi: 10.30872/jim.v11i2.212.
[10] A. Bengnga and R. Ishak, “Prediksi Jumlah Mahasiswa Registrasi Per Semester Menggunakan Linier Regresi Pada Universitas Ichsan Gorontalo,” Ilk. J. Ilm., vol. 10, no. 2, pp. 136–143, 2018, doi: 10.33096/ilkom.v10i2.274.136-143.
[11] W. M. Baihaqi, M. Dianingrum, and K. A. N. Ramadhan, “Regresi Linier Sederhana Untuk Memprediksi Kunjungan Pasien di Rumah Sakit Berdasarkan Jenis Layanan dan Umur Pasien,” J. SIMETRIS, vol. 10, no. 2, pp. 671–680, 2019.
[12] H. Santoso, I. P. Hariyadi, and Prayitno, “Data Mining Analisa Pola Pembelian Produk Dengan Menggunakan Metode Algoritma Apriori,” Tek. Inform. ISSN 2302-3805, no. 1, pp. 19–24, 2016, [Online]. Available: http://ojs.amikom.ac.id/index.php/semnasteknomedia/article/download/1267/1200.
[13] I. Virgo, S. Defit, and Y. Yunus, “Klasterisasi Tingkat Kehadiran Dosen Menggunakan Algoritma K-Means Clustering (Studi Kasus Institut Agama Islam Batusangkar),” J. Sistim Inf. dan Teknol., vol. 2, no. 1, pp. 24–29, 2020, doi: 10.37034/jsisfotek.v2i1.22.
[14] R. A. Putra and S. Defit, “Data Mining Menggunakan Rough Set dalam Menganalisa Modal Upah Produksi pada Industri Seragam Sekolah,” J. Sistim Inf. dan Teknol., vol. 1, no. 4, pp. 72–78, 2019, doi: 10.35134/jsisfotek.v1i4.18.
[15] M. Guntur, J. Santony, and Y. Yuhandri, “Prediksi Harga Emas dengan Menggunakan Metode Naïve Bayes dalam Investasi untuk Meminimalisasi Resiko,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 1, pp. 354–360, 2018, doi: 10.29207/resti.v2i1.276.
[16] A. Hidayad, S. Defit, and S. Sumijan, “Penerapan Algoritma K-Means Clustering untuk Melihat Hubungan Kegiatan Tahfiz dengan Hasil Belajar (Studi Kasus Madrasah Aliyah Negeri 1 Bukiktinggi),” J. Sistim Inf. dan Teknol., vol. 2, no. 2, pp. 41–47, 2020, doi: 10.37034/jsisfotek.v2i2.34.
[17] H. Sulastri and A. I. Gufroni, “Penerapan Data Mining Dalam Pengelompokan Penderita Thalassaemia,” J. Nas. Teknol. dan Sist. Inf., vol. 3, no. 2, pp. 299–305, 2017, doi: 10.25077/teknosi.v3i2.2017.299-305.
[18] P. A. Ariawan, “Optimasi Pengelompokan Data Pada Metode K-means dengan Analisis Outlier,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 2, pp. 88–95, 2019, doi: 10.25077/teknosi.v5i2.2019.88-95.
[19] X. Xu, Z. Sun, L. Wang, J. Fu, and C. Wang, “A Comparative Study of Customer Complaint Prediction Model of Time Series, Multiple Linear Regression and BP Neural Network,” J. Phys. Conf. Ser., vol. 1187, no. 5, 2019, doi: 10.1088/1742-6596/1187/5/052036.
[20] B. Amil et al., “No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title,” J. Chem. Inf. Model., vol. 21, no. 1, pp. 1–9, 2020, [Online]. Available: https://doi.org/10.1016/j.tmaid.2020.101607%0Ahttps://doi.org/10.1016/j.ijsu.2020.02.034%0Ahttps://onlinelibrary.wiley.com/doi/abs/10.1111/cjag.12228%0Ahttps://doi.org/10.1016/j.ssci.2020.104773%0Ahttps://doi.org/10.1016/j.jinf.2020.04.011%0Ahttps://doi.o.
[21] F. Wang, Z. Shi, A. Biswas, S. Yang, and J. Ding, “Multi-algorithm comparison for predicting soil salinity,” Geoderma, vol. 365, no. February 2019, p. 114211, 2020, doi: 10.1016/j.geoderma.2020.114211.
[22] H. Rawashdeh et al., “Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage,” Comput. Biol. Chem., vol. 85, no. February, p. 107233, 2020, doi: 10.1016/j.compbiolchem.2020.107233.
Published
2021-06-30
How to Cite
Riandari, F., & Tamando Sihotang , H. (2021). Linear Regression Analysis To Predict The Length Of Thesis Completion. INFOKUM, 9(2, June), 527-534. Retrieved from http://seaninstitute.org/infor/index.php/infokum/article/view/204
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Articles
