Regression Analysis to Predict the Length of Time to Complete a Thesis based on the Title

Al Aminuddin, Rahmat Hidayat, Gita Sastria, Astried Astried

Abstract


The selection of thesis titles by students is an important thing to do as part of the graduation requirements in completing undergraduate studies. In general, the difficulty or complexity of the thesis can be reflected through the title of the thesis that is appointed. This can indicate that the more difficult a thesis title, the longer it will take to complete the thesis research. This study utilizes the data mining method using machine learning, namely linear regression, in predicting how long it will take to complete a thesis title. The data used is obtained from the words or text in the thesis title as a feature or independent variable and the completion time in days as the dependent variable to predict the time required for students starting from a thesis proposal seminar to a comprehensive seminar or thesis final session. The regression model produces an evaluation value of the coefficient of determination of 0.999, which is close to the maximum value equal to 1.

Keywords


Data Mining, Linier Regression, Thesis, TF-IDF

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References


P. A. Sanistasya et al., “Coaching Clinic Skripsi Hack bagi Mahasiswa Administrasi Bisnis Universitas Mulawarman,” JMM (Jurnal Masyarakat Mandiri), vol. 7, no. 3, pp. 2577–2587, 2023, [Online]. Available: https://journal.ummat.ac.id/index.php/jmm/article/view/14069

A. Lusi, A. P. Nalle, and K. R. Saba, “Hubungan Antara Kecemasan Akademik dengan Self-Efficacy pada Mahasiswa yang sedang menyusun Skripsi di Rumpun Ilmu Pendidikan FKIP Universitas Nusa Cendana,” Jurnal Bimbingan Konseling Flobamora, vol. 1, no. 2, 2023, [Online]. Available: https://ejurnal.undana.ac.id/index.php/JBKF/article/view/12292

A. Winyo, T. Trisno, and T. Kurra, “Analisis Algoritma Asosiasi untuk memilih Judul Mahasiswa Skripsi Stimkom Stella Maris Sumba,” Multidisciplinary Indonesian Center Journal (MICJO), vol. 1, no. 1, pp. 404–411, 2024, [Online]. Available: https://e-jurnal.jurnalcenter.com/index.php/micjo/article/view/46

F. Marsela, A. Bakar, and R. Shopia, “Analisis Faktor Penyebab Keterlambatan Penyelesaian Studi pada Mahasiswa Prodi Bimbingan dan Konseling,” Syifaul Qulub: Jurnal Bimbingan dan Konseling Islam, vol. 4, no. 1, pp. 46–53, Jul. 2023, doi: 10.32505/syifaulqulub.v4i1.6169.

G. Maulana and R. D. Dana, “Prediksi Hasil Produksi Jagung di Jawa Barat dengan Metode Algoritma Regresi Linear menggunakan Google Collab,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 827–837, 2024, [Online]. Available: https://ejournal.itn.ac.id/index.php/jati/article/view/8816

R. Ridlo Al-Hakim et al., “Predict the Thyroid Abnormality Particular Disease Likelihood of The Symptoms’ Certainty Factor Value and its Confidence Level: A Regression Model Analysis,” 2023. [Online]. Available: http://sistemasi.ftik.unisi.ac.id

A. Pangestu et al., “Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19,” Sistemasi: Jurnal Sistem Informasi, vol. 13, no. 1, pp. 167–176, 2024.

S. Supardi et al., “Peran Data Mining dalam memprediksi Tingkat Penjualan Sepatu Adidas menggunakan Metode Algoritma Regresi Linear Sederhana,” Jurnal Ekonomi Manajemen Sistem Informasi, vol. 4, no. 5, pp. 883–890, 2023, [Online]. Available: https://dinastirev.org/JEMSI/article/view/1556

R. Andrianto and F. Irawan, “Implementasi Metode Regresi Linear Berganda pada Sistem Prediksi Jumlah Tonase Kelapa Sawit di PT. Paluta Inti Sawit,” Jurnal Pendidikan Tambusai, vol. 7, no. 1, pp. 2926–2936, 2023, [Online]. Available: https://jptam.org/index.php/jptam/article/download/5658/4751

W. Andriani, G. Gunawan, and A. E. Prayoga, “Prediksi Nilai Emas menggunakan Algoritma Regresi Linear,” Jurnal Ilmiah Informatika Komputer, vol. 28, no. 1, pp. 27–35, 2023, [Online]. Available: https://ejournal.gunadarma.ac.id/index.php/infokom/article/view/8096

M. Edi, E. Utami, and A. Yaqin, “Prediksi Harga pada Trading Forex Pair USDCHF menggunakan Regresi Linear,” Jurnal Manajemen Informatika (JAMIKA), vol. 13, no. 2, pp. 109–119, 2023, [Online]. Available: https://ojs.unikom.ac.id/index.php/jamika/article/view/9826

A. T. Nurani, A. Setiawan, and B. Susanto, “Perbandingan Kinerja Regresi Decision Tree dan Regresi Linear Berganda untuk Prediksi BMI pada Dataset Asthma,” Jurnal Sains dan Edukasi Sains, vol. 6, no. 1, pp. 34–43, 2023, [Online]. Available: https://ejournal.uksw.edu/juses/article/view/8438

D. Yanti, Martanto, and A. Bahtiar, “Prediksi Hasil Panen Padi Tahun 2023 menggunakan Metode Regresi Linier di Kabupaten Indramayu,” Jurnal Informatika Terpadu, vol. 9, no. 1, pp. 18–23, Mar. 2023, doi: 10.54914/jit.v9i1.657.

Y. Aqsho Ramadhan, A. Faqih, and G. Dwilestari, “Prediksi Penjualan Handphone di Toko X menggunakan Algoritma Regresi Linear,” Jurnal Informatika Terpadu, vol. 9, no. 1, pp. 40–44, Mar. 2023, doi: 10.54914/jit.v9i1.692.

A. Wibowo, D. Iskandar, and W. A. S. Wibowo, “Data Mining dalam Prediksi Jumlah Pasien dengan Regresi Linear dan Exponential Smoothing,” Jurnal Sistem Informasi dan Sains Teknologi, vol. 5, no. 1, 2023, [Online]. Available: https://dirdosen.budiluhur.ac.id/0007097901/2022-1/B_Data_Mining_Dalam_Prediksi.pdf

P. Herwanto, N. Marliani, and R. Rosida, “Prediksi Kinerja Keuangan PT Astra International Tbk dengan Regresi Linier dan Exponential Smoothing,” Infotronik : Jurnal Teknologi Informasi dan Elektronika, vol. 8, no. 1, p. 12, Jun. 2023, doi: 10.32897/infotronik.2023.8.1.2734.

F. M. Sarimole and K. Kudrat, “Analisis Sentimen terhadap Aplikasi Satu Sehat pada Twitter menggunakan Algoritma Naive Bayes dan Support Vector Machine,” Jurnal Sains dan Teknologi, vol. 5, no. 3, pp. 783–790, 2024, [Online]. Available: http://ejournal.sisfokomtek.org/index.php/saintek/article/view/2702

E. Miranda, V. Gabriella, S. A. Wahyudi, and J. Chai, “Text Classification for Analysing Indonesian People’s Opinion Sentiment for Covid-19 Vaccination,” SISTEMASI, vol. 12, no. 2, p. 438, May 2023, doi: 10.32520/stmsi.v12i2.2759.

S. N. Cahyani and G. W. Saraswati, “Implementation of Support Vector Machine Method in Classifying School Library Books with Combination of TF-IDF and Word2Vec,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 6, pp. 1555–1566, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1536.

S.-W. Kim and J.-M. Gil, “Research Paper Classification Systems based On Tf-Idf and Lda Schemes,” Human-Centric Computing and Information Sciences, vol. 9, no. 1, p. 30, Dec. 2019, doi: 10.1186/s13673-019-0192-7.

M. B. Gultom, P. A. Simbolon, and N. S. Nainggolan, “Prediksi Tingkat Pengangguran berdasarkan Pendidikan menggunakan Regresi Linear (Studi Kasus : Kota Medan),” 2024. [Online]. Available: https://www.kaggle.com/

E. C. Sitohang, F. E. Ginting, and Y. M. B. Sembiring, “Prediksi Jumlah Perokok dan Dampaknya terhadap Kesehatan Masyarakat menggunakan Regresi Linear,” in Seminar Nasional Inovasi Sains Teknologi Informasi Komputer, 2024, pp. 512–516. [Online]. Available: https://ejournal.ust.ac.id/index.php/SNISTIK/article/view/3683

V. R. Prasetyo, H. Lazuardi, A. A. Mulyono, and C. Lauw, “Penerapan Aplikasi RapidMiner untuk Prediksi Nilai Tukar Rupiah terhadap US Dollar dengan Metode Linear Regression,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 7, no. 1, pp. 8–17, May 2021, doi: 10.25077/TEKNOSI.v7i1.2021.8-17.

D. H. Perkasa and M. Magito, “Determinan Faktor Blue Economy dalam Aplikasi Praktis SDM Perhotelan di Pulau Tidung Kepulauan Seribu,” Jesya, vol. 7, no. 1, pp. 840–852, Jan. 2024, doi: 10.36778/jesya.v7i1.1495.




DOI: https://doi.org/10.32520/stmsi.v14i2.4994

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