Pembuatan aplikasi prediksi ketepatan waktu kelulusan mahasiswa teknik industri universitas pelita harapan menggunakan algoritma naive bayes dengan program python

Antonny, Antonny (2020) Pembuatan aplikasi prediksi ketepatan waktu kelulusan mahasiswa teknik industri universitas pelita harapan menggunakan algoritma naive bayes dengan program python. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Perguruan tinggi merupakan suatu jenjang pendidikan tingkat lanjut dari jenjang pendidikan menengah. Dalam perguruan tinggi, terdapat akreditasi yang merupakan jaminan mutu dari perguruan tinggi itu sendiri. Selain perguruan tinggi, terdapat pula akreditasi untuk program studi. Terdapat beberapa faktor yang menentukan akreditasi yang diberikan pada setiap program studi dimana salah satunya adalah waktu kelulusan. Namun pada tahun ajaran 2018/2019, terdapat 47% mahasiswa yang mendapatkan surat peringatan drop out. Berdasarkan hasl yang didapat juga masih terdapat banyak Mahasiswa yang lulus tidak tepat waktu. Oleh karena ini, penelitian ini bertujuan untuk membuat sarana yang dapat memprediksi waktu kelulusan mahasiswa secara dini sehingga dapat diberikan solusi dalam menangani masalah ini. Data yang digunakan yaitu data historis mahasiswa angkatan 2013-2015 yang digunakan dalam pembuatan model, serta data mahasiswa angkatan 2017 dan 2018 sebagai data yang digunakan untuk melakukan uji coba. Mengingat banyaknya jumlah data, digunakan teknik data mining guna mempermudah pengolahan data. Selain itu, digunakan pula algoritma Naïve Bayes untuk mengolah data dan Python untuk membuat aplikasi. Dari penelitian yang telah dilakukan, didapatkan 5 model yaitu model untuk mahasiswa yang telah menyelesaikan 3 semester, 4 semester, 5 semester, 6 semester, dan 7 semester. Kelima model tersebut berturut-turut memiliki tingkat keakuratan yaitu 82%, 87%, 92%, 92%, dan 95%. Selain itu, masih terdapat 40% mahasiswa angkatan 2017 yang lulus tidak tepat waktu, dan masih terdapat 67% mahasiswa angkatan 2018 yang lulus tidak tepat waktu. Dengan hasil yang didapatkan, pihak program studi diharapkan dapat mencari solusi untuk mengrurangi jumlah mahasiswa yang lulus tidak tepat waktu./ University is an advanced level of education from the level of High School. In University, there is accreditation which is used as a guarantee of quality from the University itself. Besides there are also accreditations for study programs. There are several factors that determine the accreditation granted to each study program where one of them is the time of graduation. But in the 2018/2019 school year, there were 47% of students who received a drop out warning letter. Based on the results obtained, there were still many students who did not graduate on time. Because of this, this research is aimed to create something that can predict the time of graduation of students as an early warnings so that solutions can be given in dealing with this problem. The data used are the historical data of the 2013-2015 class of students used in making models, as well as the 2017 and 2018 class of student data as data trials. Given the large amount of data, data mining techniques are used to facilitate data processing. In addition, the Naïve Bayes algorithm is used to process data and Python to create applications. From the research that has been done, obtained 5 models, namely models for students who have completed 3 semesters, 4 semesters, 5 semesters, 6 semesters, and 7 semesters. The five models have an accuracy rate of 82%, 87%, 92%, 92%, and 95%, respectively. In addition, there are still 40% of 2017 class students who do not graduate on time, and there are still 67% of class 2018 students who do not graduate on time. With the results obtained, the study program is expected to find solutions to reduce the number of students who do not graduate on time.

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