Prediksi Ketepatan Waktu Lulus Mahasiswa Teknik Informatika Menggunakan Algoritma Naive Bayes

Amosea, Salsha Armenia (2021) Prediksi Ketepatan Waktu Lulus Mahasiswa Teknik Informatika Menggunakan Algoritma Naive Bayes. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Mahasiswa merupakan komponen utama dari sebuah perguruan tinggi yang diharapkan dapat meningkatkan kualitas akademik maupun non-akademik. Data mahasiswa yang terkumpul dari setiap tahunnya dapat berguna jika dimanfaatkan dengan maksimal. Salah satu upaya untuk menunjukan mutu kualitas pendidikan pada sebuah perguruan tinggi yaitu penilaian akreditasi yang diberikan oleh Badan Akreditasi Nasional Perguruan Tinggi (BAN-PT). Akreditasi yang baik pada suatu perguruan tinggi dapat menjadi acuan standar dan cerminan pendidikan yang berkualitas. Salah satu kriteria penilaian akreditasi yaitu Persentase kelulusan tepat waktu untuk setiap program. Di teknik Informatika ITS belum memiliki model yang dapat memprediksi ketepatan lulus mahasiswa. Solusi untuk memprediksi ketepatan lulus pada mahasiswa dapat diselesaikan menggunakan proses data mining. Tugas Akhir ini membahas mengenai prediksi ketepatan waktu lulus mahasiswa menggunakan model klasifikasi dengan studi kasus mahasiswa Teknik Informatika ITS menggunakan dua Algoritma Naïve bayes berbeda yaitu, GaussianNB dan ComplementNB yang masing- masing akan diimplementasikan pada tiga pengujian fitur berbeda. Berdasarkan hasil pengujian, model terbaik yaitu menggunakan implementasi GaussianNB dengan fitur nilai IP per Semester mahasiswa dengan akurasi 96%. Dengan memprediksi ketepatan lulus mahasiswa lebih dini, Departemen Teknik Informatika diharapkan dapat menjadikannya sebagai dasar evaluasi yang lebih baik guna mencegah mahasiswa yang diprediksi tidak lulus tepat waktu serta perbaikan pola belajar bagi mahasiswa yang bersangkutan.
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Students are the main component of a university which is expected to
improve academic and non-academic quality. Student data collected from
each year can be useful if it is used optimally. One of the efforts to show
the quality of education at a university is the accreditation assessment
given by the National Accreditation Board for Higher Education (BAN�PT). Good accreditation at a university can be a standard and a reflection
of quality education. One of the accreditation assessment criteria is the
percentage on time for each program. In Informatics, ITS does not yet
have a model that can predict the punctuality of graduating students. The
solution to ensure the accuracy of graduating students can be completed
using a data mining process. This Final Project discusses the timeliness
of graduating students using a classification model with a case study of
ITS Informatics Engineering students using two Naive Bayes Algorithms,
namely GaussianNB and ComplementNB, each of which will be
implemented in three different feature tests. Based on the test, the best
model is to use the GaussianNB implementation with the IP value feature
per semester for students with an accuracy of 96%. By ensuring the
accuracy of graduating students early, the Department of Informatics is
expected to be used as a better basic evaluation to prevent students who
are predicted to not graduate on time and improve learning patterns for
the students concerned.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	ComplementNB, data mining, GaussianNB, ketepatan lulus mahasiswa, klasifikasi, Naïve Bayes, prediksi, classification, ComplementNB, data mining, GaussianNB, graduation punctuality, Naïve Bayes, prediction
Subjects:	T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T57.74 Linear programming T Technology > T Technology (General) > T58.5 Information technology. IT--Auditing T Technology > T Technology (General) > T58.6 Management information systems
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Salsha Armenia Amosea
Date Deposited:	22 Aug 2021 08:01
Last Modified:	22 Aug 2021 08:01
URI:	http://repository.its.ac.id/id/eprint/88649

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