RANCANG BANGUN WEBSITE PREDIKSI PELUANG MASUK UNIVERSITAS NEGERI MELALUI SELEKSI NASIONAL BERDASARKAN PRESTASI PADA KOMUNITAS GAPAI PTN

Fransiskus Ramaditya Arief Nursanto, . (2023) RANCANG BANGUN WEBSITE PREDIKSI PELUANG MASUK UNIVERSITAS NEGERI MELALUI SELEKSI NASIONAL BERDASARKAN PRESTASI PADA KOMUNITAS GAPAI PTN. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Polemics in the world of education follow developments from the Merdeka Campus in 2023, especially for the National Selection Based on Achievement (SNBP) program scheme for prospective students who still have not met the target of 100% acceptance to occupy available seats at every state university in Indonesia. As for the 22,497 schools that have finalized the School and Student Data Base (PDSS) with a total number of 1,243,063 applicants, only 143,805 were deemed eligible and passed accepted at the intended state university. This condition is also experienced by Gapai State University (Gapai PTN) as an educational community that has a program to help predict SNBP, so research needs to be done to find out the range between problems and needs with the ultimate goal of being able to build a website as a solution to these conditions. On the other hand, the transparency and grading systems of universities throughout Indonesia are not transparent, so there is no benchmark to find out a definite weight for scores, certificates, and other variables in contributing to passing the SNBP program. This problem also occurs in the work program for rationalizing and predicting from the Gapai PTN community which currently has limitations because it does not have sufficient audiences, the mathematical formulation does not have standard rules, and the whole process is done manually so that the final result is not optimal. Guided by the Software Development Life Cycle (SDLC), application design with the MERN concept supported by Flask technology will create a website that has machine learning with the Naïve Bayes algorithm from the Multi-Class Classification concept in the form of a REST-API model. At the end of the black box test, the results of research conducted on 130 student data with this design solution obtained a prediction accuracy rate of 74% to optimize the SNBP prediction pass process for Gapai PTN community administrators.

Item Type:	Thesis (Skripsi)
Additional Information:	[No.Panggil: 1910512002] [Pembimbing: Kraugusteeliana] [Penguji 1: Tjahjanto] [Penguji 2: Ruth Mariana Bunga Wadu]
Uncontrolled Keywords:	College Prediction Application, Flask, website, Multi-Class Classification Naïve Bayes Algorithm, & National Selection Based on Achievement (SNBP)
Subjects:	L Education > L Education (General) L Education > LB Theory and practice of education Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Divisions:	Fakultas Ilmu Komputer > Program Studi Sistem Informasi (S1)
Depositing User:	Fransiskus Ramaditya Arief Nursanto
Date Deposited:	15 Aug 2023 03:06
Last Modified:	15 Aug 2023 03:06
URI:	http://repository.upnvj.ac.id/id/eprint/25072

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