Hamidah, Dhini (2025) APLIKASI EVALUASI PEMBELAJARAN MAHASISWA JURUSAN TEKNIK INDUSTRI UNIVERSITAS SULTAN AGENG TIRTAYASA BERBASIS MACHINE LEARNING. S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.
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Abstract
Student evaluation is an important stage in the process of assessing students and majors in higher education. Evaluation of students in higher education can be seen from the level at which students complete their studies. During the 3 academic years starting from 2019-2020 to 2021-2022 at the Department of Industrial Engineering, Sultan Ageng Tirtayasa University, there was a tendency for the rate of students graduating not on time to increase. Based on this, to reduce the upward trend, an application was designed as a student evaluation tool that is able to motivate students to plan academic and non-academic processes better so as to prevent delays in graduation which can be detrimental to both students and the university and maintain accreditation in the future. The aim of this research is to obtain an algorithm that produces the highest ROC-AUC score is Random Forest to design a student evaluation application for student graduation. This research uses the Google Collaborator editor's Python programming language with Machine Learning stages and the Streamlit framework. The results of the research are an algorithm with the highest ROC-AUC value in semester 7 is Random Forest where the algorithm for designing student evaluation applications is named "GoLulus".
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | Evaluasi mahasiswa merupakan tahapan penting dalam peroses penilaian mahasiswa dan jurusan di perguruan tinggi. Evaluasi mahasiswa di perguruan tinggi dapat dilihat dari tingkat kelulusan mahasiswa menyelesaikan studinya. Selama 3 Tahun Ajaran mulai dari 2019-2020 hingga 2021-2022 di Jurusan Teknik Industri Universitas Sultan Ageng Tirtayasa, tingkat mahasiswa lulus tidak tepat waktu terjadi kecendrungan kenaikan. Berdasarkan hal tersebut, untuk mengurangi kecendrungan kenaikan maka dirancang sebuah aplikasi sebagai alat evaluasi mahasiswa yang mampu memotivasi mahasiswa dalam merencanakan proses akademik dan non akademik dengan lebih baik sehingga mencegah keterlambatan kelulusan yang dapat merugikan baik bagi mahasiswa dan universitas serta mempertahankan akreditasi di masa mendatang. Tujuan penelitian ini untuk mendapatkan algoritma terbaik yang menghasilkan nilai ROC-AUC tertinggi dalam aplikasi evaluasi pembelajaran mahasiswa yaitu Random Forest dan merancang aplikasi evaluasi pembelajaran mahasiswa. Penelitian ini menggunakan bahasa pemograman Python editor Google Collaborator dengan tahapan Machine Learning dan framework Streamlit. Hasil penelitian berupa algoritma dengan nilai ROC-AUC tertinggi pada semester 7 yaitu Random Forest dimana algoritma tersebut untuk merancang aplikasi evaluasi mahasiswa diberi nama “GoLulus”. | |||||||||
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
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| Divisions: | 03-Fakultas Teknik 03-Fakultas Teknik > 26201-Jurusan Teknik Industri |
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| Depositing User: | Dhini Dhini Hamidah Hamidah | |||||||||
| Date Deposited: | 17 Feb 2025 06:51 | |||||||||
| Last Modified: | 17 Feb 2025 08:46 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/45962 |
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