Sistem Rekomendasi Mata Kuliah Pilihan dan Pengayaan Menggunakan K-means Clustering Pada Web Learning Management System

hadin, banin fawwaz (2022) Sistem Rekomendasi Mata Kuliah Pilihan dan Pengayaan Menggunakan K-means Clustering Pada Web Learning Management System. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Seluruh mahasiswa Teknik Komputer Insitut Teknologi Sepuluh Nopember diwajibkan untuk merencanakan pengambilan mata kuliah penjurusan yang akan diambil mulai dari semester kelima. Menurut survey, masih banyak mahasiswa yang mengalami kesulitan dalam memeilih mata kuliah penjurusan dan mata kuliah pengayaan yang sesuai. Tidak hanya itu, departemen Teknik Komputer Institut Teknologi Sepuluh Nopember juga membutuhkan alat bantu untuk menentukan mata kuliah apa saja yang akan dibuka untuk kebutuhan tiap semester. Oleh karena itu, diperlukan sebuah sistem rekomendasi
mata kuliah yang dapat membantu mahasiswa menentukan mata kuliah apa saja yang ingin diambil. Sistem rekomendasi ini menggunakan algoritma Demographic Filtering untuk mengatasi cold start pada mahasiswa baru, Content Based Filtering untuk membuat agar rekomendasi lebih terarah, digabungkan dengan K-means Clustering untuk mata kuliah pengayaan.
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All Computer Engineering students at the Institut Teknologi Sepuluh Nopember are required to plan to take elective course that will be taken starting from the fifth semester. According to the survey, there are still many students who have difficulty in choosing the appropriate elective course and enrichment courses. Not only that, the Department of Computer Engineering at the Institut Teknologi Sepuluh Nopember also needs a tool to determine what courses will be opened for each semester’s needs. Therefore, we need a course recommendation system that can help students determine what courses they want to take. This recommendation system uses the Demographic Filtering algorithm to overcome cold starts for new students, Content Based Filtering to make recommendations more focused, combined with K-means Clustering for enrichment courses.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Rekomendasi, Rule Based, K-means, Moodle. Recommender System, Rule Based, K-means, Moodle.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science. EDP
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Computer Engineering > 90243-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	17 Jun 2026 06:47
Last Modified:	17 Jun 2026 06:47
URI:	http://repository.its.ac.id/id/eprint/133858

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