IDENTIFIKASI TINGKAT BAKAT MAHASISWA MENENTUKAN UKM MENGGUNAKAN METODE FORWARD CHAINING

SINDI FEBRIANI, NPM 2209100117 (2026) IDENTIFIKASI TINGKAT BAKAT MAHASISWA MENENTUKAN UKM MENGGUNAKAN METODE FORWARD CHAINING. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini bertujuan untuk mengidentifikasi tingkat bakat dan minat mahasiswa dalam menentukan Unit Kegiatan Mahasiswa (UKM) yang sesuai dengan menggunakan pendekatan sistem pakar berbasis metode Forward Chaining dan algoritma Decision Tree. Permasalahan yang diangkat adalah masih banyak mahasiswa yang belum mampu menentukan pilihan UKM yang sesuai dengan potensi diri. Data penelitian diperoleh melalui penyebaran kuesioner kepada 100 responden mahasiswa, dengan variabel yang meliputi aspek keagamaan, sosial, kepemimpinan, olahraga, dan seni yang kemudian ditransformasikan menjadi variabel keputusan berupa rekomendasi UKM seperti PMK, LDK, PMI, BEM, MENWA, Olahraga, dan Seni. Proses penelitian meliputi tahapan pengumpulan data, transformasi data, konversi fakta, pemodelan menggunakan Decision Tree, serta evaluasi model menggunakan metode Cross Validation pada aplikasi Orange Data Mining. Hasil penelitian menunjukkan bahwa sistem mampu memberikan rekomendasi UKM secara tepat berdasarkan karakteristik responden, dengan tingkat akurasi yang tinggi. Dan dapat ditunjukkan oleh nilai pada diagonal utama yaitu, dimana mahasiswa yang tertarik mengikuti UKM BEM ada 4 (empat) mahasiswa, dan yang tertarik mengikuti MENWA ada 10 (sepuluh) mahasiswa, Olahraga ada 11 (sebelas) mahasiswa, PMI ada 20 (dua puluh) mahasiswa yang tertarik, PMK ada 11(sebelas) mahasiswa, SENI ada 8 (delapan) mahasiswa yang tertarik, LDK ada 32 (tiga puluh dua) mahasiswa yang tertarik, dan Tidak Ada atau yang tidak tertarik mengikuti UKM ada 5 (lima) mahasiswa. Dengan demikian, sistem ini diharapkan dapat membantu mahasiswa dalam memilih UKM yang sesuai serta mendukung pengembangan minat dan bakat secara optimal. Kata Kunci: Sistem Pakar, Forward Chaining, Decision Tree, Data Mining, UKM, Orange Data Mining ============================================================================================= This study aims to identify the level of talent and interest of students in determining the appropriate Student Activity Unit (UKM) using an expert system approach based on the Forward Chaining method and the Decision Tree algorithm. The problem raised is that many students are still unable to determine the choice of UKM that suits their potential. The research data was obtained through distributing questionnaires to 100 student respondents, with variables covering religious, social, leadership, sports, and arts aspects which were then transformed into decision variables in the form of UKM recommendations such as PMK, LDK, PMI, BEM, MENWA, Sports, and Arts. The research process includes the stages of data collection, data transformation, fact conversion, modeling using Decision Tree, and model evaluation using the Cross Validation method in the Orange Data Mining application. The results of the study show that the system is able to provide appropriate UKM recommendations based on the characteristics of respondents, with a high level of accuracy. And it can be shown by the value on the main diagonal, namely, where there are 4 (four) students interested in joining UKM BEM, and 10 (ten) students interested in joining MENWA, there are 11 (eleven) students in Sports, 20 (twenty) students interested in PMI, 11 (eleven) students interested in PMK, 8 (eight) students interested in ARTS, 32 (thirty-two) students interested in LDK, and 5 (five) students who are not interested in joining UKM. Thus, this system is expected to help students in choosing the appropriate UKM and support the development of interests and talents optimally. Keywords: Expert System, Forward Chaining, Decision Tree, Data Mining, UKM, Orange Data Mining

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Sistem Pakar, Forward Chaining, Decision Tree, Data Mining, UKM, Orange Data Mining================Expert System, Forward Chaining, Decision Tree, Data Mining, UKM, Orange Data Mining
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	09 Jul 2026 03:16
Last Modified:	09 Jul 2026 03:16
URI:	http://repository.ulb.ac.id/id/eprint/2587

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