Implementasi Algoritma K-Nearest Neighbour dalam Menganalisis Sentimen Terhadap Program Merdeka Belajar Kampus Merdeka (MBKM)

Dewi Sartika

doi:10.24002/jbi.v14i01.7178

Authors

Dewi Sartika Program Studi Teknik Informatika Fakultas Ilmu Komputer, Universitas Indo Global MandiriUnivesitas Indo Global Mandiri https://orcid.org/0000-0001-6659-3572

DOI:

https://doi.org/10.24002/jbi.v14i01.7178

Keywords:

MBKM, NLP, K-NN, F1-Score

Abstract

K-Nearest Neighbor Algorithm Implementation in sentiment analysis towards Merdeka Belajar Kampus Merdeka (MBKM) Program. Merdeka Belajar Kampus Merdeka (MBKM) is a program that supports students to improve their skills by having direct experience in the work environment to prepare for competition and a future career. MBKM program has been implemented by Indonesia's Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) since 2020. Every policy needs to be evaluated; a simple evaluation can be done through sentiment analysis to determine public responses to the MBKM program. The results are used as suggestions for program improvement. Sentiment analysis is done by applying the Natural Language Processing (NLP) algorithm to process crawled data from Twitter, then classified using the K-NN Algorithm. Based on the results, the sentiment is neutral. This illustrates that people are only partially interested in the MBKM program policy. The accuracy of the classification model using the K-NN algorithm is 95%, and an F1-score value of 0.96 for the classification model with a ratio of 80% training data and 20% test data.
Keywords: MBKM, NLP, K-NN, F1-Score

Program Merdeka Belajar Kampus Merdeka (MBKM) merupakan suatu kebijakan dalam mendukung pemberian kebebasan terhadap mahasiswa untuk mengasah kemampuan dengan merasakan langsung pengalaman di dunia kerja sebagai bekal untuk menghadapi persaingan dan persiapan berkarir di masa mendatang. Program MBKM mulai diberlakukan oleh Kementerian Pendidikan Kebudayaan Riset dan Teknologi (Kemendikbudristek) Republik Indonesia sejak tahun 2020. Setiap kebijakan tentunya perlu dievaluasi, evalusi sederhana dapat dilakukan melalui analisis sentimen untuk mengetahui tanggapan masyarakat mengenai program MBKM. Hasilnya digunakan sebagai saran perbaikan untuk pengembangan program. Analisis sentimen dilakukan dengan menerapkan algoritma Natural Language Processing (NLP) untuk memproses data hasil crawling dari Twitter, selanjutnya diklasifikasikan menggunakan algoritma K-NN. Berdasarkan hasil analisis diperoleh bahwa sentimen masyarakat bersifat netral. Hal ini menggambarkan bahwa masyarakat tidak sepenuhnya tertarik terhadap kebijakan program MBKM, sedangkan untuk tingkat akurasi model klasifikasi menggunakan algoritma K-NN sebesar 95% dan nilai F1-score sebesar 0,96 untuk model klasifikasi dengan perbandingan 80% data latih dan 20% data uji.
Kata Kunci: MBKM, NLP, K-NN, F1-Score

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