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ANALISIS SENTIMEN RESPONS MASYARAKAT TERHADAP KARTU PRAKERJA MENGGUNAKAN ALGORITMA K-NN, NAÏVE BAYES DAN SVM

NUGRAHA, TRI PUTRA ADIMAS (2022) ANALISIS SENTIMEN RESPONS MASYARAKAT TERHADAP KARTU PRAKERJA MENGGUNAKAN ALGORITMA K-NN, NAÏVE BAYES DAN SVM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The Covid-19 virus that entered Indonesia had an impact on the decline due to the implementation of social distancing regulations by the Government. Many companies impacted to reduce the number of workers, which means more people lose their jobs. The government try a many way to resolved and reduce the unemployment rate in Indonesia, one of solution is launching the Pre-Employment Card program. Twitter as a social media with a much users by Indonesian people to explain their opinion about Pre-Employment Card Program. Sentiment analysis is usually used to determine the value of an opinion from the research subject, whether it is negative or positive. The use of sentiment analysis to find out whether the majority of public opinion on Twitter towards the Pre-Employment Card Program is positive or negative. The KNN algorithm is a method for classifying objects based on learning data that is closest to the object by tested. The Naïve Bayes method applies an approach to statistical calculations by making assumptions between classes. Support Vector Machine (SVM) is a classification method with a basic concept that maximizes the hyperplane boundary that separates a data set. For this reason, the KNN, SVM and Naïve Bayes algorithms are proper to use the process of sentiment analysis on public opinion on Twitter about Pre-Employment Cards. Key words: Machine Learning, Pre-employment card, Classification, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, TF-IDF. Virus Covid-19 yang memasuki Indonesia berdampak pada penurunan akibat pemberlakuan peraturan social distancing oleh Pemerintah. Banyak perusahaan yang terpaksa mengurangi jumlah pekerja yang artinya semakin banyak masyarakat yang kehilangan mata pencahariannya. Banyak upaya yang dilakukan pemerintah dalam menekan angka pengangguran yang terjadi di Indonesia, salah satunya yaitu meluncurkan program Kartu Prakerja. Twitter sebagai media sosial yang kerap kali digunakan oleh masyarakat Indonesia untuk menyatakan opini mereka terhadap Program Kartu Prakerja tersebut. Analisis sentimen biasa digunakan untuk mengetahui nilai dari sebuah opini dari subjek penelitian, apakah bernilai negatif atau positif. Penggunaan analisa sentimen dalam mengetahui apakah mayoritas opini dari masyarakat di twitter terhadap Program Kartu Prakerja mengarah ke positif atau negatif. Algoritma KNN adalah sebuah metode untuk melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek yang diuji. Metode Naïve Bayes menerapkan pendekatan pada perhitungan statistik dengan melakukan asumsi antar kelas. Support Vector Machine (SVM) adalah salah satu metode klasifikasi dengan konsep dasar yang memaksimalkan batas hyperplane yang memisahkan suatu set data. Untuk itu algoritma KNN, SVM dan Naïve Bayes dirasa tepat untuk diterapkan dalam melakukan proses analisis sentimen terhadap opini masyarakat di twitter tentang Kartu Prakerja. Kata kunci: Machine Learning, Kartu Prakerja, Klasifikasi, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, TF-IDF

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 22 125
NIM/NIDN Creators: 41518010057
Uncontrolled Keywords: Machine Learning, Kartu Prakerja, Klasifikasi, K-Nearest Neighbor, Support Vector Machine, Naïve Bayes, TF-IDF
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum
000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: WADINDA ROSADI
Date Deposited: 19 Oct 2022 06:06
Last Modified: 19 Oct 2022 06:06
URI: http://repository.mercubuana.ac.id/id/eprint/70599

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