Perbandingan Performa Algoritma Machine Learning untuk Analisis Sentimen Kebijakan Kominfo pada Twitter

NURTAMA, RIDHO ADITYA (2023) Perbandingan Performa Algoritma Machine Learning untuk Analisis Sentimen Kebijakan Kominfo pada Twitter. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

PSE policy is a policy made by the Ministry of Communication and Information Technology (Kominfo) to all digital service providers in Indonesia. This rule was made to create a safe and healthy internet. However, on July 30, 2022, Kominfo conducted a mass blocking of digital platforms that did not register PSE such as Steam, Epic Games Store to Paypal, this reaped pros and cons in the community and became a trending topic on platforms such as Twitter. Many Twitter users in Indonesia gave opinions and commented on this case. Therefore, a sentiment analysis was conducted on the case of the PSE policy issued by Kominfo. The data is taken from public comments regarding responses to this PSE policy on Twitter, in the form of positive and negative comments. This research also aims to compare which algorithms are effective in sentiment analysis in this case, including Support Vector Machine (SVM), Random Forest, and Decision Tree. In this case the Support Vector Machine (SVM) algorithm gets the greatest classification results by producing an accuracy value of 84.25%, followed by the Decision Tree algorithm by getting an accuracy value of 78.17%, and the Support Vector Machine (SVM) algorithm gets an accuracy value of 73.46%. Kata Kunci: Sentiment Analysis, PSE Policy, Kominfo, Support Vector Machine, Random Forest, Decision Tree Kebijakan PSE merupakan kebijakan yang dibuat oleh Kementerian Komunikasi dan Informatika (Kominfo) kepada seluruh penyedia layanan digital di Indonesia. Aturan ini dibuat untuk bertujuan menciptakan internet yang aman dan sehat. Namun pada tanggal 30 Juli 2022, Kominfo melakukan pemblokiran massal pada platform digital yang tidak mendaftar PSE seperti Steam, Epic Games Store hingga Paypal, hal ini menuai pro kontra pada masyarakat dan menjadi trending topik pada platform seperti Twitter. Banyak pengguna Twitter di Indonesia yang memberikan pendapat dan berkomentar tentang kasus ini. Oleh karena itu, dilakukan analisis sentimen pada kasus kebijakan PSE yang dikeluarkan oleh Kominfo. Data diambil dari komentar masyarakat mengenai tanggapan pada kebijakan PSE ini di Twitter, berupa komentar positif dan negatif. Penelitian ini juga bertujuan untuk membandingkan algoritma mana yang efektif dalam analisis sentimen pada kasus ini, diantaranya Support Vector Machine (SVM), Random Forest, dan Decision Tree. Pada kasus ini algoritma Support Vector Machine (SVM) mendapatkan hasil klasifikasi yang paling besar dengan menghasilkan nilai akurasi sebesar 84.25%, disusul dengan algoritma Random Forest dengan mendapatkan nilai akurasi sebesar 78.17%, dan algoritma Decision Tree mendapatkan nilai akurasi sebesar 73.46%. Kata Kunci: Analisis Sentimen, Kebijakan PSE, Kominfo, Support Vector Machine, Random Forest, Decision Tree

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 077
NIM/NIDN Creators:	41519010050
Uncontrolled Keywords:	Analisis Sentimen, Kebijakan PSE, Kominfo, Support Vector Machine, Random Forest, Decision Tree
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 > 003 Systems/Sistem-sistem > 003.5 Computer Modeling and Simulation/Model dan Simulasi Komputer 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 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 > 004.1 General Works on Specific Types of Computers/Karya Umum tentang Tipe-tipe Khusus Komputer 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	CALVIN PRASETYO
Date Deposited:	15 Sep 2023 07:51
Last Modified:	15 Sep 2023 07:51
URI:	http://repository.mercubuana.ac.id/id/eprint/80958

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