PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES UNTUK MENILAI KEPUASAN MASYARAKAT TERHADAP PELAYANAN KESEHATAN MELALUI MEDIA SOSIAL TWITTER

ZOSA, EREN (2022) PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES UNTUK MENILAI KEPUASAN MASYARAKAT TERHADAP PELAYANAN KESEHATAN MELALUI MEDIA SOSIAL TWITTER. S1 thesis, Universitas Mercu Buana Jakarta.

Preview	Text (HAL COVER) 01 Cover.pdf Download (1MB) | Preview
Preview	Text (ABSTRAK) 02 Abstrak.pdf Download (24kB) | Preview
	Text (BAB I) 03 BAB 1.pdf Restricted to Registered users only Download (41kB)
	Text (BAB II) 04 BAB 2.pdf Restricted to Registered users only Download (73kB)
	Text (BAB III) 05 BAB 3.pdf Restricted to Registered users only Download (109kB)
	Text (BAB IV) 06 BAB 4.pdf Restricted to Registered users only Download (199kB)
	Text (BAB V) 07 BAB 5.pdf Restricted to Registered users only Download (124kB)
	Text (DAFTAR PUSTAKA) 08 Daftar Pustaka.pdf Restricted to Registered users only Download (85kB)
	Text (LAMPIRAN) 09 Lampiran.pdf Restricted to Registered users only Download (518kB)

Abstract

During these few years, we are faced with various diseases that will not endanger our lives or not. And also at this time there is still a corona virus (Covid-19) that still needs to be watched out so as not to be affected by the disease. Many things have been tried by the government so that the community is not affected by the existing disease, by providing health facilities such as health centers, hospitals, or clinics as places for health checks. But there are also people who have difficulty with the facilities provided, because the system is more concerned with finances than one's life. This has led to discussions that have both positive and negative impacts on what people experience using these facilities. Many of these problems were conveyed through social media Twitter, which often found various kinds of public responses that they conveyed about the health services, both negative and positive. In this study, an analysis of the comparison of the agortima was carried out to determine the public's opinion on this public service for health. The method used is the Naïve Bayes classification algorithm and the Support Vector Machine assisted by Google Collab and using the Python language. The experimental results show that the Support Vector Machine algorithm provides the highest accuracy value, which is 62.36% and is followed by the Naive Bayes algorithm, which is 40.76% for the tested dataset regarding public services to health. Key words: Machine Learning, Support Vector Machine, Naive Bayes, Classification, Health facility Selama beberapa tahun ini, kita dihadapkan berbagai penyakit yang tidak tau akan dapat membahayakan nyawa kita atau tidak. Dan juga saat ini masih adanya virus corona (Covid-19) yang masih perlu diwaspadai agar tidak terkena dampak dari penyakit tersebut. Banyak hal yang telah diupayakan oleh pemerintah agar masyarakat tidak terkena dampak dari penyakit yang ada, dengan cara menyediakan sarana kesehatan seperti puskesmas, rumah sakit, ataupun klinik menjadi tempat kita memeriksa kesehatan. Tetapi ada juga masyarakat yang kesulitan dengan fasilitas yang disediakan, karena sistem yang mementingkan keuangan dibandingkan nyawa seseorang. Hal tersebut menyebabkan adanya pembicaraan yang memberikan dampak positif maupun negatif pada apa yang dialami oleh masyarakat menggunakan fasilitas tersebut. Untuk masalah tersebut banyak dibicarakan melalui media sosial Twitter, yang sering dijumpai berbagai macam tanggapan masyarakat yang mereka sampaikan mengenai pelayanan masyarakat kesehatan tersebut baik negatif maupun positif. Pada penelitian ini dilakukan suatu analisa mengenai perandingan agortima untuk mengetahui pendapat masyarakat terhadap pelayanan masyarakat terhadap kesehatan ini. Metode yang digunakan adalah algoritma klasifikasi Naïve Bayes dan Support Vector Machine dengan dibantu dengan Google Collab dan menggunakan bahasa Python. Hasil eksperimen menunjukkan bahwa algoritma Support Vector Machine memberikan nilai akurasi paling tinggi yaitu 62,36% dan diikuti oleh algortima Naive Bayes yaitu 40,76% untuk dataset yang diuji mengenai pelayanan masyarakat terhadap kesehatan. Kata kunci: Machine Learning, Support Vector Machine, Naive Bayes, Klasifikasi, Sarana Kesehatan

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 22 089
NIM/NIDN Creators:	41518010024
Uncontrolled Keywords:	Machine Learning, Support Vector Machine, Naive Bayes, Klasifikasi, Sarana Kesehatan
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 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 620.1-620.9 Standard Subdivisions of Engineering and Applied Operations/Subdivisi Standar Dari Ilmu Teknik dan operasi Terapan
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	WADINDA ROSADI
Date Deposited:	01 Oct 2022 05:01
Last Modified:	03 Oct 2022 07:38
URI:	http://repository.mercubuana.ac.id/id/eprint/69759

Actions (login required)

View Item