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All Journal Buana Ilmu Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks)
Ayu Ratna Juwita
Universitas Buana Perjuangan Karawang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice

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ANALISIS SENTIMEN TWITTER TERHADAP UU OMNIBUS LAW MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN NAÏVE BAYES CLASSIFIER (NBC) Chorirotun Cholifah; Hanny Hikmayanti Handayani; Ayu Ratna Juwita
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 4 No 4 (2022): EDISI 14
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.135 KB) | DOI: 10.51401/jinteks.v4i4.2191

Abstract

Dalam media sosial orang dapat mengutarakan opininya secara bebas sesuai dengan UU ITE. Salah satu media sosial yang dipakai oleh banyak orang adalah Twitter. Jumlah pengguna Twitter sebanyak 21.05% dari total seluruh pengguna internet di Indonesia. Analisis sentimen twitter bertujuan untuk menentukan isi dari dataset yang berbentuk teks berupa (kalimat dan paragraf) yang bersifat positif atau negatif. Alur kerja yang digunakan merupakan framework yang pada umumnya digunakan pada teknik analisis. Penelitian ini menggunakan teknik crawling, teknik ini memanfaatkan API pada Twitter, sehingga di dapatkan data tweet berdasarkan kata kunci tertentu. Hasil datanya sebanyak 3018 data. Untuk mengetahui akurasinya, Analisis sentimen twitter terhadap UU OmnibusLaw diklasifikasikan menggunakan metode Naïve Bayes Classifier (NBC) dan metode Support Vector Machine (SVM) dengan menggunakan tipe dokumen Comma Separated Values (CSV) sebagai inputan data untuk melakukan klasifikasi data. Hasil klasifikasi dataset tweets terhadap UU OmnibusLaw bahwa kedua metode tersebut dapat melakukan performa yang baik, pada metode Naïve Bayes nilai accuracy sebesar 75%, untuk nilai precision sebesar 77% dan untuk nilai recall sebesar 79%. Sedangkan pada metode SVM nilai accuracy sebesar 77%, untuk nilai precision sebesar 88% dan untuk nilai recall sebesar 67%.
Analysis of Sentiment Adiraku App Reviews on Google Play Store Using Vector Machine Support Algorithm and Naïve Bayes Bayu Padilah; Adi Rizky Pratama; Ayu Ratna Juwita
JURNAL SISFOTEK GLOBAL Vol 13, No 1 (2023): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v13i1.2943

Abstract

The Adiraku application is considered to be able to facilitate and facilitate customers so that there is no need to come to the branch office to get information related to the number of installments that must be paid, due dates, credit simulations, and Adira Finance information offers to customers. A large number of reviews from users received makes it difficult for developers to read them, it will take too much time and effort if they have to read and analyze them manually. To find out which reviews are classified as positive or negative reviews. need a sentiment analysis of the review. This study aims to find out how the opinions or opinions of its users on the services of the application, by analyzing these sentiments through a classification process using two algorithms, namely Support Vector Machine and Naïve Bayes. The data used amounted to 2000 data obtained from Google Playstore. Data is labeled into 2 classes namely positive class and negative. Furthermore, the data is divided into 70% training data and 30% testing data and methods used for testing using Bernoulli Naïve Bayes and Linear Kernel. It was concluded that the number of user reviews of the Adiraku application on the Google Play Store showed more positive comments, amounting to 1412 positive and negative reviews, which was 588 reviews. The Support Vector Machine algorithm performs better by getting the best accuracy value of 96%, while the Naïve Bayes algorithm gets an accuracy value of 85%.
IMPLEMENTASI TEKNIK FAILOVER RECURSIVE GATEWAY Tohirin Al Mudzakir; Adi Rizky Pratama; Ayu Ratna Juwita
BUANA ILMU Vol 7 No 2 (2023): Buana Ilmu
Publisher : Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bi.v7i2.5365

Abstract

The development of information technology and the internet in Indonesia every year shows very rapid progress in terms of reliable infrastructure, users, hardware, software and information systems. At Buana Perjuangan University, Karawang has two internet lanes designated for users of all academics, both LAN and wifi, and one more lane for servers. These conditions sometimes create problems when one of the internet lines is down which results in hampering service activities. Based on the background above, the authors propose the implementation of a Failover Recursive Gateway, with the aim that there will be no more cessation of administrative services when one of the internet lines is down.
Co-Authors Adi Rizky Pratama Bayu Padilah Chorirotun Cholifah Hanny Hikmayanti Handayani Mudzakir, Tohirin Al