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Journal : Jurnal Sintaks Logika (JSilog)

A Rancang Bangun Sistem Informasi Booking & Sewa Alat Musik Studio Menggunakan Metode Prototype (Studi Kasus : Studio 55 Nyalindung): Design and Build an Information System for Booking & Rental of Studio Musical Instruments Using the Prototype Method (Case Study: Studio 55 Nyalindung) Tisna, Eza Anbiya; Rizal Setiawan, Iwan; Arsiyanik, Arsiyanik
Jurnal Sintaks Logika Vol. 3 No. 3 (2023): September 2023
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v3i3.2485

Abstract

Studio 55 merupakan salah satu dari sekian banyak studio musik di Kabupaten Sukabumi yang bergerak pada bidang jasa penyewaan alat dan ruangan latihan musik. Terdapat permasalahan dikarenakan sistem yang dilakukan masih dengan cara manual sehingga seringkali terjadi bentrokan jadwal dan kesalahan-kesalahan lainnya. Berdasarkan permasalahan tersebut, dirancang sebuah sistem informasi penyewaan pada Studio 55 berupa aplikasi berbasis web menggunakan metode potorype dengan model Software Development Lifecyle (SDLC). Tujuan dari penelitian ini adalah untuk rancang bangun sebuah sistem informasi berbasis web untuk memudahkan Customer dan Pemilik Studi 55 dalam mendapatkan informasi booking studio, penjadwalan dan administrasi yang baik dan efektif.
Analisis Sentimen Terhadap Isu Kecurangan Pemilu 2024 Pada Platfom Twitter (X) Dengan Metode Naive Bayes Multinomial Dan Cosine Similiarity putra, Muhamad Giani; Rizal Setiawan, Iwan; Indrayana, Didik
Jurnal Sintaks Logika Vol. 5 No. 1 (2025): Januari 2025
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v5i1.3562

Abstract

In an increasingly complex digital era, sentiment analysis has become a vital instrument in understanding the nuances of public opinion. This technique, which utilizes artificial intelligence and Machine Learning, allows us to extract knowledge about people's attitudes, emotions and perceptions towards various issues. This research examines public sentiment regarding the issue of fraud in the 2024 Election on the social media platform Twitter using a text mining-based sentiment analysis approach. Data was obtained through a crawling process using the Python programming language. The research methodology includes a series of stages, starting from data cleaning to improve quality, continuing with word weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and ending with modeling using the Naïve Bayes Classifier algorithm. Model evaluation was carried out systematically by applying the Naive Bayes, Confusion Matrix and K-Fold Cross Validation methods to measure the level of accuracy and effectiveness of the model developed. This research aims to produce in-depth knowledge regarding the trends and dynamics of public sentiment regarding the issue of fraud in the 2024 Election in the realm of social media, especially Twitter (X). Based on the research results, it shows a percentage of 67.7%.