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All Journal SKANIKA: Sistem Komputer dan Teknik Informatika VIDHEAS: Jurnal Nasional Abdimas Multidisiplin
Setiawan, Septhiyanthi
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering

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KLASIFIKASI TEKS ULASAN APLIKASI NETFLIX PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SVM Khoirunnisaa, Nabiilah; Nabila Nastiti Kesuma, Kaylista; Setiawan, Septhiyanthi; Yunizar Pratama Yusuf, Ajif
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 1 (2024): Jurnal SKANIKA Januari 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i1.3138

Abstract

Netflix is a subscription streaming platform that presents various shows, such as TV series, documentaries, and films, connected to a device connected to the internet. One of the most popular sites for streaming videos is Netflix, throughout the world and is now starting to apply data analysis and machine learning technology to improve its user services. Through the Google Play Store, users can submit various reviews about the Netflix application. It is possible to extract significant hidden information from this vast quantity of review data that is helpful for assessing an application's quality. Therefore this research aims to classify text reviews of the Netflix application by comparing the two algorithms applied, that is, Support Vector Machine (SVM) and Naive Bayes. With the aim of finding out which algorithm performs better in terms of accuracy. The dataset was obtained through the Google Play Store and applied to the scraping method, totaling 1000 reviews, and processed utilizing the Python programming language. Then the Netflix application review data that was obtained was divided into 70% train data and 30% test data. 82% of the accuracy results were obtained using the Naive Bayes approach., while the support vector machine (SVM) yielded 85% accuracy. It therefore demonstrates that support vector machines (SVM) are no more successful than the outcomes of applying the Naive Bayes method. (SVM).
Strategi Pemasaran Untuk UMKM Melalui Pemanfaatan Media Sosial dan Teknologi Informasi Mugiarso; Setiawan, Septhiyanthi; Hilmy Dwinanda, Muhammad; Arby Ansya, dkk, Muhammad
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 1 No. 2 (2023): Desember 2023
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v1i2.43

Abstract

Before the era of Information Technology, Micro, Small and Medium Enterprises (MSMEs) were faced with a number of problems that could hinder their development. Limited market access and lack of information about market trends and customer preferences often make it difficult for MSMEs to develop effective marketing strategies. Observation activities are carried out by conducting direct observations of the situation and environmental conditions of the community. MSMEs now have the opportunity to overcome most of these problems. The aim of MSMEs in Sawarna Village is to carry out marketing that can improve the quality of human resources who have high abilities and interest in marketing the Sitaman Bumi application, Batour. Utilization of online platforms that will be used to help MSMEs
Co-Authors Arby Ansya, dkk, Muhammad Hilmy Dwinanda, Muhammad Khoirunnisaa, Nabiilah Mugiarso Nabila Nastiti Kesuma, Kaylista Yunizar Pratama Yusuf, Ajif