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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Journal Of Computer Science And Technology
Bagus Adhi Kusuma
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Game Genshin Impact pada Play Store Menggunakan Naïve Bayes Clasifier Primandani Arsi; Pungkas Subarkah; Bagus Adhi Kusuma
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 3 No. 1 (2023): Maret: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v3i1.1962

Abstract

Online games are an entertainment medium that cannot be separated from some groups of people, especially during the co-19 pandemic. The existence of a policy of violence reduces the interaction of people in the world, including in Indonesia, resulting in an increase in online activity, especially playing games. Game is an interesting online gaming platform, where players can still interact socially boldly. Genshin Impact is a mobile game that provides other platforms such as PC, PlayStation and Nintendo Switch. Although some people like this game, others are not satisfied with the game play. Sentiment analysis is also very useful when game developers want to know what users think about the game experience. This research aims to build a model of the classification of reviews on the Genshin Impact game available on the Google Play platform, so that the resulting model can provide recommendations for developers for improvement. The results obtained are reviews on the Google Play Store tend to be positive with an accuracy score of 87%, precision of 67%, memory of 98%, and f1 score of 67%. Evaluation is done by comparing the model that has been obtained in this study with the previous model with the same algorithm.
Klasifikasi Spesies Hiu Dengan Arsitektur Bahar, Ahmad; Bagus Adhi Kusuma
Journal of Computer Science and Technology (JOCSTEC) Vol 1 No 3 (2023): JOCSTEC - September
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v1i3.120

Abstract

Ikan hiu adalah kelompok hewan yang menarik dan menakutkan di dunia laut. Mereka termasuk dalam kelas Chondrichthyes bersama dengan pari dan hiu bersirip. Mereka dapat ditemukan di berbagai perairan dan memiliki peran penting sebagai indikator kesehatan ekosistem laut. Namun, populasi ikan hiu terancam karena penangkapan ikan ilegal dan kurangnya konservasi. Dalam proses mengidentifikasi dan mengklasifikasikan spesies hiu, peneliti membuat program dengan menggunakan teknik pengenalan visi komputer, seperti Convolutional Neural Network (CNN), dapat sangat membantu. Salah satu arsitektur CNN yang efektif adalah ResNet50, yang terbukti berhasil dalam klasifikasi gambar. Dengan menggunakan 4720 data citra ikan hiu dari 14 kelas model ResNet50 berhasil mencapai akurasi 86% dalam klasifikasi spesies ikan hiu. Model ini dapat digunakan untuk mengidentifikasi spesies hiu dengan baik, kecuali pada beberapa kelas tertentu yang perlu diperbaiki.
IMPROVEMENT OF NAIVE BAYES ALGORITHM IN SENTIMENT ANALYSIS OF SHOPEE APPLICATION REVIEWS ON GOOGLE PLAY STORE Elistiana, Khoerotul Melina; Bagus Adhi Kusuma; Subarkah, Pungkas; Awal Rozaq, Hasri Akbar
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1486

Abstract

Reviews of the shopee application on the google play store are included in useful information if processed properly. Old or new users can analyze app reviews to get information that can be used to evaluate services. The activity of analyzing application reviews is not enough just to see the number of stars, it is necessary to see the entire contents of the review comments to be able to know the intent of the review. A sentiment analysis system is a system used to automatically analyze a review to obtain information including sentiment information that is part of an online review. The data is classified using Naive Bayes. A total of 1,000 shopee app user reviews were collected to form the sample dataset. The purpose of this study is to determine the sentiment analysis of shopee application reviews in the Google Play Store using the Naive Bayes algorithm. The stages of this research include, data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage there are 6 stages, namely Cleaning, Case folding, Word Normalizer, Tokenizing, Stopword Removal and Stemming. TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighting. The data will be grouped into two categories, namely negative and positive. The data will then be evaluated using accuracy parameter testing. The test results show an accuracy value of 81%, this result shows that shopee application reviews tend to be negative.
Co-Authors Awal Rozaq, Hasri Akbar Bahar, Ahmad Elistiana, Khoerotul Melina Primandani Arsi Pungkas Subarkah Subarkah, Pungkas