Social media is the most effective way to facilitate fast information, unfortunately, there are some elements who use social media to add hoax or deception to give misleading opinions to the public. Therefore a method is needed to classify hoax news and non-hoax news on social media. Naive Bayes is a simple classification algorithm but has high qualifications, but Naive Bayes has a very sensitive shortcoming in the selection of features and therefore the Particle Swarm Optimization method is needed to improve the expected results. After conducting research with the Naive Bayes method and the Naive Bayes method based on Particle Swarm Optimization, the results obtained are Naive Bayes yielding 74.67% while the Naive Bayes based on Particle Swarm Optimization with an accuracy value of 85.19%. The purpose of this study is to see a large comparison. Swarm Optimization particles to improve accuracy in the classification of hoax news on social media using the Naive Bayes classifier. After using Particle Swarm Optimization the test results increased by 10.52%.
Penggunaan bahasa daerah kini mulai ditinggalkan karena banyak orang yang lebih menyukai menggunakan bahasa asing, maka dari itu sebagai warga yang baik kita harus melestarikan bahasa daerah yang ada contohnya adalah bahasa Jawa. Untuk mempermudah mempelajari bahasa jawa maka kita membutuhkan aplikasi penerjemah. Melihat dari permasalahan tersebut maka penulis merancang sebuah aplikasi kamus digital Jawa–Indonesia dan Indonesia–Jawa pada perangkat mobile khususnya handphone yang berbasis Android. Tahapan yang penulis lakukan untuk melakukan proses pembangunan aplikasi tersebut meliputi tahapan analisis permasalahan dan kebutuhan, perancangan aplikasi dan desain antar muka aplikasi, sehingga aplikasi yang terbentuk menjadi mudah untuk digunakan dan memiliki fungsi yang optimal dalam menterjemahkan bahasa.
Covid-19 merupakan penyakit menular melalui mulut dan hidung seseorang yang terinfeksi saat sedang berbicara, batuk maupun bersin dan menyebar secara luas didunia sehingga ditetapkan sebagai pandemi. Banyak upaya pemerintah yang dilakukan untuk menekan penyebaran Covid-19, salah satunya adalah penerapan PPKM untuk wilayah Jawa dan Bali. Pemberlakuan PPKM ini menimbulkan pro-kontra antar masyarakat, ada yang setuju dan ada yang tidak setuju diberlakukannya PPKM. Oleh sebab itu peneliti melakukan penelitian sentimen masyarakat terhadap pemberlakuan PPKM wilayah Jawa dan Bali. Komentar masyarakat diambil dari media sosial yaitu twitter berupa komentar positif dan negatif, kemudian data diolah menggunakan text editor Jupyter dan bahasa pemrograman Python serta menggunakan algoritma SVM. Penelitian ini memiliki tujuan apakah algoritma SVM dapat menjadi pengklasifikasi teks yang baik untuk analisis sentimen pemberlakuan PPKM, membandingkan Kernel pada SVM antara Kernel Linier dengan Kernel RBF, serta menilai apakah penerapan PPKM untuk wilayah Jawa dan Bali terbukti berhasil menekan angka penyebaran virus Covid-19. Algoritma SVM dengan kernel linier terbukti menjadi algoritma pengklasifikasi text yang baik pada analisis sentimen pemberlakuan PPKM wilayah Jawa dan Bali dengan nilai akurasi sebesar 86%. Serta Dilihat dari hasil analisis sentimen penerapan PPKM untuk wilayah Jawa dan Bali terbukti berhasil menekan angka penyebaran Covid-19
The problem examined in this study is about the user’s trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the classification method and the proposed selection feature as a consideration in determining the use of digital learning applications. This study compares the classification method, the Naïve Bayes algorithm and the genetic algorithm (GA) as feature selection with the Naïve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Naïve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.
There are various software related to the implementation of IT Service Management (ITSM) for a company, including those that are open source and commercial. An input is needed for companies in determining what software to choose from various software, especially for small and medium enterprises (SMEs) that have limited human and financial resources. In this study, we have contributed in evaluating open source ITSM software (including OTRS, ITOP, and SDP) that are suitable for use by small and medium-sized companies. In the evaluation process, we evaluated various appropriate criteria, so that we finally chose the quick-win criteria from ITIL V3 (one of the best practices that are widely used in ITSM). The method used for training is Fuzzy SIR (Superiority and Inferiority Ranking) with assessment criteria for data taken in the form of quantitative data from experts who have used and had certificates in the field of ITSM. The results showed that OTRS was the best software with a value of 0.86, SDP was in second place with a value of 0.77, and ITOP was in the last place with a value of 0.04.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.