Congestion major cities in Indonesi caused by the proliferation of the use of private vehicles. Some expressing he thinks about busway user through the social media and other web site, This opinion can be used as a sentiment analysis to see if the user busway proposes a review of positive or negative. The results of the analysis sentiment can help in the sight of and evaluate the use of busway, also expected to improve and transjakarta facility from so they tend to have an opinion positive. Based on the results of the analysis, sentiment it is hoped people will switch to using the will of course will reduce congestion. In the study also added the stages preprocesing by using the framework gataframework to complete the process that cannot be done on tools rapidminer. The methodology that was used in this research was it is anticipated that analysis the sentiment of the by the application of an genetic algorithm for an election features with an algorithm naive bayes. From the results of the testing to the case in research it is found that classification algorithm naive bayes based genetic algorithm having the kind of accuracy that good enough 88,55 % and value of auc reached 0,813 % with the level of the diagnosis classifications good. So that in this research classification algorithm naive bayes based genetic algorithm can be recommended as algorithms classifications good enough to analyze the busway user sentimen. Based on analysis is expected to private transport users will switch to using the busway will reduce congestion
Analisis sentimen adalah proses untuk menentukan konten dataset berbasis teks yang positif atau negatif. Saat ini, opini publik menjadi sumber penting dalam keputusan seseorang dalam menemukan solusi. Algoritma klasifikasi seperti Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) diusulkan oleh banyak peneliti untuk digunakan dalam analisis sentimen untuk pendapat ulasan. Namun, klasifikasi sentimen teks memiliki masalah pada banyak atribut yang digunakan dalam dataset. Fitur pemilihan dapat digunakan sebagai proses optimasi untuk mengurangi set fitur asli ke subset yang relatif kecil dari fitur yang secara signifikan meningkatkan akurasi klasifikasi untuk cepat dan efektif. Masalah dalam penelitian ini adalah pemilihan pemilihan fitur untuk meningkatkan nilai akurasi Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) dan membandingkan akurasi tertinggi untuk analisis sentimen tweet / komentar yang menggunakan tagar # 2019GantiPresiden. Algoritma perbandingan, SVM menghasilkan akurasi 88,00% dan AUC 0,964, kemudian dibandingkan dengan SVM berdasarkan PSO dengan akurasi 92,75% dan AUC 0,973. Data hasil pengujian untuk akurasi algoritma k-NN adalah 88,50% dan AUC 0,948, kemudian dibandingkan untuk akurasi dengan PSO berbasis k-NN sebesar 75,25% dan AUC 0,768. Hasil pengujian algoritma PSO dapat meningkatkan akurasi SVM, tetapi tidak mampu meningkatkan akurasi algoritma k-NN. Algoritma SVM berbasis PSO terbukti memberikan solusi untuk masalah klasifikasi tweets/ komentar yang menggunakan tagar # 2019GantiPresiden di Twitter agar lebih akurat dan optimal.
The development of information technology which is increasingly advanced and dynamic and the increasing competition in the global market has made all competition, both labor and business sectors, tighter. Of course this affects all types of businesses and business actors must start thinking about new breakthroughs so that their business can survive and be able to compete with other business competitors. The lack of breakthrough in marketing is experienced by the Judo Karawang Branch Management, where marketing is the spearhead of a business. So, if the marketing strategy is blunt, it will have an impact on the process of business activities. In order for these businesses to survive, the use of information technology is of course the main choice in business development, especially in terms of promotion and sales of services so that they can compete with other businesses. To increase competitiveness and to obtain other business opportunities, this can be done by taking advantage of developments in Information and Communication Technology (ICT). The system development method used in this study is the Rapid Application Development model. This model is used because it is considered a development method that has a short and fast development time. Referring to previous research, the researcher chose this RAD model because it was considered appropriate for the e-commerce system that the researcher built. E-commerce will make it easier for producers in marketing activities and also cut operating costs for trading and marketing activities so that it will increase sales volume and increase revenue. This increase in income will eventually expand the business.
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