Bambang Kelana Simpony
@simpony.web.id
Fakultas Teknik dan Informatika
Universitas Bina Sarana Informatika
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Tuti Alawiyah, Agung Baitul Hikmah, Wildan Wiguna, Mira Kusmira, Herlan Sutisna, and Bambang Kelana Simpony
IOP Publishing
Saeful Bahri, Miftah Farid Adiwisastra, Tuti Alawiyah, Dini Silvi Purnia, and Bambang Kelana Simpony
IOP Publishing
This paper presents sentiment analysis that will be used as Decision Support in employee recruitment. Sentiment analysis used Term Frekuensi.Index Document Frekuensi (TF.IDF) weight calculations. Weighting results were classified using the Support Vector Machine (SVM) method into several categories, namely negative sentiment, positive sentiment and neutral. the results of this study showed an accuracy value of 0.65 which was the best accuracy for text classification
Erfian Junianto, Mayya Nurbayanti Shobary, Rizal Rachman, Ai Ilah Warnilah, and Bambang Kelana Simpony
IEEE
Science, Technology, and Medicine (STM) is a field of research that has a characteristic in each document. These characteristics are different from most documents that are used as a corpus in mining text research. Documents derived from Newswire are more dominant in previous research. However, in this study will try to classify documents from STM field. Complex technical terms, symbols, position information, and the number of citations would be a challenge itself. Previous studies have used the Naive Bayes Classifier (NBC) classification method. There are also those who apply Particle Swarm Optimization to assist its classification. From the Newswire field generated a fairly high accuracy Therefore, it would be applied to the optimization method with PSO and combine it with NBC method. This study produced accuracy value in classification model without using PSO equal to 82,73%. While in the classification model using PSO, the accuracy value is 87.27%. This shows that the use of PSO optimization is very influential on the classification