Bambang Kelana Simpony

@simpony.web.id

Fakultas Teknik dan Informatika
Universitas Bina Sarana Informatika



                          

https://researchid.co/simpony
4

Scopus Publications

Scopus Publications

  • NLP Optimization for SVM-Based Stock Price Sentiment Classification
    Saeful Bahri, Miftah Farid Adiwisastra, Tuti Alawiyah, Bambang Kelana Simpony, Dini Silvi Purnia, and Herlan Sutisna
    IEEE

  • Generation of Rectangular Matrix Key for Hill Cipher Algorithm Using Playfair Cipher
    Tuti Alawiyah, Agung Baitul Hikmah, Wildan Wiguna, Mira Kusmira, Herlan Sutisna, and Bambang Kelana Simpony
    IOP Publishing

  • Sentiment Analysis for Decision Support Systems of Employee
    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

  • Classification of Science, Technology and Medicine (STM) Domains with PSO and NBC
    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

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