SMART AND INTEGRATED LABORATORY REPORT ASSESSMENT SYSTEM (SILARAS) DENGAN METODE PROBABILISTIC LATENT SEMANTIC ANALYSIS
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Wicaksono, Bayu
Arianto, Rakhmat
Abdurrasyid, Abdurrasyid
Arianto, Rakhmat
Abdurrasyid, Abdurrasyid
Subject
Teknik Informatika
Datestamp
2022-09-23 02:40:29
Abstract :
This study discusses the making of SILARAS application (Smart and Integrated Laboratory Report Assessment System) which purposed to correct a document report on Algorithm & Programming 2 courses by using PLSA (Probabilistic Latent Semantic Analysis) text mining calculation method. PLSA is one of the methods in text mining used to summarize a text document purposed at obtaining a topic discussion group contained in the document or can be called as topic modeling by calculating the probability value of the word on the topic, the probability of the topic on the document, and the probability of the word and document on topic. Before the calculation of PLSA is done first pre-processing step to reduce the number of matrix dimensions to be easier when used as input parameters into the PLSA method that includes tokenizing, filtering, and stemming. It then calculates the number of occurrences of word / term queries in the document being tested and used as input parameters for the calculation of the PLSA method. Performance testing results of PLSA using accuracy method by calculating how percentage probability topics in documents successfully calculated and yield accuracy of 74.99%. Based on these percentages can be concluded that the PLSA method can be an option to summarize the text of the document so that it can assist the laboratory assistant in correcting student reports.
This study discusses the making of SILARAS application (Smart and Integrated Laboratory Report Assessment System) which purposed to correct a document report on Algorithm & Programming 2 courses by using PLSA (Probabilistic Latent Semantic Analysis) text mining calculation method. PLSA is one of the methods in text mining used to summarize a text document purposed at obtaining a topic discussion group contained in the document or can be called as topic modeling by calculating the probability value of the word on the topic, the probability of the topic on the document, and the probability of the word and document on topic. Before the calculation of PLSA is done first pre-processing step to reduce the number of matrix dimensions to be easier when used as input parameters into the PLSA method that includes tokenizing, filtering, and stemming. It then calculates the number of occurrences of word / term queries in the document being tested and used as input parameters for the calculation of the PLSA method. Performance testing results of PLSA using accuracy method by calculating how percentage probability topics in documents successfully calculated and yield accuracy of 74.99%. Based on these percentages can be concluded that the PLSA method can be an option to summarize the text of the document so that it can assist the laboratory assistant in correcting student reports.
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