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Application of document spelling checker for Bahasa Indonesia
Aqsath Rasyid N.a, Kamayani M.a, Reinanda R.a, Simbolon S.a, Soleh M.Y.a, Purwarianti A.a
a School of Electrical and Informatics Engineering, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]The needs of document spelling checker of Bahasa Indonesia is highly required. Unfortunately, there is no available application of document spelling checker for Bahasa Indonesia. The existing researches on Indonesian spelling checker have not developed into a complete document spelling checker. Here in this research, we compare several methods employed for Indonesian spelling checker especially in the word error detection and analyzed best methods employed in the building of Indonesian document spelling checker application. The main idea is to employ a complete word list as the reference. The Indonesian document spelling checker consists of 5 main components, namely document preprocess, word error detection, word error correction, word candidate ranking, and user feedback. The document preprocess is to process the document into a list of unique word which will be analyzed further in the spelling checker. In the word error detection, a binary search and hashing are used to do the searching faster. In the word error correction, the forward reverse and a similarity measure score are employed. In the candidate ranking, HMM is used to select the best correct word candidate. Using 13,000 words as the lexicon resource and 10 documents as the tested documents, the experimental results achieved 93.7% accuracy. The errors are caused by the word absence in the lexicon resource and the special repetition word form. © 2011 Universitas Indonesia.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Binary search,Indonesia,Main component,Preprocess,Similarity measure,Spelling checker,Unique word,User feedback,Word lists[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]