Multiprocessing Stemming: A Case Study of Indonesian Stemming

Yusliani, Novi; Primartha, Rifkie; Marieska, Mastura Diana

doi:10.5120/ijca2019918476

Cited by 14 publications

(8 citation statements)

References 4 publications

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“…Stemming is the stage of converting words with affixes into their base form. Stemming is a method based on the morphological rules of the Indonesian language, which classifies affixes as prefixes, infixes, suffixes, and combinations of prefixes and suffixes [16].…”

Section: Methodsmentioning

confidence: 99%

A sentiment analysis of madura island tourism news using C4.5 algorithm

Savitri,

Sa’id,

Husni

et al. 2024

J. Soft Comput. Explor.

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Over the past few years, the tourism sector has experienced significant growth in its contribution. The tourism potential on Madura Island is widespread across four regencies, namely Bangkalan, Sampang, Pamekasan, and Sumenep. This potential can be harnessed to support the local government's economy and the communities in the surrounding areas. This research aims to analyze the sentiment of Madura tourism news from online sources using the Decision Tree (C4.5) method. The data used in this study consist of 100 Madura tourism news articles collected from online news portals, which will be classified using the Decision Tree (C4.5) method. The test results show that this method has an average accuracy rate of 76.5% in 10 tests. The average accuracy results demonstrate that the use of the Decision Tree (C4.5) method in this research yields a sufficiently high accuracy level in the sentiment analysis of tourism news.

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Section: Methodsmentioning

confidence: 99%

A sentiment analysis of madura island tourism news using C4.5 algorithm

Savitri,

Sa’id,

Husni

et al. 2024

J. Soft Comput. Explor.

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“…Stemming removes a word's prefix or suffix to get the basic word form. For example, registered words and registrations share a common term, stem list [31].…”

Section: Methodsmentioning

confidence: 99%

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Gunawan,

Fitria,

Setiawan

et al. 2023

Knowl. Eng. Data Sci.

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Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.

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“…In this study, public complaints on the Indonesian government social media will be analyzed. For the analysis, we will use Sastrawi which is one of the most popular corpus for the mining in the Indonesian language [32], [35]. This corpus has a standard in stop-words and is unable to stem some local or contextual words.…”

Section: Application-based Public Complaintsmentioning

confidence: 99%

Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

2022

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Citizen science has emerged in many countries to contribute to the prompt resolution of individual field problems and has been shifted toward Information System (IS) research. In the domain of IS, a citizen report mechanism has been introduced in many local governments to understand regional problems based on the public participation. The rising of social media enforces many organizations including the local governments to utilize any information from the citizens including texts. Text mining has been utilized in various types of analyses such as sentiment analysis. However, it shows many challenges when it comes to the local context. The local context of words could cause various conflation errors that highly affect the learning task such as classification methods. This study aims to propose a context-based text processing and feed the proposed approach into a machine learning framework to classify the data of citizen reports-. The context-based text preprocessing utilized statistical-and semantic-based measurements to extract the local context and elaborate domain expertise to verify the misinterpretation for further text processing such as feature extractions. Subsequently, the n-gram language models together with the Term Frequency and Inverse Document Frequency schemes were performed to build the features. The result showed that the context-based text preprocessing improved the classification performance in majority classifiers in about 3% with the combinations of n-gram features.

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Multiprocessing Stemming: A Case Study of Indonesian Stemming

Cited by 14 publications

References 4 publications

A sentiment analysis of madura island tourism news using C4.5 algorithm

A sentiment analysis of madura island tourism news using C4.5 algorithm

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

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