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All Journal Brilliance: Research of Artificial Intelligence
Ari Purno Wahyu Wibowo
Widyatama University, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Analysis of Public Sentiment Text Clustering on Tax Increases using Orange Data Mining on Twitter Ibnu Azhar Maulana; Ari Purno Wahyu Wibowo
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5787

Abstract

Taxes play an important role in the life of a nation and state, particularly in the implementation of national development. Recently, Indonesia issued a new policy to increase VAT to 12%. This policy has sparked a range of both negative and positive opinions from the public. As a result, various reactions and sentiments have been expressed by citizens regarding the policy. To analyze these public sentiments, text mining was carried out using the Orange Data Mining application, utilizing data from the Twitter platform to observe and evaluate Indonesian citizens' reactions. A total of 100 tweets were collected using relevant keywords to find content related to the policy. The results were then categorized into several sentiment groups based on the similarity of their content. After the text classification, the data was stored in a table showing the number of positive, negative, and neutral sentiments. This data was later visualized in a graph, which revealed that the most common reaction was disappointment, followed by confusion, enthusiasm, and lastly, anger. The results of this study indicate that many Indonesian citizens are disappointed with the VAT increase policy. Many believe that the government's use of tax funds has not been satisfactory. Therefore, the government is urged to improve its programs so that citizens can feel the benefits of the taxes they pay.
Design of an Automatic Help Desk Response Module Using Natural Language Processing Tri Mur Fridayanto; Ari Purno Wahyu Wibowo
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6575

Abstract

Manual help desk systems in enterprise environments often suffer from delayed response times and repetitive queries, reducing service efficiency. This research aims to design an automated help desk response module by applying Natural Language Processing (NLP) techniques, specifically within the asset management context of an ERP system. The module uses Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity to classify incoming queries and retrieve relevant answers from a predefined knowledge base. Python, Django, PostgreSQL, Scikit-learn, and NLTK were used to implement the module. Testing was conducted using 50 sample queries, resulting in an accuracy of 90% based on confusion matrix evaluation. The system successfully retrieves appropriate responses for most frequent user issues. This design is expected to support organizations in streamlining their help desk operations and improving response time and consistency. Future developments may involve semantic matching and machine learning-based improvements to enhance understanding of unstructured queries.
Co-Authors Ibnu Azhar Maulana Tri Mur Fridayanto