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Combining Feature Embedding Based on the BERT Architecture and Random Forest Algorithm to Identify the Handling Section and Risk Priority Level in an ISP Company in Indonesia

CommIT (Communication and Information Technology) Journal · Vol. 20 No. 2 (2026) · hlm. 207–218

DOI
10.21512/commit.v20i2.13022
ISSN
2460-7010, 1979-2484
Terbit
2026-07-27
Penerbit
Bina Nusantara University
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Presently, Internet usage in society has grown considerably. It presents significant opportunities for companies to develop business related to Internet Service Providers (ISPs). In Indonesia, both domestic and foreign ISPs are competing. To survive and remain popular with Indonesian customers, ISPs need to improve their products and services quickly. Hence, companies need to collect user feedback through customer loyalty applications and reviews to improve their service or technology. However, manually auditing thousands of textual reviews is labor-intensive and subject to human error, making it highly inefficient for agile, real-time corporate decision-making. The research aims to address this operational bottleneck by improving the automation process and providing decision-making information to ISP management. More specifically, the research contributes to implementing and comparing two transfer learning models: Bidirectional Encoder Representations from Transformers (BERT) and IndoBERT for tokenization and embedding. The embedding results are then fed into a random forest for classification. The dataset is collected via a scraping process from the Play Store application, focusing on user feedback and ratings. A total of 1,192 records of user feedback reviews from ISP loyalty apps on the Play Store in March 2024 were gathered. These are manually labeled by a general manager from the company into binary and multi-class categories. Splitting the dataset into 70% for training and 30% for testing achieves a classification accuracy of 70% for the problem domain with IndoBERT + Random Forest. However, multi-class classification for categorizing user feedback into a priority score model achieves an accuracy of 58%.

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.binus.ac.id/index.php/commit/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Combining Feature Embedding Based on the BERT Architecture and Random Forest Algorithm to Identify the Handling Section and Risk Priority Level in an ISP Company in Indonesia</dc:title>
	<dc:creator>Maulana, Rhemzy Putra</dc:creator>
	<dc:creator>Yulianto, Yulianto</dc:creator>
	<dc:subject xml:lang="en-US">Sentiment Analysis</dc:subject>
	<dc:subject xml:lang="en-US">Bidirectional Encoder Representations from Transformers (BERT)</dc:subject>
	<dc:subject xml:lang="en-US">Indonesian Bidirectional Representations from Transformers (BERT)</dc:subject>
	<dc:subject xml:lang="en-US">Random Forest</dc:subject>
	<dc:subject xml:lang="en-US">Natural Language Processing</dc:subject>
	<dc:description xml:lang="en-US">Presently, Internet usage in society has grown considerably. It presents significant opportunities for companies to develop business related to Internet Service Providers (ISPs). In Indonesia, both domestic and foreign ISPs are competing. To survive and remain popular with Indonesian customers, ISPs need to improve their products and services quickly. Hence, companies need to collect user feedback through customer loyalty applications and reviews to improve their service or technology. However, manually auditing thousands of textual reviews is labor-intensive and subject to human error, making it highly inefficient for agile, real-time corporate decision-making. The research aims to address this operational bottleneck by improving the automation process and providing decision-making information to ISP management. More specifically, the research contributes to implementing and comparing two transfer learning models: Bidirectional Encoder Representations from Transformers (BERT) and IndoBERT for tokenization and embedding. The embedding results are then fed into a random forest for classification. The dataset is collected via a scraping process from the Play Store application, focusing on user feedback and ratings. A total of 1,192 records of user feedback reviews from ISP loyalty apps on the Play Store in March 2024 were gathered. These are manually labeled by a general manager from the company into binary and multi-class categories. Splitting the dataset into 70% for training and 30% for testing achieves a classification accuracy of 70% for the problem domain with IndoBERT + Random Forest. However, multi-class classification for categorizing user feedback into a priority score model achieves an accuracy of 58%.
</dc:description>
	<dc:publisher xml:lang="en-US">Bina Nusantara University</dc:publisher>
	<dc:date>2026-07-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.binus.ac.id/index.php/commit/article/view/13022</dc:identifier>
	<dc:identifier>10.21512/commit.v20i2.13022</dc:identifier>
	<dc:source xml:lang="en-US">CommIT (Communication and Information Technology) Journal; Vol. 20 No. 2 (2026): CommIT Journal ; 207-218</dc:source>
	<dc:source>2460-7010</dc:source>
	<dc:source>1979-2484</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.binus.ac.id/index.php/commit/article/view/13022/5805</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Rhemzy Putra Maulana, Yulianto</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>