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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Enthusiastic : International Journal of Applied Statistics and Data Science
Setiono, Nisrina Hanifa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Exploring the Impact of Back-Translation on BERT's Performance in Sentiment Analysis of Code-Mixed Language Data Setiono, Nisrina Hanifa; Sari, Yunita
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.104757

Abstract

Social media, particularly Twitter, has become a key platform for communication and opinion-sharing, where code mixing, the blending of multiple languages in a single sentence, is common. In Indonesia, Indonesian-English code mixing is widely used, especially in urban areas. However, sentiment analysis on code-mixed text poses challenges in natural language processing (NLP) due to the informal nature of the data and the limitations of models trained on formal text. This study applies back translation to address these challenges and optimize BERT-based sentiment analysis. The method is tested on the INDONGLISH dataset, consisting of 5,067 labeled tweets. Results show that applying back translation directly to raw tweets yields better performance by preserving original meaning, improving model accuracy. However, when back translation follows monolingual translation, accuracy declines due to semantic distortions. Repeated translation modifies sentence structure and sentiment labels, reducing reliability. These findings indicate that each additional translation step risks decreasing sentiment analysis accuracy, particularly for code-mixed datasets, which are highly sensitive to linguistic shifts. Back translation proves to be an effective approach for formalizing data while maintaining contextual integrity, enhancing sentiment analysis performance on code-mixed text
Recommending E-Commerce Platforms for MSMEs: A Sentiment Analysis Approach Adiyana, Imam; Kurniawan, Angga; Rahmatika, Alfilia Hilda; Setiono, Nisrina Hanifa; Gumelar, Satya Fajar
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art8

Abstract

The rapid growth of e-commerce in Indonesia presents significant opportunities for micro, small, and medium enterprises (MSMEs), yet the diversity of marketplace platforms complicates the selection of an optimal sales channel. This study addressed this challenge by developing a data-driven recommendation system based on sentiment analysis of user reviews. Utilizing a dataset of 80,000 reviews scraped from four major platforms on the Google Play Store (Shopee, Tokopedia, Lazada, and Blibli), two classification approaches were implemented and compared: support vector machine (SVM) and long short-term memory (LSTM). Both models demonstrated a competitive performance, enabling effective sentiment categorization. Furthermore, multinomial logistic regression was employed to analyze the influence of key variables rating, number of likes, and marketplace brand on sentiment outcomes. The analysis revealed that Shopee yielded the highest probability of receiving positive reviews (97.82%) and showed no significant association with negative sentiment. Consequently, this study recommends Shopee as the primary platform for MSMEs to enhance their digital presence and sales performance. The primary contribution lies in integrating machine learning-based sentiment analysis with statistical modelling to generate actionable, evidence-based marketplace recommendations for MSMEs.
Co-Authors Adiyana, Imam Angga Kurniawan Gumelar, Satya Fajar Rahmatika, Alfilia Hilda Yunita Sari