Abstract : Indonesia’s reliance on subsidized Liquefied Petroleum Gas (LPG) for household cooking places a significant burden on the national energy subsidy budget and increases dependence on imported fossil fuels. As part of the clean energy transition strategy, the Indonesian government has promoted the conversion from LPG stoves to electric induction stoves. However, public acceptance and actual post-use experiences at the household level remain diverse and insufficiently examined empirically. This study aims to analyze public sentiment toward induction stove use based on post-adoption user reviews to identify factors that encourage interest and reveal existing adoption barriers. This study employs a machine learning–based sentiment analysis approach using primary data collected through open-ended questionnaires distributed to induction stove users. A total of 265 valid textual responses were analyzed. Text preprocessing was conducted using Python with the NLTK and Sastrawi libraries, including data cleaning, case folding, tokenization, stopword removal, stemming, and duplicate removal. Sentiment classification was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Naive Bayes algorithm, while WordCloud visualization was applied to identify dominant keywords. The results indicate a relatively balanced sentiment distribution, with positive sentiment accounting for 33.6%, neutral sentiment 32.5%, and negative sentiment 34.0%. Positive sentiment is mainly associated with energy efficiency, safety, and ease of use, whereas negative sentiment is driven by concerns regarding initial costs and electricity dependence. Neutral sentiment reflects an evaluative phase among users. These findings provide empirical insights to support user-oriented policies and strategies for accelerating the sustainable adoption of induction stove technology in Indonesia’s clean energy transition.
Maharani et al. (Sat,) studied this question.