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.

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Keywords :

Clean Energy Transition, Induction Stove Adoption, Machine learning, Sentiment analysis, User Experience

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References :

1. D. Pratiwi, R. W. Damayanti, and P. W. Laksono, “Public Sentiment Analysis to Support Indonesian Government Induction Stove Program,” E3S Web of Conferences, vol. 465, pp. 02006–02006, Jan. 2023, doi: https://doi.org/10.1051/e3sconf/202346502006
2. I. al Irsyad, T. Anggono, C. Anditya, I. Ruslan, D. G. Cendrawati, and R. Nepal, “Assessing the feasibility of a migration policy from LPG cookers to induction cookers to reduce LPG subsidies,” Energy for Sustainable Development, vol. 70, pp. 239–246, Oct. 2022, doi: https://doi.org/10.1016/j.esd.2022.08.003. Available: https://www.sciencedirect.com/science/article/pii/S0973082622001296. [Accessed: Feb. 18, 2024]
3. Amelina, “Peran Hukum di Indonesia dalam Penanggulangan Dampak Perubahan Iklim,” Jurnal Hukum Lingkungan Indonesia, vol. 1, no. 1, p. 181, Feb. 2020, doi: https://doi.org/10.38011/jhli.v1i1.9
4. W. Damayanti, S. Dhea, P. W. Laksono, H. Setiadi, and Nisa, “Drivers of the Sustainability Performance of Induction Stove Conversion Program in Indonesia,” International Journal of Sustainable Energy Planning and Management, vol. 43, pp. 5–30, Jan. 2025, doi: https://doi.org/10.54337/ijsepm.8414
5. F. Dinda, “Pengembangan Model dan Usulan Strategi Adopsi Kompor Induksi di Indonesia,” Uns.ac.id, 2023, doi: https://doi.org/Kartika,%20Fitria%20Dinda%20(2023)%20Pengembangan%20Model%20dan%20Usulan%20Strategi%20Adopsi%20Kompor%20Induksi%20di%20Indonesia. Available: https://digilib.uns.ac.id/dokumen/detail/107564/Pengembangan-Model-dan-Usulan-Strategi-Adopsi-Kompor-Induksi-di-Indonesia. [Accessed: Jan. 25, 2026]
6. P. H. Febrian and U. Wikarya, “The Economic Impact of the Induction Stove Conversion Program in Indonesia,” Economics Development Analysis Journal, vol. 13, no. 1, pp. 109–124, 2024, Available: https://journal.unnes.ac.id/sju/edaj/article/view/78946
7. “Presiden Perintahkan Konversi LPG ke Kompor Induksi, PLN: Kami Sudah Siap,” PT PLN (Persero), Nov. 2021. Available: https://web.pln.co.id/media/siaran-pers/2021/11/presiden-perintahkan-konversi-lpg-ke-kompor-induksi-pln-kami-sudah-siap?
8. Dahmen, J. von Bergmann, and M. Das, “Teardown Index: Impact of property values on carbon dioxide emissions of single family housing in Vancouver,” Energy and Buildings, vol. 170, pp. 95–106, Jul. 2018, doi: https://doi.org/10.1016/j.enbuild.2018.03.012
9. Antara News, “Kementerian ESDM-PLN uji coba konversi kompor listrik di dua kota,” Antara News, Sep. 22, 2022. Available: https://www.antaranews.com/berita/3132905/kementerian-esdm-pln-uji-coba-konversi-kompor-listrik-di-dua-kota?. [Accessed: Jan. 25, 2026]
10. L. Rizky, R. W. Damayanti, and P. W. Laksono, “Technology Readiness for Induction Stove Adoption in a Government Pilot Program: A Case Study in Surakarta, Indonesia,” Jurnal Sosioteknologi, vol. 24, no. 2, pp. 190–203, Jul. 2025, doi: https://doi.org/10.5614/sostek.itbj.2025.24.2.4
11. Feng and W. Wei, “An Empirical Study on User Experience Evaluation and Identification of Critical UX Issues,” Sustainability, vol. 11, no. 8, p. 2432, Apr. 2019, doi: https://doi.org/10.3390/su11082432
12. G. Majumder, S. D. Gupta, and J. Paul, “Perceived usefulness of online customer reviews: A review mining approach using machine learning & exploratory data analysis,” Journal of Business Research, vol. 150, pp. 147–164, Nov. 2022, doi: https://doi.org/10.1016/j.jbusres.2022.06.012
13. Fernandes, R. Panda, V. G. Venkatesh, B. N. Swar, and Y. Shi, “Measuring the impact of online reviews on consumer purchase decisions – A scale development study,” Journal of Retailing and Consumer Services, vol. 68, no. 103066, Sep. 2022, doi: https://doi.org/10.1016/j.jretconser.2022.103066. Available: https://www.sciencedirect.com/science/article/abs/pii/S096969892200159X
14. Chen, P. Samaranayake, X. Cen, M. Qi, and Y.-C. Lan, “The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence from an Eye-Tracking Study,” Frontiers in Psychology, vol. 13, no. 13, pp. 1–13, 2022, doi: https://doi.org/10.3389/fpsyg.2022.865702. Available: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.865702/full
15. Yao and A. Gillen, “Public Opinion Evaluation on Social Media platforms: a Case Study of High Speed 2 (HS2) Rail Infrastructure Project,” UCL Open Environment, vol. 5, p. e063, 2023, doi: https://doi.org/10.14324/111.444/ucloe.000063. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503540/
16. Bello-Orgaz, J. J. Jung, and D. Camacho, “Social Big data: Recent Achievements and New Challenges,” Information Fusion, vol. 28, no. 1, pp. 45–59, Mar. 2016, doi: https://doi.org/10.1016/j.inffus.2015.08.005. Available: https://www.sciencedirect.com/science/article/pii/S1566253515000780
17. Pang and L. Lee, “Opinion Mining and Sentiment Analysis,” Foundations and Trends® in Information Retrieval, vol. 2, no. 1–2, pp. 1–135, 2008, doi: https://doi.org/10.1561/1500000011. Available: http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf
18. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, May 2012, doi: https://doi.org/10.2200/s00416ed1v01y201204hlt016
19. Medhat, A. Hassan, and H. Korashy, “Sentiment Analysis Algorithms and applications: a Survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, Dec. 2014, doi: https://doi.org/10.1016/j.asej.2014.04.011
20. Tumasjan, T. Sprenger, P. Sandner, and I. Welpe, “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 4, no. 1, pp. 178–185, May 2010, doi: https://doi.org/10.1609/icwsm.v4i1.14009
21. C. Aggarwal, Machine Learning for Text. Cham: Springer International Publishing, 2018. doi: https://doi.org/10.1007/978-3-319-73531-3. Available: https://rd.springer.com/book/10.1007/978-3-319-73531-3
22. A. Palomino and F. Aider, “Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis,” Applied Sciences, vol. 12, no. 17, p. 8765, Jan. 2022, doi: https://doi.org/10.3390/app12178765. Available: https://www.mdpi.com/2076-3417/12/17/8765
23. Li and Y. Tao, “Daily PM10 Concentration Forecasting Based on Multiscale Fusion Support Vector regression,” Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3833–3844, Jun. 2018, doi: https://doi.org/10.3233/jifs-169555
24. -T. Duong and T.-A. Nguyen-Thi, “A review: Preprocessing Techniques and Data Augmentation for Sentiment Analysis,” Computational Social Networks, vol. 8, no. 1, Jan. 2021, doi: https://doi.org/10.1186/s40649-020-00080-x
25. Heimerl, S. Lohmann, S. Lange, and T. Ertl, “Word Cloud Explorer: Text Analytics Based on Word Clouds,” 2014 47th Hawaii International Conference on System Sciences, Jan. 2014, doi: https://doi.org/10.1109/hicss.2014.231
26. Venkatesh and F. D. Davis, “A Theoretical Extension of the Technology Acceptance model: Four Longitudinal Field Studies,” Management Science, vol. 46, no. 2, pp. 186–204, Feb. 2000.