Kuantifikasi Prioritas Pengembangan Fitur Aplikasi dengan Kerangka Outcome-Driven Innovation: Studi Kasus Aplikasi Pembelajaran Bahasa Asing
DOI:
https://doi.org/10.55123/insologi.v4i6.6762Keywords:
Mobile Application, Outcome-Driven Innovation, User-Centered Design, Features DevelopmentAbstract
This study aims to identify user satisfaction, importance, and development priorities for the English Language Speech Assistant (ELSA) application by integrating Outcome-Driven Innovation (ODI) and sentiment analysis methods. Using reviews collected from the Google Play Store via ScrapeStorm, two sentiment models were applied. Namely, Valance Aware Dictionary and sEntiment Reasoner (VADER) for overall sentiment polarity and Aspect-Based Sentiment Analysis (ABSA) for feature-spesific sentiment extraction. The result show that 86.23% of user reviews express positive sentiments, reflecting high satisfaction with ELSA’s functionality and learning experience. Correlation testing confirms strong validity between VADER and ABSA models (r = 0.98), and moderate alignment with user rating (r 0.55), confirming both construct and external validity. The ODI analysis further identifies Pronunciation as the highest-priority aspect (opportunity = 12.1), followed by Learning (opportunity = 2.6), highlighting key areas for innovation. Other aspects, such as Support and Subscription, show balanced performance with low opportunity values. The integration of ODI and sentiment analysis provide a data-driven framework for product improvement, enabling developers to prioritize enhancements based on user-perceived value. The findings contribute to informed strategic decision-making in the development of digital language-learning applications.
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References
Awajan, I., Mohamad, M., & Al-Quran, A. (2021). Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data from Online Reviews. IEEE Access, 9, 47338–47353. https://doi.org/10.1109/ACCESS.2021.3067844
Chai, C. P. (2023). Comparison of text preprocessing methods. Natural Language Engineering, 29(3), 509–553. https://doi.org/10.1017/S1351324922000213
Corral Abad, E., Gómez García, M. J., Diez-Jimenez, E., Moreno-Marcos, P. M., & Castejón Sisamon, C. (2021). Improving the learning of engineering students with interactive teaching applications. Computer Applications in Engineering Education, 29(6), 1665–1674. https://doi.org/10.1002/cae.22415
Featherstone, J. D., & Barnett, G. A. (2020). Validating Sentiment Analysis on Opinion Mining Using Self-Reported Attitude Scores. 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS), 1–4. https://doi.org/10.1109/SNAMS52053.2020.9336540
Fu, Y., Wang, Y., Ye, X., Wu, W., & Wu, J. (2023). Satisfaction with and Continuous Usage Intention to-wards Mobile Health Services: Translating Users’ Feedback into Measurement. Sustainability, 15(2). https://doi.org/10.3390/su15021101
Handayani, L. N. C., Sukerti, G. N. A., Nugroho, I. M. R. A., Wicaksana, K. A. B., & Ardika, I. W. D. (2022). Development and Implementation of a Mobile-Based Accounting Terms Application as Self-Learning Kit in English for Vocational Purposes. Proceedings of the International Confer-ence on Applied Science and Technology on Social Science 2021, 179–187. https://doi.org/10.2991/assehr.k.220301.030
Haug, S., Benke, I., & Maedche, A. (2023). Aligning Crowdworker Perspectives and Feedback Outcomes in Crowd-Feedback System Design. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1–28. https://doi.org/10.1145/3579456
Hutto, C. J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Me-dia, 8, 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
Kim, M. K. (2021). A design experiment on technology-based learning progress feedback in a graduate-level online course. Human Behavior and Emerging Technologies, 3(5), 649–667. https://doi.org/10.1002/hbe2.308
Liu, W. (2023). The theory of second language development for international students. Journal for Mul-ticultural Education, 17(3), 367–378. https://doi.org/10.1108/JME-08-2022-0106
Martens, D., & Maalej, W. (2019). Release Early, Release Often, and Watch Your Users’ Emotions: Les-sons from Emotional Patterns. IEEE Software, 36(5), 32–37. https://doi.org/10.1109/MS.2019.2923603
Nam, S., Yoon, S., Raghavan, N., & Park, H. (2021). Identifying Service Opportunities Based on Out-come-Driven Innovation Framework and Deep Learning: A Case Study of Hotel Service. Sus-tainability, 13(1). https://doi.org/10.3390/su13010391
Ribeiro, Á. H. P., Monteiro, P. R. R., & Luttembarck, L. (2019). The Use of the “Job to Be Done” meth-odology to identify value co-creation opportunities in the context of the Service Dominant Logic. Brazilian Business Review, 16(1). https://doi.org/10.15728/bbr.2019.16.1.3
Serna-Herrera, A., Caicedo Rendón, O. M., & Rivera Martínez, W. (2025). An Approach for the Devel-opment and Maturation of ICT Products. Administrative Sciences, 15(10). https://doi.org/10.3390/admsci15100383
Valtonen, T., Leppänen, U., Hyypiä, M., Kokko, A., Manninen, J., Vartiainen, H., Sointu, E., & Hirsto, L. (2021). Learning environments preferred by university students: a shift toward informal and flex-ible learning environments. Learning Environments Research, 24(3), 371–388. https://doi.org/10.1007/s10984-020-09339-6
van Atteveldt, W., van der Velden, M. A. C. G., & Boukes, M. (2021). The Validity of Sentiment Analy-sis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learn-ing Algorithms. Communication Methods and Measures, 15(2), 121–140. https://doi.org/10.1080/19312458.2020.1869198
Yang, S., Liao, H., & Kóczy, L. T. (2025). Preference mining and fuzzy inference for hotel selection based on aspect-based sentiment analysis from user-generated content. Journal of the Operational Re-search Society, 76(7), 1414–1431. https://doi.org/10.1080/01605682.2024.2437128
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