Indriadika, Ayu (2026) Perancangan Usulan Perbaikan Aplikasi E-Government Berdasarkan Analisis Ulasan Pengguna dengan Pendekatan Design Thinking dan Metode LDA-Topic Modeling (Studi pada M-Pajak di Google Play Store). Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penelitian ini bertujuan untuk menganalisis penyebab rendahnya rating aplikasi e-government M-Pajak berdasarkan ulasan pengguna bintang 1 dan 2 di Google Play Store. Rendahnya rating aplikasi M-Pajak mengindikasikan adanya permasalahan serius dalam pengalaman pengguna yang berpotensi menurunkan kepercayaan masyarakat terhadap layanan perpajakan digital. Penelitian ini menggunakan pendekatan Design Thinking yang difokuskan pada tiga tahap awal, yaitu empathize, define, dan ideate, serta didukung oleh analisis teks menggunakan metode Latent Dirichlet Allocation (LDA) Topic Modeling. Pada tahap empathize, data dikumpulkan melalui web scraping terhadap ulasan pengguna aplikasi M-Pajak pada periode 23 Maret hingga 27 Agustus 2025. Data yang dianalisis dibatasi pada ulasan bintang 1 dan 2 karena dianggap merepresentasikan ketidakpuasan pengguna secara paling eksplisit. Tahap define dilakukan dengan menerapkan LDA Topic Modeling untuk mengidentifikasi pola keluhan utama pengguna dari data ulasan tidak terstruktur. Hasil pemodelan topik menunjukkan bahwa permasalahan pengguna terkonsentrasi pada aspek autentikasi dan verifikasi, khususnya kegagalan login, keterlambatan atau kegagalan OTP, validasi NPWP yang bermasalah, serta kesulitan pemulihan akun. Selanjutnya, pada tahap ideate, temuan pain points hasil LDA diterjemahkan menjadi artefak desain berupa user needs, requirements, user stories, dan acceptance criteria melalui expert judgement yang melibatkan praktisi di bidang product management, UX, dan quality assurance. Hasil penelitian ini menghasilkan rekomendasi perbaikan terstruktur dan dapat ditindaklanjuti sebagai dasar peningkatan kualitas pengalaman pengguna aplikasi M-Pajak, serta sebagai masukan strategis bagi pengembangan dan integrasi sistem M-Pajak dengan Coretax di masa mendatang.
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This study aims to analyze the underlying causes of poor ratings in the M-Pajak e-government application based on 1-star and 2-star user reviews on the Google Play Store. The low rating of the M-Pajak application indicates significant user experience issues that may undermine public trust in digital tax services. This research adopts a Design Thinking approach, focusing on the empathize, define, and ideate stages, supported by text analysis using Latent Dirichlet Allocation (LDA) Topic Modeling. During the empathize stage, user reviews were collected through web scraping from the Google Play Store within the period of March 23 to August 27, 2025. The analysis was limited to 1-star and 2-star reviews, as these ratings are considered to represent explicit user dissatisfaction. In the define stage, LDA Topic Modeling was applied to identify dominant complaint patterns from unstructured textual data. The results reveal that user issues are primarily concentrated on authentication and verification processes, including repeated login failures, delayed or unsuccessful OTP delivery, problematic NPWP validation, and difficulties in account recovery. In the ideate stage, the identified pain points were translated into design artifacts, including user needs, requirements, user stories, and acceptance criteria, through expert judgment involving professionals in product management, user experience design, and quality assurance. The findings provide structured and actionable recommendations to improve the user experience of the M-Pajak application and serve as strategic input for future system enhancements and integration with the Coretax platform.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | e-Government, M-Pajak, LDA Topic Modelling, ulasan pengguna, Ulasan Buruk, Pengalaman Pengguna, Design Thinking, user Review, Bad Rating, User Experience |
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HA Statistics H Social Sciences > HM Sociology > HM24 Social prediction. T Technology > T Technology (General) T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Interdisciplinary School of Management and Technology (SIMT) > 61101-Master of Technology Management (MMT) |
| Depositing User: | Ayu Indriadika |
| Date Deposited: | 29 Jan 2026 02:50 |
| Last Modified: | 29 Jan 2026 02:50 |
| URI: | http://repository.its.ac.id/id/eprint/130583 |
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