Analisis Kepuasan Pengguna Aplikasi Bhumi Kementerian Agraria dan Tata Ruang Dengan Pendekatan Model End-User Computing Satisfaction
Bibliographic record
Abstract
The Bhumi ATR/BPN application was developed as an interactive geospatial information system to support the digital transformation of public services at the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN). This study aims to analyze user satisfaction with the Bhumi application using the End-User Computing Satisfaction (EUCS) model, which includes five dimensions: content, accuracy, format, ease of use, and timeliness. A quantitative approach was applied through the distribution of questionnaires to 427 users. Regression analysis revealed that content (β=0.237), accuracy (β=0.181), and ease of use (β=0.292) significantly influenced user satisfaction (p<0.01), while format and timeliness did not show significant effects. Collectively, the five variables explained 46% of the variance in user satisfaction (R²=0.460). The limitation of this study lies in the use of purposive and convenience sampling methods, which tend to represent active users with reliable digital access. The findings provide important recommendations for the development of public sector information systems, particularly in improving the quality of content, data accuracy, and user-friendliness of the Bhumi ATR/BPN application. Aplikasi Bhumi ATR/BPN dikembangkan sebagai sistem informasi geospasial interaktif untuk mendukung transformasi digital layanan publik Kementerian Agraria dan Tata Ruang/Badan Pertanahan Nasional (ATR/BPN). Penelitian ini bertujuan untuk menganalisis kepuasan pengguna aplikasi Bhumi dengan menggunakan model End-User Computing Satisfaction (EUCS) yang mencakup lima dimensi: konten, akurasi, format, kemudahan penggunaan, dan ketepatan waktu. Pendekatan kuantitatif digunakan melalui penyebaran kuesioner terhadap 427 pengguna. Analisis regresi menunjukkan bahwa konten (β=0,237), akurasi (β=0,181), dan kemudahan penggunaan (β=0,292) berpengaruh signifikan terhadap kepuasan pengguna (p<0,01), sedangkan format dan ketepatan waktu tidak menunjukkan pengaruh yang signifikan. Kelima variabel secara simultan menjelaskan 46% variansi kepuasan pengguna (R²=0,460). Batasan penelitian ini terletak pada metode pengambilan sampel secara purposive dan convenience sampling, yang cenderung merepresentasikan pengguna aktif dengan akses digital yang baik. Hasil penelitian ini memberikan masukan bagi pengembangan sistem informasi sektor publik, terutama dalam peningkatan kualitas konten, keakuratan data, dan kemudahan penggunaan Bhumi Kementerian ATR/BPN.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".