Faktor-Faktor yang Memengaruhi Kepuasan Pengunjung di Objek Kawasan Wisata Religi Masjid Raya Al-Jabbar
Bibliographic record
Abstract
Abstract. Religious tourism is increasingly popular in Indonesia, including the Al-Jabbar Grand Mosque in Bandung as an attractive destination. However, its management still faces various challenges, such as the parking problem that is often complained about, the ticket system for the Rasulullah Museum gallery, as well as narrow road access and minimal public transportation. In addition, some visitors consider the available facilities to be inadequate, which can affect the tourist experience and reduce interest in visiting. This study aims to identify the main elements that influence visitor satisfaction, including attractions, amenities, accessibility, and additional services. This study also serves as an evaluation to improve the quality of the destination. The method used is quantitative descriptive statistics with SEM modeling through SmartPLS and a qualitative approach. The results of the study show that amenities, attractions, and additional services have a significant influence on tourist satisfaction. Overall, visitors are satisfied with the services received. This study can be a reference for stakeholders in designing religious tourism development strategies to make it more attractive and comfortable for tourists. Abstrak. Wisata religi semakin diminati di Indonesia, termasuk Masjid Raya Al-Jabbar Bandung sebagai destinasi menarik. Namun, pengelolaannya masih menghadapi berbagai tantangan, seperti masalah parkir yang sering dikeluhkan, sistem tiket untuk galeri Museum Rasulullah, serta akses jalan yang sempit dan minimnya transportasi umum. Selain itu, beberapa pengunjung menganggap fasilitas yang tersedia kurang memadai, yang dapat memengaruhi pengalaman wisata dan menurunkan minat kunjungan. Penelitian ini bertujuan mengidentifikasi elemen utama yang memengaruhi kepuasan pengunjung, mencakup atraksi, amenitas, aksesibilitas, dan layanan tambahan. Kajian ini juga berfungsi sebagai evaluasi untuk meningkatkan kualitas destinasi. Metode yang digunakan adalah statistik deskriptif kuantitatif dengan pemodelan SEM melalui SmartPLS serta pendekatan kualitatif. Hasil penelitian menunjukkan bahwa amenitas, atraksi, dan layanan tambahan memiliki pengaruh signifikan terhadap kepuasan wisatawan. Secara keseluruhan, pengunjung merasa puas dengan layanan yang diterima. Studi ini dapat menjadi referensi bagi pemangku kepentingan dalam merancang strategi pengembangan wisata religi agar lebih menarik dan nyaman bagi wisatawan.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".