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All Journal International Journal of Artificial Intelligence Research Journal of Innovative and Creativity
Krisna Natawijaya
Politeknik Negeri Sriwijaya
Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Analisis Tren Kunjungan Wisatawan Terhadap Sektor Pariwisata Di Kabupaten Musi Banyuasin Rizky Novrianty; Afra Nazhirah; Wanda Septian; Krisna Natawijaya
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.9591

Abstract

Penelitian ini lahir dari konteks pemulihan sektor pariwisata Kabupaten Musi Banyuasin pascapandemi, yang ditandai dengan tren peningkatan jumlah kunjungan wisatawan sekaligus lonjakan penyelenggaraan event pariwisata daerah. Tujuan penelitian adalah menganalisis tren pertumbuhan kunjungan wisatawan, menelaah hubungan deskriptif antara peningkatan event dengan kenaikan jumlah wisatawan, serta mengkaji kesiapan kualitas sumber daya manusia (SDM) sektor pariwisata dalam mendukung pembangunan ekonomi daerah. Penelitian menggunakan jenis penelitian kombinasi deskriptif–analitis dengan pendekatan kuantitatif. Populasi mencakup seluruh data kunjungan wisatawan, jumlah event pariwisata, dan data tenaga kerja pariwisata di Kabupaten Musi Banyuasin periode 2022–2025 yang berasal dari Dinas Pemuda, Olahraga, dan Pariwisata, sehingga sampel berupa sensus terhadap setiap tahun pada periode tersebut. Instrumen penelitian berupa dokumentasi sekunder dari database Dinas Pariwisata, BPS Musi Banyuasin, dan laporan kinerja nasional, kemudian dianalisis dengan teknik analisis tren, rasio deskriptif, dan interpretasi hubungan event–kunjungan–SDM dalam konteks koefisien determinasi. Hasil penelitian menunjukkan bahwa jumlah kunjungan wisatawan meningkat dari 49.314 orang (2022) menjadi 54.745 orang (2024), dengan total pertumbuhan sekitar 10,9%. Peningkatan jumlah event dari 4 kegiatan (2023) menjadi 8 kegiatan (2024) berkorelasi positif dengan konsistensi tren kenaikan kunjungan, meskipun peningkatan kuantitas event tidak sepenuhnya linier dengan laju pertumbuhan wisatawan. Tingkat sertifikasi SDM sektor pariwisata baru mencapai 13,2%, sehingga masih terdapat kesenjangan antara pertumbuhan kunjungan dan kesiapan layanan profesional. Kesimpulan penelitian menekankan bahwa peningkatan event dan pemulihan pariwisata telah mendorong kenaikan kunjungan wisatawan, akan tetapi keberlanjutan pertumbuhan tersebut bergantung pada peningkatan kualitas SDM dan promosi digital. Peran sinergi antara event, kinerja kunjungan, dan profesionalisme SDM menjadi kunci dalam membangun daya saing destinasi pariwisata di Kabupaten Musi Banyuasin
Performance Evaluation of Optical Character Recognition in SmartScan Rivai Based on Document Quality Variations Sulistiyanto Sulistiyanto; Bima Saputra; Krisna Natawijaya; Fitrianto Puja Kusuma
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1745

Abstract

Optical Character Recognition (OCR) plays a key role in this process by converting text contained in scanned documents into machine-readable information. However, OCR performance is highly dependent on document image quality, which may be affected by factors such as blur, low illumination, physical deterioration, and perspective distortion. This study evaluates the performance of the OCR module implemented in the SmartScan Rivai application under varying document quality conditions. Five document conditions were considered: normal, blurred, worn, low illumination, and skewed perspective. The evaluation focused on three performance indicators: Customer ID recognition accuracy, document classification accuracy, and OCR processing time. Experimental results show that the OCR system successfully recognized Customer IDs in 22 out of 25 test cases, achieving an overall accuracy of 88%. The highest recognition accuracy (100%) was obtained for normal and worn documents, whereas blurred documents, low illumination, and skewed perspectives reduced the accuracy to 80%. Document classification achieved an overall accuracy of 67%, indicating that this task is more challenging because it depends on the successful recognition of multiple textual features rather than individual characters. In addition, all OCR processes were completed in less than five seconds per document, demonstrating the operational feasibility of the proposed system. The findings confirm that document quality significantly influences OCR performance and highlight the importance of incorporating image preprocessing techniques to improve recognition accuracy under challenging document conditions. Overall, SmartScan Rivai provides an effective solution for operational document digitization while offering opportunities for further enhancement through advanced image processing and artificial intelligence-based OCR techniques.
Co-Authors Afra Nazhirah Bima Saputra Fitrianto Puja Kusuma Rizky Novrianty Sulistiyanto Sulistiyanto Wanda Septian