THE DEVELOPMENT STRATEGY OF LAKE KELIMUTU TOURIST ATTRACTION IN ENDE REGENCY
Bibliographic record
Abstract
The purpose of this study is to analyze the potential and the development of Lake Kelimutu tourist attraction in Ende Regency. The data were collected through observation, documentation, and interviews with stakeholders, such as the Kelimutu National Park Office, Government Tourism Office, Community, and Visitors. The data was then analyzed descriptively for later determined of its development strategies using SWOT. The results of the study showed that the potential of Lake Kelimutu tourist attraction, besides the uniqueness of the three crater lakes, is also a diversity of flora and fauna, and it was concluded that the appropriate alternative strategy for developing Lake Kelimutu tourist attraction was the S-O strategy (strength and opportunity), they are: creating an integrated tourist package marketing strategy for natural and cultural tourism, using various existing social media to promote the uniqueness of the ever-changing colors of the lake, working with various travel agents to provide special discounts or special services for tourists, and creating special transport routes to Lake Kelimutu from Ende. Keywords: potential, development strategy, tourist site, Lake Kelimutu. References Bunghez, C.L. (2016). The Importance of Tourism to a Destination’s Economy. Journal of Eastern Europe Research in Business & Economics, 1-9. Damanik & Weber. (2006). Perencanaan Pariwisata: Dari Teori ke Aplikasi. Yogyakarta: Andi. Engelhard. (2005). Culturally and Ecologically Sustainable Tourism Development through Local Community Management. Culture and Sustainable Development in the Pacific: ANU Press. Fandeli, C. (2002). Perencanaan Kepariwisataan Alam. Yogyakarta: Fakultas Kehutanan Universitas Gadjah Mada. Gaol, H.L. (2014). Potensi Pariwisata Danau Tiga Warna Gunung Kelimutu dan Usaha Kerajinan Kain Tenun Lio Sebagai Atraksi Wisata. JDP, 1(1),31-50. Ismayanti. (2010). Pengantar Pariwisata. Jakarta: PT Gramedia Widisarana Indonesia. Kruja, A. (2012). The Impact of Tourism Sector Development in the Albanian Economy. Economia Seria Management, 15(1), 204-218. Marpaung, H. (2000). Pengetahuan Kepariwisataan. Bandung: Alfabeta. Mohammed, Guellil, Moestefa, Belmokaddem, Mohammed, Sahraoui.(2015). Tourism Spending-Economic Growth Causality in 49 Countries: A Dynamic Panel Data Approach. 2nd Global Conference on Business, Economic, Management and Tourism, 1613-1623. Moleong, J.L. (2007). Metodologi Penelitian Kualitatif (Rev. ed.). Bandung: Remaja Rosdakarya. Pendit, S.N. (1999). Ilmu Pariwisata Sebuah Pengantar Perdana. Jakarta: PT. Pradnya Paramita. Pitana, I Gede & Diarta, I Ketut Surya (2008). Pariwisata sebagai Disiplin Ilmu yang Mandiri. Badan Pengembangan Sumber Daya, Departemen Kebudayaan dan Pariwisata. Rangkuti, F. (2008). Teknik Mengukur dan Strategi Meningkatkan Kepuasan Pelanggan. Jakarta: PT Gramedia Pustaka Utama. Soekadijo. (2000). Anatomi Pariwisata. Jakarta: PT Gramedia Pustaka Utama. Sugiyono. (2013). Metode Penelitian Kombinasi (Mixed Methods). Bandung: Alfabeta. Sukmadinata. (2008). Metode Penelitian Pendidikan. Bandung: Remaja Rosdakarya. Tabash, M.I. (2017). The Role of Tourism Sector in Economic Growth: An Empirical Evidence from Palestine. International Journal of Economic and Financial Issues, 7(2), 103-108. Weiler, B.,& Hall, M.C. (1992). Special Interest Tourism.New York& Toronto: Halsted Press. Yoeti, O.A. (2008). Ekonomi Pariwisata: Introduksi, Informasi, dan Aplikasi.Jakarta: Kompas. Copyright (c) 2019 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".