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Strategi Pengembangan Fasilitas Umum di Daya Tarik Wisata Solok Radjo Aia Dingin Kabupaten Solok Aulia Ilham; Yuliana Yuliana
Jurnal Manajemen Pariwisata dan Perhotelan Vol. 2 No. 2 (2024): Mei : Jurnal Manajemen Pariwisata dan Perhotelan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jmpp-widyakarya.v2i2.3115

Abstract

The problem in this research is that there are inadequate toilets at the Solok Radjo Tourist Attraction, because there are only two public toilets at this Tourist Attraction. The prayer room is still made of wood and can only accommodate 2-5 people in worship. Insufficient number of rubbish bins at the Solok Radjo Tourist Attraction. There is no special parking area that is neat and well arranged at the Solok Radjo Tourist Attraction. This type of research is a type of descriptive research with qualitative data. This research was conducted at the Solok Radjo Aia Winter Tourist Attraction, Solok Regency, and data collection will be carried out in October-November 2022. The results of this research are 1) Internal factors that become strengths and weaknesses in the development of public facilities In the Solok Radjo Aia Winter tourist attraction, Solok Regency, the strength is that public toilets which are used by visitors for urinating, defecating and performing ablution are very available, where the Solok Radjo Aia Winter tourist attraction has 12 public toilets. Apart from that, the parking area is large so it can accommodate many vehicles, both motorbikes and cars. Meanwhile, the weakness is that the prayer room is only made of wood with a tarpaulin, mat and this is the place used by visitors for prayer. The next weakness is that the rubbish bins around the tourist attraction location are limited, namely only 5 rubbish bins. 2) External factors that become opportunities (Opportunities) and threats (Threats) regarding public facilities at the Solok Radjo Aia Winter Tourist Attraction, Solok Regency, namely the opportunity (Opportunities) is the construction of a Mushalla/Mosque with magnificent architecture which has the opportunity to develop religious tourism. Furthermore, the opportunity (Opportunities) is the cleanliness of tourist locations which can be realized through the availability of organic and non-organic rubbish bins at every point in the Solok Radjo Aia Winter tourist attraction area, Solok Regency. Meanwhile, the threat is the number of visitors who lose their items or have items scattered in the public toilets at the Solok Radjo Aia Winter tourist attraction. Furthermore, another threat is the parking lot which is sometimes frequented by many thugs.
MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND Aulia Ilham; Marza Ihsan Marzuki
International Journal of Remote Sensing and Earth Sciences Vol. 14 No. 2 (2017)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2017.v14.a2820

Abstract

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for wide data observation. In remote sensing, machine learning method could be used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.