SVM Approach for Forecasting International Tourism Arrival In East Java

Purnaningrum, Evita; Athoillah, Muhammad

doi:10.1088/1742-6596/1863/1/012060

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…The use of machine learning has been widely used in the field of tourism, including to predict tourism demand (Ahmed et al, 2007;Li, 2022;Yu & Chen, 2022), marketing strategies for rural tourism (Xie & He, 2022), and recommendations for smart tourism strategies (Ho, 2022). For the case of tourism in Indonesia, its use is still limited, including predicting international tourist arrivals during the Covid-19 period (Andariesta & Wasesa, 2022) and estimating international tourists (Purnaningrum & Athoillah, 2021). The image of data mining through machine learning is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Predicting determinant factors and development strategy for tourist villages

Ariyani¹,

Fauzi²,

Umar³

2023

10.5267/j.dsl

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Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.

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Section: Methodsmentioning

confidence: 99%

Predicting determinant factors and development strategy for tourist villages

Ariyani¹,

Fauzi²,

Umar³

2023

10.5267/j.dsl

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“…SVM is one of the machine learning algorithms used for classification problems. The SVM algorithm has been applied to various tasks, including biology, medicine, and meteorology [22][23][24]. A simple explanation of the thinking concept of the SVM algorithm is Cite this article as: M. Athoillah, E. Purnaningrum, and R. K. Putri, "Modified Multi-Kernel Support Vector Machine for Mask Detection", CommIT Journal 16(2), 159-166, 2022.…”

Section: Multi-kernel Support Vector Machine (Svm)mentioning

confidence: 99%

Modified Multi-Kernel Support Vector Machine for Mask Detection

Athoillah

Purnaningrum

Putri

2022

CommIT (Communication and Information Technology) Journal

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Indonesia is one of the countries most affected by the Coronavirus pandemic with millions confirm cases. Hence, the government has increased strict procedures for using face masks in public areas. For this reason, the detection of people wearing face masks in public areas is needed. Face mask detection is a part of the classification problem. Thus Support Vector Machine (SVM) can be implemented. SVM is still known as one of the most powerful and efficient classification algorithms. The research aims to build an automatic face mask detector using SVM. However, it needs to modify it first because it only can classify linear data. The modification is made by adding kernel functions, and a Multi-kernel approach is chosen. The proposed method is applied by combining various kernels into one kernel equation. The dataset used in the research is a face mask image obtained from Github. The data are public datasets consisting of faces with and without masks. The results present that the proposed method provides good performance. It is proven by the average value. The values are 83.67% for sensitivity, 82.40% for specificity, 82.00% for precision, 82.93% for accuracy, and 82.77% for F1-score. These values are better than other experiments using single kernel SVM with the same process and dataset.

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“…In the field of tourism, machine learning, for instance, has been used to forecast demand for tourism (Ahmed et al, 2007;Li, 2022;Yu & Chen, 2022), marketing strategies for rural tourism (Xie & He, 2022), and recommendations for smart tourism strategies (Ho, 2022). The use of machine learning for tourism cases in Indonesia is limited to several aspects, such as predictions of international tourist arrivals during the Covid-19 period (Andariesta & Wasesa, 2022) or estimation of international tourists (Purnaningrum & Athoillah, 2021). There remains a lot of room for tourism analysis with machine learning that can be done for the current case, such as predicting how the tourist village will develop or not and what antecedent factors determine the development of the tourist area.…”

Section: Introductionmentioning

confidence: 99%

Predicting and determining antecedent factors of tourist village development using naive bayes and tree algorithm

Ariyani

Fauzi

Umar

2023

IJASTE

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This study aims to predict the progress status of tourism villages in the Kedung Ombo area, Java, Indonesia, and find the antecedent factors of the progress of tourism villages in Indonesia. This study uses a modern approach, namely data mining. Data sources for tourist villages use the data available on the Google link and the observation method. The prediction technique uses the Naïve Bayes machine learning algorithm and Tree Decision on Orange 3.3.0 software. The number of tourist villages analyzed was 126. The results showed that all tourist villages in the Kedung Ombo area were at the development level of the four tourist village classifications of the Ministry of Tourism and Creative Economy. The antecedent factors for the progress of tourism villages are the completeness of ICT facilities, multi-stakeholder partnerships, strong government support, community involvement, and various attractions. Another finding is that the Tree Decision algorithm provides better predictions than the Naïve Bayes method. The results of this study can be used to design policies for developing tourist villages throughout Indonesia.

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SVM Approach for Forecasting International Tourism Arrival In East Java

Cited by 9 publications

References 11 publications

Predicting determinant factors and development strategy for tourist villages

Predicting determinant factors and development strategy for tourist villages

Modified Multi-Kernel Support Vector Machine for Mask Detection

Predicting and determining antecedent factors of tourist village development using naive bayes and tree algorithm

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