Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

Saifullah, Shoffan

doi:10.26555/jifo.v13i2.a15268

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…The concept of literacy studies is used to solve anxiety cases in Indonesia today, mainly based on social media comments. This social media can identify comments and feedback from users [32]. Besides, data collection was carried out directly using observation to obtain appropriate data.…”

Section: Methodsmentioning

confidence: 99%

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

2021

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All groups of people felt the impact of the COVID-19 pandemic. This situation triggers anxiety, which is bad for everyone. The government's role is very influential in solving these problems with its work program. It also has many pros and cons that cause public anxiety. For that, it is necessary to detect anxiety to improve government programs that can increase public expectations. This study applies machine learning to detecting anxiety based on social media comments regarding government programs to deal with this pandemic. This concept will adopt a sentiment analysis in detecting anxiety based on positive and negative comments from netizens. The machine learning methods implemented include K-NN, Bernoulli, Decision Tree Classifier, Support Vector Classifier, Random Forest, and XG-boost. The data sample used is the result of crawling YouTube comments. The data used amounted to 4862 comments consisting of negative and positive data with 3211 and 1651. Negative data identify anxiety, while positive data identifies hope (not anxious). Machine learning is processed based on feature extraction of count-vectorization and TF-IDF. The results showed that the sentiment data amounted to 3889 and 973 in testing, and training with the greatest accuracy was the random forest with feature extraction of vectorization count and TF-IDF of 84.99% and 82.63%, respectively. The best precision test is K-NN, while the best recall is XG-Boost. Thus, Random Forest is the best accurate to detect someone's anxiety based-on data from social media.

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Section: Methodsmentioning

confidence: 99%

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

2021

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“…It is one of the decision-making methods with a set of related criteria to provide the priority for each criterion and support sensitivity analysis of the results [17]. This method helps to choose the best from numerous alternatives [18], assessed using a few criteria [19].…”

Section: E Analytical Hierarchy Process Modelmentioning

confidence: 99%

Developing an integrated business model in the manufacturing industry – An AHP approach

Chien¹,

Chan²

2022

SITech

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Due to the market competition in the current business environment, there are many pressures in the Small and Medium-sized Enterprise (SME) manufacturers. Most SMEs are facing challenges in the market change from Mass Production (MP) to Mass Customization (MC). The purpose of this study is mainly based on drilling down into an SME manufacturer, exploring the limitation in its current business model and determining the boundaries of its operation process. This research paper designs an Analytical Hierarchy Process (AHP) model to develop a new business model to resolve the MC issue in the SME manufacturers. The aims of the new business model should be to improve company profit. Thereby seven criteria in the AHP model are profit, Minimum Order Quantity (MOQ), flexibility, inventory control, delivery time, revenue from existing customers and revenue from new customers for customized products. The methodology is pilot-run the new business model and compares the results among the AHP, current and new business models. The results prove that the new business model not only solves the problems in MC but also create a synergic effect on the business. The study provides a method that uses an AHP model for developing an integrated business model for SME manufacturers.

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“…The form of the dataset in this research is a matrix with a size of 10 x 980 of data [29]. The dataset has 2 distributions, namely actual data and normalization data distribution.…”

Section: A Analysis Of Dataset Based On Distribution Valuementioning

confidence: 99%

The Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

Saifullah

2022

JIKO

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This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP methods of tourism. The fuzzy-AHP method will rank tourism trends based on data from social media. Social media is one of the channels with the largest source of data input in determining tourism development. The development uses social media interactions based on the facilities visited, including reviews, stories, likes, forums, blogs, and feedback. This experimental analysis aims to prioritize facilities that are the trend of tourism. The priority ranking uses the fuzzy-AHP method for the process of determining weight criteria and the ranking process. The highest-ranking is on the Parks/Picnic Spots attraction and make it a priority to develop.

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Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

Cited by 8 publications

References 17 publications

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data

Developing an integrated business model in the manufacturing industry – An AHP approach

The Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

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