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All Journal ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tecnoscienza
Marniyati Husain Botutihe
Universitas Pohuwato
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics Neuroscience Transportation Other

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Journal : ILKOM Jurnal Ilmiah

 1 
Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection Annahl Riadi; Marniyati Husain Botutihe
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.791.117-124

Abstract

This study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta beach. The government needs to know the level of visitor satisfaction to attempt to improve and develop the Pohon Cinta beach tourist attraction. Thus, to solve the problem above, a method that can help predict visitor satisfaction is needed. This study aims to measure visitor satisfaction through predictions using the Backpropagation algorithm and PSO feature selection to assist the government in developing tourism potential in Pohuwato Regency. The method used is the backpropagation algorithm for prediction and Particle Swarm Optimization which is considered effective in overcoming optimization problems. This algorithm is considered capable of solving problems in the backpropagation algorithm. The accuracy value of the backpropagation algorithm model is 84.67%, the accuracy value of the PSO-based backpropagation algorithm model is 85.00%, and the difference in accuracy is 0.33. The results of the application of the Backpropagation algorithm and Particle Swarm Optimization can increase the predictive accuracy value of visitor satisfaction at the Cinta Tree Beach tourist attraction.
Predicting the success of the government’s program of lomaya (Regional PKH) in reducing poverty Ruhmi Sulaehani; Marniyati Husain Botutihe
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1149.323-328

Abstract

Poverty reduction is one indicator of the success of development. The form of support from the Pohuwato Regency Government through the Social Service is to organize PKH-D, which is known as LOMAYA. It is one of the implementations of the Community Movement Towards Independent Prosperity (Gerakan Masyarakat Menuju Sejahtera Mandiri). This research was conducted to assist the government in predicting the level of development success indicated by the satisfaction of beneficiaries of lomaya. The method employed was the Naïve Bayes method and forward feature selection. The research data was obtained from a survey of lomaya beneficiaries in the last two years. The accuracy result obtained using the Naïve Bayes algorithm was 94.19%, while Naïve Bayes with the Forward Selection feature was only 94.03%. Therefore, the Naïve Bayes algorithm method is better than the Forward Selection based Naïve Bayes algorithm. Forward selection does not improve accuracy because the selection process causes many attributes to be discarded because they are considered irrelevant. This happened because of the inaccuracy of the data after being selected for its attributes using the Forward Selection feature resulting 1 attribute  only as a determinant.
Co-Authors Annahl Riadi Robby Anto Wijaya Sri Ferawati Hasan Sulaehani, Ruhmi Yanhar Hamzah Yuliyanti Tahir Zubair Biki