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All Journal Applied Science and Technology Research Journal KINERJA : Jurnal Ekonomi dan Bisnis
Apriyanto, Bagas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Determination Of Freshwater Consumption Fish Diseases Using Artificial Neural Network Methods Apriyanto, Bagas; Riyadi, Ahmad; Kusuma Wardana, Ari; Nasrun Mohd Nawi, Mohd
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 3 No. 1 (2024): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v3i1.6217

Abstract

Artificial Neural Network (ANN) is a branch of Artificial Intelligence (AI) that uses neuron-based computational methods to identify and solve problems. In this study, we tried to use ANN to identify diseases of freshwater fish commonly consumed by the public, using one of the ANN methods, namely backpropagation. This research produces a website-based system that uses the backpropagation Neural Network method so that it can be used to help freshwater fish breeders or cultivators identify fish diseases that are kept more accurately than conventional methods. In addition, it is hoped that this system can anticipate more severe infections in fish belonging to cultivators. The results of system testing show that regarding the appearance of the system, 38.5% of respondents answered that the design was beautiful, related to the ease of use of the system, 46.2% of respondents answered that the approach was straightforward to use, regarding the performance of the system 53.8% of respondents answered that the system performance was excellent. Regarding the system's benefits, 76.9% of respondents answered that the system was beneficial
THE INFLUENCE OF AFFILIATE MARKETING AND PROMOTIONS ON THE PRODUCT PURCHASE DECISION ON TIKTOK SOCIAL COMMERCE Apriyanto, Bagas; Affar, Muhammad
Kinerja Vol 8 No 01 (2025): KINERJA : Jurnal Ekonomi dan Bisnis
Publisher : Fakultas Ekonomi dan Bisnis Universitas Islam As-Syafi'iyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34005/kinerja.v8i01.4893

Abstract

This study aims to analyze the influence of affiliate marketing and promotion on product purchase decisions on the TikTok social commerce platform. The method used is a quantitative approach with multiple linear regression analysis and classical assumption tests. The population in this study is TikTok social commerce users in Jatiasih District.This study uses questionnaires as data collection materials with a sample of 130 respondents, this study uses the SPSS program in conducting data processing. The test results showed the data met classical assumptions and significant regression models. Affiliate Marketing (β = 0.385) and Promotion (β = 0.311) have a positive and significant effect on Purchase Decisions (Sig. < 0.05). An R² value of 0.498 indicates that both variables explain 49.8% of the variation in purchasing decisions, and the remaining 50.2% is influenced by other factors outside the model. The F test also showed significant simultaneous influence (F = 62.943; Sig. = 0.000). Thus, it can be concluded that Affiliate Marketing and Promotion partially or simultaneously have a significant effect on product purchase decisions on TikTok social commerce. This model can be used as a basis for decision-making digital marketing strategies on social commerce platforms.
Determination Of Freshwater Consumption Fish Diseases Using Artificial Neural Network Methods Apriyanto, Bagas; Riyadi, Ahmad; Kusuma Wardana, Ari; Nasrun Mohd Nawi, Mohd
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 3 No. 1 (2024): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v3i1.6217

Abstract

Artificial Neural Network (ANN) is a branch of Artificial Intelligence (AI) that uses neuron-based computational methods to identify and solve problems. In this study, we tried to use ANN to identify diseases of freshwater fish commonly consumed by the public, using one of the ANN methods, namely backpropagation. This research produces a website-based system that uses the backpropagation Neural Network method so that it can be used to help freshwater fish breeders or cultivators identify fish diseases that are kept more accurately than conventional methods. In addition, it is hoped that this system can anticipate more severe infections in fish belonging to cultivators. The results of system testing show that regarding the appearance of the system, 38.5% of respondents answered that the design was beautiful, related to the ease of use of the system, 46.2% of respondents answered that the approach was straightforward to use, regarding the performance of the system 53.8% of respondents answered that the system performance was excellent. Regarding the system's benefits, 76.9% of respondents answered that the system was beneficial
Co-Authors Affar, Muhammad Ahmad Riyadi Kusuma Wardana, Ari Nasrun Mohd Nawi, Mohd