CFP last date
22 July 2024
Reseach Article

Design of Smartphone Recommendation Application (Phone Finder) with Specifications of Mobile-based User Needs

by Muhammad Tahta Andhika, Muhammad Fachrie
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 11
Year of Publication: 2024
Authors: Muhammad Tahta Andhika, Muhammad Fachrie
10.5120/ijca2024923467

Muhammad Tahta Andhika, Muhammad Fachrie . Design of Smartphone Recommendation Application (Phone Finder) with Specifications of Mobile-based User Needs. International Journal of Computer Applications. 186, 11 ( Mar 2024), 35-38. DOI=10.5120/ijca2024923467

@article{ 10.5120/ijca2024923467,
author = { Muhammad Tahta Andhika, Muhammad Fachrie },
title = { Design of Smartphone Recommendation Application (Phone Finder) with Specifications of Mobile-based User Needs },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2024 },
volume = { 186 },
number = { 11 },
month = { Mar },
year = { 2024 },
issn = { 0975-8887 },
pages = { 35-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number11/design-of-smartphone-recommendation-application-phone-finder-with-specifications-of-mobile-based-user-needs/ },
doi = { 10.5120/ijca2024923467 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-03-23T00:18:05.467801+05:30
%A Muhammad Tahta Andhika
%A Muhammad Fachrie
%T Design of Smartphone Recommendation Application (Phone Finder) with Specifications of Mobile-based User Needs
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 11
%P 35-38
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research aims to design and develop a smartphone recommendation application that can provide suggestions based on user requirements specifications. The application development method uses an Android application-based approach. This research involves the stages of analyzing user needs, designing recommendation algorithms, implementing applications, and evaluating performance. The recommendation algorithm is developed by considering user preferences for certain features in smartphones. The test results show that this application can provide recommendations that match user needs with a sufficient level of accuracy. By offering recommendations tailored to users' interests and preferences, users no longer need to manually browse app stores or conduct intensive research.

References
  1. R. Kasauli, E. Knauss, J. Horkoff, G. Liebel, and F. G. de Oliveira Neto, “Requirements engineering challenges and practices in large-scale agile system development,” Journal of Systems and Software, vol. 172, p. 110851, 2021.
  2. S. Al-Saqqa, S. Sawalha, and H. AbdelNabi, “Agile software development: Methodologies and trends.,” International Journal of Interactive Mobile Technologies, vol. 14, no. 11, 2020.
  3. S. Pargaonkar, “A Comprehensive Research Analysis of Software Development Life Cycle (SDLC) Agile & Waterfall Model Advantages, Disadvantages, and Application Suitability in Software Quality Engineering,” International Journal of Scientific and Research Publications (IJSRP), vol. 13, no. 08, 2023.
  4. R. A. Hamid et al., “How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management,” Comput Sci Rev, vol. 39, p. 100337, 2021.
  5. I. H. Sarker, M. M. Hoque, M. K. Uddin, and T. Alsanoosy, “Mobile data science and intelligent apps: concepts, ai-based modeling and research directions,” Mobile Networks and Applications, vol. 26, pp. 285–303, 2021.
  6. S. S. Goswami and D. K. Behera, “Evaluation of the best smartphone model in the market by integrating fuzzy-AHP and PROMETHEE decision-making approach,” Decision, vol. 48, pp. 71–96, 2021.
  7. S. Al-Saqqa, S. Sawalha, and H. AbdelNabi, “Agile software development: Methodologies and trends.,” International Journal of Interactive Mobile Technologies, vol. 14, no. 11, 2020.
  8. R. A. Hamid et al., “How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management,” Comput Sci Rev, vol. 39, p. 100337, 2021.
  9. M. Cordella, F. Alfieri, C. Clemm, and A. Berwald, “Durability of smartphones: A technical analysis of reliability and repairability aspects,” J Clean Prod, vol. 286, p. 125388, 2021.
  10. F. Cheng, Y. Ming, and H. Qu, “Dece: Decision explorer with counterfactual explanations for machine learning models,” IEEE Trans Vis Comput Graph, vol. 27, no. 2, pp. 1438–1447, 2020.
  11. Z. H. \.Ipek, A. I. C. Gözüm, S. Papadakis, and M. Kallogiannakis, “Educational Applications of the ChatGPT AI System: A Systematic Review Research.,” Educational Process: International Journal, vol. 12, no. 3, pp. 26–55, 2023.
  12. A. Sharma, J. S. Pandher, and G. Prakash, “Consumer confusion and decision postponement in the online tourism domain: the moderating role of self-efficacy,” Journal of Hospitality and Tourism Insights, vol. 6, no. 2, pp. 1092–1117, 2023.
Index Terms

Computer Science
Information Sciences

Keywords

Smartphone Recommendations Android Apps Mobile Device Selection