Paper's Title:

C^*-algebras Associated Noncommutative Circle and Their K-theory

Author(s):

Saleh Omran

Taif University,
Faculty of Science,
Taif,
KSA

South Valley University,
Faculty of Science,
Math. Dep.
Qena,
Egypt

salehomran@yahoo.com

Abstract:

In this article we investigate the universal C^*-algebras associated to certain 1 - dimensional simplicial flag complexes which describe the noncommutative circle. We denote it by S₁^nc. We examine the K-theory of this algebra and the subalgebras S₁^nc/I_k, I_k . Where I_k, for each k, is the ideal in S₁^nc generated by all products of generators h_s containing at least k+1 pairwise different generators. Moreover we prove that such algebra divided by the ideal I₂ is commutative.

Paper's Title:

Indonesia's GDP Forecast: Evidence From Fuzzy Time Series Model Using Particle Swarm Optimization Algorithm

Author(s):

Ismail Djakaria¹, Djihad Wungguli², Regina Sugi Pakadang³, Sri Endang Saleh⁴, Maman Abdurachman Djauhari⁵

^1,2,3Universitas Negeri Gorontalo,
Department of Statistics, Gorontalo,
Indonesia.

⁴Universitas Negeri Gorontalo,
Department of Development Economics, Gorontalo,
Indonesia.

⁵Indonesian Institute of Education,
Jl. Terusan Pahlawan 32, Garut 44151,
Indonesia.

E-mail: iskar@ung.ac.id
URL: https://orcid.org/0000-0003-1358-2356

Abstract:

Gross Domestic Product (GDP) is a principal indicator used to measure the economic condition of a country. Indonesia's GDP growth from 2017 to 2019 was approximately 6 percent; however, it experienced a decline in 2020 and 2021, with rates of only -0.02 percent and 2.41 percent, respectively. In the process of economic development planning, a forecasting system is required to determine GDP in the future. The forecasting method employed in this research is fuzzy time series optimized using Particle Swarm Optimization (PSO), to enhance the accuracy and convergence of forecasted values. The dataset used comprises secondary data, specifically 54 sets of Indonesian GDP data spanning from the first quarter of 2010 to the second quarter of 2023. The analysis results indicate that the proposed method is better than the conventional fuzzy time series approach. The former method provides a predictive value for one period in the future with a Mean Absolute Percentage Error (MAPE) value of 4.40%. In contrast, the latter yields higher predictive values with a MAPE value of 7.93%.

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