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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Pendidikan Ekonomi & Bisnis Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Aplikasi Statistika & Komputasi Statistik EAJ (ECONOMICS AND ACCOUNTING JOURNAL) AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis Journal of Mathematics, Computation and Statistics (JMATHCOS)
Valentika, Nina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Developing Marketplace-Based Online Store as an Adaptation to Online Purchase Trends Fitriani, Sabrina; Valentika, Nina
AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis Vol. 3 No. 2 (2023): AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis
Publisher : Perhimpunan Sarjana Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37481/jmeb.v3i2.592

Abstract

The presence of the Covid-19 pandemic is the main cause of the online buying trend. This is because the whole world is imposing social restrictions simultaneously, thus encouraging the birth of consumption pattern adaptations, with the digital purchasing model. These conditions require business actors to develop digital business concepts or e-commerce. One of the digital sales models is to build a marketplace-based online store. This research was conducted aiming to analyze the effectiveness of the influence of marketplace-based digital shops on purchase intention mediated by trust. To answer this goal, a quantitative study was carried out using the structural equation analysis method. The number of respondents is set at 200 people from millennial workers. Research data was collected primary by distributing questionnaires. The results of the study explain that there has been a change in purchasing patterns, from direct purchases to online purchases. Then, consumers feel that online purchases provide more guarantees of trust in service, price and satisfaction. So consumers assess, there is no doubt to make buying and selling transactions online.
Dimensionality Reduction Evaluation of Multivariate Time Series of Consumer Price Index in Indonesia Valentika, Nina; Sumertajaya, I Made; Wigena, Aji Hamim; Afendi, Farit Mochamad
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34151

Abstract

Multivariate time series (MTS) analysis of the Consumer Price Index (CPI) in Indonesia often encounters challenges such as outliers, missing data, and inter-variable correlations. Principal Component Analysis (PCA) is a practical approach for dimensionality reduction; however, its performance may vary depending on the data characteristics. This study is a quantitative comparative study that integrates empirical analysis and Monte Carlo simulation based on a first-order Vector Autoregressive (VAR(1)) model to evaluate three PCA approaches: Classical PCA, Robust PCA (RPCA), and PCA of MTS. These methods were applied to weekly price data of eight strategic food commodities across 70 districts and cities in Indonesia. The evaluation employed three criteria: (1) dimensionality reduction efficiency (empirical and simulation), (2) reconstruction accuracy measured using Root Mean Square Error (RMSE) (empirical), and (3) robustness to outliers and inter-variable correlations (simulation). Empirical results indicate that Classical PCA (lag 1) and RPCA (lag 1) are both efficient and effective in reducing dimensionality with minimal information loss. Using the first three principal components, all three methods were able to explain at least 85% of the total variance, with lag 1 identified as optimal. Simulation results reveal that RPCA (lag 1) provides the most stable and consistent performance in the presence of outliers, while Classical PCA (lag 2) performs better under conditions of high inter-variable correlation and a low proportion of outliers. These findings suggest that robust covariance estimation can improve the accuracy of dimensionality reduction and enhance the stability of multivariate time-series analysis for food price data in Indonesia.
Co-Authors Agusyarif Rezka Nuha Aji Hamim Wigena Azis, Irfani Bagus Sartono Chasanah, Sri Istiyarti Uswatun Farit Mochamad Afendi Fitriani, Sabrina Harahap, Irwan Leo Putra I Made Sumertajaya Ilmadi, Ilmadi Kusdaniyama, Nunung La Ode Nashar Maulidi, Ikhsan Nunung Kusdaniyama Nursyirwan , Vivi Iswanti Purwana E.S., Dedi Rahman, Gusti Arviana Sunardi Syarif Abdullah Syazali, Muhammad Usep Suhud Vina Apriliani Waryanto, Hendro Wibowo, Bonno Andri