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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ARBITRASE: JOURNAL OF ECONOMICS AND ACCOUNTING VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Utomo, Agung Tri
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Peran Komitmen Organisasi Dalam Memediasi Pengaruh Job Demand dan Stres Kerja Terhadap Kinerja Karyawan dengan Percevied Organizational Support Sebagai Variabel Moderasi (Studi Kasus di Satuan Kerja Inspektorat Jenderal Kementerian XYZ) Utomo, Agung Tri
ARBITRASE: Journal of Economics and Accounting Vol. 3 No. 3 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/arbitrase.v3i3.688

Abstract

The purpose of this study was to analyze the effect of job demand and work stress on organizational commitment. To analyze the effect of job demand, work stress and organizational commitment on employee performance. To analyze whether perceived organizational support can weaken the negative effect of job demand and work stress on employee performance. This study uses a quantitative approach with an explanatory design. The population in this study were all employees of the Inspectorate General of the XYZ Ministry. The sample method used is probability sampling, the sampling technique uses simple random sampling, so the sample size used in this study is 100 respondents. The data analysis method uses the Structural Equation Model (SEM) using the Smart PLS program version 3.2.9. The results of the study prove that job demand and work stress affect the organizational commitment of employees working at the Inspectorate General of the XYZ Ministry. Job demand, work stress and organizational commitment affect the performance of employees working at the Inspectorate General of the XYZ Ministry. Perceived organizational support cannot moderate the effect of job demand and work stress on performance of employees working at the Inspectorate General of the XYZ Ministry.
Analisa Data Penjualan Untuk Memprediksi Penjualan Barang Menggunakan Algoritma Asosiasi Dan Apriori Utomo, Agung Tri
JATISI Vol 11 No 3 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i3.7973

Abstract

Data mining serves as a pivotal instrument for enhancing business performance through the utilization of information gleaned from information systems. This research employs association algorithms, notably the Apriori algorithm, to examine concurrent purchasing patterns at OnlyThrift.id, a thrift clothing store located in Salatiga. The investigation entails an analysis of sales transactions spanning one year, with the Apriori algorithm utilized to unearth association rules among items. Findings reveal the Apriori algorithm's capability to furnish solutions with commendable accuracy in discerning combinations among itemsets and aiding in the formulation of more optimal inventory arrangements. Disparities in processing time and the quantity of rules generated between the Apriori and FP-Growth algorithms are observed, with Apriori demonstrating swifter processing time albeit yielding a reduced number of rules. The research underscores the significance of implementing association algorithms, particularly Apriori, to optimize sales patterns and inventory management practices. It is envisaged that this study will provide value to researchers by expanding insights into association algorithm analysis and offering tangible solutions for enhancing business performance, particularly within the sales sector. Keywords — Data mining, association algorithms, apriori algorithm, purchasing patterns
Perbandingan Model Value-at-Risk (VaR) Hybrid GARCH-EVT dan Model Standar dalam Pengukuran Risiko Ekstrem pada Portofolio Saham Sektoral di Indonesia Annisa Syalsabila; Ikhwana, Nur; Utomo, Agung Tri; Rahmanda, Lalu Ramzy; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm461

Abstract

This study aims to construct an optimal portfolio and compare the accuracy of various Value-at-Risk (VaR) models in measuring the risk of stock portfolios in the Indonesia Stock Exchange (IDX). The optimal portfolio is formed using the Minimum Variance Portfolio (MVP) method based on 11 sector-representative stocks for the period 2019–2025. The risk performance of this portfolio is then evaluated using six VaR models: Variance–Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), and the hybrid GARCH–EVT model. Model accuracy is assessed through backtesting using the Kupiec Proportion of Failures (POF) test and the Christoffersen Conditional Coverage (CC) test at the 95% and 99% confidence levels. The optimization results indicate that the MVP portfolio is dominated by defensive sectors such as consumer non-cyclicals (ICBP.JK) and large-cap banking (BBCA.JK). Backtesting results show that although all models perform adequately at the 95% level, standard models (VC, MC, GARCH) fail to capture extreme risk at the 99% level. In contrast, the GARCH–EVT model satisfies the backtesting criteria and emerges as the most accurate and superior model for predicting extreme losses.Penelitian ini bertujuan untuk membangun portofolio optimal dan membandingkan akurasi berbagai model Value-at-Risk (VaR) dalam mengukur risiko portofolio saham di Bursa Efek Indonesia (BEI). Portofolio optimal dibentuk menggunakan metode Minimum Variance Portfolio (MVP) dari 11 saham perwakilan sektor periode 2019-2025. Kinerja risiko portofolio ini kemudian diukur menggunakan enam model VaR: Variance-Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), dan model hybrid GARCH-EVT. Akurasi model diuji menggunakan backtesting Uji Kupiec (POF) dan Uji Christoffersen (CC) pada tingkat kepercayaan 95% dan 99%. Hasil optimisasi menunjukkan portofolio MVP didominasi oleh sektor defensif seperti consumer non-cyclicals (ICBP.JK) dan perbankan big-cap (BBCA.JK). Hasil backtesting menunjukkan bahwa meskipun semua model akurat pada tingkat 95%, model standar (VC, MC, GARCH) gagal mengukur risiko ekstrem pada tingkat 99%. Sebaliknya, model GARCH-EVT terbukti memenuhi uji dan menjadi model yang paling akurat dan superior untuk memprediksi kerugian ekstrem.
Co-Authors Annisa Syalsabila Ikhwana, Nur Rahmanda, Lalu Ramzy Rais, Zulkifli