Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Fundamental Management Journal Jurnal Ilmu Hukum, Humaniora dan Politik (JIHHP) fundamental management journal International Journal of Technology and Modeling
Angelia
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
THE EFFECT OF DEBT DEFAULT, PREVIOUS AUDIT OPINION, LIQUIDITY AND SOLVABILITY ON GOING CONCERN AUDIT OPINIONS FOR 2014 TO 2018 (Case Study on Manufacturing Company Listed on the Indonesia Stock Exchange) Angelia; Sidharta , Juaniva; Lumbantoruan , Rutman
Fundamental Management Journal Vol. 5 No. 2 (2020): ISSN: 2540-9816 (print) 2540-9220 (online) Volume:5 No.2 Oktober 2020
Publisher : Universitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/fjm.v5i2.2231

Abstract

Basically, a goingMconcernMopinionlismannopinionngivennbyltheiauditor if the sustainability ofnancompany is in doubt, in providing a going concern opinion an auditor will pay attention tonthenfactorsnthatnare considered toninfluence the issuance ofnthe goingbconcernbopinion. For this reason, thenresearch’sngoals werentonexaminentheneffectnofnauditnopinionnreceivednin debt default, previous year's audit opinion, liquidity and solvability of angoingnconcernsaudit opinions, ThezsampleszofzthiszresearchzwerezthezcompanyzthatzsufferedWlosses two years in a row and listedzin Indonesia StockzExchange in 2014 - 2018. Then the sampling was done by using a purposive sampling method and obtained a sample of 37 companies. The data used in this research is secondary data obtained from the IDX website in the form of financial reports and annual reports. The data were then tested and analyzed with SPSS version 24 through logistic regression analysis. Result of this research showedzthat thezdebt default, previouszyear's auditzopinion and liquidity haszsignificantzeffect, butzthe solvability variables weren’tzsignificantlyzinfluencezwithzgoingzconcernzopinion Keywords: Goingeconcerneauditeopinion, Debt default, previous year's audit opinion, liquidity, solvency
THE EFFECT OF DEBT DEFAULT, PREVIOUS AUDIT OPINION, LIQUIDITY AND SOLVABILITY ON GOING CONCERN AUDIT OPINIONS FOR 2014 TO 2018 (Case Study on Manufacturing Company Listed on the Indonesia Stock Exchange) Angelia; Sidharta , Juaniva; Lumbantoruan , Rutman
Fundamental Management Journal Vol. 5 No. 2 (2020): ISSN: 2540-9816 (print) 2540-9220 (online) Volume:5 No.2 Oktober 2020
Publisher : Universitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/fjm.v5i2.2231

Abstract

Basically, a goingMconcernMopinionlismannopinionngivennbyltheiauditor if the sustainability ofnancompany is in doubt, in providing a going concern opinion an auditor will pay attention tonthenfactorsnthatnare considered toninfluence the issuance ofnthe goingbconcernbopinion. For this reason, thenresearch’sngoals werentonexaminentheneffectnofnauditnopinionnreceivednin debt default, previous year's audit opinion, liquidity and solvability of angoingnconcernsaudit opinions, ThezsampleszofzthiszresearchzwerezthezcompanyzthatzsufferedWlosses two years in a row and listedzin Indonesia StockzExchange in 2014 - 2018. Then the sampling was done by using a purposive sampling method and obtained a sample of 37 companies. The data used in this research is secondary data obtained from the IDX website in the form of financial reports and annual reports. The data were then tested and analyzed with SPSS version 24 through logistic regression analysis. Result of this research showedzthat thezdebt default, previouszyear's auditzopinion and liquidity haszsignificantzeffect, butzthe solvability variables weren’tzsignificantlyzinfluencezwithzgoingzconcernzopinion Keywords: Goingeconcerneauditeopinion, Debt default, previous year's audit opinion, liquidity, solvency
Penerapan Uang Pesangon bagi Pekerja Tetap atas Pemutusan Hubungan Kerja secara Sepihak Angelia; Lie, Gunardi
Jurnal Ilmu Hukum, Humaniora dan Politik Vol. 5 No. 2 (2025): (JIHHP) Jurnal Ilmu Hukum, Humaniora dan Politik
Publisher : Dinasti Review Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jihhp.v5i2.3189

Abstract

Pemberian pesangon merupakan hak pekerja tetap yang diatur oleh hukum ketenagakerjaan dan bertujuan untuk memberikan perlindungan finansial setelah Pemutusan Hubungan Kerja (PHK). Penelitian ini menelaah bagaimana pelaksanaan pembayaran pesangon dilakukan, terutama ketika PHK terjadi tanpa kesepakatan dari pihak pekerja, serta peran peraturan perundang-undangan dalam menjamin hak-hak pekerja yang terkena PHK secara sepihak dan mengeksplorasi upaya hukum yang dapat ditempuh oleh pekerja ketika hak pesangon tidak diberikan sesuai peraturan ketenagakerjaan, yakni melalui langkah-langkah yang dapat dilakukan pekerja, seperti penyelesaian melalui mediasi di Dinas Ketenagakerjaan ataupun jalur pengadilan. Metode pendekatan yang digunakan bersifat yuridis normatif, yakni menggunakan data sekunder atau kepustakaan yang terdiri dari bahan hukum primer, sekunder, dan bahan non-hukum. Penelitian ini menunjukkan bahwa dibutuhkannya pengawasan ketat dan sosialisasi aturan terkait oleh pihak pemerintah agar hak-hak pekerja tetap terlindungi sesuai amanat Undang-Undang Ketenagakerjaan.
Determining the Optimal Chemical Concentration with the Regula Falsi Method Bandiyah, Salza Nur; Angelia; Hidayat, Rafi
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.100

Abstract

Determination of optimal chemical concentrations is one of the important aspects in industrial research and applications, especially in chemical reaction processes. In this article, the use of the Regula Falsi method as a numerical approach to determine optimal concentration based on the mathematical model of non-linear functions is discussed. The Regula Falsi method was chosen for its simplicity and ability to iteratively converge solutions with high accuracy. The target function is defined from the relationship between concentration variables and the efficiency of chemical reactions. In this study, simulations were carried out using several reaction parameter data scenarios to evaluate the performance of the method. The results show that the Regula Falsi method consistently provides accurate results in determining the root of the target function that represents the optimal concentration. The error rate is calculated to ensure that the resulting solution is within an absolute error tolerance of 0.01. The advantage of this method lies in the speed of convergence compared to other numerical methods, such as the Division by Two method. In addition, sensitivity analysis was carried out to assess the effect of parameter changes on the calculation results. This article concludes with a discussion of the potential applications of the Regula Falsi method in other chemical fields, including the optimization of reaction processes on an industrial scale. With this approach, it is hoped that the Regula Falsi method can be an effective tool to support data-based decision-making in chemical research and process technology.
Implementing LU Decomposition to Improve Computer Network Performance Angelia; Bandiyah, Salza Nur; Marine, Yoni
International Journal of Technology and Modeling Vol. 4 No. 2 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i2.101

Abstract

The application of LU decomposition in computer networks has great potential to improve system performance, especially in processing and analyzing complex and large-sized data. LU decomposition is a technique in linear algebra that breaks down a matrix into two triangular matrices, namely the lower (L) and upper (U) matrices, which facilitates the solution of a system of linear equations. In the context of computer networks, these algorithms can be applied to accelerate the analysis and processing of network traffic data, resource management, and traffic scheduling. Large matrices are often used to model networks in applications such as route mapping, bandwidth allocation, and network performance monitoring. The use of LU decomposition allows efficiency in handling such big data, speeds up calculations and reduces latency time in network information processing. This study proposes the application of LU decomposition to optimize several aspects in computer networks, such as dynamic routing, network fault detection, and more effective resource allocation. With LU decomposition, the process of load analysis and problem identification can be carried out more quickly, increasing the throughput and stability of the system. The results of the experiments conducted show that the application of LU decomposition can reduce the computational load and accelerate the system's response to changes in network conditions. Overall, the application of these methods can contribute to improving the efficiency and performance of modern computer networks, especially in the face of increasingly high and complex data traffic demands.
Stock Price Prediction with Mathematical Model Based on Secant Method Nabila, Andini Dara; Angelia
International Journal of Technology and Modeling Vol. 3 No. 3 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i3.102

Abstract

Stock price prediction is a complex problem involving various factors, including market volatility and historical data. This study proposes a mathematical model based on the secant method to predict stock prices. The secant method, as a simple but effective numerical algorithm, is used to approximate nonlinear solutions to stock price trends. Historical stock data is analyzed to form a function that represents the pattern of price changes. This function is the basis for applying the secant method to predict stock prices at a certain time. The study was conducted using stock data from several companies, with performance evaluation based on the level of prediction error compared to actual data. The results show that the secant method is able to produce predictions with a low average error rate and high computational efficiency. This makes it an attractive choice compared to more complex models, especially in resource-constrained environments. However, accuracy decreases in highly volatile market conditions, indicating the need for further development. This method offers a simple yet reliable approach to stock price prediction, so it can be used as a tool for investors or market analysts, taking into account its limitations.  
Co-Authors Bandiyah, Salza Nur Hidayat, Rafi Lie, Gunardi Lumbantoruan , Rutman Marine, Yoni Nabila, Andini Dara Sidharta , Juaniva