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All Journal Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Data Science: Journal of Computing and Applied Informatics NaCoLET
Harry Patria
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Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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8 rules for better data storytelling Harry Patria
National Conference on Language, Education, and Technology Proceeding Vol. 2 No. 1 (2022): December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32534/nacolet.v2i1.3620

Abstract

PORTFOLIO OPTIMIZATION MODELING IN THE CONSUMER GOODS INDUSTRY Muthia Ulfa; Ahmad Fauzi Amrullah; Laksmi Ayudyanti; Harry Patria
Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Vol. 4 No. 6 (2022): Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan
Publisher : Departement Of Accounting, Indonesian Cooperative Institute, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.813 KB) | DOI: 10.32670/fairvalue.v4i6.1092

Abstract

Portfolio optimization refers to the process of choosing the proportions of assets to be placed in a portfolio. The objective of this research is to analyze the performance of various portfolio optimization models. This study uses Statistical Calculation R software to analyze the performance of portfolio optimization models, including Monte Carlo with Sharpe Ratio. We will analyze the multi-asset data of the 5 con-stituent consumer goods stocks listed on the Indonesia Stock Exchange (IDX) for 1 year and a half. Then use R to test the stock performance of the model. By using additional risk indicator to assess equity performance, such as volatility, Sharpe ra-tio (SR), risk parity (RP) the result shows that ICBP.JK with 41.5% SR and 28.1% RP that could be a recommendation to invest stocks in this consumer goods industry.
IMPACT OF COVID-19 PANDEMIC ON MARKET SHARE AND RETURN IN CONSTRUCTION INDUSTRY Fernando Y. Solar; Rosyida Tri Nurdyana; Velasri Vebraudia; Harry Patria
Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Vol. 4 No. Spesial Issue 3 (2022): Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan
Publisher : Departement Of Accounting, Indonesian Cooperative Institute, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.675 KB) | DOI: 10.32670/fairvalue.v4iSpesial Issue 3.1174

Abstract

Covid-19 pandemic has had a significant impact on all sectors and industries in Indonesia, including the construction industry. Completion of infrastructure development projects which are controlled by construction SOEs is largely hampered. The purpose of this study was to analyze the impact of Covid-19 on the market share and returns of state-owned enterprises in the construction industry compared to conditions before the pandemic. The study used stock price data in the building construction industry during the period March 2018 - 2020 and March 2020 - August 2021. The method used is Modern Portfolio Theory with Monte Carlo Simulation for portfolio optimization. The results showed that the Sharpe Ratio of the Portfolio in the period before the COVID-19 pandemic had a return around minus 4.9% to 33% and the risk was around 30.9% to 41.2%. During the pandemic, although the stock returns provided by the portfolio improved slightly, the risk increased between 53.7% to 63.3%. Therefore, the government and investors should pay more attention to selectively investing in the construction sector..
Predicting the Oil Investment Decision through Data Mining Empirical Evidence in Indonesia Oil Exploration Sector Harry Patria
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i1-7539

Abstract

Petroleum investment decision remains subject to economic and financial research for decades. Due to capital intensive and higher risk on oil exploration, the investment decision has become more important than ever before. This study aims to shed some light on this issue by conducting four machine learning algorithms to predict the decision applying the dataset from 2007-2019. This study includes the Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine. A comparative performance analysis is the illustrated using confusion matrix, Cohen’s Kappa value, and the accuracy of each model and Area under the ROC Curve. In this study, a machine learning approach was carried out on the oil exploration data. The findings demonstrate that Naïve Bayes has the most accurate performance for the classification (94.5%), followed by Decision Tree (92.9%), Random Forest (90.9%), and Support Vector Machine (89.6%). In practice, the selected Naïve Bayes model was applied to assess the decision using a new data test. The findings can diminish the subjective blindness and confirmation bias in the investment decision and bring about a reasonable and orderly exploration and development of petroleum reserves.
Co-Authors Ahmad Fauzi Amrullah Fernando Y. Solar Laksmi Ayudyanti Muthia Ulfa Rosyida Tri Nurdyana Velasri Vebraudia