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The Role of Success Rate, Discovery, Appraisal Spending, and Transitioning Region on Exploration Drilling of Oil and Gas in Indonesia in 2004-2015 Patria, Harry
Economics and Finance in Indonesia Vol. 67, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Petroleum exploration decision remains a subject of petroleum and economic studies for decades. Most of the studies discuss the investment decision by focusing on either a technical or economic perspective. In reality, economic, geological, and environmental factors are expected to determine the way investors make a decision. This study aims to increase the understanding of best practices in decision-making by scrutinizing integrative perspectives applying panel data of 32 basins in Indonesia in 2004-2015. This study provides several contributions to optimize decisions on wells drilled. First, this study derives an empirical model examining several plausible factors of economy, geology, and environment. Second, the findings demonstrate how to empirically examine which factors significantly determine wells drilled by companies. The last contribution is to empirically support a technical transformation from Western to Eastern exploration due to the natural depletion of oil fields.

PORTFOLIO OPTIMIZATION FOR SHIPPING & DELIVERY SERVICES WITH R: BEFORE AND AFTER PANDEMIC COVID-19 Anggraeni, Dina; Sugiyanto, Kris; Zam Zam, M. Irwan; Patria, Harry
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

The objective of this research is to construct an optimum investment portfolio of courier services sector stocks during period 2018-2021 using Modern Portfolio Theory model and to analyse risk and return generated by optimal portfolio before and after Covid-19 using R programming. Furthermore, we would also examine the impact of the Covid-19 on stock prices before and after Covid-19 to formulate investment decisions. The sample are 5 biggest courier services stocks (by market capitalization) that are listed consistently and did not stock split or reverse stock and the number of observations in 4825 stocks prices during the period January 2018 to October 2021. Based on the result of optimum investment research, we can observe that the best performing stocks with high tangency is AMZN, in fact way ahead of other sample emitens. The result will be expected to help investors to bid the best possible portfolio in courier services.

Dampak pandemi Covid-19 terhadap portofolio saham bisnis logistik transportasi laut di Indonesia Margono, Hanif Nur Fauzi; Daulay, Iman Taufiq; Patria, Harry
Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Vol. 5 No. 1 (2022): Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan
Publisher : Departement Of Accounting, Indonesian Cooperative Institute, Indonesia

The Covid-19 pandemic has had an impact on the global economy, particularly in the logistics industry, which includes Indonesia. The goal of this study is to identify the portfolio performance of the top five Indonesian companies in the logistics sector, particularly sea transportation, and analyze the impact before and during the pandemic using a Monte Carlo simulation of the optimal risk asset portfolio two years before the Covid-19 pandemic and two years during the pandemic. Covid-19. Before (during) the Covid-19 pandemic, the optimal portfolio weight value of BULL.JK 4,99% (0,05%), PSSI.JK 92,01% (34,65%), SMDR.JK 1,40% (31,12%), TMAS.JK 1,29% (30,42%) dan WINS.JK 0,31% (3,77%). Based on the sharpe ratio, the performance of the transportation sector stock portfolio provided a high return compared to risk during the Covid-19 pandemic, from 41.71% to 297.59%, with a level of volatility that tends to be stable before and after the Covid-19 pandemic, from 41.65% to 44.56%. Based on these findings, it can be concluded that the logistics sector in sea transportation can improve performance during the Covid-19 pandemic and is worthy of investment as an investor consideration.

Pengaruh strategi korporasi terhadap kinerja perusahaan foods & beverages di Indonesia selama pandemi covid-19 Satya, Candra Harry; Nugroho, Antonius Agung; Patria, Harry
Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan Vol. 4 No. 12 (2022): Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan
Publisher : Departement Of Accounting, Indonesian Cooperative Institute, Indonesia

This study aims to analyze 4 F&B companies listed on the Indonesia Stock Exchange (IDX), namely PT Indofood Sukses Makmur Tbk. (INDF.JK), PT. Mayora Indah Tbk. (MYOR.JK), PT Garudafood Putra Putri Jaya Tbk. (GOOD.JK), and PT Siantar Top Tbk. (STTP.JK). This study uses secondary data obtained from Yahoo Finance for the period March 11, 2020 to March 31, 2022. By using a Monte-Carlo simulation. Based on the results of the Monte-Carlo simulation, the sharpe ratio increased to 261.87% and volatility decreased to 43.16%. These results show that investment in the F&B sector in Indonesia is still very profitable and even experienced a fairly large increase in returns compared to before the COVID-19 pandemic. This is also in line with the government's prediction that with the economic recovery after the pandemic, the F&B industry sector in Indonesia will also grow. This study also found that during the COVID-19 pandemic, there was a significant change in the weight of the optimal investment portfolio, where GOOD.JK's performance far outperformed the other 3 companies due to the successful implementation of their corporate strategy in 2020 and 2021.

Assessing the investment viability of Indonesia's upstream electric vehicle (EV) sector stocks amidst the COVID-19 pandemic Patria, Harry; Rahim, Djuwita A.; Hermawan, Heri; Prasetyo, Heru Budi
JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen Vol. 20 No. 2 (2023): JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31106/jema.v20i2.19508

Amidst the global push for sustainability, the burgeoning electric vehicle (EV) industry has driven increased demand for batteries, placing the Indonesian nickel ore sector in a pivotal position due to its vast reserves. This study thoroughly examines the investment landscape of this sector, utilizing advanced portfolio optimization techniques to analyze four major nickel ore mine firms in Indonesia. Through Monte Carlo simulations, the study evaluates the optimal portfolios of risky assets, comparing their performance before and during the COVID-19 pandemic. Findings reveal a significant shift in portfolio composition during the Pandemic, reflecting investors' response to global disruptions by diversifying their holdings. Notably, the Sharpe Ratio, a risk-adjusted return measure, demonstrates an impressive increase in return relative to risk during the Pandemic, emphasizing the sector's resilience and attractiveness for investment, especially in times of economic uncertainty like the COVID-19 pandemic. The transformation in portfolio weights and the corresponding increase in risk-adjusted returns highlight the sector’s potential as a lucrative investment avenue, especially during periods of global economic uncertainty like the COVID-19 pandemic.

Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning) Patria, Harry
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (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.i2-8309

The number of financial transactions has the potential to cause many violations of the law (fraud). Conventional machine learning has been widely used, including logistic regression, random forest, and gradient boosted. However, the machine learning can work as long as the dataset contains fraud. Many new financial technology companies need to anticipate the potential for fraud, which they have not experienced much. This potential for a crime can also be experienced by old service providers with a low frequency of previous fraud. With the data imbalance, traditional machine learningis likely to produce false negatives so that they do not accurately predict potential fraud. This study optimizes the machine learning approach based on Neural Networks to improve model accuracy through the integration of KNIME and Python Programming with KERAS and TensorFlow models. The study also conducts a comparative analysis to scrutinize the performance of Adam and Adamax Optimizer. Using data from European cardholders in 2013, this study proves that workflows and neural network algorithms can detect with up to 95% accuracy even with a very small fraud sample of only 0.17% or 492 of 284,807 transactions. In addition, the Adam optimizer performs higher accuracy than the Adamax optimizer. The implication is that this supervisory technology innovation can be developed to minimize transaction crimes in the financial services sector.

Deciphering the Key Drivers of Sustainability : Harnessing Artificial Intelligence in Data Analytics to Unravel the Dynamics of Decarbonisation in Pursuit of Sustainable Development Patria, Harry; Djuwita A. Rahim
Data Science: Journal of Computing and Applied Informatics Vol. 8 No. 2 (2024): 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.v8.i2-15005

In the epoch where climate change poses an existential threat to humanity, understanding the intricate dynamics of CO2 emissions is more critical than ever. This study embarks on an ambitious journey to unravel the complex interplay of factors influencing carbon emissions, leveraging the prowess of Artificial Intelligence (AI) and the analytical capabilities of Power BI. Anchored in the context of the United Nations' Sustainable Development Goals (SDGs), this research transcends traditional analytical boundaries, offering a novel lens to view and interpret environmental data. At the heart of this exploration lies the UN SDG dataset, a rich tapestry of environmental, economic, and social indicators. The study's methodology is a fusion of advanced AI techniques with Power BI's visualization influencers, a combination that not only promises precision but also an unprecedented depth of insight. This dual approach enables a multifaceted analysis, capturing the nuances and subtleties often lost in conventional studies. The findings of this research are both revealing and transformative. They shed light on the significant yet varied factors that drive CO2 emissions across different geographical and socio-economic landscapes. The study unveils a striking correlation between increased access to electricity and GDP per capita with rising carbon emissions, a pattern particularly pronounced in developing regions. Conversely, in more developed contexts, the analysis reveals a complex interplay between emissions, literacy rates, and fertility rates, suggesting indirect yet potent pathways through which socio-economic development influences environmental outcomes. The insights gleaned offer a beacon for policymakers, illuminating the pathways to tailor environmental strategies that resonate with the unique needs of different regions. For developing nations, the study advocates for policies that intertwine educational and family planning initiatives with environmental objectives. In contrast, for developed countries, it underscores the need for technological innovation and efficiency improvements. The study's innovative use of AI and Power BI sets a new precedent in environmental research, demonstrating the immense potential of these tools in shaping sustainable futures.

Pengelompokan Negara Berdasarkan Indikator Kesejahteraan Dengan Metode Unsupervised Learning-Clustering: Bukti Empiris dari 167 Negara Farih, Imaduddin; Fadillah, Lukman; Nadira, N; Aromy, Verry Dina; Patria, Harry
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.423

One of the goals of the countries is to do continuous development in a positive direction so that the welfare of the country is guaranteed. To assess the development of a country can be seen from various factors such as socioeconomic and health factors. Some of the indicators used including GDP, health, income, export-import and others. This analysis can be used an evaluation of each country to improve its level. In addition, it is also used as a basis for determining which countries are entitled to receive assistance from funding institutions, so that the people of these countries can have a better life. Based on these problems, the authors analyze data of countries in the world using the Machine Learning Unsupervised which is Clustering method with KNIME. This analysis aims to determine the effect of indicators on the level of a country. The data to be studied are 167 countries in the world with socioeconomic and health factors. Based on research to avoid multicolenarity the authors use the PCA method. From this study, the authors used 4 PCA which represented 90% of the data and obtained 3 optimal clusters with an average silhouette value of 0.443.

Analisis Konsumsi Energi Listrik Pelanggan Dan Biaya Pokok Produksi Penyediaan Energi Listrik dengan Machine Learning Nugraha, Raditya Hari; Yuwono, Eko; Prasetyohadi, Latif; B, Yanuardhi Arief; Patria, Harry
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.424

PT PLN (Persero) during the Covid-19 pandemic was one of the companies whose sales growth was affected by the decline in electricity consumption in several sectors. Another condition is that several power plant and substation construction projects have fulfilled the realization commitment to the RUPTL from PT PLN (Persero). This has resulted in PT PLN (Persero) being faced with an over supply condition between power capacity and customer usage load. Realization of sales growth until July 2021 was 4.44% (144,788 TWh). Energy consumption in July 2021 was 20.55 TWh where the growth of kWh sales in July 2021 comparing with July 2020 began to show a recovery of +1.82%. The factor that most affected business and industrial growth was the manufacturing sector in Indonesia experiencing a slowdown/contraction as reflected in the PMI (Purchasing Managers Index) which decreased from 53.5 to 40.1. Growth is strongly influenced by consumer behavior in responding to government regulations, especially related to controlling the spread of Covid-19 in Indonesia in the form of restrictions on social activities (PSBB, PPKM, or Lockdown) which have been effectively implemented since April 2020 until now. Based on the analysis of the customer's electrical energy consumption data per industrial sector, as well as using technical data on the availability of power per electrical sub-system and the cost of producing electrical energy in an area, an evaluation model will be obtained that can be used in selecting the criteria for prospective customers who will be given program offers "SEMAKIN PRODUKTIF". By using "SEMAKIN PRODUKTIF" program data modeling, it is hoped that prospective customers will be given program offers so that they can be an opportunity to increase sales growth of electrical energy which is targeted to grow 6% in December 2021

Pengelompokan Negara Berdasarkan Indikator Kesejahteraan Dengan Metode Unsupervised Learning-Clustering: Bukti Empiris dari 167 Negara Farih, Imaduddin; Fadillah, Lukman; Nadira, N; Aromy, Verry Dina; Patria, Harry
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.423

One of the goals of the countries is to do continuous development in a positive direction so that the welfare of the country is guaranteed. To assess the development of a country can be seen from various factors such as socioeconomic and health factors. Some of the indicators used including GDP, health, income, export-import and others. This analysis can be used an evaluation of each country to improve its level. In addition, it is also used as a basis for determining which countries are entitled to receive assistance from funding institutions, so that the people of these countries can have a better life. Based on these problems, the authors analyze data of countries in the world using the Machine Learning Unsupervised which is Clustering method with KNIME. This analysis aims to determine the effect of indicators on the level of a country. The data to be studied are 167 countries in the world with socioeconomic and health factors. Based on research to avoid multicolenarity the authors use the PCA method. From this study, the authors used 4 PCA which represented 90% of the data and obtained 3 optimal clusters with an average silhouette value of 0.443.

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