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All Journal Asia-Pacific Management and Business Application JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan J-SAKTI (Jurnal Sains Komputer dan Informatika) Data Science: Journal of Computing and Applied Informatics J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal of Advanced Science Computing and Engineering Economic and Finance in Indonesia
Patria, Harry
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Social Sciences

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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

Abstract

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
Predicting Peer to Peer Lending Loan Risk Using Classification Approach Zulfikri, Fahmi; Tryanda, Dendy; Syarif, Allevia; Patria, Harry
International Journal of Advanced Science Computing and Engineering Vol. 3 No. 2 (2021)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.08 KB) | DOI: 10.62527/ijasce.3.2.57

Abstract

Technological innovations have affected all sectors of life, especially, the financial sector with the emergence of financial technology. One of them is marked by the emergence of Peer-to-Peer Lending ("P2P Lending). Credit Risk Management is essential to P2P Lending as it directly affects business results, therefore it is important for P2P Lending to predict borrowers with the highest probability to become good or bad loans based on their profile or characteristics. In the experiments, five classification algorithms are used, which are Gradient Boosted Trees, Naïve Bayes, Random Forest, Decision Tree and Logistic Regression. The result is two modelling performed well that is Random Forest with accuracy 93.38% and Decision Tree with 92.35%.
Bayesian Regression for Predicting Price Empirical Evidence in American Real Estate Patria, Harry
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): 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.v7.i1-10082

Abstract

The two foremost aims of classical regression are to assess the structure and magnitude of the relationship between variables. Despite the aforementioned benefits, unlike classical regression, which only offers a point estimate and a confidence interval, Bayesian regression offers the whole spectrum of inferential solutions. The results of this study demonstrate the Bayesian approach's suitability for regression tasks and its advantage in accounting for additional a priori data, which often strengthens studies. Using data from Boston Housing provided by from UCI ML Repository, this study proves that the prior distributions have the benefit of producing analytical, closed-form conclusions, which eliminates the need to use numerical techniques like Markov Chain Monte Carlo (MCMC). Second, software implementations are offered together with formulas for the posterior outcomes that are supplied, clarified, and shown. The assumptions supporting the suggested approach are evaluated in the third step using Bayesian tools. Prior elicitation, posterior calculation, and robustness to prior uncertainty and model sufficiency are the three processes that are essential to Bayesian inference.
Price Prediction with Bayesian Inference and Visualization: Empirical Evidence in India Real Estate Patria, Harry
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 2 (2023): 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.v7.i2-11434

Abstract

Classical regression serves two primary purposes: evaluating the structure and strength of the relationship between variables. However, while classical regression provides only a point estimate and confidence interval, Bayesian regression offers a comprehensive range of inferential solutions. This study demonstrates the suitability of the Bayesian approach for regression tasks and its advantage in incorporating additional a priori information, which can strengthen research. To illustrate, we utilized data from the Indian Housing dataset provided by the Kaggle Repository. We found that prior distributions produce analytical, closed-form conclusions, eliminating the need for numerical techniques like Markov Chain Monte Carlo (MCMC). Furthermore, this study provides software implementations, along with formulas for the posterior outcomes that are explained and presented clearly. In the third step, Bayesian tools were employed to evaluate the assumptions that underlie the proposed approach. Specifically, the essential processes of Bayesian inference - prior elicitation, posterior calculation, and robustness to prior uncertainty and model sufficiency - were assessed.
The Role of Leadership and Decision-Making under Crisis: A bibliometric analysis and scientific evolution from 1962 to 2020 Patria, Harry
APMBA (Asia Pacific Management and Business Application) Vol. 10 No. 1 (2021)
Publisher : Department of Management, Faculty of Economics and Business, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.apmba.2021.010.01.3

Abstract

The unprecedented importance of leadership and decision-making under recent pandemic and economic crises boosts the development of this research domain. This study shed light on the published works of leadership and decision-making under crises which have had the greatest contribution and evolutionary scientific paths over the decades, which are: (1) inspect the scientific anatomy of earlier works and their main structures; (2) scrutinize the scientific trends and the evolutionary path, and (3) recognize theoretical and practical implications. This study generates its analysis based on R programming language with a package of ‘bibliometrix’ (a) multidimensional data analysis, (b) intellectual structure and network analysis, (c) conceptual structure and factorial analysis, (d) strategic diagrams and evolution maps, and (e) historical citation network and research collaboration across the world. From this bibliometric study covering 692 articles published in the academic journal from 1962 to 2020, the findings open up an opportunity of how leaders overcome plausible crises by making the right decision through organizational resources, technological capability, people management. Subsequently, the findings can explain the way decisions are made so that prevent the potential crisis in the stage of planning and lessening the harm in the stage of crisis intervention. For theoretical contributions, it appears that future research needs to explore the emerging themes of data mining, artificial intelligence, information system, and information management. In the era of the COVID-19 pandemic, healthcare and crisis management are likely to be addressed by unleashing cutting-edge digital technology such as Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT).
Co-Authors Aromy, Verry Dina B, Yanuardhi Arief Daulay, Iman Taufiq Dina Anggraeni, Dina Djuwita A. Rahim Fadillah, Lukman Farih, Imaduddin Heri Hermawan Margono, Hanif Nur Fauzi Nadira, N Nugraha, Raditya Hari Nugroho, Antonius Agung Prasetyo, Heru Budi Prasetyohadi, Latif Rahim, Djuwita A. Satya, Candra Harry Sugiyanto, Kris Syarif, Allevia Tryanda, Dendy Yuwono, Eko Zam Zam, M. Irwan Zulfikri, Fahmi