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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Prosiding Seminar Nasional Official Statistics
Ningrum, Icha Wahyu Kusuma
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Analisis Regresi Spasial Persentase Kemiskinan di Kawasan Timur Indonesia Tahun 2022 Huda, Achmad Choirul; Az-Zahra, Afifah; Yasmin, Fatia Putri; Ningrum, Icha Wahyu Kusuma; Putra, Wildhan Surya; Budiasih, Budiasih
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1792

Abstract

Poverty is a multidimensional problem that occurs in Indonesia. Poverty is closely related to the level of food security in an area. Eastern Indonesia is a concern because of the high poverty rate. The purpose of this research is to look at the constituent variables of food security that have a significant effect on the percentage of poverty at the regency/city level in Eastern Indonesia. The spatial heterogeneity identification carried out shows that the use of the Geographically Weighted Regression (GWR) model is better than multiple linear regression to show the relationship between the variables constituting food security and the percentage of poverty. A local GWR model was generated to see spatial heterogeneity at the regency/city level with a focus on the variable per capita normative consumption ratio (NCPR), the percentage of households with a proportion of food expenditure of more than 65% of income (PP), and its combination with other variables. It was found that the poverty rate at the regency/city level in Eastern Indonesia is influenced by different constituent variables of food security, especially between PP variables with access to electricity and life expectancy.
Predicting Startup Success Using Machine Learning Approach Ningrum, Icha Wahyu Kusuma; Ridho, Farid; Wijayanto, Arie Wahyu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8338

Abstract

Predicting startup success is important because it helps investors, entrepreneurs, and stakeholders allocate resources more efficiently, minimize risks, and enhance decision-making in an uncertain and competitive environment. Therefore, investors need to predict whether a startup will succeed or fail. Investors conduct this assessment to determine if a startup is worthy of funding. The company's founders mark success here by receiving a sum of money through the Initial Public Offering (IPO) or Merger and Acquisition (M&A) process. If the startup closes, we will consider it a failure. The data used consists of 923 startup companies in the United States. We carried out the classification using four methods: Random Forest, Support Vector Machines (SVM), Gradient Boosting, and K-Nearest Neighbor (KNN). We then compare the results from the four methods with and without feature selection. We determine the feature selection based on the relative importance of each method. The results of this study indicate that the Random Forest method with feature selection has the best accuracy, precision, recall, and F1 score than the other methods, respectively 81.85%, 80.19%, 87.09%, and 83.44%.
Simulasi Dampak Kebijakan Moneter terhadap Perekonomian dan Emisi CO2 Per Kapita di Indonesia Ningrum, Icha Wahyu Kusuma; Agustini, Peni; Nabilah, Yasmin Nur Alya
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2247

Abstract

In running an economy, energy is needed as an input for the production process. However, energy needs are still dominated by energy from fossils that produce CO2 emissions. CO2 emissions can cause global climate change where the United Nations (UN) is struggling to combat climate change and its impacts through the 13th Sustainable Development Goals. This study aims to examine the simultaneous relationship between gross domestic product, CO2 emissions, and gross fixed capital formation in Indonesia and the variables that influence the three indicators using a simultaneous equation model with the two stage least squares (2SLS) method. In addition, a simulation will be carried out when an intervention is made on monetary policy against the three indicators. As a result, the scenario that can improve the economy and CO₂ emissions per capita is by lowering interest rates. While the scenario that can reduce the economy and CO2 emissions per capita is by raising interest rates.
Co-Authors Agustini, Peni Arie Wahyu Wijayanto Az-Zahra, Afifah Budiasih Budiasih Huda, Achmad Choirul Nabilah, Yasmin Nur Alya Putra, Wildhan Surya Ridho, Farid Yasmin, Fatia Putri