Article Per Year (5 Year)
p-Index From 2021 - 2026
2.782
P-Index
This Author published in this journals
All Journal Media Statistika Ekonomi Bisnis Jurnal Gaussian Analisis Kebijakan Pertanian Media Trend: Berkala Kajian Ekonomi dan Studi Pembangunan Jurnal Ilmiah Ekonomi dan Bisnis Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Ekonomi dan Bisnis Jurnal Pendidikan Lingkungan dan Pembangunan Berkelanjutan EIGEN MATHEMATICS JOURNAL Jurnal Varian Analisis Kebijakan Pertanian Cendekia Niaga JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Ruwa Jurai: Jurnal Kesehatan Lingkungan Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) Jurnal Ketenagakerjaan Indonesian Journal of Economics and Management Efficient: Indonesian Journal of Development Economics Optimum: Jurnal Ekonomi dan Pembangunan Eko-Regional: Jurnal Pembangunan Ekonomi Wilayah
Fitri Kartiasih
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
The Effectiveness of ECM - MIDAS Based on Principal Component Analysis (PCA) in Predicting GDP in Indonesia Fajar Fithra Ramadhan; Dea Malaika; Ni Kadek Dwi Utami; Fitri Kartiasih
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.411-422

Abstract

GDP is closely related to monetary policy, because changes in GDP often affect decisions taken by the central bank in formulating policies to maintain economic stability. This study aims to predict the value of Gross Domestic Product (GDP) by developing a more accurate and efficient model. The variables analyzed include primary money, net domestic assets, net foreign position, and foreign exchange reserves as independent variables, and gross domestic product (GDP) as the dependent variable. The method used combines the Error Correction Model (ECM) into the Mixed Data Sampling (MIDAS) and Principal Component Analysis (PCA) models, this approach provides a more comprehensive analytical framework to capture complex interactions between variables with different frequencies, while taking into account long-term and short-term dynamics that influence each other. The results of the study indicate that the combination approach of PCA and MIDAS with the Almon distribution is more effective in capturing data patterns than other approaches that only use PCA with the average or median of economic indicators. The ECM-MIDAS-PCA model with the Almon weight function showed the best results, marked by an Adjusted R-Square value of 22.33% and low prediction error. The Error Correction Term (ECT) coefficient of -0.1579 indicates a correction towards long-term equilibrium of 15.79% per quarter, so that the process towards equilibrium can be achieved in 6.33 quarters.
Global Economic Dynamics and Its Impact on Indonesian Trade: Multi-Regional Input-Output Analysis Approach Annisa Muthi Zajidah; Dyah Widyastuti; Immanuel Nicholas Fransepta Samosir; Patrick Noel Simamora; Fitri Kartiasih
Cendekia Niaga Vol. 9 No. 1 (2025): Jurnal Cendekia Niaga : Trade and Development Studies
Publisher : Pusat Pengembangan Kompetensi Aparatur Perdagangan

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

Abstract

This study investigates the impact of economic dynamics on international trade using a multiregional analysis of Input-Output table. Multiplier effect analysis is used to study how a sector affects both the domestic economy and the economies of partner countries. Employing forward linkage and backward linkage values to analyze the linkages, determine which sectors are superior in international trade operations. The impact of partner countries' economies on Indonesia's economy is then simulated. The results show that Indonesia tends to rely on imports and exports connected to manufacturing. The manufacturing, chemical and chemical goods, and paper and printing industries are the main drivers of foreign trade. Furthermore, when Indonesia goes through a recession, the ASEAN nations typically have a big influence on the national economy. So economic diversification, domestic consumption stimulus, and building economic resilience are needed.
The Impact of Mining and Metal Industry Investment on The Economy of Southeast Sulawesi Province: Input-Output Analysis Hasna Arifah Nur Fatih; Ikhlasul A’mal; Rafael Agintha Tarigan; Yuniar Yudhi Tirana; Fitri Kartiasih
Ekonomi dan Bisnis Vol 13 No 1 (2026): EKONOMI DAN BISNIS
Publisher : Fakultas Ekonomi dan Bisnis, Universitas Pembangunan Nasional Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35590/jeb.v13i1.12377

Abstract

Purpose: This research aims to provide an overview of the input-output structure in the metal ore mining and basic metal industries, determine the leading sectors and their role in other sectors, and assess the impact of investment in these sectors on the economy of Southeast Sulawesi. Design/methodology/approach: This study uses data from the 2016 Input-Output Table of Southeast Sulawesi Province, which was updated to 2023 using the RAS method. The analysis used in this study is multiplier analysis, linkage analysis, and impact analysis. Findings: The results show differences in output structure between the metal ore mining sector and the basic metals industry. From an input perspective, both are efficient because primary input is greater than intermediate input. Metal ore mining is also one of the leading sectors in Southeast Sulawesi. Simulating investment in both sectors simultaneously yields a larger output increase compared to investing in only one sector. Research limitations/implications: The research is limited to data from the Input-Output Table for Southeast Sulawesi Province, and therefore has not yet been able to capture the relationship between the input-output structure and other regions. Further studies are recommended using the Interregional Input-Output (IRIO) Table, which allows for a more comprehensive analysis of interregional linkages. Practical implications: The government needs to utilize the output of the basic metal industry as raw materials for other sectors to stimulate economic growth. In addition, it is also necessary to prioritize downstreaming of metals thru the construction of smelters to increase added value while maintaining environmental sustainability. Originality/value: This research offers a novel contribution by integrating the mining sector and its downstream industries, namely the base metal industry, into the provincial-level analysis to provide a more representative picture of mineral resource potential in Southeast Sulawesi.
Nowcasting of Indonesia's Gross Domestic Product Using Mixed Sampling Data Regression and Google Trends Data Niken Yuliana; Shashella Zelicha Ramadanty; Umu Arifatul Syifa; Fitri Kartiasih
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.187

Abstract

This study aims to compare the results of the GDP nowcasting of the accommodation and food service activities sector without and with the pandemic time using the MIDAS method. The MIDAS method is an econometric approach used to predict economic development using real-time available high-level and low-frequency data. In this study, Google Trend acts as a predictor variable consisting of 16 search categories which are then reduced by Principal Component Analysis, resulting in several principal components. For GDP data, the data period collected is Quarter I 2010 to Quarter I 2023. This period will later be partitioned into the period before the COVID-19 pandemic, namely Quarter I 2010 to Quarter IV 2019 and a combined period, namely Quarter I 2010 to Quarter I of 2023. This partition was carried out to see the performance and sensitivity of the model before and after the shock due to the COVID-19 pandemic. From the models that have been made, nowcasting is carried out and it is found that the RMSE and MAE values for the pre-pandemic model are smaller than the combined model. The RMSE values for each of the pre-pandemic and combined models were 0.005753 and 0.056032 and the MAE values were 0.00359 and 0.048976 for the pre-pandemic and combined models. However, from this study it is not advisable to make predictions on the nominal GDP of the accommodation and food service activities sector because the results of the nowcasting predictions are still far from the actual value, but can be a reference if you want to predict the growth direction of the accommodation and food service activities sector.
Analysis of Carbon Emissions in ASEAN Manufacturing: Input-Output and Panel Data Approach Imella Mendita Sandi; Naufal Raffie Abioga; Randy Daffa Aditya; Rizka Sabrina; Fitri Kartiasih
Efficient: Indonesian Journal of Development Economics Vol. 8 No. 1 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/efficient.v8i1.14301

Abstract

This research aims to identify the leading sectors in four ASEAN countries and analyze the influence of production in these sectors and the consumption of renewable energy on carbon dioxide emissions in these countries. The analytical methods used include the analysis of inter-sector linkages and identifying leading sectors using input-output tables, followed by further analysis with panel data regression. The results indicate that while the leading sectors vary among the four countries, the manufacturing sector predominantly leads. Additionally, GDP in the manufacturing sector and renewable energy consumption significantly affect carbon dioxide emissions in the four ASEAN countries
Co-Authors Adi Setiawan Adi Setiawan Adinda Ayu Pramesthi Almira Utami Anggi Dwi Puspita Annisa Muthi Zajidah Aqsal Rizky Ramadhani Astri Nur Innayah Avril Irene Hutauruk Bagus Rohmad Bernica Tiyas Belantika christian Putra ginting David R. Hutasoit Dea Malaika Dhea Prawidia Dhevri Leonardo Hutajulu Dwi Intan Sulistiana Dyah Widyastuti Fais Jefli Fajar Fithra Ramadhan Fat’hul Mubin Gufron Firman Emmanuel Declarantius Parulian Hasna Arifah Nur Fatih Hawa Dwi Nur Arandita Heru Aryo Prasetio I Made Joel Jaya Dilaga Ikhlasul A’mal Imella Mendita Sandi Immanuel Nicholas Fransepta Samosir Indahtul Ghaista Innas Khoirun Chisan Kezia Dianrani Hutagalung Kurnia Anisya Fitri Lia Devita Malem Sembiring Lidya Zhafirah M. Yandre Febrian Muh Yusuf. S Mutiara Friska Amalia Nadine Rolanda Cantika Nasya Zahira Putri Naufal Raffie Abioga Ni Kadek Dwi Utami Ni Luh Putu Ari Susiawati Ni Putu Esti Utami Barsua Niken Yuliana Nur Yudha Jati Prakoso Nurul Izzah Nurzikri Saputra Patrick Noel Simamora Pikata Aselnino Rafael Agintha Tarigan Raihan Rahmanda Junianto Randy Daffa Aditya Ribut Nurul Tri Wahyuni Rizka Sabrina Rizky Rahmadani Shashella Zelicha Ramadanty Syfriza Davies Raihannabil Uma Sahu Umu Arifatul Syifa Wahyuni Andriana Sofa yeny puspita ningsih Yulia Nawang Wulandari Yuniar Yudhi Tirana