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All Journal RAGAM: Journal of Statistics and Its Application Jurnal Teknoinfo
Fuad Muhajirin Farid
Universitas Lambung Mangkurat
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Public Health

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ANALISIS PENGARUH INDUSTRI MIKRO DAN KECIL TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA DENGAN PENDEKATAN EKONOMETRIKA REGRESI SPASIAL DATA PANEL Jonathan Adi Winata; Fuad Muhajirin Farid; Selvi Annisa
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12799

Abstract

AbstractOne indicator to assess the economic condition of a country is Gross Domestic Product (GDP) at the national level or Gross Regional Domestic Product (GRDP) at the regional level. The sector that contributes the most to Indonesia's GDP is the manufacturing industry. One of the most crucial components within the manufacturing sector is the micro and small-scale industry (MSI). The presence of MSIs significantly contributes to economic development, closely tied to the geographical location among regions, thereby exerting spatial influence on the GRDP of a region. Hence, an analysis of GRDP considering spatial aspects is necessary, investigating the impact of the Micro and Small-scale Industry (MSI) sector on economic growth in Indonesia using spatial panel data regression. The spatial models constructed in this study include the Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) involving fixed-effect influence. This research aims to describe and identify the factors within MSIs that influence economic growth in each province of Indonesia. The results indicate that the appropriate model used is the Spatial Autoregressive Model Fixed Effect (SAR-FE). Overall, there are two independent variables significantly affecting economic growth, namely the number of micro and small-scale industries (X1) and inflation (X6). The results show that an increase in the percentage of these two variables will decrease the economic growth rate. Keywords:   Gross Regional Domestic Product, Economic Growth, Micro and Small Industries, Spatial Autoregressive Model Fixed Effect 
ANALISIS REGRESI ROBUST M ESTIMATOR UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI LAMA STUDI MAHASISWA S1 STATISTIKA FMIPA UNIVERSITAS LAMBUNG MANGKURAT Widawati Annisa Putri; Fuad Muhajirin Farid; Selvi Annisa
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12798

Abstract

Robust regression is a statistical technique commonly used to model relationships between variables by minimizing the impact of outlier data. The use of robust regression M Estimator works well when there are outliers in the data. In this study, robust regression M estimator analysis will be applied to student study period data. The aim of this research is to determine the significant factors influencing the study period of Statistics undergraduate students at the Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University. The results of the research show that the residual data characteristics are not normal and there are outliers in the data. Using the Robust Regression M Estimator, the F test results show that F calculated 6.2492 > F table 2.173112, which means rejecting H0, indicating that the independent variables collectively have a significant effect on the dependent variable. From the t-test, it is known that the Guidance Process for students while working on their final project, the Employment Status of students, and the GPA of students significantly affect the Study Period of students. Keywords:   Robust Regression M Estimator, Study Period of Students, ULM
Pengaruh Faktor Makroekonomi Terhadap Return Harga Saham Jakarta Islamic Index (JII) Menggunakan Metode Regresi Data Panel Intan Salsabila; Sigit Dwi Prabowo; Fuad Muhajirin Farid
RAGAM: Journal of Statistics & Its Application Vol 4, No 1 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i1.15389

Abstract

The Indonesian capital market has experienced developments that show its role as an important component in the economy. As one of the countries with a Muslim population, the Jakarta Islamic Index (JII) is used as an investment in stocks based on sharia. Every investor in the capital market, whether based on sharia or conventional, really needs relevant information about what macroeconomic factors can affect investment activities, especially on stock returns. The purpose of this study is to analyze the effect of macroeconomic factors, namely inflation, interest rates, and exchange rates on stock returns in companies listed on the Jakarta Islamic Index (JII) for the 2019-2022 period using the panel data regression analysis method. A method that is more likely to build and test more complex regression models. Based on the results of panel data regression processing, using the common effect model approach as the best model, the results of the f test showed that inflation, interest rates and exchange rates simultaneously had a significant effect on stock returns. The results of the t test also showed that inflation, interest rates, and exchange rates partially had a significant effect on stock returns.
Comparison of SMOTE and ADASYN in Optimizing Random Forest Model for Imbalanced Financial Ratio Bankruptcy Prediction Novanda Rizky Ramadhana; Fuad Muhajirin Farid; Yeni Rahkmawati
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.1056

Abstract

Classification is a data analysis process that can predict classes based on predefined characteristics. In the era of big data, classification can be performed using machine learning. The problem of machine learning in classification analysis is imbalance data which often affect model performance. SMOTE and ADASYN are oversampling techniques to solve this problem. This study aims to evaluate the effectiveness of SMOTE and ADASYN in improving the performance of the Random Forest model on imbalanced data in the case of company bankruptcy using financial ratios. Models were built using training data with various splitting data and oversampling techniques. Then, the resulting models will be tested using testing data. The results show that the best model was achieved with a combination of splitting data 70:30 using SMOTE technique, which produced the highest f1-score of 40.57%, compared to ADASYN technique with 36.11% (a decrease of 4.46%), and without oversampling techniques with 19.51% (a decrease of 21.06%). The findings indicate SMOTE and ADASYN can identify minority values which are the main problem of imbalance data, with SMOTE showing better performance compared to ADASYN.
Co-Authors Intan Salsabila Jonathan Adi Winata Novanda Rizky Ramadhana Selvi Annisa Sigit Dwi Prabowo Widawati Annisa Putri Yeni Rahkmawati