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VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Published by Universitas Negeri Makassar
ISSN : -     EISSN : 26847590     DOI : http://dx.doi.org/10.35580/variansiunm26374
Core Subject : Economy, Science, Education,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics
VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian pustaka.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 84 Documents
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Robust Standard Error Panel Regression of Firm Size, Leverage, Profitability on Firm Value: Indonesian Mining 2022–2024 Muh Qodri Alfairus; Muhammad Raihan Mubaraq; Alia Rezki Amalia
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm515

Abstract

Corporate financial information such as firm size, leverage, and profitability sends signals to the market that are reflected in firm value. However, previous studies have yielded inconsistent results, likely due to differences in estimation methods and the disregard of violations of classical assumptions in panel data. This study aims to analyze the effects of firm size (Size), leverage (DER), and profitability (ROA) on firm value (PBV) by applying panel data regression with robust standard error correction. Data were collected from 21 mining sector companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period, yielding 63 observations. The model selected based on the Chow Test (p=1.46E-09) and the Hausman Test (p=0.002) is the Fixed Effects Model (FEM). The results of the classical assumption tests indicate violations of heteroscedasticity (p=0.029) and autocorrelation (p=0.005), so the estimation was continued using cluster-robust standard errors (clustering by time). After adjusting for the model, it was found that all three variables simultaneously had a significant effect on firm value (F-statistic, p = 0.0538). Partially, firm size had a significant negative effect (coefficient -0.481; p=0.038), leverage had a significant positive effect (coefficient 0.672; p=0.018), and profitability had a marginally significant negative effect (coefficient -0.796; p=0.092). An R-squared value of 17.6% indicates that there are still other factors outside the model that influence firm value. The conclusion of this study confirms that in the context of the Indonesian mining sector in the post-pandemic period, the market responds negatively to companies with large assets and high profitability, but responds positively to increased debt. These findings imply that investors should not focus solely on short-term profitability, and that company management should determine the optimal capital structure to increase firm value.
Trend, Cycle, and Forecasting Analysis of Monthly Inflation in Indonesia Using the Hodrick–Prescott Filter and ARIMA Nur Ikhwana; Annisa Syalsabila; Nalto Batty Mangiri; Lalu Ramzy Rahmanda
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm526

Abstract

This study aims to analyze the structure of inflation and forecast monthly inflation in Indonesia using a time series approach. The method used is the Hodrick–Prescott Filter to decompose data into trend and cycle components, and the ARIMA model to forecast inflation. The data used is monthly inflation data for the period 2010–2025. The decomposition results show that inflation has a relatively stable long-term trend with short-term fluctuations reflecting the presence of economic shocks. Based on model identification, the best model is ARIMA(2,0,1)(1,0,1)[12] which is able to capture past influences, seasonal components, and short-term shocks. The evaluation results show that the model meets the white noise assumption and is suitable for use in forecasting. The forecasting results show that inflation tends to be stable with a moderate increasing tendency, although uncertainty increases over longer periods. This study shows that the combination of structural analysis and time series modeling provides a more comprehensive understanding of inflation dynamics and produces relevant predictions to support decision making.
Implementasi K-Affinity Propagation dalam Pengelompokan Provinsi di Indonesia Berdasarkan Kasus Pencemaran Lingkungan Hidup Derliani Natalia D.; Suwardi Annas; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm475

Abstract

Indonesia memiliki tingkat pencemaran lingkungan yang berbeda di setiap provinsi. Penelitian ini bertujuan untuk mengetahui gambaran dan hasil pengelompokan provinsi di Indonesia berdasarkan indikator pencemaran lingkungan yang meliputi pencemaran air, tanah, dan udara akibat limbah rumah tangga maupun limbah pabrik. Metode yang digunakan adalah K-Affinity Propagation (K-AP) dengan uji validasi Davies-Bouldin Index. Hasil analisis menunjukkan bahwa jumlah cluster optimum adalah 2, dimana Cluster 1 yang terdiri atas 3 provinsi dengan tingkat pencemaran lingkungan tertinggi, serta Cluster 2 yang terdiri atas 35 provinsi lainnya dengan tingkat pencemaran lebih rendah. Oleh karena itu, pemerintah perlu memberikan perhatian khusus pada provinsi yang masuk dalam Cluster 1, melalui pengawasan industri, pengelolaan limbah, serta peningkatan kesadaran masyarakat mengenai pentingnya pelestarian lingkungan.
APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) TO MODEL THE FACTORS AFFECTING THE PERCENTAGE OF POOR POPULATION IN INDONESIA Nur Shanty; Ruliana; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm518

Abstract

Poverty is one of the social and economic problems that Indonesia continues to face today. The Multivariate Adaptive Regression Spline (MARS) is a nonparametric regression model that estimates the functional relationship between the response variable and predictor variables when the relationship form is unknown. This study aims to estimate the parameters of the Multivariate Adaptive Regression Spline (MARS) method for the percentage of poor population in Indonesia and to identify the factors that significantly affect the percentage of poor population. The results of this study found that the best model was obtained with a combination of BF = 21, MI = 1, and MO = 3, with GCV = 0,3102717. Based on the MARS model, the variables that significantly affect the percentage of the poor population are the percentage of formal workers (x3), percentage of households with access to proper sanitation (x4), and Gini Ratio (x7) with a coefficient of determination (????²) of 81,44%.

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