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COVER + Dewan Redaksi ., .
Jurnal Legislasi Indonesia Vol 15, No 3 (2018): Jurnal Legislasi Indonesia - September 2018
Publisher : Direktorat Jenderal Peraturan Perundang-undang, Kementerian Hukum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54629/jli.v15i3.264

Abstract

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JDP Volume 3 Nomor 2 Mei 2018 ., .
Jurnal Dinamika Pengabdian Vol. 3 No. 2 (2018): JURNAL DINAMIKA PENGABDIAN VOL. 3 NO. 2 MEI 2018
Publisher : Departemen Budidaya Pertanian Fakultas Pertanian UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jdp.v3i2.4382

Abstract

Jurnal Dinamika Pengabdian (JDP) Volume 3 Nomor 2 Edisi Bulan Mei 2018 memuat tulisan hasil kegiatan pengabdian pada masyarakat. Pada edisi ini, tulisan yang dipublikasi meliputi kegiatan KKN PPM, IbM dan IbIKK.
Struktur Redaksi ., .
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 4 No 2: Tahun 2019
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (101.874 KB) | DOI: 10.17605/jti.v4i2.612

Abstract

Susunan Dewan Redaksi Selain Reviewer
Struktur Redaksi ., .
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 4 No 2: Tahun 2019
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (101.874 KB) | DOI: 10.17605/jti.v4i2.612

Abstract

Susunan Dewan Redaksi Selain Reviewer
Price Forecasting of Shallots Using the Machine Learning Approach of Random Forest Regression Supporting Price Stabilization ., .; Ibrahim, Muhammad Naufal Rauf
Jurnal Keteknikan Pertanian Vol. 13 No. 3 (2025): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.013.3.449-461

Abstract

Shallots (Allium cepa L.) are a major horticultural commodity in Indonesia, with a production of 1.98 million tons in 2022, representing 13.59% of the total national vegetable production. Accurate forecasting of agricultural commodity prices is fundamental to sustainable development in the agricultural sector and contributes to broader economic stability. This study uses the random forest regression algorithm, a supervised machine learning technique that utilizes ensemble learning to combine multiple decision trees. This approach offers advantages in modeling non-linear relationships for agricultural price prediction while also reducing the risk of overfitting, resulting in more accurate and stable forecasts compared to individual decision trees. The purpose of this research is to develop and optimize a shallot price forecasting model using random forest regression. The optimized model, using 50 decision tree estimators, successfully predicted up to 15 months ahead of monthly prices and achieved an RMSE of 2363.15 and a MAPE of 8.71% in validation, then a MAPE of 10.31% in test evaluation.
Back Mater ., .
ARISTO Vol 10 No 1 (2022): January
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/ars.v10i1.5140

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Front Mater ., .
ARISTO Vol 10 No 1 (2022): January
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/ars.v10i1.5141

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Financial Constraints and Tax Avoidance in Mining Companies Listed on the Indonesia Stock Exchange 2020–2022 .; ., .; Mira; Wahyuni, Wahyuni; Nurdiani
Jurnal Riset Perpajakan: Amnesty Vol 8 No 2 (2025): November 2025
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/902dn988

Abstract

This study investigates the effect of financial constraints on tax avoidance among mining companies listed on the Indonesia Stock Exchange (IDX) during the period 2020–2022. Mining firms operate in a capital-intensive and highly regulated environment, making them vulnerable to fluctuations in commodity prices, financing frictions, and liquidity pressures. These conditions heighten the relevance of examining whether financial constraints influence corporate tax planning behavior. Using a quantitative research design, the study analyzes panel data from 24 mining companies that consistently reported complete audited financial statements over the three-year period, resulting in 72 firm-year observations. Financial constraints are measured using the Hadlock–Pierce (HP) Index, while tax avoidance is proxied by the Cash Effective Tax Rate (CETR), which captures real cash taxes paid relative to pre-tax income. Several control variables—firm size, profitability, leverage, and capital intensity—are included to account for operational and structural characteristics of mining firms. Panel regression analysis is conducted using the Hausman test to determine the appropriate model, supplemented by classical assumption testing to ensure statistical validity. The results are expected to provide empirical evidence on whether financially constrained mining firms engage more aggressively in tax avoidance as a strategy to preserve liquidity during periods of economic uncertainty, including the COVID-19 pandemic. The study contributes to the literature by offering sector-specific insights into the financial determinants of tax avoidance in a highly regulated extractive industry and provides implications for policymakers, investors, and corporate managers regarding financial pressure, compliance behavior, and fiscal governance.