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All Journal Jurnal Profiet Jurnal Statistika dan Matematika (Statmat) Jurnal Ekonomi, Manajemen, Akuntansi
Apriliana, Linda
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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PENGARUH FINANCIAL DISTRESS DAN LEVERAGE TERHADAP MANAJEMEN LABA PADA PERUSAHAAN MANUFAKTUR SUBSEKTOR FOOD AND BAVERAGE YANG TERDAFTAR DI BURSA EFEK INDONESIA Apriliana, Linda; Nisa, Lita Khoirun; Prastika, Rikke
Jurnal Profiet Vol 5 No 1 (2024): Jurnal Profiet
Publisher : STIE Perbankan Indonesia

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Abstract

This study aims to examine the influence of financial distress and leverage on earnings management in food and beverage manufacturing companies listed on the Indonesia Stock Exchange for the period 2019-2023. Using a quantitative approach, this research analyzed a sample of 10 companies selected through purposive sampling. The research employed multiple linear regression analysis to test the hypotheses. The results show that financial distress has a significant negative effect on earnings management, contrary to the initial hypothesis. Leverage, however, does not show a significant influence on earnings management. When analyzed together, financial distress and leverage demonstrate a significant impact on earnings management, with the research model explaining 64.3% of the variation in earnings management practices. These findings provide new insights for investors, regulators, and other stakeholders in assessing the quality of company financial reports and open opportunities for further research on financial reporting dynamics under various financial conditions of companies in Indonesia. Keywords: Earnings Management, Financial Distress, Leverage
Analisis Sumber Dan Penggunaan Modal Kerja Dalam Meningkatkan Likuiditas Perusahaan : Studi Kasus Pada PT Yura Abadi Perkasa Apriliana, Linda; Luayyi, Sri; Fauziyah, Fauziyah
EKOMA : Jurnal Ekonomi, Manajemen, Akuntansi Vol. 4 No. 6: September 2025
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/ekoma.v4i6.11349

Abstract

Penelitian ini bertujuan untuk menganalisis sumber dan pemanfaatan modal kerja dalam meningkatkan likuiditas perusahaan, dengan menggunakan studi kasus pada PT Yura Abadi Perkasa, sebuah perusahaan pengembang properti perumahan. Penelitian ini berfokus pada periode tahun 2022 hingga 2024, dengan menerapkan pendekatan deskriptif kuantitatif berdasarkan data yang diperoleh dari laporan keuangan tahunan perusahaan. Temuan menunjukkan bahwa likuiditas perusahaan mengalami fluktuasi dengan tren menurun, sebagaimana tercermin dari penurunan rasio lancar dari 1,61 pada tahun 2022 menjadi 1,36 pada tahun 2024. Meskipun masih dalam kisaran yang wajar untuk sektor properti (1,2 hingga 2,0), yang menunjukkan perusahaan dapat memenuhi kewajiban jangka pendeknya, rasio kas masih sangat rendah, berkisar antara 0,01 hingga 0,03 selama tiga tahun. Ini jauh di bawah kisaran yang dapat diterima industri sebesar 0,1 hingga 0,3, yang menunjukkan bahwa kemampuan perusahaan untuk menyelesaikan kewajiban secara langsung melalui kas belum optimal. Oleh karena itu, pengelolaan kas sebagai bagian dari modal kerja menjadi fokus penting dalam meningkatkan likuiditas jangka pendek perusahaan.
Forecasting Ferry Passenger Traffic in New York City Using the Seasonal Arima (SARIMA) Model Aribah, Rana; Apriliana, Linda; Darmawan, Gumgum
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.54879

Abstract

This study addresses the seasonal and long-term fluctuating passenger volume patterns typical of water transportation systems such as NYC Ferry, necessitating practical forecasting methods to support operational decision-making and public transportation planning. The research aims to develop a forecasting model for NYC Ferry passenger counts using the Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology. The analysis utilizes monthly historical passenger data from January 2020 to December 2024 for training data. Key analytical steps include testing data stationarity, splitting the dataset into training and testing subsets, modeling via RStudio, forecasting, and evaluating model accuracy using Mean Absolute Percentage Error (MAPE) compared against actual observations. Results indicate that the SARIMA(1,0,0)(0,1,1)12 model outperforms other methods, yielding the lowest MAPE of 5.04%, compared to Multiplicative Winters (8.57%), SARFIMA (17.62%), and Holt-Winters (32.93%). The SARIMA model effectively captures both seasonal and monthly trends, producing accurate passenger volume predictions. These findings demonstrate SARIMA’s efficacy in monthly NYC Ferry ridership forecasting, contributing to time series literature, particularly within public transportation forecasting. Furthermore, the results offer practical insights for policymakers to strategize service capacity and enhance data-driven management of waterborne transit systems more efficiently.
Co-Authors Aribah, Rana Fauziyah Fauziyah Gumgum Darmawan Nisa, Lita Khoirun Prastika, Rikke Sri Luayyi