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Implementasi Regresi-PLS Dalam Analisis Financial Distress : (Studi Pada Perusahaan Sub Sektor F&B Di BEI) Susanti, Sussy; Mulyani Azis, Yunia
Bisman (Bisnis dan Manajemen): The Journal of Business and Management Vol. 7 No. 1 (2024): Februari 2024
Publisher : Program Studi Manajemen, Fakultas Ekonomi, Universitas Islam Majapahit, Jawa Timur, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36815/bisman.v7i1.3215

Abstract

There are several factors that affect Financial Distress, namely financial factors and non-financial factors. For financial factors, indicators that can be used include financial ratios such as profitability, liquidity, leverage, and activity that can be seen in financial reports. The purpose of this paper is to analyse the factors that influence Financial Distress in companies listed on the IDX in 2022 in the food and beverage sub-sector using partial least square (PLS) regression analysis. There are only 28 companies that fit the criteria for complete financial reports for the B&F subsector in 2022, which according to the central limit postulate will not meet the assumption of data normality if using multiple linear regression analysis so that the use of the PLS method can overcome assumption violations and the results of data analysis obtained a coefficient of determination of 0.885, which means that the contribution of the five predictor variables can explain the variance in the response variable, namely Financial Distress by 88.5 percent. The predictor variables that have the highest influence on the response variable are Return on Asset (ROA) and Debt to Asset (DTA) with VIP values of 1.345 and 1.226, respectively. By using PLS, violations in the fulfilment of classical linear regression assumptions can be overcome and the model can be used for the purpose of predicting financial distress.
Internet of things-based fuzzy controller for automatic irrigation and NPK nutrient monitoring of grapes Sarosa, Moechammad; Wirayoga, Septriandi; Kusumawardani, Mila; Firmanda Al Riza, Dimas; Mulyani Azis, Yunia
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9715

Abstract

Grape cultivation has gained increasing attention due to its short growing period and the high market value of its sweet, refreshing fruits. However, achieving optimal growth requires precise environmental and nutrient management, which can be challenging under conventional farming practices. This research aims to develop an automatic watering system that integrates soil moisture and nutrient monitoring to optimize grape cultivation. The system utilizes Nitrogen Phosphorus Potassium (NPK) sensors, soil moisture sensors, and a camera for growth observation, all connected through the internet of things (IoT) for remote monitoring via Android devices. A fuzzy logic controller is implemented to regulate watering duration based on environmental conditions such as temperature and humidity. Experimental results show that the system effectively adjusts watering duration to approximately six seconds when the temperature is between 25–32 °C and humidity is around 60%. The DS18B20 temperature sensor achieved an average error rate of only 0.12%, while the humidity sensor demonstrated 0.2% error, indicating high accuracy levels of 99.8%. Despite minor limitations related to internet stability and sensor calibration, the system demonstrates strong potential for commercial-scale smart farming applications, promoting resource-efficient and data-driven grape cultivation.