Diagnosis Penyakit Utama Pisang karena Jamur Patogen dengan Dempster-Shafer

Pisang merupakan sumber penting karbohidrat, vitamin dan mineral, dapat ditemui hampir di seluruh bagian wilayah Indonesia. Budidaya pisang menghadapi beberapa masalah penting, salah satu faktornya adalah serangan hama dan penyakit. Ketidaktahuan para pembudidaya tanaman buah pisang dan masih sedikitnya dilakukan diagnosis penyakit tanaman pisang, menyebabkan turunnya kualitas pisang dan dapat menjadi ancaman turunnya kuantitas produksi pisang. Teori Dempster-Shafer evidence memungkinkan seseorang untuk menggabungkan evidence dari berbagai sumber dan sampai pada fungsi kepercayaan dengan memperhitungkan semua evidence yang tersedia. Sehingga Dempster-Shafer diusulkan untuk diterapkan pada 32 data uji simulasi yang dilakukan secara acak. Kesesuaian hasil diagnosis simulasi perhitungan Dempster-Shafer dengan hasil diagnosis pakar ditunjukkan dengan nilai akurasi sebesar 93%. Perbedaan diagnosis penyakit dan hasil simulasi dengan Dempster-Shafer menjadi hal yang penting untuk dilakukan penelitian lanjutan

GENDER DIVERSITY DAN GOOD CORPORATE GOVERNANCE TERHADAP FINANCIAL DISTRESS

This study aims to examine and analyze the effect of gender diversity and good corporate governance on financial distress. The criteria for financial distress in this study are measured by negative net income for two consecutive years. Good corporate governance in this study uses elements including the board of commissioners, independent commissioners, and institutional ownership. This research refers to research by Ayuningtyas (2013) and Kristanti, et al. (2016). The population in this study were all manufacturing companies listed on the Indonesia Stock Exchange (IDX) during 2014-2016. The sample in this study obtained 380 companies selected based on purposive sampling method. The data analysis used in this study is logistic regression analysis with SPSS ver 20 software. The results of this study prove that the board of commissioners and gender diversity have a negative effect on financial distress. While the independent board of commissioners has no effect on financial distress and institutional ownership has a positive effect on financial distress.  Penelitian ini bertujuan untuk menguji dan menganalisis pengaruh gender diversity dan good corporate governance terhadap financial distress. Kriteria financial distress dalam penelitian ini diukur dengan laba bersih negatif selama dua tahun berturut-turut. Good gorporate governance dalam penelitian ini menggunakan elemen-elemen diantaranya dewan komisaris, dewan komisaris independen, dan kepemilikan institusional. Penelitian ini mengacu pada penelitian oleh Ayuningtyas (2013) dan Kristanti, Rahayu, & Huda (2016). Populasi dalam penelitian ini adalah seluruh perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia (BEI) selama tahun 2014-2016. Sampel dalam penelitian ini diperoleh sebanyak 380 perusahaan yang dipilih berdasarkan metode purposive sampling. Analisis data yang digunakan dalam penelitian ini adalah analisis regresi logistik dengan software SPSS ver 20. Hasil penelitian ini membuktikan bahwa dewan komisaris dan keragaman gender berpengaruh negatif terhadap financial distress. Sedangkan dewan komisaris independen tidak berpengaruh terhadap financial distress dan kepemilikan institusional berpengaruh positif terhadap financial distres

An overview of bankruptcy prediction models for corporate firms: a systematic literature review

Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.Peer Reviewe

Predicting financial distress: Applicability of O-score and logit model for Pakistani firms

Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders.The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 firms with 45 distressed and 245 healthy firms for the period 2006-2016 and covers all sectors of Pakistan Stock Exchange. The study provides important insights on the role of different financial ratio in predicting financial distress and shows that estimated logit model produces higher accuracy rate in predicting financial distress

Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework

An estimation of the default probabilities of Spanish non-financial corporations and their application to evaluate public policies

Este documento modeliza la probabilidad de impago a un año de las sociedades no financieras españolas utilizando información del período 1996-2019. Mientras que, en general, la literatura previa considera que una empresa está en situación de impago si solicita concurso de acreedores, aquí se define dicha situación como tener préstamos dudosos durante al menos tres meses en un mismo año. Esta definición más amplia permite predecir problemas financieros en una fase más temprana, antes de que estos sean demasiado graves y las empresas tengan que recurrir a procedimientos formales de insolvencia o a reestructuraciones privadas de deuda, lo que generalmente no puede ser observado por el investigador. En concreto, se estiman mediante regresiones logísticas tanto un modelo general que hace uso de todas las empresas de la muestra como seis modelos para diferentes combinaciones de tamaño y sector productivo. Las variables explicativas seleccionadas son cinco ratios financieras, que resumen la calidad crediticia de las empresas, y el crecimiento agregado del crédito a las sociedades no financieras para capturar el papel de la disponibilidad de crédito en mitigar el riesgo de impago. Finalmente, se llevan a cabo dos aplicaciones prácticas de estos modelos de predicción: se construyen matrices de transición de calificaciones crediticias y se evalúa el programa de ayudas directas del Gobierno español durante la crisis del COVID-19.We model the one-year ahead probability for default of Spanish non-financial corporations using data for the period 1996-2019. While most previous literature considers that a firm is in default if it files for bankruptcy, we define default as having non-performing loans during at least three months of a given year. This broader definition allows us to predict firms’ financial distress at an earlier stage that cannot generally be observed by researchers, before their financial conditions become too severe and they have to file for bankruptcy or engage in private workouts with their creditors. We estimate, by means of logistic regressions, both a general model that uses all the firms in the sample and six models for different size-sector combinations. The selected explanatory variables are five accounting ratios, which summarise firms’ creditworthiness, and the growth rate of aggregate credit to non-financial corporations, to take into account the role of credit availability in mitigating the risk of default. Finally, we carry out two applications of our prediction models: we construct credit rating transition matrices and evaluate a programme implemented by the Spanish government to provide direct aid to firms severely affected by the COVID-19 crisis

Risk prediction of product-harm events using rough sets and multiple classifier fusion:an experimental study of listed companies in China

With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm)

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy