Sebelas Maret University

About: Sebelas Maret University is a education organization based out in Surakarta, Indonesia. It is known for research contribution in the topics: Population & Public health. The organization has 10901 authors who have published 10832 publications receiving 33057 citations. The organization is also known as: Universitas Negeri Surakarta & Universitas Sebelas Maret.

Manajemen laba di indonesia: studi sebuah bibliograpi

Abstract: This research tries to describe the development of earnings management research in Indonesia which is from 6 accreditation journals in Indonesia which representing 3 biggest provinces in Java island. Samples obtained from 1999 to 2016 obtained 67 articles discussing earnings management. We classify this article based on the research topics, methods and models used by the researchers. The results obtained are most of the articles on earnings management in Indonesia published in Jurnal Akuntansi & Keuangan Indonesia (JAKI) and Jurnal Akuntansi dan Keuangan(JAK). Antecedent topics of earnings management is a topic that is widely studied. Analytical method and accrual modified Jones model is the most popular model used because this model is considered the most appropriate to measure the accrual of earnings management compared to other models.

Preservice English teachers’ perceptions of English as an international language in Indonesia and Korea

Abstract: Preservice English teachers’ perceptions about English as an international language (EIL) have not been adequately examined in different cross-cultural contexts. To fill that void, a total of 590 p...

Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis

Abstract: Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system for diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value for accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80% , accuracy 86,30% and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors, symptoms and exercise ECG. The conclusion that can be drawn is that the proposed diagnosis system capable of delivering performance in the very good category, with a number of attributes that are not a lot of checks and a relatively low cost .