scholarly journals ANNs-Based Early Warning System for Indonesian Islamic Banks

2018 ◽  
Vol 20 (3) ◽  
pp. 325-342 ◽  
Author(s):  
Saiful Anwar ◽  
A.M Hasan Ali
Keyword(s):  
Financial Performance ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Financial Conditions ◽  
Proposed Model ◽  
Selection Of Variables ◽  
Sample Data ◽  
Out Of Sample ◽  
Macroeconomic Indicators

This research proposes a development of Early Warning System (EWS) model towards the financial performance of Islamic bank using financial ratios and macroeconomic indicators. The result of this paper is ready-to-use algorithm for the issue that needs to be solved shortly using machine learning technique which is not widely applied in Islamic banking. The research was conducted in three stages using Artificial Neural Networks (ANNs) technique: the selection of variables that significantly affect financial performance, developing an algorithm as a predictor and testing the predictor algorithm using out of sample data. Finally, the research concludes that the proposed model results in 100% accuracy for predicting Islamic bank’s financial conditions for the next two consecutive months.

scholarly journals Perbandingan Kinerja Keuangan Perusahaan Asuransi Syariah Berdasarkan Early Warning System dan Risk-Based Capital pada PT Asuransi Jiwa Syariah Jasa Mitra Abadi Tbk Sebelum dan Sesudah Go Public

2020 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
Hizrina Awaliyah ◽  
Benny Barnas
Keyword(s):  
Financial Performance ◽  
Early Warning ◽  
Early Warning System ◽  
Insurance Industry ◽  
Descriptive Analysis ◽  
Warning System ◽  
Going Public ◽  
Financial Ratio ◽  
Financial Ratio Analysis ◽  
Before And After

The sharia insurance industry in 2019 experienced a slowdown, as seen from the indicators of assets, contributions and gross claims. In the sharia insurance industry, the only company that has gone public is PT Asuransi Jiwa Syariah Jasa Mitra Abadi Tbk. This study aims to determine how the company's financial performance is, and to find out whether there are differences in the company's financial performance before and after going public for the 2015-2019 period. The research method used is financial ratio analysis using the Early Warning System (EWS) and Risk Based Capital (RBC) methods. The data analysis method used is a comparative descriptive analysis with a quantitative approach. The results showed that the company's financial performance after going public did not improve significantly.

scholarly journals An Affordable Fast Early Warning System for Edge Computing in Assembly Line

2018 ◽  
Vol 9 (1) ◽  
pp. 84 ◽  
Author(s):  
Muhammad Syafrudin ◽  
Norma Fitriyani ◽  
Ganjar Alfian ◽  
Jongtae Rhee
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Early Warning System ◽  
Assembly Line ◽  
Warning System ◽  
Edge Computing ◽  
Environmental Data ◽  
Proposed Model ◽  
Smart Factories ◽  
Abnormal Events

Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.

scholarly journals ViEWS2020: Revising and evaluating the ViEWS political Violence Early-Warning System

2021 ◽  
pp. 002234332096215
Author(s):  
Håvard Hegre ◽  
Curtis Bell ◽  
Michael Colaresi ◽  
Mihai Croicu ◽  
Frederick Hoyles ◽  
...  
Keyword(s):  
Political Violence ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Retrospective Evaluation ◽  
Distant Future ◽  
Evaluation Procedures ◽  
Out Of Sample ◽  
Overall Performance ◽  
New Infrastructure

This article presents an update to the ViEWS political Violence Early-Warning System. This update introduces (1) a new infrastructure for training, evaluating, and weighting models that allows us to more optimally combine constituent models into ensembles, and (2) a number of new forecasting models that contribute to improve overall performance, in particular with respect to effectively classifying high- and low-risk cases. Our improved evaluation procedures allow us to develop models that specialize in either the immediate or the more distant future. We also present a formal, ‘retrospective’ evaluation of how well ViEWS has done since we started publishing our forecasts from July 2018 up to December 2019. Our metrics show that ViEWS is performing well when compared to previous out-of-sample forecasts for the 2015–17 period. Finally, we present our new forecasts for the January 2020–December 2022 period. We continue to predict a near-constant situation of conflict in Nigeria, Somalia, and DRC, but see some signs of decreased risk in Cameroon and Mozambique.

scholarly journals PENGEMBANGAN EARLY WARNING SYSTEM UNTUK DELISTING SAHAM SYARIAH MENGGUNAKAN SUPPORT VECTOR MACHINE (SVMs)

2020 ◽  
Vol 5 (02) ◽  
pp. 198-208
Author(s):  
Zulnani Tinggi ◽  
Sakum
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sampling Technique ◽  
Stock Index ◽  
Support Vector ◽  
Vector Machines ◽  
Sample Data ◽  
Turn Over

This study aim to produce Early Warning System in predicting the occurrence of delisting in Islamic stocks by using Support Vector Machines (SVM). The sample used in this research are companies listed on the Indonesian Syariah Stock Index (ISSI) for the period of 2012 - 2018. With the variables used in this research: Turn Over Asset, Long Term Debt, Interest Coverage, Debt to Equity, Quick Ratio, ROA, ROE Leverage, Current Ratio, ROIC. The population of this study is 335 Islamic stocks registered with ISSI. There are 102 companies which consists of listed and delisted companies from sharia shares as comparison for the sample data. The Method applied in this study is Purposive Sampling for The sampling technique. From the result found that accuracy rate of the best SVM models is SVM 4 models with 100% accuracy

MODIFIKASI ASESMEN EARLY WARNING SYSTEM UPAYA PENINGKATAN PENERAPAN KESELAMATAN PASIEN

2020 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Veronika Hutabarat ◽  
Enie Novieastari ◽  
Satinah Satinah
Keyword(s):  
Action Research ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Planning Action

Salah satu faktor dalam meningkatkan penerapan keselamatan pasien adalah ketersediaan dan efektifitas prasarana dalam rumah sakit. Early warning system (EWS) merupakan prasarana dalam mendeteksi perubahan dini  kondisi pasien. Penatalaksanaan EWS masih kurang efektif karena parameter dan nilai rentang scorenya belum sesuai dengan kondisi pasien. Tujuan penulisan untuk mengidentifikasi efektifitas EWS dalam penerapan keselamatan pasien. Metode penulisan action research melalui proses diagnosa, planning action, intervensi, evaluasi dan  refleksi. Responden dalam penelitian ini adalah  perawat yang bertugas di area respirasi dan pasien dengan kasus kompleks respirasi di Rumah Sakit Pusat Rujukan Pernapasan Persahabatan Jakarta. Analisis masalah dilakukan dengan menggunakan diagram fishbone. Masalah yang muncul belum optimalnya implementasi early warning system dalam penerapan keselamatan pasien. Hasilnya 100% perawat mengatakan REWS membantu mendeteksi kondisi pasien, 97,4 % perawat mengatakan lebih efektif dan 92,3 % perawat mengatakan lebih efesien mendeteksi perubahan kondisi pasien. Modifikasi EWS menjadi REWS lebih efektif dan efesien dilakukan karena disesuaikan dengan jenis dan kekhususan Rumah Sakit dan berdampak terhadap kualitas asuhan keperawatan dalam menerapkan keselamatan pasien. Rekomendasi perlu dilakukan monitoring evaluasi terhadap implementasi t.erhadap implementasi REWS dan pengembangan aplikasi berbasis tehnologi

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