scholarly journals IDENTIFIKASI POTENSI TINGGALAN ARKEOLOGI KLASIK DI KECAMATAN SAROLANGUN, JAMBI: PENDEKATAN PREDICTIVE MODELLING

2021 ◽  
Vol 15 (1) ◽  
pp. 59-70
Author(s):  
Nainunis Aulia Izza ◽  
Ari Mukti Wardoyo Adi ◽  
Nugrahadi Mahanani
Keyword(s):  
Predictive Modelling

Penelitian ini dilakukan atas dasar hipotesis tentang keberadaan tinggalan-tinggalan masa klasik yang berada di Daerah Aliran Sungai Batanghari. Kecamatan Sarolangun dipilih karena hingga kini belum pernah diteliti potensinya tentang tinggalan pemukiman arkeologi klasik. Tinggalan arkeologi klasik yang pernah dilaporkan hanyalah arca Ganesha yang saat ini disimpan di Museum Sultan Mahmud Badaruddin II, Palembang. Penelitian ini dilakukan dengan metode predictive modelling dengan menggunakan perangkat Sistem Informasi Geografis untuk dapat membantu memperkirakan titik-titik yang mengandung potensi tinggalan arkeologi. Variabel prediksi yang digunakan adalah laporan temuan, model lokasi situs, informasi masyarakat, serta potensi temuan permukaan. Hasil penelitian menunjukkan bahwa terdapat beberapa lokasi di Kecamatan Sarolangun yang memiliki sensitivitas tinggi terhadap tinggalan arkeologi klasik. Sensitivitas tinggalan arkeologi ini kemudian diturunkan dalam bentuk peta potensi. Tujuan utama dari pembuatan peta tersebut adalah agar dapat menentukan strategi riset lanjutan.

Sugar factory audits, debottlenecking and efficiency improvement

2020 ◽  
pp. 607-612
Author(s):  
Bernard Coûteaux
Keyword(s):  
Evaluation Method ◽  
Predictive Modelling ◽  
Cane Sugar ◽  
Sugar Factory ◽  
Low Energy Consumption ◽  
Process Simulations ◽  
Capital Efficiency ◽  
Potential Improvement ◽  
Primary Focus

This paper elaborates on the key solutions offered by De Smet Engineers & Contractors (DSEC) to optimize the efficiency of cane sugar producing and processing facilities. In order to meet customer needs, DSEC offers proprietary predictive models built using the latest versions of specialized software. These models allow factory managers to envision the whole picture of increased operational and capital efficiency before it becomes reality. An integrated energy model and the CAPEX/OPEX evaluation method are discussed as ways to estimate and optimize costs, both for new greenfield projects and revamping of existing factories. The models demonstrate that factory capacities can be successfully increased using equipment that is already available. Special attention is paid to crystallization and centrifugation process simulations and the potential improvement of the global energy balance. One case study shows the transformation of a beet sugar factory into a refinery to process raw cane sugar after beet crop season and the second case shows the integration of a refinery into a cane sugar factory. The primary focus of the article is optimization of the technological process through predictive modelling. DSEC’s suggested solutions, which lead to great improvements in a plant’s efficiency and its ability to obtain very low energy consumption, are discussed.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

Predictive modelling of cervical disc implant wear

2008 ◽  
Vol 41 (15) ◽  
pp. 3177-3183 ◽  
Author(s):  
C.U. de Jongh ◽  
A.H. Basson ◽  
C. Scheffer
Keyword(s):  
Predictive Modelling ◽  
Cervical Disc ◽  
Implant Wear

scholarly journals Big data and predictive modelling for the opioid crisis: existing research and future potential

2021 ◽  
Vol 3 (6) ◽  
pp. e397-e407
Author(s):  
Chrianna Bharat ◽  
Matthew Hickman ◽  
Sebastiano Barbieri ◽  
Louisa Degenhardt
Keyword(s):  
Big Data ◽  
Predictive Modelling ◽  
Opioid Crisis ◽  
Future Potential
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