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Diponegoro University

EducationSemarang, Indonesia
About: Diponegoro University is a education organization based out in Semarang, Indonesia. It is known for research contribution in the topics: Population & Stock exchange. The organization has 17704 authors who have published 18601 publications receiving 56365 citations. The organization is also known as: Universitas Diponegoro & University of Diponegoro.

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Journal ArticleDOI
TL;DR: In this article, the authors compared different macromolecular additives, such as polyvinylpyrrolidone (PVP), poly(ethylene glycol) (PEG), polyethylene oxide (PE), polypropylene oxide (OPO), bovine serum albumin (Bovine albumin), and poly(polyethylene polyoxide) (Pluronic®, Plu), in order to determine the additive that should be preferred.

465 citations

Journal ArticleDOI
TL;DR: Gelatin films incorporated with bergamot (BO) and lemongrass oil (LO) at various concentrations as glycerol substitute were prepared and characterised.

446 citations

Journal ArticleDOI
TL;DR: In this paper, the authors used line-intercept transect surveys on 15 reefs in three regions of Indonesia to estimate the relative decrease in within-habitat coral species diversity associated with different types of reef degradation.

383 citations

Journal ArticleDOI
TL;DR: RVM outperforms SVM based battery health prognostics and SampEn and estimated state of charge (SOH) are employed as data input and target vector of learning algorithms, respectively.
Abstract: In this paper, an intelligent prognostic for battery health based on sample entropy (SampEn) feature of discharge voltage is proposed. SampEn can provide computational means for assessing the predictability of a time series and also can quantity the regularity of a data sequence. Therefore, when it is applied to discharge voltage battery data, it could serve an indicator for battery health. In this work, the intelligent ability is introduced by utilizing machine learning methods namely support vector machine (SVM) and relevance vector machine (RVM). SampEn and estimated state of charge (SOH) are employed as data input and target vector of learning algorithms, respectively. The results show that the proposed method is plausible due to the good performance of SVM and RVM in SOH prediction. In our study, RVM outperforms SVM based battery health prognostics.

323 citations

Journal ArticleDOI
TL;DR: Coral growth and reef growth were decoupled, in that coral growth rates did not reliably predict rates of reef accretion, illustrating the need for a whole-reef perspective on coral reef health.

306 citations


Showing all 17790 results

Nor Aishah Saidina Amin5627710672
Richard A Williams5438112263
Michael J. Risk521337027
Iis P. Tussyadiah361115096
Willy M. Nillesen34684880
Peter Gell331213956
Evan N. Edinger31793550
Abdul Rohman283994031
Mark V. Erdmann271103074
Dirk J. Schipper262162244
Agus Purwanto232022083
Abdullah Abdullah222021797
Sultana M.H. Faradz221191684
Agus Purwanto222792466
Zainal Arifin211601327
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No. of papers from the Institution in previous years