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Sofia Najwa Ramli
Universiti Tun Hussein Onn Malaysia

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An extensive review of energy storage system for the residential renewable energy system M. S. A. Mustaza; M. A. M. Ariff; Sofia Najwa Ramli
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp242-250

Abstract

Energy storage system (ESS) plays a prominent role in renewable energy (RE) to overcome the intermittent of RE energy condition and improve energy utilization in the power system. However, ESS for residential applications requires specific and different configuration. Hence, this review paper aims to provide information for system builders to decide the best setup configuration of ESS for residential application. In this paper, the aim is to provide an insight into the critical elements of the energy storage technology for residential application. The update on ESS technology, battery chemistry, battery charging, and monitoring system and power inverter technology are reviewed. Then, the operation, the pro, and cons of each variant of these technologies are comprehensively studied. This paper suggested that the ESS for residential ESS requires NMC battery chemistry because it delivers an all-rounded performance as compared to other battery chemistries. The four-stages constant current (FCC) charging technique is recommended because of the fast charging capability and safer than other charging techniques reviewed. Next, the battery management system (BMS) is recommended to adapt in advance machine learning method to estimate the state of charge (SOC), state of health (SOH) and internal temperature (IT) to increase the safety and prolong the lifespan of the batteries. Finally, these recommendations and solutions aimed to improve the utilization of RE energy in power system, especially in residential ESS application and offer the best option that is available on the shelf for the residential ESS application in the future.
Video spam comment features selection using machine learning techniques Nabilah Alias; Cik Feresa Mohd Foozy; Sofia Najwa Ramli; Naqliyah Zainuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp1046-1053

Abstract

Nowadays, social media (e.g., YouTube and Facebook) provides connection and interaction between people by posting comments or videos. In fact, comments are a part of contents in a website that can attract spammer to spreading phishing, malware or advertising. Due to existing malicious users that can spread malware or phishing in the comments, this work proposes a technique used for video sharing spam comments feature detection. The first phase of the methodology used in this work is dataset collection. For this experiment, a dataset from UCI Machine Learning repository is used. In the next phase, the development of framework and experimentation. The dataset will be pre-processed using tokenization and lemmatization process. After that, the features to detect spam is selected and the experiments for classification were performed by using six classifiers which are Random Tree, Random Forest, Naïve Bayes, KStar, Decision Table, and Decision Stump. The result shows the highest accuracy is 90.57% and the lowest was 58.86%.
Co-Authors Cik Feresa Mohd Foozy M. A. M. Ariff M. S. A. Mustaza Nabilah Alias Naqliyah Zainuddin