Tenaga Nasional Berhad (Malaysia)

DeLone and This paper critically examines the meaning of four of these constructs and the evidence of relationships between them. It then provides results from empirical tests of these relationships. Tests are conducted using both conventional ordinary least squares regression path analysis and structural equation modeling -with substantially similar results. The empirical results provide substantial support for the "up stream" two thirds of DeLone and McLean's model. Three factors. System Quality, Information Quality, and Usefulness, are found to explain 75% of the variance in the overall User Satisfaction measure. The empirical results also provide substantial support for the use of Usefulness as an IS Success measure, and of the hitherto-unreported importance of "Importance of the task" in user perceptions of IS Usefulness.

Electricity consumer dishonesty is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. This paper presents a new approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) Sdn. Bhd. in peninsular Malaysia to reduce its NTLs in the distribution sector due to abnormalities and fraud activities, i.e., electricity theft. The fraud detection model (FDM) developed in this research study preselects suspected customers to be inspected onsite fraud based on irregularities in consumption behavior. This approach provides a method of data mining, which involves feature extraction from historical customer consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. The result yields customer classes which are used to shortlist potential suspects for onsite inspection based on significant behavior that emerges due to fraud activities. Model testing is performed using historical kWh consumption data for three towns within peninsular Malaysia. Feedback from TNB Distribution (TNBD) Sdn. Bhd. for onsite inspection indicates that the proposed method is more effective compared to the current actions taken by them. With the implementation of this new fraud detection system TNBD's detection hitrate will increase from 3% to 60%.

The environmental impacts and high long-term costs of poor waste disposal have pushed the industry to realize the potential of turning this problem into an economic and sustainable initiative. Anaerobic digestion and the production of biogas can provide an efficient means of meeting several objectives concerning energy, environmental, and waste management policy. Biogas contains methane (60%) and carbon dioxide (40%) as its principal constituent. Excluding methane, other gasses contained in biogas are considered as contaminants. Removal of these impurities, especially carbon dioxide, will increase the biogas quality for further use. Integrating biological processes into the bio-refinery that effectively consume carbon dioxide will become increasingly important. Such process integration could significantly improve the sustainability of the overall bio-refinery process. The biogas upgrading by utilization of carbon dioxide rather than removal of it is a suitable strategy in this direction. The present work is a critical review that summarizes state-of-the-art technologies for biogas upgrading with particular attention to the emerging biological methanation processes. It also discusses the future perspectives for overcoming the challenges associated with upgradation. While biogas offers a good substitution for fossil fuels, it still not a perfect solution for global greenhouse gas emissions and further research still needs to be conducted.

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

The ever increasing trend of renewable energy sources (RES) into the power system has increased the uncertainty in the operation and control of power system. The vulnerability of RES towards the unforeseeable variation of meteorological conditions demands additional resources to support. In such instance, energy storage systems (ESS) are inevitable as they are one among the various resources to support RES penetration. However, ESS has limited ability to fulfil all the requirements of a certain application. So, hybridization of multiple ESS to form a composite ESS is a potential solution. While integrating these different ESS, their power sharing control plays a crucial role to exploit the complementary characteristics of each other. Therefore, this article attempts to bring the numerous control strategies proposed in the literature at one place. Various control techniques implemented for HESS are critically reviewed and the notable observations are tabulated for better insights. Furthermore, the control techniques are classified into broad categories and they are briefly discussed with their limitations. From the carried-out analysis, the challenges faced towards the implementation of HESS for standalone and grid connected microgrid systems are presented. Finally, the future directions are laid out for the researchers to carry out the research and implementation of HESS technologies. Overall, this article would serve as a thorough guide on various control techniques implemented for HESS including their features, limitations and real-time applications.

The explosive popularity of small-cell and Internet of Everything devices has tremendously increased traffic loads. This increase has revolutionised the current network into 5G technology, which demands increased capacity, high data rate and ultra-low latency. Two of the research focus areas for meeting these demands are exploring the spectrum resource and maximising the utilisation of its bands. However, the scarcity of the spectrum resource creates a serious challenge in achieving an efficient management scheme. This work aims to conduct an in-depth survey on recent spectrum sharing (SS) technologies towards 5G development and recent 5G-enabling technologies. SS techniques are classified, and SS surveys and related studies on SS techniques relevant to 5G networks are reviewed. The surveys and studies are categorised into one of the main SS techniques on the basis of network architecture, spectrum allocation behaviour and spectrum access method. Moreover, a detailed survey on cognitive radio (CR) technology in SS related to 5G implementation is performed. For a complete survey, discussions are conducted on the issues and challenges in the current implementation of SS and CR, and the means to support efficient 5G advancement are provided.

Rainfall plays a main role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. In this study, several models and methods were applied to predict the rainfall data in Tasik Kenyir, Terengganu. The comparative study was conducted focusing on developing and comparing several Machine Learning (ML) models, evaluating different scenarios and time horizon, and forecasting rainfall using two types of methods. Data involved for this research consist of taking the average rainfall from 10 stations around the study area using Thiessen polygon to weight the station area and projected rainfall. The forecasting model uses four different ML algorithms, which are Bayesian Linear Regression (BLR), Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR) and Neural Network Regression (NNR). On the other hand, the rainfall was predicted on different time horizon by using different ML’s algorithms which is method 1 (M1): Forecasting Rainfall Using Autocorrelation Function (ACF) and method 2 (M2): Forecasting Rainfall Using Projected Error. In M1, the best regression developed for ACF is BDTR since it has the highest coefficient of determination, R2, after tuning the hyperparameter. The results show coefficient between 0.5 and 0.9 with the highest of each scenarios for daily (0.9739693), weekly (0.989461), 10-days (0.9894429) and monthly (0.9998085). In M2, overall model performances show that normalization using LogNormal is preferably giving a good result of each categories except for 10-days with BDTR and DFR are the most acceptable result than NNR and BLR. It is concluded that, two different methods have been applied with different scenarios and different time horizons, and M1 shows a rather high accuracy than M2 using BDTR modeling.

Activated carbon was produced from palm kernel shell (PKS) and coconut shell (CS) through physical steam activation and chemical activation. The optimum activation temperature for physical activation is 800oC and for chemical activation is 550oC. The activated carbons produced also loaded with different metal oxides (BaO, MgO, CuO, TiO2 and CeO2). Both loaded and unloaded activated carbons are characterized using ultimate analysis, BET surface area measurement method, fourier transform infrared (FT-IR) analysis and x-ray diffraction (XRD) analysis. The BET surface area and pore volumes showed that the activated carbons prepared from PKS by chemical activation (PCAC) and from CS by physical activation (CPAC) were found to be much higher than others activated carbons. Hence, the metal oxides were loaded on these two activated carbons. The loaded and unloaded activated carbons produced will be tested for application of CO2 adsorption from the simulated flue gas.

The Malaysian Government has set an ambitious target to achieve a higher penetration of Renewable Energy (RE) in the Malaysian energy mix. To date, Malaysia has approximately 2% of its energy coming from RE generation sources compared to the total generation mix and targets achieving 20% penetration by 2025. The current energy mix for Malaysia power generation is mainly provided by natural gas and coal. The discussion will cover the traditional sources of generation including natural gas, coal and big hydro stations. In addition, the paper will cover in depth the potential of RE in the country, challenges, and opportunities in this sector. This study can give an initial evaluation of the Malaysian energy industry, especially for RE and can initiate further research and development in this area in order to support the Government target to achieve RE target of 20% by 2025.

Fossil fuels have provided humans with an enormous and stable primary energy source to rapidly develop advanced technologies, increasingly efficient machines and industries, as well as greater varieties of consumer products. While humans enjoy the conveniences of the modern world, critical global issues have also been created, such as an exponential hike in waste generation, rapid depletion of finite fossil fuels, greater environmental pollution, and climate change. Two major anthropogenic wastes are biomass waste and plastic waste. Each year, 464 million tonnes of plastic waste are generated globally, with only 20% recycled, 25% incinerated, while 55% landfilled. Likewise, 140 billion tonnes of biomass from the agriculture sector and 181.5 billion tonnes of lignocellulosic biomass from forestry and agricultural residues are produced worldwide annually, with only 40% and 4.5% biomass reuse respectively. This mountainous underutilised biomass and plastic wastes presented a good opportunity for recycling into biofuels instead of landfilling or open dumping, thus promoting circular bioeconomy. Pyrolysis stands out as a promising thermochemical route to synthesize biofuels, and co-pyrolysis of biomass and plastic benefits from synergistic interactions between both feedstocks, enhancing the yield and quality of biofuels. Therefore, in this review, the synergistic mechanism of biomass and plastic co-pyrolysis is described, and recent advances in this field are comprehensively presented. The importance of applying circular bioeconomy frameworks on biomass and plastic wastes are also highlighted.

Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized.

A microgrid (MG) is a small-scale power system with a cluster of loads and distributed generators operating together through energy management software and devices that act as a single controllable entity with respect to the grid. MG has become a key research element in smart grid and distribution power systems. MG mainly contains different renewable energy sources (RESs) that use various technological advancements, such as power electronics-based technologies. However, it has an unstable output, thereby causing different types of power quality (PQ) events. As a result, standards and mitigation methods have been developed in recent years. To mitigate PQ issues due to MG integration, various methods and standards have been proposed over the last years. Although these individual methods are well documented, a comparative overview had not been introduced so far. Thus, this study aims to fill the gap by reviewing and comparing the prior-art PQ issues, solutions, and standards in MGs. We compare the main issues related to voltage sag, voltage swell, voltage and current harmonics, system unbalances, and fluctuations to ensure high-quality MG output power. The new technologies associated with MGs generate harmonics emission in the range of 2-150 kHz, thereby causing a new phenomenon, namely, supraharmonics (SH) emission, which is not sufficiently covered in the literature. Therefore, the characteristics, causes, consequences, and measurements of SH are highlighted and analyzed. The mitigation strategies, control, and devices of PQ issues are also discussed. Moreover, a comparison is conducted between the most popular devices used to mitigate the PQ issues in MG in terms of cost, rating, and different aspects of performance. This review study can strengthen the efforts toward the mitigation and standards development of PQ issues in MG applications, especially SH. Finally, some recommendations and suggestions to improve PQ of MG, including SH, are highlighted.

Nowadays, advanced communication technologies are being utilized to develop intelligent transportation management and driving assistance. Through the ability to exchange traffic and infotainment information between road infrastructure and vehicles, vehicular ad-hoc networks (VANETs) promise to improve transport efficiency, accident prevention, and pedestrians comfort. The deployment of VANETs in the real world is based on the message’s correctness and timely delivery and assuredness of privacy protection and data security. In this regard, many researchers have conducted surveys and studies that present models and solutions related to the improvement of VANET from different aspects such as architectural design, networking, and data security. Motivated by these influences, this study presents a detailed survey of VANETs to provide a complete picture of particular VANET applications, networking, and challenges. None of them collected all data in one survey. VANET communication techniques and their improvements are the focus of this study. The contributions of this paper are as follows. First, a complete taxonomy of VANET wireless access techniques has been provided based on various parameters. Second, a detailed discussion and classification VANET services and applications are provided. Third, the challenges related to VANET according to the applicability area, data networking, and resource management are explored in detail. Based on this classification, a complete description of the challenges for each category, including the proposed solutions and development models, is provided to overcome such challenges. Finally, the integration of evolutionary technologies with VANET is comprehensively presented. In this regard, a thorough explanation is provided for each technology, including the challenges, solutions, and suggestions for further improvements. This study enables various users working in the vehicular networking domain to select one of the proposals based on its relative advantages.

Many countries around the world are planning to reach 100% renewable energy use by 2050. In this context and due to the recent sharp increase in RE utilization in the global energy mix along with its progressive impact on the world energy sector, the evaluation and investigation of its effect on achieving sustainable development goals are not covered sufficiently. Moreover, an assessment of the emerging role of artificial intelligence for renewable energy utilization toward achieving SDGs is conducted. A total of 17 SDGs were divided into three groups, namely, environment, society, and economy, as per the three key pillars of sustainable development. Renewable energy has a positive impact toward achieving 75 targets across all sustainable development goals by using an expert elicitation method-based consensus. However, it may negatively affect the accomplishment of the 27 targets. In addition, artificial intelligence can help renewable energy enable the attainment of 42 out of 169 targets. However, with the current exponential growth of renewable energy share and artificial intelligence development and addressing certain present limitations, this impact may cover additional targets in the future. Nevertheless, recent research foci overlook essential aspects. The exponential growth of renewable energy share and rapid evolution of artificial intelligence need to be accompanied through the requisite regulatory insight and technology regulation to cover additional targets in the future.

In recent years, serious reduction in petroleum resources and concerns about the usage of synthetic plastics have prompted global communities to accept the use of natural fibres and biopolymers in many products. Lignocellulosic fibre polymer biocomposites have attracted the attention of scientists and engineers because of their wide availability, low carbon emission and biodegradability. Currently, oil palm is one of the main crops cultivated in Malaysia and Indonesia and is regarded as a potential source of lignocellulosic fibres for biocomposites. The cellulosic content of oil palm fibres (OPFs) enhances the mechanical properties of composites. Ensuring the compatibility of OPFs as main constituents with other materials in composites for a specific applications is essential. Mechanical performance in terms of tensile, flexural and impact strength determines an OPF's compatibility. However, no comprehensive reviews focusing on the mechanical properties of OPF biocomposites have been published, and factors influencing mechanical performance, such as interfacial adhesion, stacking sequence, additive, type of polymer and fibre size have not been explored. Some studies have identified the research gaps and deduced that the potential applications of OPFs as reinforcement materials in composites have not been explored.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Anak usia dini merupakan usia emas. Pada usia ini diperhatikan tugas perkembangannya. Media pembelajaran akan membantu keefektifan proses pembelajaran dalam penyampaian pesan dan isi pelajaran. Terkadang guru mengabaikan dalam penggunaan media, padahal dengan menggunakan media pembelajaran khususnya media audio visual. Bertujuan untuk motivasi belajar anak sehingga mudah penangkapan isinya oleh anak. Langkah dalam pembelajaran menggunakan media audio visual, mempersiapkan laptop, sound, kabel dan video yang akan ditayangkan, memperhatikan posisi duduk peserta didik dalam keadaan nyaman dan pada saat akan mengajak peserta didik menyimak video, guru menyampaikan tujuan pembelajaran dan teknis pembelajaran, kemudian peserta didik siap menyaksikan tayangan video dan diberikan tindak lanjut berupa pertanyaan berkaitan dengan isi video.

Abstract Radio frequency (RF) magnetron sputtering was used to deposit tungsten disulfide (WS 2 ) thin films on top of soda lime glass substrates. The deposition power of RF magnetron sputtering varied at 50, 100, 150, 200, and 250 W to investigate the impact on film characteristics and determine the optimized conditions for suitable application in thin-film solar cells. Morphological, structural, and opto-electronic properties of as-grown films were investigated and analyzed for different deposition powers. All the WS 2 films exhibited granular morphology and consisted of a rhombohedral phase with a strong preferential orientation toward the (101) crystal plane. Polycrystalline ultra-thin WS 2 films with bandgap of 2.2 eV, carrier concentration of 1.01 × 10 19 cm −3 , and resistivity of 0.135 Ω-cm were successfully achieved at RF deposition power of 200 W. The optimized WS 2 thin film was successfully incorporated as a window layer for the first time in CdTe/WS 2 solar cell. Initial investigations revealed that the newly incorporated WS 2 window layer in CdTe solar cell demonstrated photovoltaic conversion efficiency of 1.2% with V oc of 379 mV, J sc of 11.5 mA/cm 2 , and FF of 27.1%. This study paves the way for WS 2 thin film as a potential window layer to be used in thin-film solar cells.

Abstrak: Penelitian ini bertujuan untuk mengembangkan media pembelajaran video animasi yang layak dan efektif pada materi pahlawan pergerakan nasional untuk meningkatkan motivasi belajar dan karakter tanggung jawab siswa kelas V Sekolah Dasar Gugus 02 Kecamatan Srandakan. Penelitian ini merupakan penelitian dan pengembangan. Subjek pada penelitian ini adalah 27 siswa kelas V SD Proketen sebagai kelas kontrol, 33 siswa SD 1 Godegan sebagai kelas eksperimen dan 15 siswa SD Talkondo sebagai kelas uji coba. Data dianalisis menggunakan analisis deskriptif kuantitatif dan analisis perbedaan melalui uji Anova. Hasil penelitian menunjukkan bahwa media video animasi layak digunakan untuk pembelajaran pada materi pahlawan pergerakan nasional kelas V SD Gugus 02 Kecamatan Srandakan. Kelayakan media video animasi oleh ahli materi mendapat penilaian dengan kategori “Baik”, dan oleh ahli media mendapat penilaian “Sangat Baik”. Hasil uji kelayakan media video animasi oleh guru pada uji coba lapangan operasional pada kategori “Baik”. Hasil uji t pada motivasi belajar antara kelompok eksperimen dan kelompok kontrol menunjukkan nilai t=2,513 pada taraf signifikansi p= 0,015, (p0,05) dan karakter tanggung jawab antara kelompok eksperimen dan kelompok kontrol menunjukkan nilai t= 3,810 pada taraf signifikansi p= 0,000 (p0,05). Jadi, pembelajaran dengan menggunakan media video animasi efektif untuk meningkatkan motivasi belajar dan karakter tanggung jawab siswa. Kata Kunci: media video animasi, motivasi belajar, karakter tanggung jawab. DEVELOPING ANIMATED VIDEO MEDIA TO IMPROVE THE LEARNING MOTIVATION AND RESPONSSIBILITY CHARACTER OF THE FIFTH GRADE Abstract: This study aims to develop feasible and effective animated learning media on the material of national hero movement to improve the learning motivation and responssibility character of the students of Cluster 02 elementary schools in Srandakan. This study was research and development (R D). The subjects were 27 fifth grade students of SD Proketen as the control class, 33 students of SD 1 Godegan as the experimental class and 15 students of SD Talkondo as the trial class. The data were analyzed using the descriptive quantitative analysis and t test analysis of differences through Anova. The results show that the animated video media is feasible for teaching the national movement hero learning material to the fifth grade students of Cluster 02 elementary schools in Srandakan District. The results of the feasibility of the animated video media by subject matter experts gain "Good"category, and by media experts gain "Very Good" category. The result of the feasibility testing of the animated video media by teachers in the operational field test gain "Good" category. The result of the t test on the learning motivation between the experimental group and control group shows that the t value= 2.513 at the significance level of p= 0.015 (p0.05) and the character of responssibility between the experimental group and control group shows that the t value = 3.810 at the significance level of p = 0.000 (p0.05). So, teaching by using media is effective to improve the learning motivation and responssibility character of students.Keywords: animated video media, learning motivation, responssibility character.

Microalgae are one of the promising feedstock that consists of high carbohydrate content which can be converted into bioethanol. Pre-treatment is one of the critical steps required to release fermentable sugars to be used in the microbial fermentation process. In this study, the reducing sugar concentration of Chlorella species was investigated by pre-treating the biomass with dilute sulfuric acid and acetic acid at different concentrations 1%, 3%, 5%, 7%, and 9% (v/v). 3,5-Dinitrosalicylic acid (DNS) method, FTIR, and GC-FID were employed to evaluate the reducing sugar concentration, functional groups of alcohol bonds and concentration of bioethanol, respectively. The two-way ANOVA results (p < 0.05) indicated that there was a significant difference in the concentration and type of acids towards bioethanol production. The highest bioethanol yield obtained was 0.28 g ethanol/g microalgae which was found in microalgae sample pre-treated with 5% (v/v) sulfuric acid while 0.23 g ethanol/g microalgal biomass was presented in microalgae sample pre-treated with 5% (v/v) acetic acid. The application of acid pre-treatment on microalgae for bioethanol production will contribute to higher effectiveness and lower energy consumption compared to other pre-treatment methods. The findings from this study are essential for the commercial production of bioethanol from microalgae.