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THE CONCEPT OF FATWA DSN-MUI ON WAQF INSURANCE BENEFITS AND INVESTMENT BENEFITS IN SHARIA LIFE INSURANCE: HISTORICAL STUDY
No full textIn general, Indonesian people still do not understand waqf management in Islam correctly and thoroughly. If Muslims examine the benefits of waqf more deeply, it can be ascertained that the benefits can be reaped in this world and the hereafter. The discourse of productive waqf and cash waqf is echoed in Indonesia by several Indonesian figures, thinkers, and scholars so if the main obstacles to waqf are not addressed, it will have an impact on misuse and fraud in waqf. The Indonesian Waqf Board (BWI) should cooperate with the DSN-MUI to issue this waqf fatwa. This article aims to review and analyze the benefits of waqf insurance and investment benefits in Sharia life insurance based on DSN-MUI Fatwa No. 106 of 2016. This research uses a qualitative method with a literature study approach. The results of this study indicate that the principle of this fatwa uses the legal basis of the Qur\u27an and Hadith by using the istislahi pattern, which is the consideration of benefits based on general nash. In this pattern, general verses are collected to give birthto several general rules that are used to protect or bring certain benefits. They are based on Maqashid Sharia, so that they can help optimize the waqf program in Indonesia. The issuance of the fatwa on waqf insurance benefits and investment benefits in Sharia life insurance is due to a request from an Islamic financial institution, namely Sun Life Financial Syariah and Al-Azhar Waqf Institution, which applied for the determination of the Sharia aspects of life insurance investment benefits for waqf products and waqf insurance policies. For this reason, this DSN-MUI fatwa can be said to be in accordance with the general principle that fatwas are responsive, proactive and anticipatory
MASLAHAH AND ITS APPLICATION IN ISLAMIC FINANCE
No full textIn light of the complexity of contemporary Islamic banking and finance difficulties and the fact that certain answers cannot be found in traditional legal texts, maslahah or the public interest has emerged as a secondary source of Shariah to address these difficulties and issues. Maslahah which embodies the notion of achieving the public interest and preventing harm, has been crucial to the development and evolution of products and services in Islamic finance. The purpose of this study is to analyze the concept of maslahah as the secondary source of Shariah whereby the definition, classification, validity of maslahah and the conditions of validity are discussed. The study also highlighted its applications in Islamic finance products and services besides conducting library research analysis on literature from both classical and contemporary maslahah literature as well as the current application of maslahah in Islamic finance products and services. The study explained maslahah mu’tabarah, maslahah mulgha, and maslahah mursalah as the three classifications of maslahah which are considered valid when they are genuine (haqiqiyyah), general (kulliyah) and not in conflict with clear Quranic verse according to the Muslim jurists. The study also found that the concept of maslahah is applied in the establishment of Islamic banking and finance institutions as well as the Islamic capital market whereby policies were developed by the government for the Islamic banking institutions to ensure the products are Shariah-compliant. Additional collateral is required, basedon the principles of usul fiqh, for the renewal of mudarabah transactions. On the other hand, ta’widh and gharamah are introduced by Islamic banks to safeguard the interests of both banks and the customers. Finally, zakat financing can help entrepreneurs repay their existing financial obligations
An Enhanced Ant Colony Optimisation Algorithm with the Hellinger Distance for Shariah-Compliant Securities Companies Bankruptcy Prediction
No full textThis study addresses the challenge of applying ant colony optimisation algorithms to imbalanced datasets, focusing on a bankruptcy dataset. The application of ant colony optimization (ACO) algorithms has been limited by their performance on imbalanced datasets, particularly within bankruptcy prediction where the some of bankruptcy cases lead to skewed data distributions. Traditional ACO algorithms, including the original Ant-Miner, often fail to accurately classify minority classes, which is a critical shortcoming in the context of financial distress analysis. Hence, this study proposes an improved algorithm, the Hellinger Distance Ant-Miner (HD-AntMiner), which employs Hellinger distance as the heuristic for ants to gauge the similarity or dissimilarity between probability distributions. The effectiveness of HD-AntMiner is benchmarked against established classifiers—PART and J48—as well as the conventional Ant-Miner, using public datasets and a specialized dataset of 759 Shariah-compliant securities companies in Malaysia. Utilising the Friedman test and F-score for validation, HD-AntMiner demonstrates superior performance in handling imbalanced datasets compared to other algorithms, as affirmed by the Friedman test. The F-score analysis highlights HD-AntMiner’s excellence, achieving the highest F-score for Breast-cancer and Credit-g datasets. When applied to the Shariah-compliant dataset, HD-AntMiner is compared with Ant-Miner and validated through a t-test and F-score. The t-test results confirm HD-AntMiner’s higher accuracy than Ant-Miner, while the F-score indicates superior performance across multiple years in the Shariah-compliant dataset. Although the number of rules and conditions is not statistically significant, HD-AntMiner emerges as a robust algorithm for enhancing classification accuracy in imbalanced datasets, particularly in the context of Shariah-compliant securities prediction
Hybrid Real-Value-Genetic-Algorithm and Extended-Nelder- Mead Algorithm for Short Term Energy Demand Prediction
No full textEnergy consumption planning of an area is very important. It is essential to accurately predict the amount of short-term power required by an area using a highly effective prediction technique. The real-value-genetics-algorithm (RVGA) is the most effective technique that is currently used. However, the RVGA has some drawbacks, including the fact that it gets caught in premature convergence even when the search is performed over long iterations. This study proposes a hybrid prediction algorithm which comprises the RVGA and the extended-Nelder-Mead (ENM) algorithm. The ENM was implemented to speed up the search for the best among all solutions produced by the RVGA. The RVGA was configured to run under small iterations, and the ENM was used to achieve convergence. Experiments were performed on historical datasets containing the monthly electricity demand of the Gorontalo area, a region in Indonesia. The performance of the hybrid algorithm was compared to the hybrid Genetic Algorithm-Particle Swarm Optimisation (GA-PSO) and Real Coded-Genetic Algorithm (RC-GA) energy demand models based on the mean-absolute-percentage-error (MAPE), mean-square-error (MSE), root-mean-square-error (RMSE), and mean-absolute-deviation (MAD) error rates. The results showed that the proposed hybrid algorithm’s MAPE, MSE, RMSE, and MAD errors were 2.95 percent, 0.13 percent, 0.36 percent and 1.29 percent, respectively. Based on the accuracy measure obtained from this study, it implies that the RVGAENM hybrid is the best model for forecasting monthly electricity demand
Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models
No full textOld Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.
Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering
No full textThe fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm utilises the properties of the Beta distribution to weight sample features, thus not only circumventing the time cost problem of the traditional ordered mechanism but also reducing the influence of noise. Experiments were conducted on six UCI datasets to validate the effectiveness of the BDFCOM, comparing its performance against seven other clustering algorithms using six evaluation indices. The results show that compared to the average of the other seven algorithms, BDFCOM improves about 15 percent on F1-score, 11 percent on Rand Index, 13 percent on Adjusted Rand Index, 3 percent on Fowlkes-Mallows Index and 16 percent on Jaccard Index. For the other two ordered mechanism FCM algorithms, the time consumption was also reduced by 90.15 percent on average. The proposed algorithm, which designs a new way of feature weighting for ordered mechanisms, advances the field of ordered mechanisms.And, this paper provides a new method in the application field where there is a lot of noise in the dataset.
Factors influencing fast food consumption among public university students: A case study at Universiti Utara Malaysia
No full textThe prevalence of fast food consumption among students is on the rise. Although there is a growing study investigating consumption of fast food among adolescents and adults in Malaysia, little attention has been paid to university students. This is perhaps the first study to examine factors influencing fast food consumption within a sample of students in Universiti Utara Malaysia (UUM). Primary data from a survey were used. An ordered logistic regression analysis was utilised to estimate the odds of consuming fast food. The explanatory variables consisted of demographic factors, peer influence, knowledge about fast food, lifestyle and mental health. Findings of the present study showed that most of the students in UUM consumed 1 to 2 times of fast food per week. Males tended to consume more fast food than females. Fast food consumption was lower among Chinese students than Malays, Indians and those of other ethnicities. Students who have excellent academic performance were less likely to consume fast food than those with poor academic performance. High personal income was associated with increased odds of consuming fast food. Students who were influenced by their peers were more likely to consume fast food compared to those who were not. Living a healthy lifestyle was associated with reduced odds of fast food consumption. These findings are important in the sense that they can assist UUM and government in developing more effective measures aimed at lowering students’ fast food intake. Intervention measures directed toward reducing fast food consumption among UUM students who are males, are Malays, have poor academic performance, have high income and adopt unhealthy lifestyles may yield promising outcomes
Assessing the mediating role of student engagement on the relationship between student interactions and student performance in entrepreneurship education
No full textPrevious literature suggested that more studies are needed in the context of technology mediated learning in entrepreneurship education. Hence, this study aims is to gauge the understanding of the factors influencing student performance for the Fundamentals of Entrepreneurship (ENT300) subject. This study is underpinned by the Social Learning Theory: Groups, Nets, and Sets. This study was conducted in the Universiti Teknologi MARA Perlis Branch (UiTM Perlis) using survey. 281 students had participated in this study. Data were analyzed using SmartPLS. Since the research model for the study was reflective-formative, the second order approach was used to assess the structural model. The results of this study reveal positive influence between Student Interactions and Student Engagement, as well as between Student Engagement and student performance variables (Satisfaction, Efficiency, and Effectiveness). Also, Student Engagement plays a significant role as a mediator between Student Interactions and student performance variables. This study contributes to the literature of the development of Social Learning Theory: Groups, Nets, and Sets. Finally, this study makes practical contributions to the higher learning institution that plans to use a technology mediated learning approach for large enrolment subject
URBAN WATER MANAGEMENT IN MALAYSIA: A REVIEW OF LEGISLATIVE FRAMEWORK
No full textThe presence of urban rivers poses significant challenges for local authorities in their efforts to create sustainable and habitable urbanriver environments. This situation has led to various repercussions for the health of urban river ecosystems. To mitigate these issues,it is imperative that development projects’ environmental impacts are effectively managed by the relevant authorities, ensuring thesustainability of urban development and urbanization. It is worth emphasizing that in environmentally sensitive areas, particularly when planning large projects, the consideration of environmental factors should be a top priority. As such, this discussion will focus on how local planning authorities in the country devise strategies to address the challenges associated with urban river development projects
DECENTRALIZED ELECTRONIC VOTING WITH ETHEREUM BLOCKCHAIN IN DEMOCRATIC AND POLITICAL ELECTIONS
No full textThe Ethereum blockchain-based electronic voting (e-voting) systems can emerge as a viable strategy in this era of contemporary democracies to revolutionize political elections and augment the efficacy of the electoral process. There are myriad advantages that the Ethereum blockchain has to offer, from fairness to increased voting rates. Unlike traditional voting protocols, the Ethereum blockchain can assure substantial cost savings and eliminate the necessity for electoral intermediaries. The use of the Ethereum blockchain in political contexts also ensures that elections are held with integrity while preserving the voters’ privacy. Due to its popularity, provision of smart contracts logic, and various promising advantages, this systematic review aims to examine the potential deployment of decentralized e-voting systems integrated with Ethereum blockchain technology for democratic political elections. A systematic literature review (SLR) and the PICO approach, which stands for population, intervention, control, and outcomes, were adopted in this study to systematically analyze the existing literature. Key technological approaches identified in the voting system include the hybrid blockchain and privacy-preserving score voting. Among the noteworthy findings are the following: while adoption and complexity remain challenges across numerous e-voting frameworks, scalability, end-to-end security, enhanced efficiency, and effectiveness are key benefits. An exploration into the prospective future innovations, such as the integration of artificial intelligence and big data analytics into the Ethereum blockchain, was also included to further improve the reliability of the e-voting systems. It is believed that the Ethereum blockchain has a promising transformative impact on electoral politics and democratic processes, presenting a ray of hope for future elections