Maqashid Sharia Implementation in Indonesia and Bahrain

This study aims to analyze the performance of Islamic banking in Indonesia and Bahrain from the perspective of the maqashid shariah index. Performance is the success of an organization in implementing its strategy so that its achievement on the targets set. Maqashid shariah is a measurement of Islamic banking performance following the objectives and characteristics of Islamic banking. The research method used is descriptive and comparative methods, while the data analysis technique used is the independent t-test. The results of this study are that there is no significant difference between the application of Islamic maqashid in Indonesia and the implementation of Islamic maqashid in Bahrain. Thus, the application of Islamic values and the application of sharia maqashid has been integrated with Islamic bank business activities so that different government policies or regulations because each country has a specific constitution, then the impact is not significant or does not affect the implementation of sharia maqashid on operational and business activities in Islamic banks.JEL Classification: M41, M48, Z12 How to Cite:Nugraha, E., Nugroho, L., Lindra, C. N., & Sukiati, W. (2020). Maqashid Sharia Implementation in Indonesia and Bahrain. Etikonomi: Jurnal Ekonomi, 19(1), 155 – 168. https://doi.org/10.15408/etk.v19i1.14655.

SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS

In line with the needs of the community and the progress of the times in the advanced field of fintech, cash payments are currently considered insecure as well as ineffective and efficient. To run a non-cash or cashless transaction program currently run by the government, PT. Pertamina invites the public to use E-Payment from the My Pertamina application in collaboration with LinkAja. In this study, the sentiments of MyPertamina application users will be analyzed based on reviews on the Google Play Store. Review data will be analyzed to determine whether the review has positive, negative, or neutral sentiments. The data analysis stage is text preprocessing to change uppercase to lowercase, clearing text, separating text, taking important words, changing essential words, and labeling data into positive, negative, and neutral classes. As well as the classification and evaluation of results. This study used the Support Vector Machine (SVM) and Naïve Bayes classification methods. To evaluate the results, the confusion matrix was used to test the accuracy, precision, recall, and F1 score value. The classification results obtained the highest accuracy value for the Support Vector Machine (SVM) method, which had accuracy (68.50%), precision (70.00%), recall (69.70%), and F1 score (68.46%). Meanwhile, the Naïve Bayes method has performance with accuracy (63.00%), precision (63.90%), recall (61.34%), and F1 score (59.55%)

A Comparative Study of Student Satisfaction Levels on Online Learning Using K-NN and Naïve Bayes

The outbreak of the Covid-19 pandemic in Indonesia led to restrictions on human social activities to minimize transmission. Teaching-learning is also affected when students must stay home and follow distance learning based on Government Regulation Number 21 of 2020, the Large-Scale Social Restrictions (PSBB) policy, issued on March 31, 2020. This has led to the emergence of learning support applications such as Zoom, Google Classroom, Google Meet, E-Learning, and many more. However, this new learning culture requires adaptation for effective implementation. During the adaptation process, researchers want to measure the level of student satisfaction and find out the best algorithm for classifying the level of student satisfaction. This measurement uses two data mining algorithms, K-Nearest Neighbour (K-NN) and Naïve Bayes, and the Islamic State University of Sultan Syarif Kasim Riau students as the research object. Different algorithms have varying strengths and weaknesses in handling specific data types and classification tasks. By comparing both algorithms, we can assess their generalization capabilities. A model that performs well on training data but fails to generalize to unseen data may not be as effective as a more robust algorithm that exhibits better generalization performance. K-NN classification with a value of k = 3 gets good results. Based on the study results, the conclusion is that K-NN is more optimal in classifying student satisfaction levels than Naïve Bayes with an accuracy ratio of 85% : 80%, precision of 85% : 84%, and recall of 99% : 93%

China's creative industries : clusters and performances

A working paper submitted to the Annual Conference of the Chinese Economist Association held at Cambridge University from 1st to 2nd April 2008Peer reviewedFinal Accepted Versio

Livestock Practices: Traditional Animal Holdings Classification in Qatar 2020 Towards Sustainable Food Security

Background: Traditional animal holdings (TAH) in Qatar face many managerial challenges, such as inadequate usage of land capacity, low levels of animal productivity and low economic returns. The top priority of the Ministry of Municipality strategy is to take care of TAH to ensure the sustainability of this activity and to maximize its role in national food security. To support future policy choices and services provision, the ministry initiated a TAH classification system. In 2020, the Social and Economic Survey Research Institute (SESRI) of Qatar University conducted a comprehensive agriculture census that followed a well-known methodology. The census form consisted of questions that guided the classification of TAH. The aim of this study is to help assess TAH performance using data from the census. Results: The Animals Holdings Classification Index (AHCI) divided the current holdings into one of five categories (A, B, C, D or E) in accordance with seven factors as classification criteria. These factors were levels of land and barn capacity utilization, livestock productivity, economic return, biosecurity measures, husbandry system and usage of technology for animal production. The results showed that most of the holdings fell into categories C and D. The lowest-scoring criteria were commitment to biosecurity measures and economic benefit. We recommended intensifying extension and enacting legislation to organize holdings to comply with biosecurity measures and initiating marketing programs and market outlets for TAH. According to Qatar’s 2021 agriculture census, there are three different types of holdings: roving holdings (mobile), 33.6%; holdings in compounds, 57.6; and holdings outside rural houses, 8.8%. Conclusions: The AHCI not only determines a holding’s actual productivity capability but also encourages holders to develop and upgrade their holdings. Furthermore, it helps the government fill gaps and provide services based on information and evidence

E-government evaluation: Reflections on three organisational case studies

The deployment of e-Government continues at a significant cost and pace in the worldwide public sector. An important area of research is that of the evaluation of e-Government. In this paper the authors report the findings from three interpretive in-depth organisational case studies that explore e-Government evaluation within UK public sector settings. The paper elicits insights to organisational and managerial aspects with the aim of improving knowledge and understanding of e- Government evaluation. The findings that are extrapolated from the analysis of the three case studies are classified and mapped onto a tentative e-Government evaluation framework and presented in terms lessons learnt. These aim to inform theory and improve e- Government evaluation practice. The paper concludes that e-Government evaluation is an under developed area and calls for senior executives to engage more with the e- Government agenda and commission e-Government evaluation exercises to improve evaluation practice

Multi-stakeholder involvement and urban green space performance

This study aimed to identify the main factors influencing urban green space performance. Therefore, a conceptual framework on the relations of multi-stakeholder involvement (MSI) and the performance was conducted by a mixed-method approach. The study covered all urban green space projects (UGSPs) published in international journals as its population which were obtained from three main databases: ISI Web of Knowledge, Scopus and Picarta. Using a few combinations of keywords, 29 relevant journals were identified, which included 42 UGSPs as the main units of analysis in this study. A content analysis was used to determine the contribution of MSI to the performance of urban green space. The main internal (state, private, society, planning/design, implementation, maintenance, input for management, and financial support) and external (regulation, good leadership and financial support) MSI indicators were further identified. The findings showed that the main indicators that significantly influence urban green space performance are 'state, society, implementation and regulation'. The study concluded that the state plays a critical role in the UGSPs' performance although it is not the only actor. The influential role of the state and society should also be considered since most of green space projects are non-profit oriented. 'Society' involvement also contributes to the performance and 'regulation' is also needed as a legal basis for green space development and management. To validate the conceptual framework and mixed-method approach developed here, it is recommended that more studies should be conducted to compare the relationship of the MSI and the UGSPs' performance in different categories

The class of shareholdings and its impacts on corporate performance: a case of state shareholding composition in Chinese publicly listed companies.

Does the class of shareholdings matter for corporate performance? To address the question, our paper starts by classifying shareholdings on the basis of the principle of ultimate ownership. At present, the shareholding structure of Chinese quoted companies is state-dominant in that 84% of public companies ultimately are found controlled by the state, compared with 16% of non-statecontrolled ones. In contrast to our identified shareholdings, the Chinese official shareholding record only reports the state and the legal person share classes that are inevitably ambiguous for the identification of ultimate owners of public corporations, which in turn has misled many previous studies in assessing the impact of shareholding classes on performance. Based on our newly established shareholding classes, we make a nested performance comparison between these different classes, such as the state direct control versus the state indirect control, and find significant evidence from the Chinese data that the class of shareholdings does matter for company performance. The least inefficient shareholding class is the holding companies that are wholly listed and have focused industrial business through the state indirect control of the downstream public corporations. This finding provides ground for us to think more about how the corporate control mechanism could be further improved in China’s current corporate governance reform

Enhancing Sensitivity Classification with Semantic Features using Word Embeddings

Government documents must be reviewed to identify any sensitive information they may contain, before they can be released to the public. However, traditional paper-based sensitivity review processes are not practical for reviewing born-digital documents. Therefore, there is a timely need for automatic sensitivity classification techniques, to assist the digital sensitivity review process. However, sensitivity is typically a product of the relations between combinations of terms, such as who said what about whom, therefore, automatic sensitivity classification is a difficult task. Vector representations of terms, such as word embeddings, have been shown to be effective at encoding latent term features that preserve semantic relations between terms, which can also be beneficial to sensitivity classification. In this work, we present a thorough evaluation of the effectiveness of semantic word embedding features, along with term and grammatical features, for sensitivity classification. On a test collection of government documents containing real sensitivities, we show that extending text classification with semantic features and additional term n-grams results in significant improvements in classification effectiveness, correctly classifying 9.99% more sensitive documents compared to the text classification baseline