Universitas Islam Kuantan Singingi: E-Journals
Not a member yet
1863 research outputs found
Sort by
Madrasah Strategy in Facing the Challenges of Globalization and Strengthening Religious Moderation: A Literature Review
No full textThe main findings show that madrasas can face various challenges, including social, political and technological changes brought by globalization. However, through the right approach, Madrasas can take advantage of globalization opportunities to strengthen the values of moderation in Islamic religious education. The strategies identified in this study include strengthening inclusive religious education, developing curricula that are relevant to global issues, intensive teacher training, use of information and communication technology, and collaboration with non-educational institutions. This research provides important insights for the development of Islamic religious education that is responsive to the challenges of globalization and promotes religious moderation in madrasas. The practical implications of these findings include policy and practice recommendations for madrasas and other stakeholders to face these challenges effectively. Madrasas can face many problems, such as social, political and technological changes brought by globalization. Nevertheless, madrasas can take advantage of the opportunities offered by globalization to strengthen the values of moderation in Islamic religious education with appropriate methods. This study found several strategies to improve inclusive religious education; improving teacher training; curriculum development that is relevant to global issues; use of information and communication technology; and collaboration with non-educational institutions. This research provides important insights for the development of Islamic religious education that is responsive to the challenges of globalization and supports religious moderation in madrasas. This finding has practical consequences. It includes policy and practice suggestions that madrasas and other stakeholders can use to address these issues successfully
Synergy of Local Community and Government Policies in Preserving River Ecology: A Case Study on Illegal Gold Mining in Kuantan Tengah
No full textThe Kuantan River in Kuantan Singingi Regency, Riau Province, has long served as a cultural and ecological lifeline for local communities. However, its sustainability is increasingly threatened by Penambangan Emas Tanpa Izin (illegal gold mining), which has caused severe water pollution, ecological degradation, and socio-economic conflicts. This study aims to analyze the roles of local government and communities in addressing river degradation, and to examine how the integration of customary (adat) law and environmental law may contribute to sustainable river governance. A qualitative case study design was applied, utilizing interviews, field observations, and document analysis to capture the dynamics of governance, community participation, and ecological impacts. The findings reveal that while the local government has introduced regulatory frameworks and monitoring programs, implementation remains weak due to limited resources and enforcement capacity. At the same time, local communities contribute significantly through grassroots initiatives such as collective clean-up activities, waste management, and cultural reinforcement via the Pacu Jalur festival, though these efforts are constrained by economic dependence on illegal mining. The study concludes that long-term sustainability requires synergy between government and community, supported by the integration of adat law’s cultural legitimacy with the formal authority of environmental law. This hybrid framework enhances compliance, legitimacy, and resilience in preserving river ecology
Sentiment Analysis Related To Covid-19 Vaccination On Social Media Using The K-Nearest Neighbor (K-NN) Method
No full textCoronavirus 19 (COVID-19) has become a topic of great concern in the past two years. To anticipate the spread of the virus, the government has made various efforts, one of which is by procuring a COVID-19 vaccination to increase the body's immunity. In carrying out the program, the government urges the public to use social media as a means of disseminating information regarding the COVID-19 vaccination. Facebook is one of the most popular social media and is chosen by agencies as a medium of information. Information regarding the vaccination is shared by the Ministry of Health of the Republic of Indonesia through its Facebook Page and the public can provide opinions in the form of comments. Given that the comments are numerous and lengthy if you have to read the manual, it is difficult to classify which one corresponds to the positive, negative or neutral opinion class, so a system is needed to analyze them. This sentiment analysis system uses the K-Nearest Neighbor (K-NN) method to classify positive, negative and neutral opinions. This study uses 750 comments obtained from posts in November 2021 with the keywords 'vaccination' and 'vaccine', with the distribution of 700 training data and 50 test data. Furthermore, the comments are pre-processed with the stages of case folding, filtering, tokenizing, normalization, stopwords and stemming, then weighted using the TF-IDF feature. System testing is carried out using the K-Nearest Neighbor (K-NN) method with a value of k = 1, k = 3, k = 5, k = 7 and k = 9 . 1 and f-measure of 0.71428571428571. Meanwhile, the lowest accuracy value is at the value of k = 7 and k = 9 with an accuracy of 0.66 and an error rate of 0.34
Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing In Product Sales Prediction
Full text linkEffective inventory management is one of the keys to a company's success, especially in the retail and distribution sectors that are highly dependent on product availability according to market demand. One common problem faced in inventory management is deadstock, which is a condition where a product is not sold for a long time, causing a buildup of goods and financial losses. This problem is generally caused by inaccuracy in predicting product sales needs. This study aims to overcome this problem by implementing the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm combined with the Grid Search optimization method to improve the accuracy of sales predictions. By utilizing the Sales Data Analysis dataset from Kaggle, the algorithm is implemented in a web-based system using Python and Flask. The results showed that the combination of Grid Search and ARRES was able to significantly increase prediction accuracy, as indicated by a decrease in the MAPE value from 2.845% (ARRES only ) to 0.877% (Grid Search + ARRES). This proves that the proposed method can help companies manage stock more efficiently, reduce the risk of deadstock, and increase the effectiveness of product sales plannin
A Deep Learning-Based Sentiment Classification for Identifying Advertorial Content in Online News
Full text linkThe rapid advancement of technology and the widespread use of the internet have brought significant positive and transformative impacts across various aspects of human life, including finance, healthcare, education, and the media industry. One notable consequence of information transparency is the vast availability and large-scale exchange of data. However, this also presents new challenges, particularly in the spread of misleading content such as disguised advertorials that resemble genuine news. This threatens the objectivity of the information received by the public. To address this issue, an automated solution is needed to identify the distinguishing characteristics of advertorials in online news content. This study proposes a deep learning approach using the Convolutional Neural Network (CNN) model to detect sentiment as an indicator of advertorial content. CNN is a widely used deep learning model for processing sequential and spatial data, capable of automatically learning features from text. The dataset comprises news articles categorized by advertorial traits, such as positive or neutral sentiment, persuasive language, and promotional content highlighting specific entities. The data undergo several processing stages, including text preprocessing, tokenization, padding, and CNN model training. Model performance is evaluated using accuracy, precision, recall, and F1-score. The experimental results show a validation accuracy of 84%, although overfitting issues were observed. Despite ongoing limitations, such as restricted data and suboptimal parameter tuning, the findings suggest that the CNN model has potential for automatically detecting advertorial content and can serve as a basis for future research using more advanced models and refined parameter adjustments
Design and Development of an E-Commerce Website Using the Waterfall Method with the Laravel Framework
Full text linkThe e-commerce sector has experienced significant growth in Indonesia in recent years. However, many small business owners still rely on manual operations through social media platforms. This study focuses on the design and implementation of an e-commerce website for Comot Langsung, a local thrifting business, using the Waterfall methodology and the Laravel framework. The sequential nature of the Waterfall method is applied through six phases: requirement analysis, system design, development, testing, deployment, and ongoing maintenance. In the analysis phase, several key features were identified, including user registration, product catalog, shopping cart, ordering system, and QRIS payment integration. The design process utilized UML diagrams to clearly and structurally visualize the system architecture and user flow. The results show that all features were successfully implemented, offering high responsiveness and ease of navigation. This website is expected to expand market reach for thrifting entrepreneurs while enhancing the online shopping experience for consumers in selecting and purchasing vintage fashion products efficiently and conveniently
Analysis of Acceptance Factors of Pospay Application Users in Surabaya City Using Modified TAM
Full text linkThis study aims to analyze the factors that influence user acceptance of the Pospay application in Surabaya by using a modified Technology Acceptance Model (TAM). Pospay is a digital platform owned by PT Pos Indonesia that offers financial transaction services. Although the app has been downloaded over 5 million times, it still faces acceptance issues, as reflected by the high number of negative reviews due to system errors, verification failures, and unstable performance. To address these challenges, the TAM model was extended by incorporating variables such as Facilitating Conditions, Lifestyle Compatibility, Quality of Internet Connection, Perceived Security, Perceived Trust, Perceived Risk, Self-Efficacy, and Satisfaction. Data were collected from 523 active users and analyzed using Structural Equation Modeling–Partial Least Squares (SEM–PLS). The results showed that Facilitating Conditions, Perceived Ease of Use, and Perceived Usefulness significantly influence Attitude. Furthermore, Perceived Ease of Use, Perceived Usefulness, Perceived Security, Self-Efficacy, and Satisfaction have a significant effect on Intention to Use. Meanwhile, variables such as Attitude, Lifestyle Compatibility, Quality of Internet Connection, Perceived Trust, and Perceived Risk did not significantly affect Intention to Use. These findings highlight the importance of improving usability, usefulness, security, satisfaction, and user confidence to enhance the acceptance and usage of the Pospay application more effectively
Classification and Mapping of Online Gambling Based on News Articles Using NER and SVM
Full text linkThe phenomenon of online gambling in Indonesia has developed rapidly, posing serious social and economic threats. This thesis aims to classify and map online gambling activities based on digital news using the Support Vector Machine (SVM) algorithm and Named Entity Recognition (NER). Data were collected from the news portals Detik.com, Kompas.com, and Tribunnews from 2017 to 2024 through a web scraping approach. The research process included setup and library import, data upload, data exploration, data labeling according to Law No. 1 of 2023, data preprocessing, data filtering, location normalization and extraction, and location data cleaning. Subsequently, the SVM model was trained for risk classification and followed by prediction. Evaluation was conducted using accuracy and F1-score metrics to assess overall model performance and classification balance. Based on the evaluation results, the Normal SVM model demonstrated the best performance with an accuracy of 96.94% and an F1-score of 0.97. The findings indicate that the combination of NER and SVM effectively identifies the location and risk level of online gambling activities. This research is expected to contribute to law enforcement authorities and policymakers in their efforts to prevent and address online gambling activities in Indonesia
Wireless Network Quality Analysis at the Tanah Abang Sub-district Office, Pali Regency Using the Quality of Service (Qos) Method
Full text linkIn the era of rapid globalization and information technology development, government institutions are required to adopt reliable and efficient technological systems to support public services. The Tanah Abang District Office, Penukal Abab Lematang Ilir Regency, South Sumatra Province, is responsible for various administrative services such as issuing birth certificates, identity cards, and other official correspondence, all of which rely heavily on stable network performance. However, current conditions indicate frequent internet slowdowns caused by unmanaged bandwidth usage and an unknown number of active wireless users, resulting in decreased service efficiency, especially during large data transfers. This study aims to analyze the implementation of the Hierarchical Token Bucket (HTB) method as a bandwidth management solution to improve network performance. Network quality is evaluated using Quality of Service (QoS) parameters, including throughput, delay, jitter, and packet loss. Data collection and traffic monitoring were conducted using the Wireshark application to capture and analyze network performance in real time. The results of this study are expected to provide insights into improving network stability and service effectiveness within government offices
Detection and Analysis of Packet Sniffing Attacks Using Wireshark on Wifi Networks: A Practical Approach to Network Security Case Study of Puskesmas Sungsang
Full text linkIn the last two decades, advances in internet technology have significantly influenced various sectors, including healthcare. The integration of information technology into health services has become essential for improving operational efficiency through integrated medical information systems, structured patient management, and digital financial administration. The Sungsang Health Center, as a community-based healthcare facility, relies heavily on networked systems to support daily activities such as patient registration and electronic medical record management, which involve highly sensitive data. This condition increases the risk of data leakage, particularly due to packet sniffing attacks. Therefore, strengthening network security policies is a critical need. This research aims to analyze potential packet sniffing threats and support the prevention of data leaks at the Sungsang Health Center by utilizing Wireshark as a network analysis tool. Wireshark enables detailed monitoring and analysis of data packets to identify suspicious activities that are not easily detected manually. The study provides insights into techniques for detecting and analyzing network vulnerabilities, which can serve as a reference for improving network security policies. The results are expected to support the development of safer and more reliable digital health systems and raise awareness of the importance of protecting patient data