Sinkron : jurnal dan penelitian teknik informatika
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Text and Image Encryption Using Symmetric Cryptography Ron Rivest Cipher 2 (RC2)
No full textIn the current context, ensuring the secure transmission of data over the internet has become a critical concern, with information technology playing a fundamental role. As society advances into the digital information age, the importance of network security issues continues to increase. Therefore, the need for cryptographic technology has emerged to overcome these challenges. Cryptography includes symmetric and asymmetric cryptography. An example of symmetric cryptography is the RC2 algorithm. RC2 is a symmetric encryption algorithm that uses a single key to encrypt and decrypt data. The ciphertext is then concealed within an image using the Stepic technique. The RC2 encryption method also utilizes symmetric encryption, ensuring the security of the encryption process while maintaining efficient encryption and decryption speeds. The result of this research is that the average percentage of MSE is 0.00%, and for PSNR and AVA are 70.85% and 34.93%. However, the AVA value is quite unstable because the average value is below 40%. Meanwhile, image encryption results in the longer the text that needs to be hidden in the image, the higher the UACI percentage. This is inversely proportional to the NPCR, the longer the text that needs to be hidden in the image, the lower the NPCR percentage. The average results obtained for UACI and NPCR values are 41.46% dan 98.13%
Information System Strategic Planning To Improve UINSU Medan Service Performance
No full textFacing increasingly rapid technological developments, UINSU Medan's efforts must be supported by developing existing information systems to meet the needs of the community and community. Currently, even though it has implemented an Information System in its activities, UINSU Medan does not yet have an Information System plan for the next 5 (five) years (2023-2027). It is hoped that this SI strategic planning will be able to improve the performance of UINSU services related to the Tri Dharma of Higher Education. The method used in this research is a qualitative survey. Data collection was carried out by observation, interviews, and literature review. The research informants are the managers of the UINSU Information Technology and Database Center (PUSTIPADA), and employees involved in the Information Systems section as well as service users such as students and lecturers. The stages of this research use Anita Cassidy's approach which consists of the visioning phase, analysis phase, direction phase, and recommendation phase. The data obtained was then analyzed using Value Chain analysis, SWOT, Porte's Five Forces, and other supports which were then confirmed by Focus Group Discussion with competent parties. This research recommends an Information Systems roadmap for UINSU Medan consisting of 34 integrated applications to be developed within 5 (five) years from 2023-2027
OPTIMIZATION ACCURACY VALUE OF AGRICULTURAL LAND FERTILITY CLASSIFICATION USING SOFT VOTING METHOD
No full textSoil fertility on an agricultural land is very influential with agricultural yields, where plants can grow well and fertile if nutrient intake is met. The purpose of this research is to improve the accuracy in predicting soil fertility by utilizing machine learning by combining two classification algorithms using soft voting methods in the classification of agricultural land fertility. In this research, one of the ensemble learning methods called soft voting is employed. Soft voting is used to enhance accuracy by optimizing the combination of algorithms based on the highest probability provided by each model. The Gaussian Naive Bayes algorithm is used to predict classes in the sample data based on the Gaussian distribution of numerical data, while the decision tree is utilized to predict classes by constructing a decision tree using soil content attributes for the classification of fertile or infertile soil. The use of the Gaussian Naive Bayes algorithm in identifying fertile and infertile soil based on existing attributes achieved an accuracy rate of 87.2%. The decision tree algorithm, based on decision tree modeling, helped identify important attributes for decision-making with an accuracy rate of 88.3%. The soft voting method played a crucial role in improving accuracy by combining both algorithms, resulting in an accuracy rate of 88.8%. Based on the accuracy results obtained, the use of soft voting optimization in predicting soil fertility has the highest accuracy because it combines the Gaussian naïve bayes algorithm and the decision tree algorithm
Prediction of Student Entrepreneurship Future Work based on Entrepreneurship Course using the Naïve Bayes Classifier Model
No full textEntrepreneurs are critical to a country's economic progress and job creation. Few people felt schools have much to offer with business a generation ago. Students are expected to be an entrepreneur as the outcome of the course. The goal of this study is building a model to predict students' future employment, particularly in the field of entrepreneurship, using big data analysis and data mining. Various educational institutions can use data mining methodologies to identify hidden patterns in data contained in databases. The feature selection technique was utilised in this study to select and assess the significance of each element. The model was built using the final parameters determined by the feature selection technique (Correlation Based Feature Selection). Using the 10-fold cross validations for training and testing dataset distribution, the Naïve Bayes classifier was used to forecast the students' future of work. The dataset for the study was gathered from a student's performance report at Universitas Negeri Medan's engineering department. The effectiveness of using feature selection algorithms was compared to the effectiveness of not using feature selection algorithms, and the results are discussed. According to the findings of this study, the accuracy of Naïve Bayes with Correlation Based Feature Selection is 87.4%, which is higher than the model that did not use any feature selection. It was also discovered that the overall accuracy of the Correlation Based Feature Selection and Naïve Bayes Classifier models appears to be higher than that of the other treatments
Implementation of Classification Decision Tree and C4.5 Algorithm in selecting Insurance Products
No full textEvery insurance customer will receive a policy card, as a sign that the person is included in the insurance and is obliged to pay the insurance premium, the amount of which has been determined by the company in accordance with the agreement. Premium payments are Insurance's biggest source of income. Unfavorable economic conditions often cause customers not to pay their premiums by the specified time limit, resulting in a delay in completing the recording of premium income. This research aims to find out the right type of insurance product for prospective customers. The research method used is Classification Decision Tree. Classification Decision Tree is a research method used to examine existing facts systematically based on research objects, existing facts to be collected and processed into data, then explained based on theory so that in the end it produces a conclusion. This research is for selecting the right type of insurance product for prospective customers based on the age and income categories of prospective customers. Insurers must be more careful, especially in selecting prospective customers, and in determining the right type of insurance product for prospective customers so that the power in selecting the right type of insurance product for prospective customers is right at the intended target
Comparison Of Naïve Bayes And Decision Trees In Determining The Best Manager Of Nurul Jadid Islamic Boarding School Based On Forward Selection
No full textIn an effort to find a solution for determining the best administrators, Islamic boarding school administrators try to determine the nominations for the best administrators using existing service data and knowledge. The process of determining nominations for the best administrators is less accurate, requiring computational methods to classify which administrators fall into the best category. In the context of data mining, classification is an important aspect. One of the classification models used is Naïve Bayes which focuses on class probability, and Decision Tree C4.5 which produces a decision tree to determine the priority of indicators that are most influential in predicting the best management. Both of these algorithms have their respective advantages. This research aims to analyze and compare the performance of the Naïve Bayes and Decision Tree classification algorithms. The comparative results of testing the Naïve Bayes and C4.5 algorithms in determining the nominations for the best administrators at the Nurul Jadid Paiton Probolinggo Islamic Boarding School on 455 administrator data tested in this study show that there is a fairly large comparison of accuracy. Naïve Bayes with Forward Selection has an accuracy rate of 91.21%, higher than Naïve Bayes itself whose accuracy results are only 87.64%. there is a difference of 3.57%. Likewise, the accuracy of C4.5 with Forward Selection has an accuracy rate of 90.99%, higher than C4.5 alone which has an accuracy rate of 90.11%. there is a difference of 0.88%. So in the comparison between 4 algorithm model trials, Naïve Bayes and Forward Selection had the most dominant accuracy with an accuracy result of 91.21%
SIAKAD Mobile With API Service To Improve Academic Services
No full textDeveloping SIAKAD (commonly called SIAM, Student Academic Information System) Mobile using API Service to improve academic services for students is the goal of researchers doing so because it supports the implementation of education to create better information distribution services for everyone who wants access to it. And this also has an impact on academic performance, it is easier to organize lecturer attendance schedules, value recapitulation, and so on. Conventional SIAKAD (SIAM) which can be accessed via a computer or laptop has limited accessibility and practicality, which can hinder students from accessing academic information flexibly. Therefore, after researchers have examined and paid attention to several systems that can be implemented to assist users in accessing them, the development of SIAKAD in the form of a mobile application is a solution to increasing accessibility and ease of access to academic information. The API service is used as a communication bridge between the SIAKAD mobile application and the backend system. Through this, the Mobile Application can communicate (send requests and receive responses from the backend system) quickly and efficiently. But to shorten the application development time we use the SCRUM method and for the business process model, we use BPMN to create, design and design this application. The results of this study the authors see a compare of the time that can increase after using Mobile in access SIAKAD (SIAM)
Improving Digital Image Clarity: A Study on the Application of Histogram Equalization for Noise Correction
No full textThis study aims to improve the clarity of digital images by examining the application of the histogram equalization method for noise correction. Noise in digital images is often a major challenge in maintaining the clarity and authenticity of visual information. Histogram equalization has been recognized as an effective method in improving image contrast and reducing the effects of noise. In this research, we conducted experiments by applying histogram equalization techniques to various types of digital images that are affected by noise. We analyzed the results by comparing the clarity and quality of the images before and after applying this method. The results of this research show that histogram equalization is able to significantly improve the clarity of digital images by reducing the effects of noise without sacrificing important details in the image. The implication of this discovery is the potential use of the histogram equalization method as an effective tool in improving the quality of digital images that are affected by noise
Analyzing UI and UX of Verval PTK: Impact on Elementary School Data Precision
No full textThis study explores the implementation of the Verval PTK application in elementary school data management in Kabupaten Jember, with emphasis on its impact and precision. The research methodology adopts a differential qualitative descriptive design, with the main focus on elementary schools in Kabupaten Jember that have integrated Verval PTK. Five Elementary Schools were involved in the study. Among others, SD Negeri Jember Lor 2, SD Negeri Kepatihan 1, SD Negeri Jember Kidul 2, SD Negeri Kebonsari 1, and SD Negeri Patrang 1. In the analysis, the study involved technical aspects such as usability tests, performance measurements using Lighthouse, and A/B tests. However, challenges related to platform accessibility and stability are still a concern. Performance measurement using Lighthouse shows excellent scores, although SEO scores require further attention to improve. A/B tests highlight significant improvements in time efficiency and data accuracy through Verval PTK implementations, but some low scores require more in-depth analysis. A number of technical recommendations were put forward to improve the security, stability, accessibility, and SEO optimization of Verval PTK. Regular software updates, efficient error management, and real-time performance monitoring are key focuses to support continuous development. This recommendation is directed to strengthen the role of Verval PTK in supporting efficient and accurate school data management
Enterprise Architecture for Efficient Integration of IoT Lighting System in Smart City Framework
No full textThis research investigates the influence of enterprise architecture design in integrating Internet of Things (IoT)-based street lighting systems into an innovative city framework, emphasizing the importance of efficient lighting infrastructure as a fundamental component of a creative urban ecosystem. With a focus on building an architectural model that supports the integration of IoT street lighting with other components of a smart city, this research addresses the knowledge gap in optimizing enterprise architecture design for integration efficiency, considering technological complexity and interoperability needs between systems. The methodology applied involved an in-depth analysis of the architectural components essential to facilitate the integration of IoT-based street lighting within the more extensive intelligent city infrastructure. The findings of this study show that a well-structured enterprise architecture model can significantly improve operational efficiency, reduce energy consumption, and provide a rich source of data for strategic decision-making regarding the management and maintenance of city infrastructure. Furthermore, these results emphasize the importance of an adaptive and unified architecture design, which not only improves the functionality of the lighting system but also strengthens the synergy between IoT technologies and innovative city operations. These discoveries have a wide range of repercussions and implications, offering new insights into designing enterprise architectures that can support the transition to more efficient and sustainable smart cities, thereby improving the quality of service for citizens