Sinkron : jurnal dan penelitian teknik informatika
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Integrating TOGAF and Big Data for Digital Transformation: Case Study on the Lending Industry
No full textIn today’s digital era, the strategic integration of enterprise architecture frameworks with Big Data technologies is crucial in driving digital transformation, especially within the lending industry. This research aims to identify and analyze how The Open Group Architecture Framework (TOGAF) can be integrated with Big Data to enhance innovation, operational efficiency, and decision-making in the lending sector. This study examines Indonesian financial institutions using qualitative case studies, exploring the intricate practices, challenges, and benefits of the combination of TOGAF and Big Data. The qualitative methodology focuses on in-depth interviews and document analysis to gather contextual insights into the implementation dynamics and impacts of these technologies. Findings indicate that integrating TOGAF and Big Data not only streamlines workflows but also significantly enhances data security and risk management—critical elements in the lending industry. A vital outcome of this study is the development of a robust integration model that serves as a blueprint for companies in similar sectors to navigate their digital transformation journeys. Additionally, this research provides strategic recommendations to overcome integration and implementation challenges. These guidelines facilitate the transition to a more cohesive and strengthened digital architecture, equipping financial institutions to manage the complexities of modern digital economies effectively. Ultimately, this study delivers a comprehensive framework that enriches theoretical understanding and offers practical insights for effective technology integration in financial services
Analysis Of Improving Service Quality At The Ssctelkom Surabaya Institute Of Technology Using The Lean Six Sigma Method
No full textStudent Service Centre (SSC) is a center that provides services and information to active students at InstitutTeknologi Telkom Surabaya (ITTS). ITTS provides SSC with academic, student, and faculty services to support its students' academic and non-academic development. One of the main services provided by SSC is the Active Certificate. However, SSC users need help obtaining the letter. This study aims to measure the quality of Active Certificate services using the Lean Six Sigma method and provide recommendations for improvement. The results showed that the quality of SSC services still needs to be improved, with a DPMO value of 289686, a sigma value of 2.07, and the highest negative gap in the Responsiveness dimension. The total Non Value Added time was obtained at 10 hours 31 minutes, and the total Value Added time was 4 hours 8 minutes. Proposed improvements include the deployment of QR Codes to provide information on document requirements and using Value Stream Mapping (VSM) to reduce the time spent on non-value added. Lean Six Sigma method can reduce the total value-added time and improve the efficiency of SSC services
Implementation of Random Forest Algorithm for Graduation Prediction
No full textUniversity also has responsibility for the period of study taken by students in accordance with the level of education taken. The prediction of student study duration is designed to support the study program in guiding students to graduate on time. In this problem, data mining techniques can be applied to make predictions, namely by using the Random Forest classification method. The stages used in this study are data collecting, namely collecting student data, the data selection stage of 300 students with 5 (five) input data attributes including personal data (gender, age, marital status, job status) and academic data (grade) and 1 (one) attribute as an output containing choices about on time and late. The next stage is preprocessing with the aim of eliminating duplication, noise, and missing values, the stage of data transformation by normalizing age attributes (young and old), grade (large and small). Then the data split stage 3 times, namely 50/50, 40/60, and 30/60, the modeling stage with random forest, and finally, the evaluation stage by analyzing the confusion matrix consisting of accuracy, precision, and recall. The results of the study show that the proposed model can do well with predictions, that is, with the same results for all three data splits. The test value is 100% accuracy, 100% recall, and 100% precision. With this value, the success rate for predicting the timeliness of student graduation will be more accurat
Enhancing Vehicle Routing Efficiency through Branch and Bound and Heuristic Methods
No full textThe Vehicle Routing Problem (VRP) is a critical challenge in logistics, impacting delivery efficiency and costs. Traditional VRP solutions often fail to address real-world dynamics such as fluctuating traffic conditions and varying customer demands. This research proposes a novel VRP model integrating real-time data to enhance route optimization. By combining the precision of the Branch and Bound (B&B) approach with the flexibility of heuristics like Genetic Algorithms and Simulated Annealing, the hybrid method dynamically adjusts routes based on live traffic and demand updates. The objective is to reduce operational costs and improve logistical performance. The hybrid model’s effectiveness is validated through comparative analysis with traditional VRP solutions, demonstrating significant improvements in cost reduction, fuel consumption, vehicle wear and tear, and customer satisfaction due to timely deliveries. These advancements highlight the potential of real-time data integration and advanced optimization techniques in providing robust solutions for modern logistics challenges. Future research should focus on incorporating more advanced data sources and testing the model in various real-world scenarios to further enhance its practicality and performance, ensuring businesses remain competitive in a dynamic market. This study underscores the importance of continuous innovation in VRP solutions to achieve sustainable, efficient, and customer-centric logistics operations
Deep Learning Approach for Traffic Congestion Sound Classification using Circular Neural Networks
No full textTraffic congestion has become one of the main problems that occur in big cities around the world. Traffic congestion also has a negative impact if not handled seriously. Traffic congestion occurs because there is a buildup of vehicle volume that exceeds the capacity of the road. The efficiency and quality of living in cities can be negatively impacted by traffic congestion, which can also result in higher fuel consumption, pollution, and delays. There needs to be a method that can overcome and identify this. Therefore, by classifying sounds, this research aims to reduce traffic congestion. The author uses deep learning with the Convolutional Neural Network (CNN) method as the algorithm model. The model employs Mel-Frequency Cepstral Coefficients (MFCC) as the primary feature extraction technique to capture the essential characteristics of the audio signals. This research is expected to be able to classify traffic congestion sounds with good accuracy, so it can be used as a solution to overcome traffic congestion. Experiments were conducted using a training dataset, and for testing, the road sound dataset has been collected at traffic light intersections. To evaluate the proposed method, the implementation showed promising results, achieving an accuracy of 97.62% on the training data and 88.19% on the test data in classifying traffic congestion sounds
Sales Trend Analysis With Machine Learning Linear Regression Algorithm Method
No full textThe development of online business in Indonesia is now very rapid, with the process being done by ordering goods through resellers or distributors using one of the social media. Item purchases are made based on product information, prices, discounts and inventory quantities using a decision model. In the sales process, Toko Serbu Aek Batu usually releases several different items to be offered to the market at different prices, but not all items are in high demand. Multiple linear regression is an analysis that describes the relationship between dependent variables and factors that affect more than one independent variable. The purpose of this study is to analyze sales trends using a linear regression method using rapidminer. The results of this study are prediction calculations using manual calculations with rapidminer the same results, predicting the price desired by buyers using a linear regression algorithm with the original price is not much different and rapidminer is very accurate to be used in predicting sales trends at the price desired by customers, so that sellers can pay more attention to things that are very influential in the sales process
Analysis of the Implementation of the XYZ Core Banking System at PT BPR Hariarta Sedana
No full text: The Core Banking System (CBS) is often referred to as the heart of a bank due to its crucial role in banking activities. Core Banking is used for processing loans, savings, customer information storage, and various other services. Real-time interaction is required to process banking activities. Each bank has its own strengths, needs, and target market in order to conduct its business. These needs must be supported by reliable, flexible information technology solutions that are ready for further development according to the bank's requirements. The transformation of Bank Perekonomian Rakyat (BPR) is a critical step in supporting economic growth for both the bank and its customers. This study highlights the latest trends in the banking industry related to banking digitalization, such as real-time online integration with delivery channels, including mobile banking services. This analysis also identifies the weaknesses of the previous banking system and the strengths relevant to the transformation desired by BPR's business needs. The availability of technology, system integration, and IT architecture are the focus to understand the existing infrastructure. For now, the implemented changes have significantly impacted the speed of operational performance and services, as well as better control of employee working hours
Agile Project Management Impacts Software Development Team Productivity
No full textThe agile nature of the software development sector calls for flexible and effective project management techniques. Agile Project Management (APM) is emerging as a significant method that supports team cooperation, iterative improvement, and flexibility. This paper looks at how agile project management might affect software development team output. This study investigates the primary Agile methodologies Scrum and their impact on team productivity by means of a thorough literature review and empirical analysis. A mixed-methods approach employs qualitative comments and quantitative measures to provide a comprehensive view of output changes. We examine several software development teams inside a mid-sized technology company over 12 months using a case study approach, comparing productivity measures before and after Agile practices, including team satisfaction, development pace, and code quality. Furthermore, team member surveys and interviews offer an understanding of the supposed advantages and difficulties of switching to Agile approaches. Teams showing more efficiency, improved communication, and better morale point to a notable rise in productivity. Notable improvements included improved adaptability to shifting project needs and a shorter time-to-market for software products. This paper offers an insightful analysis of Agile Project Management's ability to revolutionize software development processes, helping companies trying to improve project results. This study has consequences for managers and practitioners because it provides valuable instructions for implementing Agile approaches to achieve the best team performance. Future directions of study will include investigating the long-term effects of Agile methods and their relevance in various organizational settings
Analysis and Implementation of CNN in Real- time Classification and Translation of Kanji Characters
No full textThis research explores the training outcomes of the Convolutional Neural Network (CNN) algorithm applied to Kanji character recognition. It employs a CNN architecture with 10 layers for recognizing digital image of Kanji characters from N5 to N1 levels. The training of the CNN model reveals varying accuracies, influenced by factors such as architecture, training data size, and data quality. The lowest accuracy, occurring at the beginning of training, highlights challenges like poor random weight initialization and suboptimal architecture. Conversely, the highest accuracy demonstrates the optimal predictive ability of the model after multiple training iterations. The iterative training process refines the model's accuracy over time, with initial challenges paving the way for a better understanding of the data for future improvements. In the subsequent analysis and system development phase, two algorithms are compared to assess the effectiveness of the CNN algorithm in Kanji character recognition. Testing is conducted at various complexity levels to systematically evaluate accuracy. This testing involves complexity levels at each Kanji character level to assess system accuracy. The developed model shows potential for real-time classification of Kanji characters, with a focus on error rate and accuracy during training and testing. The built model can essentially be used to evaluate the ability to recognize Kanji characters effectively. The model's performance can be assessed based on the error rate and accuracy achieved during the training and testing processes
Convolutional Neural Network Activation Function Performance on Image Recognition of The Batak Script
No full textDeep Learning is a sub-set of Machine learning, Deep Learning is widely used to solve problems in various fields. One of the popular deep learning architectures is The Convolutional Neural Network (CNN), CNN has a layer that transforms feature extraction automatically so it is widely used in image recognition. However, CNN's performance using the tanh function is still relatively low, therefore it is necessary to select the right activation function to improve accuracy performance. This study analyzes the use of the activation function in image recognition of the Batak script. The result of this study is that the CNN model using the ReLU and eLU functions produces the highest accuracy compared to the CNN model using the tanh function. The CNN model using eLU produces the best accuracy performance in the training process, which is 99.71% with an error value of 0.0108. Meanwhile, in the testing process, the highest accuracy value is generated by the CNN Model using the ReLU function with an accuracy of 94.11%, an error value of 0.3282, a precision value of 0.9411, a recall of 0.9411, and an f1-score of 0.9416