Sinkron : jurnal dan penelitian teknik informatika
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Sentiment Analysis by Using Naïve Bayes Classification and Support Vector Machine, Study Case Sea Bank
No full textInformation technology is developing at a rapid pace, changing people's lives, particularly in the financial sector where customer demands are rising, and banks must innovate to convert from traditional to technological banking systems while also increasing competency and efficiency through improved services. Innovations in digital banking have arisen in Indonesia as a result of technical progress. SEA Bank is one such digital bank; it was established in Indonesia in 2021. An app that may be found on the Google Play Store is used for all transactions. However, there are instances when the application's performance falls short of users' expectations, which prompts some users to voice their dissatisfaction. In order to determine if the evaluations are either beneficial or detrimental, the author therefore carried out a sentiment analysis study on SEA Bank using the Naïve Bayes classification and Support Vector Machine techniques. This was then implemented on a website utilizing the Flask framework. In the experiments with 90% training data, 10% testing data, and k = 10, the results of this study demonstrated that the sentiment classification process using the SVM algorithm was the best classification algorithm for evaluating its accuracy, precision, and recall values of 93.99%, 94.60%, 98.87%, and an F1 score of 96.69%
Improvement Master Data Management : Case Study Of The Directorate General Of The Religious Courts Of The Supreme Court Of The Republic Of Indonesia
No full textImplementation of Master Data Management (MDM) in an organization aims to help the process of consolidating and integrating various master data sources into one separate source of truth, as well as helping to overcome data complexity that occurs in the process of synchronizing, consolidating and cleaning data from redundancy. The main obstacle at DG Badilag is that data is spread across various systems, in different formats, and is not well integrated. The aim of this research is to improve master data management at the Directorate General of Badilag using MD3M which has an impact on a more transparent, efficient and just justice system . To improve master data management, it is necessary to measure the maturity level with use Master Data Management Maturity Model (MD3M) by Spruit and Pietzka. The results of the assessment show that the MDM maturity level at the Directorate General of Badilag is 0 with an implementation level of 73% (48 out of a total of 65) of implemented capabilities. From these results, recommendations were prepared to increase the maturity level of the Directorate General of Badilag to level 2 in the three designated focus areas, namely strengthening data management in all aspects, from data structure, data quality and data protection. DG Badilag already has awareness in master data management and can increase MDM maturity to a higher level by implementing capabilities that have not been implemented and implementing activities that have not been implemented
Determining The Optimal Number of K-Means Clusters Using The Calinski Harabasz Index and Krzanowski and Lai Index Methods for Groupsing Flood Prone Areas In North Sumatra
No full textThe k-means algorithm is a partitional clustering method. K-Means has several advantages, including being easy to implement, having a high level of convergence and producing denser clusters. Meanwhile, the drawback is that it is difficult to determine the optimal number of clusters. The K-Means method will be used to solve problems in areas prone to flood disasters in North Sumatra. This research aims to find the optimal number of clusters with the Calinski Harabasz Index and Krzanowski And Lai Index based on the Cluster Tightness Measure (CTM) value. There are eleven variables used in this research. Based on the research results, it was concluded that the CTM CH result of 0.376 was smaller than the CTM KL of 0.7843. So it can be said that determining the optimal number of clusters using CH with k = 6 is better than KL with k = 2
Rice Plant Disease Detection System Using Transfer Learning with MobilenetV3Large
No full textIn this study, we address that foliar diseases of rice (Oryza sativa L.) pose a serious threat to agricultural productivity and propose an effective method for disease detection using Convolutional Neural Network (CNN). We use transfer learning on the MobilenetV3Large model to improve the model's performance. Our study involves a curated dataset containing images of infected rice leaves, followed by a careful preprocessing step. This dataset is then used to train a CNN model. The results show a commendable accuracy rate of over 90% and almost reaching 95% when the model is trained over 200 epochs. The model performance graph shows a consistent upward trend in accuracy coupled with decreasing loss during the training process. Furthermore, the classification results highlight the ability of the model to discriminate between different types of diseases affecting rice leaves. This study demonstrates the effectiveness of our proposed method and positions it as a valuable tool for leaf disease detection in rice. By providing faster and more accurate control measures, our approach has the potential to significantly improve agricultural productivity. The successful application of the CNN model using MobilenetV3Large highlights its adaptability and robust performance in addressing the pressing problem of rice leaf diseases and provides a promising path for future advances in precision agriculture
Optimizing Attendance Data Security by Implementing Dynamic AES-128 Encryption
No full textThe protection of data security is crucial, particularly when dealing with the transmission of sensitive information through communication networks. This article explores the Advanced Encryption Standard 128-bit (AES-128) algorithm as an effective and secure cryptographic solution. The paper proposes the dynamic development of the AES-128 cryptography method by implementing a dynamic key to enhance the security of employee attendance data. The dynamic key involves changing the encryption key every minute, providing an additional security layer and reducing the risk of decryption by unauthorized parties. Test results indicate that the dynamic AES-128 encryption algorithm demonstrates optimal performance. The consecutive encryption and decryption speeds for sending attendance data are 14656.78 bit/s and 21898.21 bit/s, respectively. The consistent duration of the encryption and decryption processes, at 6.66ms and 2.44ms, along with an Avalanche Effect rate of 50.73% and an Entropy of 6.67 bit/symbol, emphasizes the algorithm’s efficiency and stability. This research not only reinforces the desired level of security but also outperforms several previous studies. Analyzed performance data indicates that this method is not only efficient but also stable in maintaining data security, addressing significant variations in data length. Thus, the implementation of dynamic AES-128 cryptography in attendance systems provides a significant advantage in addressing information security challenges in the current digital era
Extraction of Shape and Texture Features of Dermoscopy Image for Skin Cancer Identification
No full textSkin diseases are increasing and becoming a very serious problem. Skin cancer in general there are 2, namely melanoma and non-melanoma. Cases that are often encountered are in non-melanoma types. A critical factor in the treatment of skin cancer is early diagnosis. Doctors usually use the biopsy method to detect skin cancer. Computer-based technology provides convenient, cheaper, and faster diagnosis of skin cancer symptoms. This study aims to identify the type of skin cancer. The data used in the study were 6 types of skin cancer, namely Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus image, Pigmented Benign Keratosis image, or Vascular Lesion, with a total of 60 dermoscopy images obtained from the Kaggle site. Dermoscopy image processing begins with a pre-processing process, which converts RGB images to LAB. After that, segmentation is carried out to separate objects from the background. The method of extracting shape and texture features is used to obtain the characteristics of dermoscopy images. As many as 2 types of shape features, namely eccentricity and metric, and 4 types of texture features, namely contrast, correlation, energy, and homogeneity. The result of this study is that it can identify the type of skin cancer based on image features that have been extracted using a program from the Matlab application. The technique of extracting shape and texture features is proven to work well in identifying the type of skin cancer. In the future it is expected to use more data, and add color features in identifying dermoscopy images
Stability Analysis of Dapodik Website: A WebQual Efficiency Model Approach
No full textThis study aims to provide a deeper understanding of how Dapodik can play a role in supporting efficiency and improving the quality of education at the primary level. The research methodology adopts qualitative research design using WebQual 4.0 model. A qualitative approach was chosen to gain an in-depth understanding of user experience regarding the stability and effectiveness of Dapodik in the management of educational data. This study involved participants from two leading elementary schools in Kabupaten Jember, Jember Lor 3 State Elementary School and Al Furqon Elementary School. In its analysis, the research instrument covers three main aspects. The findings show fluctuations in usability scores that reflect application access and performance instability. Although Dapodik shows a good focus on search engine optimization with high SEO value and best practices, improvements to the Largest Contentful Paint (LCP) and page structure are needed to improve stability and responsiveness. The results of operator interviews show the adequacy of Dapodik information, however, more attention is needed in understanding the features of the application. User service responsiveness can be improved by minimizing delays in providing guidance. Suggestions for improvement include stabilization of applications and improved understanding of features, while continued research can explore the positive impact of Dapodik in the context of student learning in various educational environments
Application of Decision Tree Method in ECG Signal Classification For Heart Disorder Detection
No full textCardiovascular Disease (CVD) is a group of diseases that affect the heart and blood vessels, and it is the leading cause of death globally. In Indonesia, Coronary Heart Disease (CHD) is one of the most prevalent CVDs. However, due to the high cost of drugs, lengthy treatment duration, and various supporting examinations required, treating CHD can be very expensive. An obstacle to treating heart disease in Indonesia is the insufficient number of cardiologists and experts experienced in interventional cardiology. Along with technological developments, the computer science community is encouraged to contribute to the medical field. For instance, using an electrocardiogram (ECG) can help prevent and minimize problems arising from heart disease. An Electrocardiogram (ECG) is a medical test that measures and records the heart’s electrical activity using a machine that detects electrical impulses. The use of Artificial Intelligence (AI) in ECG is rapidly increasing and has shown to have great potential in improving the diagnosis and treatment of cardiac patients. AI has become a valuable tool in helping doctors diagnose, predict risk, and manage heart disease with greater accuracy, speed, and precision. One of the machine learning methods used in this research is the decision tree method, which is often employed to make decisions. The decision tree method exhibited promising results, with an accuracy rate of 99% in identifying heart defects at an early stage. This method has significant potential to assist doctors in diagnosing heart defects at an early stage with high accuracy.
 
A Literature Review: Development of Electronic Medical Records In Hospital Management Information Systems
No full textIntroduction: Health technology today is developing very quickly from the initially conventional using paper to being computerized. This literature review aims to map and critically summarize the scientific evidence on the cost-effectiveness and acceptability of Computerized Physician Order Entry (CPOE) and Electronic Health Reports. This Journal have Question that need to be answered how does the development of CPOE in medical records affect cost-effectiveness improvement so that it can be accepted by many parties? Method used in this journal is literature review is conducted on journal articles related to costs and receipts in CPOE. The systematic search was conducted from 5 databases namely PubMed, Science Direct, ProQuest, DOAJ and Ebscohost. Journal articles are selected and selected following PRISMA guidelines. Twenty-five journal articles qualified based on predetermined criteria. At the end as result, Cost-effectiveness with CPOE is more likely to be found that it is easier to reach compared to conventional methods. In addition, the acceptance of patients and health workers is also high. These factors can have a positive or negative influence on the hospital management system because developing countries still need adequate resources so that they can run these methods. Discussion and conclusion CPOE systems can improve patient safety by detecting drug interactions and actions. It is necessary to develop medical records in order to provide effective financing and acceptance
Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine
No full textGenshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game