Performance Comparison of Support Vector Machine Algorithm and Logistic Regression Algorithm

According to the World Health Organization (WHO), there are around 7 million breast cancer patients each year, with about 5 million of them dying. Based on Globocan 2018 data, the death rate from breast cancer averages 17 per 100,000 people with incidents of 2.1 per 100,000 people attacking women in Indonesia. Hence breast cancer causes spread genetic mutations in the DNA of breast epithelial cells that radiate to the ducts. The purpose of this study was to classify the type of cancer (benign or malignant) that was suffered. The difference between previous research and this research is in the algorithm testing method chosen. In this study the algorithm used is SVM and Logistic Regression by applying the SMOTE technique. The K-fold cross validation method is used in testing this research. The accuracy results obtained are 1.0, precision 1.0 and recall 1.0.While the highest evaluation results for the model without SMOTE were Accuracy 0.97, precision 1.0 and recall 0.90 with the LR method. So based on the results of the comparison, it shows that the evaluation of models using SMOTE tends to be higher than models without SMOT

Brain Tumor Detection On Magnetic Resonance Imaging Using Deep Neural Network

Cancer is a heterogeneous disease that can attack all parts of the body. Cancer is caused by the abnormal and uncontrolled growth of body cells, resulting in damage to body tissue and the potential to cause death. Cancer is a type of malignant tumor that attacks the body, one of which is the brain. Every year there are 300 brain tumor patients in Indonesia, both adults and children. Generally, doctors use two methods to diagnose these tumors, namely: biopsy and magnetic resonance imaging (MRI). Although the use of biopsy is quite accurate in identifying brain tumors, the time required is relatively long, reaching 15 days. While using MRI is relatively fast, the resulting accuracy is low and depends on the experience of medical personnel. This research aims to develop a method for diagnosing brain tumors using MRI images to make it faster and more accurate. The method used in this research was a deep neural network with a convolutional neural network (CNN) architecture layer. This method was chosen because deep learning has the advantage of pattern recognition with a high level of accuracy and is directly proportional to the number of datasets. This study used a dataset of 300 MRI images with two-dimensional (2D) axial imaging. The metrics used as a basis for the performance of the deep neural network model are accuracy, sensitivity, specificity, precision, and dice similarity coefficient with the results of each metric, namely: 99.3%, 98.6%, 98%, 98%, 98.3%. The research results showed that a deep neural network can speed up the diagnosis of brain tumors with high accuracy within 0.2 seconds.

Implementation Fuzzy Mamdani Algorithm To Predict Web Based Inventory

Mamdani's fuzzy algorithm enables the use of fuzzy logic to overcome the uncertainties and ambiguities associated with inventory predictions. This study describes implementing the Mamdani fuzzy algorithm to predict web-based inventory. Fuzzy algorithms allow specific reasons to deal with the uncertainties and ambiguities associated with inventory predictions. We collect relevant inventory data, including input variables such as the number of items sold, customer demand, and other factors that affect inventory. We also use historical inventory data to create the Mamdani fuzzy model. We implement fuzzification by specifying a linguistic variable for each input variable and converting the numeric to a linguistic value using a predefined membership function, then build a  rule-based fuzzy Mamdani which includes a set of rules that relate language values as input variables with linguistic values of output variables., i.e., inventory prediction. After the inference process, we apply defuzzification using the Mamdani method to convert the linguistic values of the output variables into numeric values that can be used in practice. Through this implementation, we managed to integrate the power of Mamdani's fuzzy algorithm with web technology so that users can access the inventory prediction system online. This system can assist inventory managers in making better decisions in production planning, stock procurement, and delivery schedule. This system is expected to increase efficiency and optimize inventory availability in a rapidly changing business environment

ISO, Contract Management And Blockchain On Profit Quality

The purpose of this research is to determine the effect of ISO, Management Contracts, and Blockchain on Earnings Quality in manufacturing companies listed on the Indonesia Stock Exchange for the period of 2016-2018. The 2016-2018 period was chosen because before the COVID-19 pandemic, Inondesia's economic condition was still stable, thus providing ideal information to see the company's performance. The approach of this research is quantitative research. The population of this research is 10 companies listed on the Indonesia Stock Exchange in 2016-2018. The sampling technique is done by using purposive sampling method that produces 30 samples during 2016-2018. The data used are secondary data taken through documentation techniques consisting of manufacturing companies annual report in 2016-2018. The method of data analysis in this research is panel data regression analysis. The results showed that Blockchain has a positive influence on Earnings Quality, ISO and Contract Management have no influence on Earnings Qualit

Integrating Sentiment Analysis and Quality Function Deployment for Product Development

The development of technology and media has made online data reviews a promising data source. Through machine learning utilizing text processing, data analysis of Ventela Public Low product reviews can be carried out—sentiment analysis is used to find class groups from each data. The classification algorithm is Naïve Bayes and Support Vector Machine (SVM). A classification model with the best performance and accuracy values will be selected. Word association is then applied to obtain information from the required class. Quality Function Deployment (QFD) is a tool used to assist designers in developing products. The results of the integration of sentiment analysis into QFD show that sentiment analysis produces information by the provisions of the QFD method and can support the product development process in terms of the amount of data various data topics and reduces the subjectivity of designers at the stage of determining Voice of Customer (VOC) and performance values of products and competitor

The Influence Of Model Problem-Based Learning, Model Project-Based Learning, And Model-Based Multicultural Learning On Prosocial Behavior Primary School Students In Surabaya

This research aims to determine the influence of the problem-based learning model, project-based model, and multicultural-based learning model on the prosocial behavior of elementary school students in Surabaya. The research applied is experimental research. The design in this research uses a nonequavalent Control Group Post-test Design. The research population is SDN Surabaya with research samples of fifth-grade elementary school students at SDN Margerejo I, SDN Sumur Welut III Surabaya, SDN Dukuh Menanggal 601 Surabaya. The data collection technique uses a questionnaire with 30 questions, while the research instrument uses the Measure of Prosocial Tendencies by adapting Carlo's. The data analysis technique uses the T-test. From the results of data processing, it can be concluded that there is an influence of the Model Problem Based Learning, Model Project Based Learning, and Model Multicultural Based Learning on the prosocial behavior of elementary school students in Surabay

A Model of Indonesian Consumers' Online Shopping Behavior, an Extension of TAM

The aim of determining the online shopping behavior of Indonesian consumers was to create a model of online shopping behavior in Indonesia. The specific goal was to examine and develop a model of online shopping behavior in Indonesia using the Technology Acceptance Model, which is integrated with e-trust, security, and perceived risk as moderators. This research used a descriptive analysis method with a quantitative approach. Primary data were obtained by distributing questionnaires with 385 respondents as samples, using an online survey. The questionnaire was analyzed to determine the effect of the Technology Acceptance Model on Purchase Intention in online marketing through the moderating role of trust, security, and risk. The object of this research was online shopping users in Indonesia. The population of this study was all consumers who used the Internet to make online purchases (Blibli.com, Tokopedia.com, Bukalapak.com, Beribenka.com, Shopee.com, or other online product marketing sites) either through smartphone media or other media (PCs, and laptops). Samples were taken using the non-probability sampling technique with the purposive sampling method and analyzed using SEM. The results showed that perceived usefulness positively affected consumer attitudes, perceived ease of use positively affected consumer attitudes, perceived ease of use positively affected purchase intentions, perceived ease of use affected purchase intentions strengthened by perceived risk, consumer attitudes positively affected purchase intentions, attitudes affected purchase intentions strengthened by security, and attitudes affected purchase intentions strengthened by e-trust

Fuzzy Preference Relations-Based AHP for Multi-Criteria Supplier Segmentation

Supplier segmentation is a strategic activity for businesses. It involves dividing suppliers into distinct categories and managing them differently. Various supplier typologies based on different dimensions and factors are available in the existing literature. By highlighting two main characteristics the skills and the desire of suppliers to work with a specific company this article integrates many typologies. Almost all of the supplier segmentation criteria stated in the literature are covered by these dimensions. These dimensions can be defined utilizing a multi-criteria decision-making process for each specific case. To account for the inherent ambiguities and uncertainties in human judgment, a fuzzy Analytic Hierarchy Process (AHP) is suggested as part of the technique. This approach makes use of fuzzy preference relations. A broiler firm uses the suggested process to divide up its suppliers. A categorization of vendors according to two aggregated criteria is the end outcome. Lastly, we offer some suggestions for future research, draw some conclusions, and talk about some techniques to address distinct sector

Identify the Usability of the Netraku Application System Using the Usability Testing Method

The Netraku application is aimed at the visually impaired who want to know the name of an object or the writing of an object or printed media, as well as knowing the nominal currency conveyed via voice in the application. The application works by pointing the camera at the object or currency you want to see the name or amount of. The Netraku application will detect it, and a voice will appear stating the information you want to convey. The problem found is that the Netraku application has not carried out usability testing for blind users. Therefore, it is necessary to develop usability testing to obtain user needs from the Netraku application, determine the effectiveness and efficiency values, and provide recommendations that can be applied to the Netraku application. The methods used in usability testing are performance measurement, focus group discussion, and participatory design. The results obtained on the effectiveness value received a success rate of 83.33% and a failure rate of 16.7%, concluding that the user can complete the task effectively. The efficiency value obtained from the average processing time is 10.34 seconds

The Role of Information Technology in the Context of OVO Digital Wallet Attractiveness

The research aims to find out the attractiveness of Ovo so that people use Ovo more than other digital wallets in payment transactions. The research uses a qualitative method, with triangulation of data sources, namely observation, interviews and documentation. Informants in the research are people who have knowledge in the topic under study. Data analysis is in the form of data reduction, data presentation and conclusion drawing. The results showed that in the midst of a lot of competition, Ovo is aggressively conducting sales promotions in the form of cashback and vouchers so that consumers feel they get more benefits by using Ovo compared to other digital wallets. Ovo is superior because it has an easy feature in topping up the balance without additional administration fees that other digital wallets do not have. In addition, the attraction of Ovo is that it has a transfer feature to banks without additional administration fees. This is a favourite feature of Ovo users themselves. Currently, hundreds of merchants have partnered with Ovo. Ovo offers convenience in its features, making it easier for consumers to carry out their activities. More practical, faster and easier transactions. The presence of Ovo provides benefits such as shortening payment time, no need to carry or store large amounts of cash.