Sinkron : jurnal dan penelitian teknik informatika
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Optimization of the Artificial Neural Network Algorithm with Genetic Algorithm in Stroke Prediction
No full textThis study aims to optimize Artificial Neural Network with Genetic Algorithm in predicting stroke. This research is motivated by health problems in the community that are less considered that cause a disease such as stroke. Factors of lifestyle, poor diet and other factors that can be the cause of stroke. Therefore, where later the data that has been obtained will be processed to see what factors determine the cause of stroke. The data used, namely kaggle and mendeley, will be processed using RapidMiner, with a development method (CRISP-DM) and a testing method using a Confusion Matrix. The results of this study, stroke disease classification model accuracy kaggle Artificial Neural Network dataset with Genetic Algorithm accuracy 95.13% and AUC 0.667 and mendeley dataset accuracy 98.20% and AUC 0.712. For model evaluation with Artificial Neural Network algorithm with Artificial Neural Network algorithm with kaggle dataset genetic algorithm using X-fold validation average accuracy of 95.14% and AUC 0.686.7 and mendeley dataset resulted in accuracy of 98.20% and AUC 0.712.5. So as to produce from an algorithm a new attribute from the results of the classification model that has been carried out, namely heart disease, ever married, work type and residence type
Application of MCDM-AHP and EDAS Methods for Selection of the Best Residential Locations Areas
No full textThe population density has led to an expansion of the area where people live. This opportunity is exploited by housing developers to build many locations for the development of residential areas. The purpose of writing this paper is to provide proper consideration in housing selection which can be seen from various parameters as selection criteria. The method support that can be used in residential selection is the collaboration of the MCDM-AHP and EDAS methods. This method can be used as a recommendation against the concept of multi-criteria. The more criteria used, the higher the level of difficulty to support decision making. With the collaboration of the MDCM-AHP method, it can be used to provide an assessment of multi-criteria that have optimal values, while the EDAS method will be used as a strength in evaluating the selection of alternatives based on positive and negative distances for different types of criteria through normalized values. Determination of the weighting value of the criteria is obtained through the iteration stages using the mathematical algebra matrices method and proven by expert choice apps. The decision support results obtained provide a ranking value with the first priority being PR06 with an accumulative weight of 0.552 followed by the second and third ranks respectively PR04 and PR05 with a weight of 0.545 and 0.522 respectively. Thus supporting decision making with the recommendation of the MCDM-AHP and EDAS method collaboration can provide an optimal assessment of residential selection in a detailed and accurate manner
Comparative Analysis of Machine Learning Algorithm Performance in Predicting Stunting in Toddlers
No full textStunting is a condition where the growth of children and toddlers is stunted, which causes children to be shorter than they should be. In the long term, stunting can reduce reproductive health, study concentration, and work productivity, thereby causing significant state losses. The prevalence of stunting in Indonesia, which is still above 20 percent, shows that there are still chronic nutritional problems among toddlers. To prevent this from happening, identification as early as possible can be done using machine learning for predictions. The aim of this research is to conduct a comparative analysis of the performance of machine learning algorithms for predicting stunting in toddlers. Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting are the algorithms that are compared for their performance. The performance of each algorithm is measured using evaluation matrices such as accuracy, precision, recall, and f1-score. The research method starts with data collection, data preprocessing, data splitting, application of machine learning algorithms, evaluation of algorithm performance, and comparison of results. The performance evaluation matrix measurement results show that Random Forest has an accuracy of 99.95%, precision of 99.89%, recall of 99.94%, and f1-score of 99.91%. K-Nearest Neighbors has an accuracy of 99.93%, precision of 99.87%, recall of 99.88%, and f1-score of 99.88%. Meanwhile, Extreme Gradient Boosting has an accuracy of 99.36%, precision of 98.86%, recall of 98.95%, and f1-score of 98.90%. From the results of all performance evaluation matrices, it can be concluded that the random forest algorithm is the best algorithm for predicting stunting in toddlers
Combination of Lexical Resources and Support Vector Machine for Film Sentiment Analysis
No full textText data generated by internet users holds potentially valuable information that can be researched for new insights. One strategy for obtaining information from a text data set is to classify text into predetermined categories based on existing data. Text classification is an aspect of Text Mining. One of the popular approaches in Text Mining uses the Support Vector Machine (SVM) classification algorithm, which aims to classify text and separate data into different classes. However, in some cases, SVM classification algorithms may face difficulties in understanding the context of the text properly due to unclear wording, varying sentence structures, or a lack of understanding of interpretation. To address this problem, applying SVM classification using lexical resources can be an effective solution. In this research framework, the first step is to obtain data, which in this case is a film review dataset taken from the kaggle.com site. After obtaining the data, the next step is preprocessing. The results of the preprocessing are then divided into 80:20 percentages. The 80% training data is used to search for the form of polarization, and this training data lexicon is used for training the SVM model. Based on the modeling results, the overall model accuracy is around 85%, calculated using the confusion matrix. The precision value, which shows the proportion of correct positive predictions, reached 88%. The precision for negative predictions reached 80%, and for neutral predictions, it reached 0%. These results show that the Lexicon+SVM model has good performance, with an accuracy of 85%
A CNN Model for ODOL Truck Detection
No full textThis study developed a Convolutional Neural Network (CNN) model as one of artificial intelligence method to detect trucks experiencing over-dimension and over-loading (ODOL). The primary goal of this research is to enhance the efficiency of truck monitoring, reduce road infrastructure damage, and support the sustainability of transportation using artificial intelligence approaches. The model was trained using a dataset consisting of ODOL and non-ODOL truck images, and successfully achieved a testing accuracy of 94.23%. The confusion matrix analysis demonstrated the model's ability to classify trucks with high precision. Additional testing on truck images not included in the training or testing dataset showed the model's potential for good generalization
Comprehensive Study of Information Technology Strategy Components in Global ICT Companies Utilizing PESTLE and Ansoff Matrix
No full textThis research underscores the diversity and strategic significance of IT Strategy components in shaping the digital transformation and competitive edge of ICT companies. The formulation of IT Strategy documents is pivotal for industries, including ICT companies, as it ensures alignment with business goals and competitive positioning. This study conducts a comprehensive literature review of IT Strategy components within global ICT companies, specifically those specializing in telecommunication network infrastructure. Despite operating within the same sector, each company’s IT Strategy document comprises distinct components. The identified components include Auditor Report, Business Strategy, Leadership, Product/Service Line, Geographic Performance, Research & Development, Partnership & Acquisition, Summary Report, Corporate Governance, Vision & Mission, Financial Statement, Industry Trends, and Business Highlights. These components are essential for aiding the organization’s IT Strategic Plan and the creation of the company's roadmap. Furthermore, this study identifies PESTLE analysis and the Ansoff Matrix as crucial tools in creating strategic roadmaps tailored to each company’s unique objectives and market conditions
Model Random Forest and Support Vector Machine for Flood Classification in Indonesia
No full textPeople, especially those living in lowland areas and along rivers. This flood phenomenon significantly affects various aspects, both in terms of economics, environment, and public safety. Flooding is a disaster that often causes problems for most people, especially those living in lowland areas and on riverbanks. This flood phenomenon significantly affects various aspects, such as the economy, environment, and community safety. This research compares the Random Forest and Support Vector Machine (SVM) methods for flood classification in Jakarta. The data used is flood data from 2016 – 2020 in Jakarta, obtained from Kaggle. Model performance evaluation is carried out using accuracy, precision, recall, and F1- Score metrics. The analysis results show that both models accurately classification floods, with Random Forest showing a more stable performance than SVM
An IT Governance Analysis in Interior Contracting Industry: A COBIT 2019 Approach
No full textThe very rapid development of technology is currently having an impact on every industry, which must adapt by carrying out technological transformation to survive and have added value for customers. Many businesses, including interior contractors, use a variety of hardware and software, as well as information systems, to streamline their business processes. Under these conditions, the importance of strong IT governance to ensure that the implementation of IT investments continues to provide great benefits for the company's progress has been considered a top priority. This research explores how IT governance functions in this industry using COBIT 2019, a leading evaluation framework. The main areas of COBIT 2019 will be used to assess a company's IT capabilities. This study focused on an interior contractor company in Serpong, Indonesia, which was already using enterprise resource planning (ERP) and project management software. The analysis identified 12 out of 40 domains that need improvement to achieve certain target levels. These agreed targets aim to improve IT capabilities, such as reducing dependence on external vendors for system development and creating clear standards for managing technological change. Despite these recommendations, further investigation revealed a gap between the desired and current conditions. This research proposes solutions to bridge this gap, including achieving greater IT system independence and establishing clear guidelines for navigating technological advances
Optimization of Dimsum Production Profits Using the Branch and Bound Method
No full textThe dimsum industry in Indonesia is currently experiencing very significant development, because many businesses offer processed dimsum products for convenient consumption. The characteristics of dimsum are varied and suitable to be served as a snack. This has created an increasing number of dimsum enthusiasts, seen from the emergence of restaurants serving dimsum menus originating from China. The aim of this research is to determine the maximum profit achieved in making dimsum using the Branch and Bound technique. Using the branch and bound method because it is a mathematical model which is a development of a linear program, where all decision variables must be integers, this method limits the optimal solution to a whole by creating an upper and lower branch for each solution which has a fractional value. to be a round value so that each restriction will produce a new branch. Based on the research results, it can be concluded that the optimal production level using the Branch And Bound method is IDR 19,054,950 per month. When compared with the profits before using the Branch and Bound method, the profits obtained were IDR 18,800,000. This shows that by using the Branch and Bound method, the profit of the Mikaila Bakery cake shop increases by IDR. 1.3% or around Rp. 254,950 per month. Sensitivity analysis shows that profits will remain at optimal conditions if changes in the objective function coefficients are less than or equal to the objective function coefficients in the initial model
Analysis of Palm Oil Production Planning for Biodiesel Needs in North Sumatra
No full textPalm oil has become a leading product in the plantation business in Indonesia. Currently, Indonesia has become the country with the largest palm oil production capacity in the world. With this production capability, the opportunity to diversify energy made from palm oil is very possible. The analysis of this study was carried out using secondary data to find out the extent of the potential of palm oil as the main source of biodiesel raw materials in Indonesia. The results of the analysis state that with the production and expansion of oil palm land very massive, energy diversification is a relevant step and very feasible. The role of the government through the export levy tariff policy also determines palm oil consumption for the benefit of the domestic market