Sinkron : jurnal dan penelitian teknik informatika
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Comparison of Smartphone Technology using AHP, ELECTRE, and PROMETHEE Methods
No full textThe progress of smartphone technology is now very rapid, supported by many renewable features, even many users are competing to get the latest products without regard to the costs that have been incurred. The problem that arises is that it is increasingly difficult to select technology-based products with many criteria. The purpose of writing this paper is to provide the best solution for selecting technology-based products with multi-criteria to suit user needs by taking into account the costs incurred effectively and the use of contradictory multi-criteria applications. The presence of technology products always has many criteria that make it more difficult for users to choose products as the right choice according to their needs, thus the right method is needed as a solution to obtain technology-based products such as smartphones. The Analytic Hierarchy Process (AHP) method is used for the evaluation and selection process. This AHP method will collaborate with the ELECTRE and PROMETHEE methods as a comparison solution for smartphone product selection. The resulting comparison will be an applied model for smartphone selection that produces the best decision-making support according to user needs. The results of the collaborative implementation process of the ELECTRE and PROMETHEE methods provide a decision on the rating system. The collaborative application of the AHP method to the ELECTRE and PROMETHEE methods provides optimal decision support for the selection process, so that this can be used as a comparison material in making decisions regarding the selection of smartphones as technology-based products
Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease
No full textChronic Kidney Disease (CKD) is a highly serious health issue, affecting millions of people worldwide. Early diagnosis and accurate prediction of chronic kidney disease are key factors in successful treatment. One of the approaches used for diagnosing this disease is through machine learning algorithms, specifically the Support Vector Machine (SVM) method. By collecting CKD data that includes various clinical parameters, initial kernel selection as well as various kernels are tested. However, the accuracy of the SVM method can be further improved for better diagnosis. The objective of this research is to enhance accuracy, optimize parameters, and improve the SVM kernel by incorporating the Particle Swarm Optimization (PSO) algorithm. The results of this study indicate that the use of PSO method to improve SVM kernels can significantly enhance accuracy in CKD diagnosis compared to conventional SVM approaches, potentially aiding medical practitioners in early disease diagnosis and better CKD management, which in turn can improve patient prognosis and quality of lif
Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks
No full textThe prediction of student graduation plays a crucial role in improving higher education efficiency and as-sisting students in graduating on time. Neural networks have been used for predicting student graduation; however, the performance of neural network models can still be enhanced to make predictions more accurate. Genetic algorithms are optimization methods used to improve the performance of neural network models by optimizing their parameters. The problem at hand is the suboptimal performance of neural networks in predict-ing student graduation. Thus, the objective is to leverage genetic algorithms to improve the accuracy of stu-dent graduation predictions, measure the improvements obtained, and compare the accuracy results between the genetic algorithm-optimized neural network model and the neural network model without optimization. The training process of the neural network model is conducted using training data obtained through experiments, and the accuracy results of the neural network model with and without genetic algorithm optimization are compared. The research findings indicate that by harnessing genetic algorithms to optimize the parameters of the neural network model, the accuracy of student graduation predictions increased by 2.78%. Furthermore, the Area Under the Curve (AUC) also improved by 0.037%. These results demonstrate that integrating genetic algorithms into the neural network model can significantly enhance prediction performance. Thus, this study successfully utilized genetic algorithms to improve student graduation predictions using a neural network model. Experimental results show that prediction accuracy and AUC values significantly increased after opti-mizing the neural network model's parameters with genetic algorithms. Therefore, the use of genetic algorithms can be considered an effective approach to improving student graduation predictions, thereby assisting educa-tional institutions in improving efficiency and helping students graduate on tim
Implementation of K-Means Clustering in Food Security by Regency in East Java Province in 2022
No full textFood is the main need that society must fulfill. If food security is disrupted, it will have a negative impact on the nation's life. The agricultural sector has an important role in West Java Province. This province has a large area of agricultural land, so it has high potential to produce abundant agricultural production. However, knowing the adequate number of farmers is very important. Therefore, the implementation of K-Means Clustering can make a significant contribution to the East Java Provincial Agriculture Service in grouping farmers by district. To achieve optimal results, determining the best K value needs to be considered carefully. K-Means Cluster Analysis is a method of non-hierarchical Cluster Analysis that groups data into one or more groups. Data with the same characteristics is grouped into one cluster and data with different characteristics is grouped into another cluster. The data used in this research are land area and rice production in the Regency of East Java Province in 2022. Based on the results of research with the object of Food Security, it can be concluded that, the results of the analysis of the application of manual data mining calculations in Excel Software using the K-Means Clustering method, resulted in two types of clustering in the form of C0, namely the Highest Land Area and Production group with 4 districts: Jember Regency, Ngawi Regency, Bojonegoro Regency and Lamongan Regency, for C1 clustering, namely the Lowest Land Area and Production group with 25 districts in East Java Provinc
Analysis of Goods Stock Using the Apriori Algorithm to Aid Goods Purchase Decision Making
No full textAfter the covid-19 pandemic outbreak and the high uncertainty index during the covid-19 pandemic. The business world is experiencing a huge impact in addition to the sluggish interest of buyers is also limited in its movement. On this occasion, the researcher intends to provide an overview that can help business people, especially in purchasing goods that are useful for filling the stock of goods in the warehouse. To get maximum results and minimum error rate. Researchers use the Apriori Algorithm in analyzing stock items and use the Tanagra version 1.4 application. Research data used the sales history of the past 1 year here the data used is between May 2022 and April 2023. With a total itemset of 375. But after applying the Golden Rule (threshold), there are only 10 products with sales reaching 1623 items. This research produces a final ordered association based on the minimum support and minimum confidence that has been determined, namely 12 rules with a combination of 2 itemsets with a confidence value of 100%
Implementation of Cloud Run and Cloud Storage as REST API Service on OutfitHub Application
No full textThe development of Cloud Computing technology has progressed rapidly in recent years especially with the emergence of Google Cloud Services (GCR) which has become one of the leading cloud service providers. This research focuses on the OutfitHub application, which plays a role in assisting users in determining clothing styles using a personalized recommendation system. In developing this application, the research seeks to implement cloud computing services to improve application performance. The purpose of this research is to implement Cloud Computing, especially Cloud run and Cloud Storage services as Rest API in the Outfithub application. By implementing these two services, it is expected that there is no need to pay attention to the problem of Storage needs that are growing at any time and no need to worry about the need for server configuration because both of these things will be fully done by GCR. Implementing Cloud Computing will provide a variety of benefits in addition to those previously mentioned, such as: being able to access data from anywhere and at any time. This implementation is expected to be able to run OutfitHub applications in a Cloud environment in a serverless computing manner without requiring the design of unnecessary virtual machines
Classification of types Roasted Coffee Beans using Convolutional Neural Network Method
No full textIn the current digital era, the role of technology in the agricultural industry is very necessary to increase yields which can have an impact on the productivity and welfare of farmers. Coffee is a drink that has been very popular for many years. Due to the high demand for coffee beans, this research aims to develop a system that can classify types of roasted coffee beans based on images using the Convolution Neural Network (CNN) method. Coffee bean processing is the most important stage in the coffee industry, classifying coffee beans often requires more in-depth knowledge and extensive experience regarding coffee beans. Therefore, this system can be a more effective solution. The author collects a dataset containing types of roasted coffee beans, then the Convolutional Neural Network (CNN) can analyze in the form of visual patterns each type of coffee bean. This implementation is expected to help the coffee industry identify coffee beans quickly and accurately
Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method
No full textResearch on the Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method is an effort to understand and utilize sales data in making more informed and strategic business decisions. The main goal of this research is to extract hidden patterns from large sales data sets, which cannot be discovered by manual analysis alone. This research process is divided into several key stages, namely Data Selection, Preprocessing, Transformation, and Data Mining. The research results show that the Apriori method is effective in finding purchasing patterns. In terms of the frequency of 2 itemsets, the highest support value was found to be 1, which indicates that the combination of the two products is always purchased together in all transactions. For 3 itemsets and 4 itemsets, the high support value of 0.9 also indicates the existence of product combinations that are often purchased together. In terms of confidence, 2 itemsets show the highest value of 1.25, indicating that purchasing one product has a high tendency to be followed by purchasing other products. For 3 itemsets and 4 itemsets, the confidence values show a slightly lower trend but are still significant. Furthermore, lift analysis provides additional insight into the strength of association between itemsets, with 4 itemsets showing the highest lift value of 1.30, indicating the product combination has a very strong association compared to random expectations. This research confirms the potential of the Apriori method in finding valuable sales patterns, which can help companies make strategic decisions for increasing sales and customer satisfaction
Determination of MSMEs Business Feasibility Decisions using the Profile Matching Method
No full textMicro and Medium Enterprises (MSMEs) in Bali contribute to the local economy. When operating a business, it is crucial to evaluate the viability of MSME enterprises to enhance the calibre of business offerings and services. Nevertheless, the lack of competence to establish the parameters or criteria for evaluating the viability of a firm poses challenges for MSMEs in decision-making. This study presents a business feasibility assessment model utilising the Profile Matching method to aid in resolving issues and supporting Micro, Small, and Medium Enterprises (MSMEs) in making informed decisions for the long-term viability of their businesses. This study examines the feasibility of MSME businesses using the Profile Matching method. The method involves assessing 13 criteria and selecting from 10 alternatives. The process includes determining initial and target values, weighting criteria, grouping core and secondary factors, calculating total values, and ranking. The final results indicate which MSMEs are feasible and which ones require further evaluation. According to the calculations using the Profile Matching method, MSME 5 has a value of 27.80, indicating its feasibility
Comparison of MAGIQ, MABAC, MARCOS, and MOORA Methods in Multi-Criteria Problems
No full textDetermining the best alternative from many criteria is one of the core problems in decision making, both routine and non-routine problems. One of them is in the problem of determining egg suppliers. Eggs are one of the basic needs of the community so that the demand for eggs is always increasing, this makes the emergence of many egg agents in distributing and fulfilling needs. Selective and careful selection is needed in order to get a supplier that meets the desired expectations. Problems then arise in the selection of egg suppliers that are not in accordance with the expectations of the manager. In determining egg suppliers that have been carried out by UD Taluh Subur, only by means of a simple comparison between several factors such as price, production quantity, and quality without considering other factors. In addition to this, business managers have limited knowledge in statistical and business decision making. To optimize the supplier selection process, a Decision Support System can be used to help provide recommendations for selecting prospective suppliers of fixed eggs. Based on the situation of decision makers who have limited knowledge in statistical decision making, the MAGIQ method is suitable for weighting. To provide a more accurate ranking, additional methods such as the MABAC, MARCOS, and MOORA methods are used. The purpose of this research is to focus on which method is most recommended for the case study faced in the research based on the analysis results of the sensitivity test. The results of the sensitivity test show that the MAGIQ-MABAC method has the highest value of 4.42737%, then the MAGIQ-MOORA method with a value of 2.34415% and the MAGIQ-MARCOS method with a value of 0.45729%