International Journal of Advances in Data and Information System
Not a member yet
161 research outputs found
Sort by
Features Selection for Entity Resolution in Prostitution on Twitter
Get PDFEntity resolution is the process of determining whether two references to real-world objects refer to the same or different purposes. This study applies entity resolution on Twitter prostitution dataset based on features with the Regularized Logistic Regression training and determination of Active Learning on Dedupe and based on graphs using Neo4j and Node2Vec. This study found that maximum similarity is 1 when the number of features (personal, location and bio specifications) is complete. The minimum similarity is 0.025662627 when the amount of harmful training data. The most influencing similarity feature is the cellphone number with the lowest starting range from 0.997678459 to 0.999993523. The parameter - length of walk per source has the effect of achieving the best similarity accuracy reaching 71.4% (prediction 14 and yield 10)
K-Nearest Neighbor with K-Fold Cross Validation and Analytic Hierarchy Process on Data Classification
Get PDFThis study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making
Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path
Get PDFThe problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra\u27s algorithm in this case is better than the other two algorithms because it always gets optimal results
Multi-Attribute Decision Making using Hybrid Approach based on Benefit-Cost Model for Sustainable Fashion
Get PDFMulti-Attribute Decision Making (MADM) is used to select the best alternative from multi-alternatives based on multi-attribute (fashion material) and multi-criteria (sustainable fashion). Multi-alternatives are cotton, linen, silk, wool, acrylic, nylon, polyester, rayon, spandex, and mixed. Multi-attributes are material, texture, color, characteristic, comfort, and wearability. Multi-criteria are material fiber, smooth texture, faded color, elastic clothing, useful long, chilly and comfortable. Hybrid approaches and optimal solutions are needed to determine the best choice in decision making for both producers and consumers. The hybrid approach in MADM used is Simple Multi-Attribute Rating (SMART), Multi-Factor Evaluation Process (MFEP), Multi-Object Optimization based on Ratio Analysis (MOORA), Simple Additive Weighting (SAW), and Weighted Product (WP). SMART and MFEP are based on the Non-Benefit Cost Model while MOORA, SAW, and WP are based on a Benefit-Cost Model. The experimental results show that the SMART model with the best alternative is the rayon with the highest value (2.8333). The selection of the MFEP Model with the best alternative is rayon with the highest value (2.8330). The choice of MOORA model with the best alternative is rayon with the highest value (0.2595). The selection of the SAW Model with the best alternative is rayon with the highest value (0.8932). The selection of the WP Model with the best alternative is rayon with the highest value (0.1285). MADM using SMART, MFEP, MOORA, SAW, and WP for sustainable fashion yields the best alternative for consumption and production for the middle-class population in Indonesia
Use Ordinary Expressions to Learn How to Extract Code Feedback From the Software Program Upkeep Process
Get PDFSoftware engineering is the manner of making use of engineering studies and alertness packages to the design, improvement and renovation of software program. For software program builders or college students majoring in data engineering, the software program renovation manner is a totally complicated activity. Software renovation manner charges account for 40% to 80% of the whole software program engineering manner. The software program renovation manner is resulting from based programming, inadequate understanding domains, and application documentation. In this study, researchers attempted to apply the Java programming language and c / c ++ to deal with supply code truncation. After finishing this manner, this system code may be divided into code and remarks. This report could be used to gain data approximately the manner of knowledge this system from the software program renovation manner. For supply code slicing, the writer makes use of normal expressions, specifically textual content processing strategies or patterns. Using normal expressions can accelerate the manner of locating remarks to your application. The end result of this study is to construct software primarily based totally on open supply code (loose license) so that scholars and trendy programmers can use it to assist apprehend this system. According to the effects of the researchers\u27 testing, the recuperation price is 100% and the accuracy is 100%
Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine
Get PDFSentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively
Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm
Get PDFThis study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%
Self-Diagnosis of Web-Based Pregnancy and Childbirth Disorders Using Forward Chaining Methods
Get PDFThe high mortality rate for pregnant women and childbirth in Bali, Indonesia, is caused by a lack of initial diagnosis of the diseases and complaints experienced by pregnant women during pregnancy, as well as a lack of health medical personnel scattered throughout Bali, to be able to provide optimal health services. It is necessary to have an online information system that helps pregnant women to be able to independently and online diagnose diseases, complaints, and symptoms experienced during pregnancy. The system must be able to be accessed anytime and anywhere, with high reliability and availability, and provide fast diagnostic results. Focus of this research is design and implementation of an Information System for Diagnosis of Pregnancy Disorders Based on Cloud Computing based on Forward Chaining Method, using Design Science Research Methodology (DSRM) and tested using the Technology Acceptance Model (TAM) method. The application is placed on the Hybrid Cloud. The results of this research, can help pregnant women in diagnosing diseases and complaints online, to reduce the mortality rate for pregnant women and giving birth
A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction
Get PDFHeart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease
The Diversity of Labuhanbatu Community Culture in Android-Based Applications
Get PDFAndroid is an application system which has been developed by many people so that people can use the Android system in a work which can help them solve problems in their work. Android systems in smartphones can support complete facilities with Android. Labuhan Batu area is divided into three regions, because of this, knowledge is needed to explains about the culture of the people of Labuhanbatu main district separately of the people in the other districts. In this research, the author describes the research methods used in system design into two parts of method namely the research results obtained by methods using descriptive methods and the research conducted based on actual data by comparing theories and then drawing conclusions. The benefit of the authors working on this research is to make an Android-based application which eases the public to find out the information about the culture that exists in each district in Labuhanbatu Regency. Labuhanbatu Regency Cultural Information Application based on Android can be used by students and even the public, using an Android-based smartphone