1,720,980 research outputs found
Increasing Accuracy of C4.5 Algorithm by Applying Discretization and Correlation-based Feature Selection for Chronic Kidney Disease Diagnosis
Full text linkData mining is a technique of research necessary hidden information in a database to find interesting pattern. In the health sector, data mining can be used to diagnose a disease from the patient's medical data record. This research used a Chronic Kidney Disease (CKD) dataset obtained from UCI machine learning repository. In this dataset almost half of attributes are numeric types that are continuous. Continuous attributes can make accuracy lower because the data forms are unlimited, so it need to be transformed into discrete. In certain cases, if all attributes are used, it can produce a low level of accuracy because it is irrelevant and does not have a correlation with the target class. So, these attributes need to be selected in advance to get more accurate results. Classification is one technique in data mining. Which one of classification algorithms is C4.5. Purpose of this study is increasing accuracy of C4.5 algorithm by applaying discretization and Correlation-Based Feature Selection (CFS) for chronic kidney disease diagnosis. Accuracy improvement is done by applying discretization and CFS. Discretization is used to handle continuous value, while CFS is used as attribute selection. Experiment was conducted with WEKA (Waikato Environment for Knowledge Analysis). By applying discretization and CFS in C4.5 shows an increase in accuracy of 0.5%. The C4.5 has an accuracy of 97%. The accuracy of C4.5 with discretization are 97.25% and accuracy of C4.5 algorithm with discretization and CFS is 97.5%
Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy
Full text linkElectronic Commerce (E-Commerce) is distributing, buying, selling, and marketing goods and services over electronic systems such as the Internet, television, websites, and other computer networks. E-commerce platforms such as amazon.com and Lazada.co.id offer products with various price and quality. Sentiment analysis used to understand the product’s popularity based on customers’ reviews. There are some approaches in sentiment analysis including machine learning. The part of machine learning that focuses on text processing called text mining. One of the techniques in text mining is classification and Support Vector Machine (SVM) is one of the frequently used algorithms to perform classification. Feature and parameter selection in SVM significantly affecting the classification accuracy. In this study, we chose unigram as the feature extraction and grid search as parameter optimization to improve SVM classification accuracy. Two customer review datasets with different language are used which is Amazon reviews that written in English and Lazada reviews in the Indonesian language. 10-folds cross validation and confusion matrix are used to evaluating the experiment results. The experiment results show that applying unigram and grid search on SVM algorithm can improve Amazon review accuracy by 26,4% and Lazada reviews by 4,26%
Implementasi Cloud Computing Menggunakan Metode Pengembangan Sistem Agile
Full text linkCloud computing merupakan sebuah teknologi yang menyediakan layanan terhadap sumber daya komputasi melalui sebuah jaringan. Sumber daya yang di sediakan di dalam cloud computing meliputi mesin, media penyimpanan data, sistem operasi dan program aplikasi. Fitur dari cloud computing dipercaya akan jauh lebih hemat dan memuaskan. Masalah yang muncul adalah bagaimana mengimplementasi Cloud Computing dengan menggunakan Windows Azure Pack dan bagaimana provisioning Windows Azure Pack SQL Database. Fokus pada penelitian ini adalah pada proses deploying dan provisioning SQL Database Server. Pengimplementasian cloud computing menggunakan metode pengembangan sistem agile dengan langkah-langkah meliputi perencanaan, implementasi, pengujian (test), dokumentasi, deployment dan pemeliharaan. Untuk menjalankan proses tersebut kebutuhan perangkat yang dipersiapkan meliputi perangkat keras seperti PC Server Cisco UCS C240 M3S2, Hardisk 8753 GB, 256 GB RAM, bandwith minimal 1 Mbps dan kebutuhan perangkat lunak meliputi Windows Server 2012 R2, VMM, Windows Azure Pack, IIS, SQL Server 2012 dan Web Patform Installer. Hasil dari implementasi cloud computing menggunakan metode pengembangan sistem agile adalah terbentuknya sebuah sistem cloud hosting provider dengan menggunakan Windows Azure Pack dan SQL Server 2012 sebagai sistem utama dan pengelolaan database menggunakan Microsoft SQL Server Managemen
Penyajian Data Pelanggan pada Lima Area PT. Telekomunikasi Indonesia, Tbk. Kandatel Pekalongan Menggunakan Google Earth
Full text linkProsedur sistem penyajian data pelanggan di PT. Telekomunikasi Indonesia, Tbk. Kandatel Pekalongan khususnya bidang Divisi Business Services masih menggunakan cara manual, hanya menggunakan media Micorsoft Excel. Dalam hal ini peneliti ingin menerapkannya dalam bentuk aplikasi Google Earth untuk membuat penyajian data pelanggan, karena Google Earth dapat memetakan bumi dari superimposisi gambar yang dikumpulkan dari pemetaan satelit, fotografi udara dan globe GIS tiga dimensi sehingga akan menghasilkan data yang akurat. Penyajian data dengan menggunakan Google Earth dilakukan dengan memanfaatkan bahasa markup HTML. Dengan cara ini, Divisi Business Service akan menjadi lebih mudah ketika menyajikan data-data para pelanggan Telkom yang mencakup lima area yaitu Batang, Pekalongan, Pemalang, Tegal dan Brebes
Implementasi Logika Fuzzy Mamdani untuk Mendeteksi Kerentanan Daerah Banjir di Semarang Utara
Full text linkKerentanan (Vuinerability) adalah keadaan atau kondisi yang dapat mengurangi kemampuan masyarakat untuk mempersiapkan diri menghadapi bahaya atau ancaman bencana. Logika Fuzzy adalah cara untuk memetakan suatu ke dalam suatu ruang output. Salah satu aplikasi logika Fuzzy adalah untuk menentukan kerentanan daerah banjir di Semarang Utara. Pengujian dilakukan dengan metode Mamdani Fuzzy Inference System. secara manual dan program menggunakan 5 defuzifikasi, yaitu Centroid, SOM (Smallest Of Maximum), LOM (Large Of Maximum), MOM (Mean Of Maximum), Bisector. Dari 2 contoh kasus diperoleh hasil pengujian dengan kesimpulan yang sama
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Improved Accuracy of Naive Bayes Classifier for Determination of Customer Churn Uses SMOTE and Genetic Algorithms
Full text linkWith increasing competition in the business world, many companies use data mining techniques to determine the level of customer loyalty. The customer data used in this study is the german credit dataset obtained from UCI. Such data have an imbalance problem of class because the amount of data in the loyal class is more than in the churn class. In addition, there are some irrelevant attributes for customer classification, so attributes selection is needed to get more accurate classification results. One classification algorithm is naive bayes. Naive Bayes has been used as an effective classification for years because it is easy to build and give an independent attribute into its structure. The purpose of this study is to improve the accuracy of the Naive Bayes for customer classification. SMOTE and genetic algorithm do for improving the accuracy. The SMOTE is used to handle class imbalance problems, while the genetic algorithm is used for attributes selection. Accuracy using the Naive Bayes is 47.10%, while the mean accuracy results obtained from the Naive Bayes with the application of the SMOTE is 78.15% and the accuracy obtained from the Naive Bayes with the application of the SMOTE and genetic algorithm is 78.46%
Improving accuracy of C4.5 algorithm using split feature reduction model and bagging ensemble for credit card risk prediction
No full textVariations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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