Data Cleaning Service for Data Warehouse: An Experimental Comparative Study on Local Data

Data warehouse is a collective entity of data from various data sources. Data are prone to several complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure data quality. Data cleaning service involves identification of errors, removing them and improve the quality of data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology, involved stages and services within data warehouse environment. It also provides a comparison through some experiments on local data with different cases, such as different spelling on different pronunciation, misspellings, name abbreviation, honorific prefixes, common nicknames, splitted name and exact match. All services are evaluated based on the proposed quality of service metrics such as performance, capability to process the number of records, platform support, data heterogeneity, and price; so that in the future these services are reliable to handle big data in data warehouse

Stateful library service system design and implementation in Saudi Arabia

Service system has become one of the most challenging research issues in industry. Most of organizations in Saudi Arabia build their services with state-less technique to avoid many issues although there are some acknowledged advantages of using state-full technique. These issues are mainly related to the low number of visitors, low number of services, storage capacity and organization size. The purpose of this research is to create services that have capability in reading all required data from library management system, improving the service by applying state-full technique. Technology acceptance model is used to measure the acceptance of state-full service system through organizations and customers which gave some prediction to library high management to support them in decision making

APLIKASI INFORMATION RETRIEVAL UNTUK PEMBENTUKAN TESAURUS BERBAHASA INDONESIA SECARA OTOMATIS

In Information Retrieval, a thesaurus which could provide a term list with its similar terms can be used to search a document within a collection of documents. With the growth of information, a thesaurus is expected to help more in finding information so that more relevant document could be retrieved The purpose of this research is to find a method to form an automatic thesaurus generation. This process requires a term dictionary in specific field and a group of documents to perform calculation defining the relationship within its existing terms. The generation of the thesaurus is done by calculating the paired-occurrence value within the terms which is found in a collection of documents. Theexperiment is done by using some respondents to define the terms that are relevant with a specific term. The result showed that the system could provide accuracy in generating the thesaurus with the average recall value of 59.62 % and the average precision of66. 78 %. Keywords: Information Retrieval, automatic thesaurus generation, similarity, recal/, Indonesian thesauru

Arabic Translation Web Services: An Implementation Survey Towards Arabic Language Grid

This research proposes the development of Arabic language service. It is a servlet-based Webservice which provides a translation from English into Arabic, using techniques to develop Webservices such as Restful and API in Java language. This API is part of Language Grid, aproject in Japan to collect, share and combine as many language resources as possible bywrapping the language resources as web services, which is also known as Everything as a Service(XaaS) technology. By having Arabic language services connected in the Language Grid, therewill be a wider use of Arabic language resource in the world. An evaluation of running theservice is provided to enhance the performance and reliability of the service

Pengembangan Sistem Informasi Akademik Berdasarkan Kurikulum Dan Sistem Pengujian Berbasis Kompetensi Yang Diintegrasikan Dengan Aplikasi Monitoring Siswa Berbasis J2me

Sejak tahun ajaran 2002/2003, di Indonesia diberlakukan kurikulum baru sebagai pengganti kurikulum 1994 yang disebut dengan nama Kurikulum dan Sistem Pengujian Berbasis Kompetensi. Sebagai awalnya kurikulum ini dilaksanakan secara terbatas di 40 sekolah di seluruh Indonesia, salah satunya adalah SMUN 1 Gresik. Implikasi penerapan pendidikan berbasis kompetensi adalah sistem pengujian berbasis kemampuan dasar dimana kemampuan dasar merupakan bentuk minimum dalam mata pelajaran yang harus dikuasai oleh siswa. Selain itu, pengembangan sistem pengujian hasil kegiatan pembelajaran berbasis kompetensi ini bersifat hirarkis, secara berurutan yaitu : mata pelajaran, standar kompetensi, kemampuan dasar, jenis ujian dan indikator. Dengan tingkat penilaian yang cukup rumit ini, maka dibuat perangkat lunak guna membantu proses akademik di sekolah. Proses pembuatan perangkat lunak ini didahului dengan studi literature dan pengumpulan data di sekolah, dilanjutkan dengan perancangan sistem. Dari hasil perancangan inilah maka bisa dibuat suatu aplikasi perangkat lunak untuk kemudian dilakukan uji coba dan evaluasi guna mengetahui kehandalan dan unjuk kerja dari aplikasi yang telah dibuat. Setelah melalui serangkaian uji coba, ternyata aplikasi yang dibuat bisa dan mampu menjawab persoalan yang ada, terutama sekali yang berkaitan dengan proses penilaian dalam kegiatan akademik yang sedang berlangsung. Selain itu dengan aplikasi mobile yang juga dibuat, sangat memudahkan orang tua siswa dalam memantau perkembangan akademik putra-putrinya

Perancangan Dan Pembuatan Case Tool Software Testing Menggunakan Metode Static Data Flow Analysis (Studi Kasus Pogram Berbasis C++)

Penentuan kualitas atau mutu suatu perangkat lunak mutlak diperlukan. Kualitas suatu perangkat lunak dapat dinyatakan baik bila telah sesuai dengan requirement pengguna tanpa mengabaikan segi strukturalnya. Dengan tidak mengabaikan fungsional program, uji coba struktural juga harus dilakukan karena uji coba fungsional tak mampu menangani hal-hal yang berhubungan dengan struktural program.. Uji coba fungsional tidak dapat menentukan apakah suatu bagian program telah dieksekusi atau tidak. Dengan demikian, program yang tidak dieksekusi selama pengujian akan tersembunyi dalam package program dan bila didalamnya terdapat kesalahan maka kesalahan tersebut akan tersembunyi dalam jangka waktu tertentu. Data flow analysis adalah suatu metode yang digunakan untuk mengumpulkan informasi dalam program tanpa mengeksekusi program yang diuji coba. Metode ini merupakan bagian dari metode pengujian secara structural yang sangat efektif untuk menemukan kesalahan yang berupa data flow anomaly dengan cara memeriksa kode program. Dengan metode ini, seluruh pernyataan program yang diuji coba akan dianalisa. Metodologi yang digunakan dalam penelitian ini terdiri dari beberapa tahapan. Yang dilakukan pertama kali adalah melakukan parsing terhadap file input untuk dijadikan file teks dengan format tertentu yang sudah terpisah menjadi token yang dapat berdiri sendiri. Selanjutnya akan diambil daftar nama fungsi dalam program untuk dijadikan input fungsi. Selanjutnya isi fungsi input akan direpresentasikan menjadi sebuah graph. Dari graph inilah dapat dicari keberadaan data flow anomaly dalam fungsi dan dibuat visualisasi aliran program dalam suatu control flow graph. Tahap terakhir yang dilakukan adalah uji coba dan evaluasi. Dari uji coba dan evaluasi yang dilakukan pada berbagai macam jenis fungsi, baik fungsi sederhana yang hanya melibatkan pernyataan-pernyataan sekuensial, fungsi yang melibatkan percabangan, Perulangan maupun fungsi yang melibatkan pemanggilan terhadap fungsi lain didapatkan data flow anomaly maupun control flow graph dengan benar

Data mapping process to handle semantic data problem on student grading system

Many applications are developed on education domain. Information and data for each application are stored in distributed locations with different data representations on each database. This situation leads to heterogeneity at the level of integration data. Heterogeneity data may cause many problems. One major issue is about the semantic relationships data among applications on education domain, in which the learning data may have the same name but with a different meaning, or learning data that has a different name with same meaning. This paper discusses on semantic data mapping process to handle semantic relationships problem on education domain. There are two main parts in the semantic data mapping process. The first part is the semantic data mapping engine to produce data mapping language with turtle (.ttl) file format as a standard XML file schema, that can be used for Local Java Application using Jena Library and Triple Store. The Turtle file contains detail information about data schema of every application inside the database system. The second part is to provide D2R Server that can be accessed from outside environment using HTTP Protocol. This can be done using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. To implement the semantic data process, this paper focuses on the student grading system in the learning environment of education domain. By following the proposed semantic data mapping process, the turtle file format is produced as a result of the first part of the process. Finally, this file is used to be combined and integrated with other turtle files in order to map and link with other data representation of other applications

Content Based Image Retrieval and Support Vector Machine Methods for Face Recognition

The development of biometrics is growing rapidly. The recognition as non-trivial element in biometrics is not only using fingerprints, but also human face. The purpose of this research is to implement both Content Based Image Retrieval (CBIR) and Support Vector Machine (SVM) methods in the face recognition system with a combination of features extraction. CBIR method interprets images by exploiting several features. The feature usually consists of texture, color, and shape. This research utilizes color, texture, shape and shape coordinate features of the image. The proposed algorithms are HSV Color Histogram, Color Level Co-Occurrence Matrix (CLCM), Eccentricity, Metric, and Hierarchical Centroid. SVM method is used to train and classify the extracted vectors. The result shows that the proposed system is 95% accurate in recognizing faces with different resolutions

Automated Visual Inspection for Bottle Caps Using Fuzzy Logic

Automated Visual Inspection System (AVIS) has the capability to investigate large numbers of manufactured goods quickly and accurately. In addition, this system operates with a high level of reliability and consistency in their tasks. This study proposed an AVIS for detecting cap situations by using fuzzy logic classifiers. The objectives of this research are to develop an applicable image processing algorithm, together with a feature extraction technique, and to detect the cap for plastic bottles which is based on the average of distances. Three types of classification were compared for detecting the bottle caps. They are Mamdani, Sugeno, and production rule. The system was evaluated in a real time environment. The results are 97.91%, 97.5%, 96.66% accuracy for Mamdani, Sugeno, and production rule respectively

Development of Scoliotic spine severity detection using deep learning Algorithms

According to research conducted by Johns Hopkins' Division of Pediatric Orthopedic Surgery, around three million new instances of Scoliosis are identified each year, with the majority of cases affecting children between the ages of 10 and 12. The current method of diagnosing and treating Scoliosis, which includes spinal injections, back braces, and a variety of other types of surgery, may have resulted in inconsistencies and ineffective treatment by professionals. Other scoliosis diagnosis methods have been developed since the technology's invention. Using Convolutional Neural Network (CNN), this research will integrate an artificial intelligence-assisted method for detecting and classifying Scoliosis illness types. The software model will include an initialization phase, preprocessing the dataset, segmentation of features, performance measurement, and severity classification. The neural network used in this study is U-Net, which was developed specifically for biomedical picture segmentation. It has demonstrated reliable and accurate results, with prediction accuracy reaching 94.42%. As a result, it has been established that employing an algorithm helped by artificial intelligence provides a higher level of accuracy in detecting Scoliosis than manual diagnosis by professionals