Review implementation of linguistic approach in schema matching

Research related schema matching has been conducted since last decade. Few approach related schema matching has been conducted with various methods such as neuron network, feature selection, constrain based, instance based, linguistic, and so on. Some field used schema matching as basic model such as e-commerce, e-business and data warehousing. Implementation of linguistic approach itself has been used a long time with various problem such as to calculated entity similarity values in two or more schemas. The purpose of this paper was to provide an overview of previous studies related to the implementation of the linguistic approach in the schema matching and finding gap for the development of existing methods. Futhermore, this paper focused on measurement of similarity in linguistic approach in schema matching

An extended approach of weight collective influence graph for detection influence actor

Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method. However, this method is associated with unweighted datasets, which makes it inappropriate for social media, significantly using weight datasets. This study proposes a new CI method called the Weighted Collective Influence Graph (WCIG), which uses the weights and neighbor values to detect the influence of hate speech. A total of 49, 992 Indonesian tweets were and extracted from Indonesian Twitter accounts, from January 01 to January 22, 2021. The data collected are also used to compare the results of the proposed WCIG method to determine the influential actors in the dissemination of information. The experiment was carried out two times using parameters ∂=2 and ∂=4. The results showed that the usernames bernacleboy and zack_rockstar are influential actors in the dataset. Furthermore, the time needed to process WCIG calculations on HPC is 34-75 hours because the larger the parameter used, the greater the processing time

ANALISA DAN PERHITUNGAN REDAMAN HUJAN PADA LINK RADIO 2,4GHZ DENGAN ARAH LINK BERLAWANAN DENGAN ARAH ANGIN DI DAERAH MATARAM

Tropic area, such as the city of Mataram, generally has heavy rainfall which causes propagation attenuation affecting communication network system mainly on terrestrial radio link. Due to such a condition, it requires a design of network system that is reliable and also resistant to such a rain condition.Before designing, it is important to determine the characteristics and real circumstances on the area by means of capturing rainfall and rain attenuationdata. Rainfall is closely related to attenuation, because the higher the rainfall, the higher the attenuation occurs. The use of 2.4 GHz radio devices in Mataram is relatively very high, both as a backbone and local access. Therefore, it is necessary to obtain information concerning rain attenuation on the radio link with a frequency of 2.4 GHz in the city of Mataram to be used as means for designing terrestrial radio communication network system. Therecent study will carry out a rain attenuation measurement either by direct method or by SST (Synthetic Storm Technique) method on the 2.4 GHz radio link in the city of Mataram to provide a comparison of rain attenuation along the link considering wind direction and speed. 2.4 GHz radio link will be built on the campus of STMIK BumigoraMataram with 47.47 meterslink distance and its direction is opposite to the direction of the wind. Then, empirical rain attenuation measurementis carried out along the links and its result will be compared with the result of SSTcalculation on rain attenuation processed by Matlab, in which the data of the rainfall, wind speed, and its direction are obtained from BMKG Kediri Mataram

Penggunaan Metode Analisa Komponen Utama (PCA) untuk Mereduksi Faktor-Faktor yang Mempengaruhi Penyakit Jantung Koroner

Tujuan dari penelitian ini adalah untuk mereduksi variabel-variabel yang benar-benar mempengaruhi penyakit jantung koroner. Untuk mendapatkan informasi yang diinginkan, maka diperlukan suatu metodologi yang tepat agar dapat digunakan dalam mengolah data yang sudah ada. Ada banyak metodologi yang digunakan untuk melakukan pengurangan variabel (feature eksraction) seperti principal component analysis (PCA), rough set theory, algoritma genetika, dan lainnya. Metodologi yang akan digunakan untuk melakukan reduksi dalam penelitian ini adalah metodologi principal component analisa (PCA) atau analisa komponen utama. Metodologi PCA digunakan untuk mereduksi jumlah variabel yang ada pada dataset sehingga dari 13 variabel yang terdapat pada dataset hanya akan diketahui empat variabel yang benar- benar mempengaruhi penyakit jantung koroner dan empat variabel yang dihasilkan dengan metodologi PCA dapat mewakili 13 variabel yang ada pada dataset. Dataset yang digunakan pada penelitian ini adalah data penyakit jantung koroner yang diperoleh dari Cleveland Clinic Foundation yang merupakan koleksi database dari Universitas California, Irvine (UCI) machine learning repository

PENGGUNAAN PRINCIPAL COMPONENT ANALYSIS DAN POHON KEPUTUSAN UNTUK MENDETEKSI PENYAKIT JANTUNG KORONER

There are many ways that can be done to make the diagnosis of a disease like by seeing physical symptoms suffered by patients and utilizing technology to see inside the patient's body. Another way that can be done to detect a disease is using calculations. There are many methods or algorithms that can be used to develop disease detection system for example, artificial neural network method, Naive Bayes, decision trees, rough set theory, principal component analysis (PCA), nearest neighbor, and other. This study uses data for coronary heart disease to learn and discover patterns that can be used to detect coronary heart disease. Principal component analysis method is used to reduce existing variable in the data and result in principal component. Decision tree method is used to create the rules which are used for coronary heart disease diagnosis. From 14 variables that exist in the research data, only nine variables in the research data are considered to have significant effect in performing coronary heart disease diagnosis. Further analysis of the nine variables is done using decission tree methods and results in 25 rules for coronary heart disease detection. The use of two algorithms have accuracy of 75.42%