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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Didit Rahmat Hartadi
Politeknik Negeri Jember
Computer Science & IT Electrical & Electronics Engineering

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PENGEMBANGAN IDEAL SOLUTION SYSTEM VERSI 2.0 DENGAN MENERAPKAN MODEL PROTOTYPING Rani Purbaningtyas; Didit Rahmat Hartadi
Jurnal Teknologi Informasi dan Terapan Vol 2 No 1 (2015)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

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Applications Ideal Solution System version 1.0 is an application developed independently since 2014 to solve various problems are multiple criteria as well as having the characteristics of the benefit factor and cost factor. In manufacturing, this application implementing TOPSIS method as basic algorithms used program. But in its implementation, there is still a shortage or revision of the user. Note the revision of the user obtained during the implementation phase is the basis for the developers to develop applications. Therefore, developers are trying to develop the Ideal Solution System version 2.0, which was developed with reference to the model prototyping. Ideal Prototype Solution System version 2.0 has been tested and found viable easy use testing because it is easy and convenient for the user so operate the application.
PREDIKSI MISSING IMPUTATION UNTUK DATA PENYEBARAN MENGGUNAKAN NAÏVE BAYES Aji Seto Arifianto; Didit Rahmat Hartadi; Anik Nur Novitasari E S
Jurnal Teknologi Informasi dan Terapan Vol 3 No 1 (2016)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

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Dengue fever is a disease caused by dengue virus by Aedes Aegypti mosquitoes intermediary. There are four dengue virus serotypes, called DEN-1, DEN-2, DEN-3, and DEN-4, each of them can lead to dengue fever, either mild or fatal. Based on a survey of public health in Jember, recorded during January 2015 there were 300 cases of dengue fever, and seven of them died. Previous research using fuzzy method to determined the potential spread of Dengue in Jember. Para meters that used is the amount of rain in a period, the amount of rain in one month, free amount of larva and house index. Data that used is taken from 2009 until 2012 from 31 districts. The weaknesses in this study were not contain a way to resolve the imputation of missing data. In fact, survey data is often incomplete. Based on these issues, it will be created a prediction system of imputation of missing data on the spread of dengue by using Naïve Bayes method. The data refers to the prediction of the mi ssing data and the data were used as training data, so it can be processed further. This research has been successfully predicting missing data imputation using existing data, so that the missing data can be completed with high degree of accuracy.
Co-Authors Aji Seto Arifianto Anik Nur Novitasari E S Rani Purbaningtyas