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All Journal PROCEEDING IC-ITECHS 2014 Journal of Information Technology
Go Frendi Gunawan
STIKI Malang
Computer Science & IT Education

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The Use of Smartphone To Process Personal Medical Record By Using Geographical Information System Technology Subari Subari; Go Frendi Gunawan
IC-ITECHS Vol 1 (2014): Prosiding IC-ITECHS 2014
Publisher : IC-ITECHS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1669.678 KB)

Abstract

In this era medical record is focussed on how to arrange medical record document. The status of manual medical record will be kept on the storage, the media like paper is very easy to be broken and lost. Therefore the information of medical record data will be difficult to be gotten completely, moreover the file of medical record especially for using personal is not easy to be kept. How the people face the improvement in using the gadget and also smartphone. it should be cope with how to optimalize smartphone technology used by people by using correct information media in the implementing electronic medical record for each person. This research design also includes the technology of geographical information system for the spatial data of private doctor, hospital, medical laboratorium and related object for every using smartphone. This research will result the running application on the smartphone that can operate and manage individual medical record. It can be integrated online on the center of health and give recommendation and retrieval information about medical note for the hospital, private doctor or other health service. The invention of this application can process the medical record data from some deseases fast, more detail and easy, also the geographical visualization for the related health service
Multiple And Single Haar Classifier For Face Recognition Go Frendi Gunawan; Subari Subari
IC-ITECHS Vol 1 (2014): Prosiding IC-ITECHS 2014
Publisher : IC-ITECHS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1706.007 KB)

Abstract

Haar-Classifier is a well-known tool for face detection in an image. Open-CV library already provide a class for Haar-Classifier implementation. However, one significant problem in face detection is tilt-face-pose recognition. In this paper, we will discuss about two different solutions to overcome the problem. The solutions are written in Python which has a very simple syntax. The first solution is using multiple classifier. Each of them is trained to recognize face with different rotation-degrees. The second solution is using single classifier to recognize many face image which have been rotated by different angles.
Penerapan Metode Naïve Bayes Untuk Klasifikasi Sms Spam Menggunakan Java Rogramming Eko Ardian Pranata; Subari Subari; Go Frendi Gunawan
J-INTECH ( Journal of Information and Technology) Vol 7 No 02 (2019): J-INTECH : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v7i02.435

Abstract

Short Message Service (SMS) is one of the communication services for sending and receiving short messages in the form of text on cell phones (cellphones). SMS is still used every day because of its ease of use, simple, fast, and inexpensive. The increasing use of SMS is used by many parties to benefit, one of which is sending spam via SMS. The method used is a probabilistic approach in making inferences that is based on Bayes theorem in general. Training data used in the categorization process is obtained from journals and already has a previous category, namely SMS spam and not spam. Application in Indonesian-language SMS, which has a certain morphology in categorizing processing. The application performs several stages in processing including preprocessing in the form of case folding, and parsing, transformation in the form of stopword removal and stemming, frequency and probability calculation and naïve bayes calculation. The categorization produced by the application compared to manual categorization has an average precision of 24%, recall 88% and Confusion Matrix (Accuracy) of 62%.
Co-Authors Eko Ardian Pranata Subari