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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
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Articles 4 Documents
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Search results for , issue "Vol. 3 No. 1 (2014): IJID May" : 4 Documents clear
Patient Data Clustering using Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC) Rosalia Susilowati; Ahmad Subhan Yazid; Shofwatul Uyun
IJID (International Journal on Informatics for Development) Vol. 3 No. 1 (2014): IJID May
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (981.781 KB) | DOI: 10.14421/ijid.2014.%x

Abstract

Generally, the current system development only include the input, view, and reports. At Jogja Hospital, a system with a patient database can only provide information about the percentage of male and female patients. Its unable to extract more specific information, even though medical record data has a lot of information. The complete information should be used as a reference for the authorities to make a decision. This information can be obtained by analyzing and processing the medical record data. One way to extract information from this data is clustering. The domain of this study is patient data. Before the data is clustered, preprocessing is needed through name standardization, numeration, and data normalization. During the clustering process, the algorithms used are Fuzzy C-Means (FCM) and Agglomerative Hierarchical Clustering (AHC). Two algorithms are implemented to determine which algorithm is the most appropriate and fast to handle the processing of patient data. The results of the study show that the processing time required to do clustering with FCM algorithm is relatively faster than AHC algorithm. For data with small volumes, the iteration of FCM algorithm is more than AHC algorithm, however, the results of the clustering using FCM algorithm are easier to interpret than AHC algorithm. From the visualization of clustering results, found that the cluster pattern with FCM algorithm is better based on the three variables used as references. So the most suitable algorithm to use is Fuzzy C-Means (FCM) for processing patient data.
Recommendation System of Self-Medication for Mild Digestive Diseases with Dempster Shafer Method Sayekti Abriani; Khurin 'ien Mukhoyyaroh; Ahmad Subhan Yazid; Maria Ulfah Siregar
IJID (International Journal on Informatics for Development) Vol. 3 No. 1 (2014): IJID May
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (814.001 KB) | DOI: 10.14421/ijid.2014.%x

Abstract

Pain is a state of body discomfort. To cure diseases, people usually go to the doctor, but now if the disease is mild it can be treated with self-medication. Therefore, Self-medication (Swamedikasi) recommendation system needs to be built specially to lessen and solve the mild disease problem, in this case, is digestion. The recommendation generated using an expert system with Dempster Shafer method. The application’s output will display the possibility of mild disease in digestion system that suffered by user based on the existing symptoms. The application also shows the possibility of symptoms from the legible disease that suffered by the user. The trust value obtained by using the Dempster Shafer method.
Hospital Information System Audit Using The ISO 27001 Standard (Case Study In RSU PKU Muhammadiyah Bantul) Heri Setiawan; Khurin 'ien Mukhoyyaroh; Muhammad Dzulfikar Fauzi; Bambang Sugiantoro
IJID (International Journal on Informatics for Development) Vol. 3 No. 1 (2014): IJID May
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (190.929 KB) | DOI: 10.14421/ijid.2014.03105

Abstract

RSU PKU Muhammadiyah Bantul have been using information technology to improve health care in their area. One of the uses of information technology is in medical record information system. The existence of medical record information system will help to manage all medical record data. But with applying information system its data need to be secured, while there still less knowledge and understanding about medical record information system security. Therefore, it’s needed to have an audit using the standard of ISO 27001 to get a convenient security service for a medical record information. The audit of ISO 27001 used because this standard focus at information system security and use as the national standard of Indonesia. This standard contains complete determination to discover information system security. This research managed to give an assessment for medical record information system security of RSU PKU Muhammadiyah Bantul with maturity value of 2,2 (Repeatable but Intuitive). So medical record information system security of RSU PKU Muhammadiyah Bantul is good enough because it’s been followed the information system security procedure. But the hospital management is not paying attention regarding the understanding of their employees about information system security for their medical record information system.
Comparison of Edge Detection Method in Case of Blood Pattern Recognition Using Backpropagation Algorithm Agung Nur Hidayat; Ahmad Subhan Yazid; Shofwatul Uyun
IJID (International Journal on Informatics for Development) Vol. 3 No. 1 (2014): IJID May
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (783.789 KB) | DOI: 10.14421/ijid.2014.%x

Abstract

There are 4 types of blood: A, B, O, and AB. So far, the process of checking blood type depends on the officer’s work accuracy. To keep the validity of the results, a system is needed to help humans to recognize the blood types. This recognition can be done by computers by applying the method of blood pattern recognition through an image. The data domain of this study is a scan of blood type checks obtained from PMI Yogyakarta City. A total of 54 images were used in the training and recognition process. The image used in .bmp extension with a size of 400 x 200 pixels. Before the recognition process, first execute the preprocessing image, that is convert the image to grayscale image. The next process is edge detection with a Sobel operator or Prewitt operator. The use of these two operators aim to determine the optimal operator for recognition of blood type case. After the edge detection process, the image is converted to binary so it can be processed by feature extraction. The last step is the implementation of artificial neural network backpropagation algorithm with bipolar sigmoid activation function for hidden layer and linear activation for output. As a result, the optimal neural network architecture is three hidden layers with each hidden layer having three nodes. The optimal value for the mean squared error parameter is 1e-1 or 0.1, epoch 1000 and learning rate 0.01. In this study, Sobel operator was better than Prewitt operator in introducing blood type types. When viewed from the difference in processing time, the Prewitt operator is slightly faster than the Sobel operator with a difference of 0.000052 seconds. From 39 training data and 14 test data, the percentage of success in the recognition of blood type was 92.86%.

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