Scientific Journal of Informatics
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
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"Vol 6, No 1 (2019): May 2019"
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<![CDATA[Bayes Theorem and Forward Chaining Method On Expert System for Determine Hypercholesterolemia Drugs ]]>
<![CDATA[Perbawawati, Anna Adi]]>;
<![CDATA[Sugiharti, Endang]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.14149
<![CDATA[The development of technology capable to imitating the process of human thinking and led to a new branch of computer science named the expert system. One of the problem that can be solved by an expert system is selecting hypercholesterolemia drugs.  Drug selection starts from find the symptoms and then determine the best drug for the patient. This is consist with the mechanism of forward chaining which starts from searching for information about the symptoms, and then try to illustrate the conclusions. To accommodate the missing fact, expert systems can be complemented with the Bayes theorem that provides a simple rule for calculating the conditional probability so the accuracy of the method approaches the accuracy of the experts. This reseacrh uses 30 training data and 76 testing data of medical record that use hypercholesterolemia drugs from Tugurejo Hospital of Semarang. The variable are common symptoms and some hypercholesterolemia drugs. This research obtained a selection of hypercholesterolemia drugs system with 96.05% accuracy]]>
<![CDATA[Dataset Characteristics Identification for Federated SPARQL Query ]]>
<![CDATA[Rakhmawati, Nur Aini]]>;
<![CDATA[Fadzilah, Lutfi Nur]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.17258
<![CDATA[Nowadays, the amount of data published in the RDF format is increasing. Federated SPARQL query engines that can query from multiple distributed SPARQL endpoints have been developed recently. A federated query engine usually has different performance compared to the others. One of the factors that affect the performance of the query engine is the characteristic of the accessed RDF dataset, such as the number of triples, the number of classes, the number of properties, the number of subjects, the number of entities, the number of objects, and the spreading factor of a dataset. The aim of this work is to identify the characteristic of RDF dataset and create a query set for evaluating a federated engine. The study was conducted by identifying 16 datasets that used by ten research papers in Linked Data area.]]>
<![CDATA[Compression and Decompression of Audio Files Using the Arithmetic Coding Method ]]>
<![CDATA[Silitonga, Parasian D. P]]>;
<![CDATA[Morina, Irene Sri]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.17839
<![CDATA[Audio file size is relatively larger when compared to files with text format. Large files can cause various obstacles in the form of large space requirements for storage and a long enough time in the shipping process. File compression is one solution that can be done to overcome the problem of large file sizes. Arithmetic coding is one algorithm that can be used to compress audio files. The arithmetic coding algorithm encodes the audio file and changes one row of input symbols with a floating point number and obtains the output of the encoding in the form of a number of values greater than 0 and smaller than 1. The process of compression and decompression of audio files in this study is done against several wave files. Wave files are standard audio file formats developed by Microsoft and IBM that are stored using PCM (Pulse Code Modulation) coding. The wave file compression ratio obtained in this study was 16.12 percent with an average compression process time of 45.89 seconds, while the average decompression time was 0.32 seconds.]]>
<![CDATA[Digital Evidence Acquisition System on IAAS Cloud Computing Model using Live Forensic Method ]]>
<![CDATA[Sudyana, Didik]]>;
<![CDATA[Lizarti, Nora]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.18424
<![CDATA[Cloud Computing is a technological development that has been warmly discussed in recent years and has seen significant increases in usage, especially on the IAAS Cloud Computing model. The high rate of development of IAAS Cloud Computing model is in line with the high number of crimes involving IAAS Cloud Computing model on server virtualization. When a computer crime occurs and a digital forensic investigation will be carried out to uncover the case, it raises issues related to the acquisition of digital evidence. Because the acquisition model in general, it is done only to one operating system, while in virtualization there is more than one operating system, so the acquisition technique in general cannot be used because it takes only one operating system involved crime, and cannot acquire the whole data server related privacy data in other virtual operating systems. Therefore, research to make the acquisition system of server virtualization is needed. The focus in this research is to make system acquisition in server virtualization Proxmox using the live forensic method to produce a system that can acquire virtualization without disrupting the overall data server and in accordance with the principle of digital forensics. The resulting acquisition system can be a reference for investigators to investigate the IAAS Cloud Computing model on Proxmox virtualization and facilitate the investigator's work in the use of the system because the investigator simply chooses which virtual operating system to acquire, after which the system will work on its own the acquisition.]]>
<![CDATA[Security Login System on Mobile Application with Implementation of Advanced Encryption Standard (AES) using 3 Keys Variation 128-bit, 192-bit, and 256-bit ]]>
<![CDATA[Utami, Hamdan Dian Jaya Rozi Hyang]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.17589
<![CDATA[The development of mobile applications is unbalanced with the level of its security which is vulnerable to hacker attacks. Some important things that need to be considered in the security of mobile applications are login and database system. A login system that used the database as user authentication and passwords are very vulnerable to be hacking. In securing data, various ways had been developed including cryptography. Cryptographic algorithms used in securing passwords usually used MD5 encryption. However, MD5 as a broader encryption technique is very risky. Therefore, the level of login system security in an android application is needed to embed the Advanced Encryption Standard (AES) algorithm in its process. The AES algorithm was applied using variations of 3 keys 128-bit, 192-bit, and 256-bit. Security level testing was also conducted by using 40 SQL Injection samples which the system logins without security obtained 27.5% that be able to enter the system compared to the result of login systems that use AES algorithm 128-bit, 192-bit or 256-bit was obtained 100% that cannot enter into the system. The estimation of the average encryption process of AES 128, 192 and 256 bits are 5.8 seconds, 7.74 seconds, and 9.46 seconds.]]>
<![CDATA[INTESTINE DISEASE DIAGNOSIS SYSTEM USING CERTAINTY FACTOR METHOD ]]>
<![CDATA[Kirana, Chandra -]]>;
<![CDATA[Pradana, Harrizki Arie]]>;
<![CDATA[Sulaiman, Rahmat -]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.17908
<![CDATA[Inside the human body there are many important organs, one of which is the intestine. Intestinal disease / digestive disease is a disease that most often attacks the digestive tract in humans. There are several intestinal diseases that are dangerous and there are also harmless intestinal diseases. In this research, researchers created an android-based expert system application that can provide information to the users about diseases that are being suffered through the symptoms experienced by the user. The process of making expert system applications using the certainty factor algorithm. The certainty factor algorithm is used to accommodate the uncertainty of an expert's. The mechanism that be used in the certainty factor algorithm on each symptom uses a measure of increased belief (MB) and measure of increased disbelief (MD). Expert system applications that have been built to detect intestinal diseases based on Android have been successfully implemented with a presentation of accuracy of 99.7265625%. by that percentage, it show us that the diagnosis of symptoms of the selected disease is in suitable by the experienced of user, and has the accuracy determined by the system]]>
<![CDATA[Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis ]]>
<![CDATA[Larasati, Ukhti Ikhsani]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.14244
<![CDATA[Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.]]>
<![CDATA[Speed and Power Consumption Comparison between DES and AES Algorithm in Arduino ]]>
<![CDATA[Sukiatmodjo, Arcelina]]>;
<![CDATA[Setianto, YB Dwi]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.17838
<![CDATA[Telemedicine is commonly used to check or diagnose patients from a long distance. Its application is often combined with sensors as needed, but for delivery, a cryptography algorithm is needed so the data sent safely, illegible, and can not be changed by unauthorized people. Besides that, the algorithm must be light, fast and use less power. In this study, a comparison of the Data Encryption Standard (DES) and Advanced Encryption Standard (AES) algorithms will be implemented in the encryption module. Data from the sensor encrypted and sent to the server. The time and power consumption by DES will be compared with AES. From this research, we can conclude that the encryption time of AES is faster than DES. The average difference speed is 33413 microseconds. Then the power consumption by AES and DES does not have any significant difference, and the addition of sensors causes additional power as well.]]>
<![CDATA[The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas ]]>
<![CDATA[Astuti, Winda Try]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.16648
<![CDATA[The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of landslide data features. It conducted to the neuro fuzzy method in determining landslide prone areas. The distribution of training data and testing data was using 20 k-fold cross validation. The implementation of the neuro fuzzy method on landslide data was obtained an accuracy of 81.9231%. In the implementation of the neuro fuzzy method with information gain was conducted in classification process. The process will stop when the accuracy has decreased. The highest accuracy result was obtained of 88.489% by removing an attribute. So, it can be concluded the accuracy increase of 6.5659% in the implementation of the neuro fuzzy method and information gain in determination of landslide prone areas.]]>
<![CDATA[Optimization Neuro Fuzzy Using Genetic Algorithm For Diagnose Typhoid Fever ]]>
<![CDATA[Fata, Muhamad Nasrul]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Prasetiyo, Budi]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i1.17097
<![CDATA[Neuro Fuzzy is one method in the field of information technology used in diagnosing an disease. The application of Neuro Fuzzy is to identify disease. Genetic algorithms can be used to find solutions without paying attention to the subject matter specifically, one of which is an optimization problem. Typhoid or typhoid fever is a disease caused by Salmonella enterica bacteria, especially its derivatives. The diagnosis of typhoid fever is not an easy thing to do. This is because some of the indications experienced by patients also appear in other diseases. The number of patients with typhoid fever that requires accuracy in diagnosing typhoid fever based on indications caused. Based on this background this study aims to assist in the diagnosis of typhoid fever with 11 indication variables. This study uses medical record data for typhoid fever in 2017 Tidar Magelang Hospital. The method used is Neuro Fuzzy which optimizes the value of the degree of membership with genetic Algorithms. Then the value of the degree of neuro fuzzy membership is more optimal. The results of this optimization are the diagnosis of typhoid fever based on the variable of indications entered. From the research results obtained from the neuro fuzzy method get an 80% accuracy value and neuro fuzzy optimization results with genetic algorithms with a value of pc 0.5, pm 0.2 and max generation 25 the value of accuracy increases to 90%. Suggestions from this study, need to add more specific indication variables.]]>
