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.
Articles
564 Documents
<![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): Mei 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): Mei 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): Mei 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): Mei 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): Mei 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): Mei 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): Mei 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.]]>
<![CDATA[Face Identification Based on K-Nearest Neighbor ]]>
<![CDATA[Widiari, Ni Putu Ayu]]>
<![CDATA[ Scientific Journal of Informatics Vol 6, No 2 (2019): November 2019 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v6i2.19503
<![CDATA[Face identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biometrics and accurate results. Face recognition utilizes facial features for security purposes. The classification method in this paper is K-nearest Neighbor (KNN). The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. K-NN includes an instance-based learning group. This paper developed face identification using Principal Component Analysis (PCA) or eigenface extraction methods. The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. This research is registered 30 people, each person had 3 images used for training and 2 images used for testing. The results obtained from several trials of k values are as follows. Experiments with a value of k=1 get the best accuracy, namely 81%, k=2 get 53% accuracy, and k=3 get 45% accuracy.]]>
<![CDATA[Fuzzy Logic Inference System for Determining The Quality Assesment of Students Learning ICT ]]>
<![CDATA[Pamuji, Agus]]>
<![CDATA[ Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v4i1.7082
<![CDATA[The Assesment that held in the school is one of the learning process in education who do it by teacher. One of the course that exemined is Computer Application. In the computer application have 3 topic, they are Microsoft Word, Microsoft Excel, Microsoft Power Point. The assesment for students at politecnic about learning computer application have 3 criteria in the selection. First of all, the students have ability to operate computer system generaly, it has understanding the formula on microsoft excel, the students have skill toward any application. In this study, fuzzy logic used for determining the quality assesment of stundents learning Information and Comunication Technology (ICT) as a tools to analyze any constraint that are known as min-max method. As a result, we have found that the students have good for analyzing in the application from the each question or case of study when the course it has been examined.]]>
<![CDATA[Implementasi Logika Fuzzy Mamdani untuk Mendeteksi Kerentanan Daerah Banjir di Semarang Utara ]]>
<![CDATA[Arifin, Saiful]]>;
<![CDATA[Muslim, Much Aziz]]>;
<![CDATA[Sugiman, Sugiman]]>
<![CDATA[ Scientific Journal of Informatics Vol 2, No 2 (2015): November 2015 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v2i2.5086
<![CDATA[Kerentanan (Vuinerability) adalah keadaan atau kondisi yang dapat mengurangi kemampuan masyarakat untuk mempersiapkan diri menghadapi bahaya atau ancaman bencana. Logika Fuzzy adalah cara untuk memetakan suatu ke dalam suatu ruang output. Salah satu aplikasi logika Fuzzy adalah untuk menentukan kerentanan daerah banjir di Semarang Utara. Pengujian dilakukan dengan metode Mamdani Fuzzy Inference System. secara manual dan program menggunakan 5 defuzifikasi, yaitu Centroid, SOM (Smallest Of Maximum), LOM (Large Of Maximum), MOM (Mean Of Maximum), Bisector. Dari 2 contoh kasus diperoleh hasil pengujian dengan kesimpulan yang sama.]]>
