Articles
<![CDATA[Sistem Informasi Tracer Study Alumni Universitas Negeri Semarang Dengan Aplikasi Digital Maps ]]>
<![CDATA[Nugroho, Zulfikar Adi]]>;
<![CDATA[Arifudin, Riza]]>
<![CDATA[ Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v1i2.4021
<![CDATA[Tracer study alumni merupakan salah satu metode yang digunakan untuk menelusuri informasi mengenai alumni. Informasi yang diambil meliputi identitas pribadi alumni, riwayat pendidikan di Universitas Negeri Semarang, riwayat pekerjaan, serta masukan yang diberikan kepada Universitas Negeri Semarang. Salah satu data yang sulit untuk diperoleh adalah data valid mengenai alamat pekerjaan alumni serta cara menyajikan data alamat pekerjaan alumni. Digital Maps adalah representasi fenomena geografik yang disimpan untuk ditampilkan dan dianalisis oleh komputer. Setiap objek pada peta digital disimpan sebagai sebuah atau sekumpulan koordinat. Posisi tempat kerja atau posisi kantor merupakan salah satu data geografis berupa titik, sedangkan titik dalam data geografi merupakan bagian dari sebuah peta. Sehingga titik yang baik adalah titik yang dapat diproyeksikan kedalam sebuah peta. Dalam tulisan ini, akan dibahas rancang bangun sistem informasi Tracer Study alumni Universitas Negeri Semarang dengan aplikasi Digital Maps. ]]>
<![CDATA[Prediction The Number of Dengue Hemorrhagic Fever Patients Using Fuzzy Tsukamoto Method at Public Health Service of Purbalingga ]]>
<![CDATA[Hikmawati, Zahra Shofia]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v4i2.10342
<![CDATA[DHF (Dengue Hemorrhagic Fever) is still a major health problem in Indonesia. One of the factors that led to an increase in dengue cases is uncertain climate that causes dengue fever is difficult to be predicted. Prediction is an important thing that is used to determine future events by identifying patterns of events in the past. When knowing the events that happen, it will make everyone to make better preparation for everything. This research is aimed at determining the accuracy of Tsukamoto Fuzzy method in the number of dengue patients in Puskesmas Purbalingga. Tsukamoto Fuzzy method can be used for prediction because it has the ability to examine and identify the pattern of historical data. Tsukamoto fuzzy that used to predict the number of dengue fever patients at Puskesmas Purbalingga has several stages. The first stage is the collection of climate data includes precipitation, humidity, water temperature and the data of dengue fever patients in Puskesmas Purbalingga. The next stage is processing the data that has been obtained. The last stage is to make predictions. Based on the results of the implementation by Tsukamoto Fuzzy method in predicting the number of dengue fever patients in Purbalingga for twelve months in 2016, it was obtained a percentage error (MAPE) of 8.13% or had an accuracy rate of 91.87 %. With the small value of MAPE and high accuracy, it shows that the system can predict well. ]]>
<![CDATA[Implementation of Analytic Network Process Method on Decision Support System of Determination of Scholarship Recipient at House of Lazis Charity UNNES ]]>
<![CDATA[Rahmanda, Primana Oky]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v4i2.11852
<![CDATA[The scholarship is one of the forms of giving/ rewarding funds to individuals or students to use for sustainability during their education. Scholarships are awarded as government or institutional efforts to ease the burden of students in meeting the need for increasingly expensive education costs. The mechanism for selecting scholarship recipients, the selection team of UNNES Charity House of Lazis still use the scoring of the scholarship scores manually based on the total sum of criteria assessment without considering the priority weighted value of each criterion. So that cause the disbursement of scholarship funds that are not on target. To solve the problem, it is necessary to apply a decision support system to help provide consideration of the award of the scholarship recipient. Decision support system used requires data as a guidance assessment in the form of data criteria and alternative data by implementing Analytic Network Process method. The ANP method is used to determine the criteria and alternate priority weight values and the results are rankings. The purpose of this research is to build and implement ANP method in decision support system of awarding of scholarship recipients. The criteria used include the work of parents, parent income, the amount/ grade of Single Tuition, grade point average cumulative. The results of this study indicate that the use of ANP method implementation can determine the scholarship recipients who declared feasible or not to receive the scholarship based on the ranking results of the priority weight of the alternative.]]>
<![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[Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time ]]>
<![CDATA[Hidayat, Kukuh Triyuliarno]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Alamsyah, Alamsyah]]>
<![CDATA[ Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v5i1.12720
<![CDATA[The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.]]>
<![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[K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor ]]>
<![CDATA[Safri, Yofi Firdan]]>;
<![CDATA[Arifudin, Riza]]>;
<![CDATA[Muslim, Much Aziz]]>
<![CDATA[ Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v5i1.12057
<![CDATA[Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm.]]>
<![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.]]>
<![CDATA[The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency ]]>
<![CDATA[Devi, Feroza Rosalina]]>;
<![CDATA[Sugiharti, Endang]]>;
<![CDATA[Arifudin, Riza]]>
<![CDATA[ Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v5i2.15452
<![CDATA[The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.]]>
<![CDATA[Decision Support Systems with AHP and SAW Method for Determination of Cattle with Superior Seeds ]]>
<![CDATA[Josaputri, Clarissa Amanda]]>;
<![CDATA[Sugiharti, Endang]]>;
<![CDATA[Arifudin, Riza]]>
<![CDATA[ Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v3i2.7908
<![CDATA[Department of Animal Husbandry and Fisheries of Semarang District is an institution in charge of livestock and animal health. Basically the Animal Husbandry Department has provided standardization for quality livestock cattle with superior seeds that usually can be judged or measured by various criteria.They are weight, age and value of BCS (Body Condition Score).They needed a system that could help the Department of Livestock and Fisheries of Semarang District in determining the electoral process cattle with superior seeds. In this research, the manufacture of Decision Support Systems in the determination cattle with superior seedsis using a combination of two methods is Analytical Hierarchy Process (AHP) and the Simple Addictive Weighting (SAW). In AHP will perform an importance value calculation criteria that will be paired up with an alternative to the SAW the next process is the sum of the weight from performance rating of all the attributes to each alternative, a ranking conducted to determine the result of cattle with superior seeds. Suggestions on this system, can be developed further by combining other methods to determine the recommendation that more effective.]]>
