Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
Khazanah Informatika: Jurnal Ilmiah Komputer dan Informatika, an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
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<![CDATA[WRITER IDENTIFICATION OF LAMPUNG HANDWRITTEN DOCUMENTS BASED ON SELECTED CHARACTERS ]]>
<![CDATA[Junaidi, Akmal]]>;
<![CDATA[Trianingsih, Syifa]]>;
<![CDATA[Iqbal, Muhammad]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.8418
<![CDATA[Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.]]>
<![CDATA[PERFORMANCE OF METHODS IN IDENTIFYING SIMILAR LANGUAGES BASED ON STRING TO WORD VECTOR ]]>
<![CDATA[Sujaini, Herry]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.8199
<![CDATA[Indonesia has a large number of local languages that have cognate words, some of which have similarities among each other. Automatic identification within a family of languages faces problems, so it is necessary to learn the best performer of language identification methods in doing the task. This study made an effort to identification Indonesian local languages, which used String to Word Vector approach. A string vector refers to a collection of ordered words. In a string vector, a word is represented as an element or value, while the word becomes an attribute or feature in each numeric vector. Among Naïve Bayes, SMO, J48, and ZeroR classifiers, SMO is found to be the most accurate classifier with a level of accuracy at 95.7% for 10-fold cross-validation and 94.4% for 60%: 40%. The best tokenizer in this classification is Character N-Gram. All classifiers, except ZeroR shows increased accuracy when using Character N-Gram Tokenizer compared to Word Tokenizer. The best features of this system are the TriGram and FourGram Character. The TriGram is preferred because it requires smaller training data. The highest accuracy value in the combination experiment is 0.965 obtained at a combination of IDF = FALSE and WC = TRUE, regardless the conditions of the TF.]]>
<![CDATA[MARKET BASKET ANALYSIS TO IDENTIFY STOCK HANDLING PATTERNS & ITEM ARRANGEMENT PATTERNS USING APRIORI ALGORITHMS ]]>
<![CDATA[Prawira, Tresna Yudha]]>;
<![CDATA[Sunardi, Sunardi]]>;
<![CDATA[Fadlil, Abdul]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.8628
<![CDATA[The process of managing the pattern of handling stock of goods and the pattern of arranging goods on store shelves requires an identification process by utilizing data from sales transaction results. Market basket analysis of sales transaction data using Apriori Algorithm stages produces an information in the form of association rules with a minimum support value of 50% and a minimum confidence of 60%. It can be a reference in the arrangement of items on store shelves by referring to a combination of items that are often bought by consumers simultaneously. In addition, the stock inventory pattern can take advantage of the results of determining the high frequency value in the combination pattern 1 - itemset C1 with a minimum support value of 50% which is compared with the initial inventory.]]>
<![CDATA[MAPPING LAND SUITABILITY FOR SUGAR CANE PRODUCTION USING K-MEANS ALGORITHM WITH LEAFLETS LIBRARY TO SUPPORT FOOD SOVEREIGNTY IN CENTRAL JAVA ]]>
<![CDATA[Seta, Pramudhita Tunjung]]>;
<![CDATA[Hartomo, Kristoko Dwi]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.9027
<![CDATA[Indonesia is the largest sugar importer country in the world, this is contrary to the government's desire to realize sugar self-sufficiency. To overcome the dependence on sugar imports in order to support national food sovereignty, geographic information system technology (GIS) can be used to present information as material for consideration by the government in determining policies on the management of sugar cane land resources. The K-means algorithm is used to group regions according to production level, while the Matching method is for evaluating the suitability of sugarcane land. Presentation of data in the form of map visualization on the web using a new model in processing land data, where this model processes production grouping data, and land suitability class data in the form of GeoJSON then mapped with the help of Leaflets. This new model enables dynamic land data processing and visualization in the form of interactive maps. The results of the EUCS test for GIS mapping of Land Suitability and Cane Production are 3.23 (Satisfied) of the total score of 4, so this system can be accepted by the user.]]>
<![CDATA[DESIGNING USER EXPERIENCE MOBILITY ASSISTANT APPLICATION FOR THE PHYSICALLY-DISABLED USING THE WHEEL METHOD ]]>
<![CDATA[Fatahillah, Azman]]>;
<![CDATA[Asfarian, Auzi]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.8634
<![CDATA[This research was conducted to design a mobility assistant application for persons with physical disability using The Wheel method. This application can assist mobile activities for people with disability. The application called Kuygo was made in four stages: analysis, design, prototype, and evaluation. Analysis of user needs is carried out at Loka Bina Karya (LBK) Bogor through observation and interviews. The interaction design requirements are generated in the form of requirements statements and inventory tasks with three main tasks, namely mobility preparation, mobility comfort, and providing a review of a location. Afterwards, discussions were held in the form of design thinking and ideation sessions, with people who care about people with disabilities and the Senyum Difabel community, which produced sketches, storyboards, and wireframes. Furthermore, the design implementation is carried out by making a prototype of medium-fidelity and the results are tested using cognitive walkthrough techniques. Of the four questions that must be answered with cognitive walkthrough techniques, the average success rate of all the tasks tested is 94.62%. Based on these results, no major usability errors were found in the prototype medium-fidelity and the Kuygo application can be further developed.]]>
<![CDATA[DROUGHT ANALYSIS AND FORECAST USING LANDSAT-8 SATTELITE IMAGERY, STANDARDIZED PRECIPITATION INDEX AND TIME SERIES ]]>
<![CDATA[Maipauw, Musa Marsel]]>;
<![CDATA[Sediyono, Eko]]>;
<![CDATA[Prasetyo, Sri Yulianto Joko]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.8863
<![CDATA[A drought is a phenomenon of shortages in water supply in an area for a long time. Drought usually occurs in areas that have little rain for a long time or in areas with low precipitation. Drought have negative impacts on many sectors such as agriculture, plantations, water resources and environment. This paper describes the results of a research that aims to analyze data to get the level of drought during four yearly periods, and predict the likelihood of drought to occur in the future. The level of drought was analyzed using the Inverse Distance Weighted (IDW) method and the Standardized Precipitation Index (SPI). Least square time series was utilized to forecast the level of drought in the near future. Data consists of drought data collected from electronic news, rainfall data from BMKG, and anual Landsat-8 satellite imagery. All data are for Western Southeast Mallucas in the range of 2015-2018. Analysis using IDW and SPI methods produce similar interpretation for year 2015, i.e. mild dryness, and fro year 2018, i.e. no drought. However, the two methods show discrepancy in analysis of data for 2016 and 2017. The use of least square time series to forecast drought in 2019 gives SPI value of 0.03 which intepretes as normal weather (no drought) that is consistent with the result of field observation.]]>
<![CDATA[USABILITY TESTING ON QR CODE SCANNER APPLICATION FOR LECTURE PRESENCE ]]>
<![CDATA[Pujastuti, Eli]]>;
<![CDATA[Laksito, Arif Dwi]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.9026
<![CDATA[Universities are obliged to facilitate lectures. Many students require universities to provide a fair teaching and learning services with a minimum of student cheating. In order to improve the quality of teaching and learning, each university develops a lecture presence system electronically. The QR code scanner application became a solution offered for leak problems that previously existed on the magnetic card system. Before the application applied on a large scale, developers needed to conduct an assessment of the usability of the QR scanner application. The assessment aimed to make lectures go smoothly and to maintain the good reputation of the university. The method used is usability testing. The result of this study is a usability system at the level of 65%. This value consists of an effectiveness value of 70%, an efficiency value of 54.31%, and a satisfaction value of 70.85%. The improvements of user interface recommended in this study include adding of placeholders to inform the correct NIM format, changing the QR scanner icon into a titled icon and choosing a stimulating color, providing a zoom feature on the scanner camera, and applying a more familiar logout icon according to the mental model of the user.]]>
<![CDATA[DETECTION OF CYBER MALWARE ATTACK BASED ON NETWORK TRAFFIC FEATURES USING NEURAL NETWORK ]]>
<![CDATA[Engel, Ventje Jeremias Lewi]]>;
<![CDATA[Joshua, Evan]]>;
<![CDATA[Engel, Mychael Maoeretz]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Surakarta ]]>
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DOI: 10.23917/khif.v6i1.8869
<![CDATA[Various techniques have been developed to detect cyber malware attacks, such as behavior based method which utilizes the analysis of permissions and system calls made by a process. However, this technique cannot handle the types of malware that continue to evolve. Therefore, an analysis of other suspicious activities ? namely network traffic or network traffic ? need to be conducted. Network traffic acts as a medium for sending information used by malware developers to communicate with malware infecting a victim's device. Malware analyzed in this study is divided into 3 classes, namely adware, general malware, and benign. The malware classification implements 79 features extracted from network traffic flow and an analysis of these features using a Neural Network that matches the characteristics of a time-series feature. The total flow of network traffic used is 442,240 data. The results showed that 15 main features selected based on literature studies resulted in F-measure 0.6404 with hidden neurons 12, learning rate 0.1, and epoch 300. As a comparison, the researchers chose 12 features based on the nature of the malware possessed, with the F-measure score of 0.666 with hidden neurons 12, learning rate 0.05, and epoch 300. This study found the importance of data normalization technique to ensure that no feature was far more dominant than other features. It was concluded that the analysis of network traffic features using Neural Network can be used to detect cyber malware attacks and more features does not imply better detection performance, but real-time malware detection is required for network traffic on IoT devices and smartphones.]]>
<![CDATA[Writer Identification of Lampung Handwritten Documents Based on Selected Characters ]]>
<![CDATA[Akmal Junaidi]]>;
<![CDATA[Syifa Trianingsih]]>;
<![CDATA[Muhammad Iqbal]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v6i1.8418
<![CDATA[Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.]]>
<![CDATA[Performance of Methods in Identifying Similar Languages Based on String to Word Vector ]]>
<![CDATA[Herry Sujaini]]>
<![CDATA[ Khazanah Informatika Vol. 6 No. 1 April 2020 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia ]]>
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DOI: 10.23917/khif.v6i1.8199
<![CDATA[Indonesia has a large number of local languages that have cognate words, some of which have similarities among each other. Automatic identification within a family of languages faces problems, so it is necessary to learn the best performer of language identification methods in doing the task. This study made an effort to identification Indonesian local languages, which used String to Word Vector approach. A string vector refers to a collection of ordered words. In a string vector, a word is represented as an element or value, while the word becomes an attribute or feature in each numeric vector. Among Naïve Bayes, SMO, J48, and ZeroR classifiers, SMO is found to be the most accurate classifier with a level of accuracy at 95.7% for 10-fold cross-validation and 94.4% for 60%: 40%. The best tokenizer in this classification is Character N-Gram. All classifiers, except ZeroR shows increased accuracy when using Character N-Gram Tokenizer compared to Word Tokenizer. The best features of this system are the TriGram and FourGram Character. The TriGram is preferred because it requires smaller training data. The highest accuracy value in the combination experiment is 0.965 obtained at a combination of IDF = FALSE and WC = TRUE, regardless the conditions of the TF.]]>
