JUITA : Jurnal Informatika
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
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<![CDATA[Editor Preface and Table of Content ]]>
<![CDATA[JUITA: Jurnal Informatika]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.10595
<![CDATA[Editor Preface and Table of Content]]>
<![CDATA[Maturity Level of ITSM Analysis Using ITIL V3 Framework in State Electricity Enterprise Purwokerto ]]>
<![CDATA[Resad Setyadi]]>;
<![CDATA[Enggar Priyatiningsih]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.9594
<![CDATA[In the industrialized world, information technology has become an essential component that everyone can feel and use. However, IT's proper use can provide opportunities for increased productivity and effective and efficient company processes. The quality of the service supplied is related to managing the problems faced and handling its day-to-day service operations. The purpose of this research is to analyze the IT service management (ITSM) of the National Electricity Company (NEC) in Purwokerto with media analysis using the Information Technology Infrastructure Library (ITIL) v3 framework. The method used is a quantitative method with observation, distribution of questionnaires, and data analysis as a follow-up. Maturity level analysis becomes the target of assessment to become a reference for providing recommendations for the performance of ITSM. The results of the maturity level show that NEC Purwokerto is at the optimal level. The advice given is that NEC Purwokerto maintains IT services' performance so that the NEC Purwokerto company's business strategy continues to run well and improve]]>
<![CDATA[A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face ]]>
<![CDATA[Faisal Dharma Adhinata]]>;
<![CDATA[Diovianto Putra Rakhmadani]]>;
<![CDATA[Merlinda Wibowo]]>;
<![CDATA[Akhmad Jayadi]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.9624
<![CDATA[The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.]]>
<![CDATA[A Survey of Data Mining Techniques for Smart Museum Applications ]]>
<![CDATA[Shinta Puspasari]]>;
<![CDATA[Ermatita Ermatita]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.9247
<![CDATA[This research aims to find out what data mining techniques are effectively implemented in museums and what application trends are currently being used to improve museum performance towards modern museums based on intelligent system technology. The review was carried out on a number of articles found in journals and proceedings in the 2004-2020 period. It is found that the majority of data mining techniques are implemented in museum virtual guide applications, recommender systems, collection clustering and classification system, and visitor behaviour prediction application. Data classification, clustering, and prediction technique commonly used for museum application. Collections with historical and artistic value contain a lot of knowledge making data mining an important technique to be included in various applications in museums so that they can have an impact on the achievement of museum goals not only in the fields of education and culture but also economics and business.]]>
<![CDATA[Indonesian Plate Number Identification Using YOLACT and Mobilenetv2 in the Parking Management System ]]>
<![CDATA[I Kadek Gunawan]]>;
<![CDATA[I Putu Agung Bayupati]]>;
<![CDATA[Kadek Suar Wibawa]]>;
<![CDATA[I Made Sukarsa]]>;
<![CDATA[Laurensius Adi Kurniawan]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.9230
<![CDATA[A vehicle registration plate is used for vehicle identity. In recent years, technology to identify plate numbers automatically or known as Automatic License Plate Recognition (ALPR) has grown over time. Convolutional Neural Network and YOLACT are used to do plate number recognition from a video. The number plate recognition process consists of 3 stages. The first stage determines the coordinates of the number plate area on a video frame using YOLACT. The second stage is to separate each character inside the plat number using morphological operations, horizontal projection, and topological structural. The third stage is recognizing each character candidate using CNN MobileNetV2. To reduce computation time by only take several frames in the video, frame sampling is performed. This experiment study uses frame sampling, YOLACT epoch, MobileNet V2 epoch, and the ratio of validation data as parameters. The best results are with 250ms frame sampling succeed to reduce computational times up to 78%, whereas the accuracy is affected by the MobileNetV2 model with 100 epoch and ratio of split data validation 0,1 which results in 83,33% in average accuracy. Frame sampling can reduce computational time however higher frame sampling value causes the system fails to obtain plate region area.]]>
<![CDATA[Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection ]]>
<![CDATA[April Firman Daru]]>;
<![CDATA[Mohammad Burhan Hanif]]>;
<![CDATA[Edi Widodo]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.9941
<![CDATA[Diabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network method as a data mining algorithm is present to overcome the burden that arises as an early detection analysis of the onset of disease. However, Neural Network has slow training capabilities and can identify important attributes in the data resulting in a decrease in performance. Pearson correlation is good at handling data with mixed-type attributes and is good at measuring information between attributes and attributes with labels. With this, the purpose of this study will be to use the Pearson correlation method as a selection of features to improve neural network performance in diabetes detection and measure the extent of accuracy obtained from the method. The dataset used is diabetes data 130-US hospital UCI with a record number of 101767 and the number of attributes as many as 50 attributes. The results of this study found that Pearson correlation can improve neural network accuracy performance from 94.93% to 96.00%. As for the evaluation results on the AUC value increased from 0.8077 to 0.8246. Thus Pearson's Correlation algorithm can work well for feature selection on neural network methods and can provide solutions to improved diabetes detection accuracy.]]>
<![CDATA[Japanese Hiragana Handwriting Pattern Recognition Using Template Matching Correlation Method ]]>
<![CDATA[Imam Riadi]]>;
<![CDATA[Abdul Fadlil]]>;
<![CDATA[Putri Annisa]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.7082
<![CDATA[Hiragana is one of the traditional Japanese letters used to translate native Japanese words. The introduction of an object requires a learning process, which is obtained through the characteristic in the form of unique features on similar objects, but manually it is quite difficult to distinguish these letters. This writing explains the discussion system to differentiate between hiragana letters starting from preprocess namely grayscale and threshold, then segmenting and normalization, while image classification uses the Template Matching Correlation method. The results of tests carried out assessing the test rate of around 76% using the Matching Template Correlation method. While the remaining 14% indicates that the object identified does not match the intended results.]]>
<![CDATA[Compression Analysis Using Coiflets, Haar Wavelet, and SVD Methods ]]>
<![CDATA[Muhamad Aznar Abdillah]]>;
<![CDATA[Anton Yudhana]]>;
<![CDATA[Abdul Fadlil]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.8559
<![CDATA[The image problem lies in the amount of storage space required, to save memory as little as possible image compression is required. The image compression technique is a technique used to represent an image by reducing the quality of the original image but still retaining the information inside. This study compares the best compression method between Coiflets, Haar wavelets, and SVD with JPG image material. The comparison process has done by calculating the compression ratio (CR), Space Saving (SS), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR). The results obtained prove that the SVD method has the highest compression ratio of 3.25 while in the case of Space Saving (SS) the Coiflets method gives the best performance with a value of 73. Measurement in terms of MSE and RMSE is the best for the Coiflets method because it has an average value. -The smallest average among all methods is 0.02395 and 0.111383. provides the best performance in maintaining compression quality. The best PSNR based image quality assessment is the Coiflets method with the highest PSNR average of 63.02 dB. Overall, the Coiflets, Haar wavelet, and SVD compression methods used for JPG images can reduce file size and preserve image information and quality.]]>
<![CDATA[Decision Support System for Service Quality Using SMART and Fuzzy ServQual Methods ]]>
<![CDATA[Sri Lestari]]>;
<![CDATA[Muhammad Reza Romahdoni]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.9644
<![CDATA[The Regional Technical Implementation Unit of the Tresna Werdha Social Home for the Elderly of Natar South Lampung does not yet have a systematic calculation, which can be a parameter of the quality level of each service. This study develops a system to solve the problem of the calculation gap between perceptions and expectations in determining the quality level of each service, namely the Decision Support System using the Simple Multi-Attribute Rating Technique Method (SMART) and Fuzzy Service Quality. The results showed that the SMART method obtained an accuracy rate of 85.71%, 75.00% Precision, 100% Recall, and 100% Specificity, while the Fuzzy Service Quality method obtained an accuracy rate of 71.43%, 66.67% Precision, 66.67% Recall, and 75.00% Specificity. So that the Simple Multi-Attribute Rating Technique Method (SMART Method) is superior, so it is more appropriate to solve the problem of decision-making on the level of service quality at the Regional Technical Implementation Unit of the Tresna Werdha Elderly Social Home, Natar South Lampung.]]>
<![CDATA[Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset ]]>
<![CDATA[Maulida Ayu Fitriani]]>;
<![CDATA[Dany Candra Febrianto]]>
<![CDATA[ JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021 ]]>
Publisher : <![CDATA[Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto ]]>
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DOI: 10.30595/juita.v9i1.7983
<![CDATA[Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.]]>
