JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
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<![CDATA[Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning ]]>
<![CDATA[Julio Narabel]]>;
<![CDATA[Setia Budi]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2675
<![CDATA[In the fitness industry, the number of members is a major factor for the sustainability of its business. The ability of managers and trainers to detect members who represent traits to quit membership is critical. Four supervised learning classification methods like Support Vector Machine, Random Forest, K-Nearest Neighbor, and Artificial Neural Network were used to generate early detection using two variants of datasets that have different amounts of data. Classification results are separated into three different zones, which are Green Zone, Yellow Zone, and Red Zone. Artificial Neural Network methods using backpropagation training give 99.90% of accuracy on a dataset which has more amount of data. The evaluation has been done using the confusion matrix and AUC-ROC curves.]]>
<![CDATA[Analisis Komparatif ARIMA dan Prophet dengan Studi Kasus Dataset Pendaftaran Mahasiswa Baru ]]>
<![CDATA[Cato Chandra]]>;
<![CDATA[Setia Budi]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2676
<![CDATA[This research presents all studies, methodologies, and results about testing the accuracy of predictions on new student marketing data by region using the Prophet and Autoregressive Integrated Moving Average (ARIMA) methods. The dataset selected for this study uses 26 years of actual data that has an annual interval. The data was prepared for time series forecasting analysis, therefore, several numbers of data preprocessing were applied such as log transformation and resampling. To get efficient variables, the best variables will be sought to improve the accuracy of predictions. Both models will conduct training and test data to produce values that can be compared using the metric regression model. Based on the training conducted, Prophet has better performance than ARIMA.]]>
<![CDATA[Prediksi Pencapaian Target Kerja Menggunakan Metode Deep Learning dan Data Envelopment Analysis ]]>
<![CDATA[David Sanjaya]]>;
<![CDATA[Setia Budi]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2678
<![CDATA[Along with the rapid development of technology, especially in the computer field, several methods have been developed for target setting. Data Envelopment Analysis (DEA) is commonly employed to analyze efficiency levels based on historical data with static targets. Data Envelopment Analysis results in a low level of efficiency against the use of static targets. A new target setting solution is needed to handle dynamic targets. Based on the need, we propose a method to predict more realistic dynamic targets using Deep Learning Long Short Term Memory (LSTM) approach from the results of the Data Envelopment Analysis (DEA). This study leads to a prediction model with 71.2% average accuracy.]]>
<![CDATA[Building Acoustic and Language Model for Continuous Speech Recognition in Bahasa Indonesia ]]>
<![CDATA[Vincent Elbert Budiman]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2684
<![CDATA[Here a development of an Acoustic and Language Model is presented. Low Word Error Rate is an early good sign of a good Language and Acoustic Model. Although there are still parameters other than Words Error Rate, our work focused on building Bahasa Indonesia with approximately 2000 common words and achieved the minimum threshold of 25% Word Error Rate. There were several experiments consist of different cases, training data, and testing data with Word Error Rate and Testing Ratio as the main comparison. The language and acoustic model were built using Sphinx4 from Carnegie Mellon University using Hidden Markov Model for the acoustic model and ARPA Model for the language model. The models configurations, which are Beam Width and Force Alignment, directly correlates with Word Error Rate. The configurations were set to 1e-80 for Beam Width and 1e-60 for Force Alignment to prevent underfitting or overfitting of the acoustic model. The goals of this research are to build continuous speech recognition in Bahasa Indonesia which has low Word Error Rate and to determine the optimum numbers of training and testing data which minimize the Word Error Rate.]]>
<![CDATA[Augmentasi Data Pengenalan Citra Mobil Menggunakan Pendekatan Random Crop, Rotate, dan Mixup ]]>
<![CDATA[Joseph Sanjaya]]>;
<![CDATA[Mewati Ayub]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2688
<![CDATA[Deep convolutional neural networks (CNNs) have achieved remarkable results in two-dimensional (2D) image detection tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. In this paper, a Deep Learning system for accurate car model detection is proposed using the ResNet-152 network with a fully convolutional architecture. It is demonstrated that significant generalization gains in the learning process are attained by randomly generating augmented training data using several geometric transformations and pixel-wise changes, such as image cropping and image rotation. We evaluated data augmentation techniques by comparison with competitive data augmentation techniques such as mixup. Data augmented ResNet models achieve better results for accuracy metrics than baseline ResNet models with accuracy 82.6714% on Stanford Cars Dataset.]]>
<![CDATA[Pembangkitan Pola Batik dengan Menggunakan Neural Transfer Style dengan Penggunaan Cost Warna ]]>
<![CDATA[Yosef Ariyanto Irawan]]>;
<![CDATA[Andreas Widjaja]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2698
<![CDATA[In this research, the neural transfer styles technique was applied to transfer styles of pattern of Batik, a traditional Indonesian cloth painted using the wax-resist dyeing technique, to some certain images. The transfer was performed using a well-known convolutional neural network (CNN) architecture. Some neural transfer tests were done to produce solid color which originally came from color clustered images. The color cost function of the CNN was computed at every epoch of the iterative neural computation. The result of the transfer are images with clustered colors and a slightly apparent color gradient. The produced images can be classified as "Creative Batik".]]>
<![CDATA[Rancang Bangun Media Pembelajaran Augmented Reality Mengenal Alat Musik Degung ]]>
<![CDATA[Yunita Agustin Mulyana]]>;
<![CDATA[Iwan Rizal Setiawan]]>;
<![CDATA[Lelah Lelah]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2699
<![CDATA[Augmented Reality is one of information technology that still exists and being developed till now. This technology can show up a not real object in the real background. As we know, we can use technology to help our work and help us to make everything simple and easier. Nowadays, everything got easier and easier to do because of technology, it’s because a lot of people try to make something useful to help them with their work. Therefore, this study uses one of the technologies, augmented reality, to introduce traditional musical instruments named Degung to all people who want to know about this instrument. They who want to show Degung to other but do not know an easy way, can use the result of this study using MDLC (multimedia development life cyle) method.]]>
<![CDATA[Penerapan Kansei Engineering dalam Perbandingan Desain Aplikasi Mobile Marketplace di Indonesia ]]>
<![CDATA[Nucky Vilano]]>;
<![CDATA[Setia Budi]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2705
<![CDATA[The company's application design is very important because it displays the company's image and to attract more users to purchase/utilize the application. This research applies Kansei Engineering Method to analyze the emotion or feelings of the user towards the design of a mobile application interface. Six Kansei Words and three specimens are utilised in this research, where Kansei words are selected from words related to user experience. The participants of this research consist of 54 students from Maranatha Christian University. Participants’ responses are studied using multivariate statistical analysis (e.g., Coefficient Correlation Analysis, Principal Component Analysis, and Factor Analysis). This study explores the emotional factors that occur in designing an application. This analysis shows that there are some major factors that greatly influence the design of a mobile application interface.]]>
<![CDATA[Penerapan Metode SCRUM dalam Pengembangan Sistem Informasi Layanan Kawasan ]]>
<![CDATA[Warkim Warkim]]>;
<![CDATA[Muhamad Hanif Muslim]]>;
<![CDATA[Farham Harvianto]]>;
<![CDATA[Setiawan Utama]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2711
<![CDATA[The development of technology is very influential in the business processes of an organization to be able to carry out its duties and functions. As a government agency engaged in research, the Indonesian Institute of Sciences (LIPI) needs to make organizational changes to support its vision as a world-class research institution. One of the first steps taken is reorganizing and redistributing employees that have a high impact on the business process of service to employees because the supporting resources are placed corporately and no longer in the work units. To deal with this problem, we developed a Regional Service Information System using the Scrum methodology. The output is a web-based software that facilitates service requests needed by employees, ranging from service submission, processing by the Area Manager and Central Manager, to being received again by the service requester. The Regional Service Information System is expected to be a solution to the problems that arise as a result of the redistribution of employees at LIPI and to improve the effectiveness of employees as the research supporting resources.]]>
<![CDATA[Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa ]]>
<![CDATA[Afrillebar Putra Pratama]]>;
<![CDATA[Agi Prasetiadi]]>;
<![CDATA[Elisa Usada]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v6i2.2730
<![CDATA[The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.]]>
