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
Articles
479 Documents
<![CDATA[Optimasi Fuzzy Artificial Neural Network dengan Algoritma Genetika untuk Prediksi Harga Crude Palm Oil ]]>
<![CDATA[Anwar Rifa'i]]>
<![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.v6i2.2617
<![CDATA[Crude Palm Oil (CPO) is one of Indonesia's best export commodities. CPO production competition causes price fluctuations so that it can trigger losses. The solution that can be taken to avoid losses is to predict the price of CPO. Time series data in the previous months, starting from January 2009 until January 2020, are used as a reference to predict the next CPO price. In this research, CPO price prediction is carried out with a combination of artificial intelligence concepts, namely Radial Basis Function Neural Network (RBFNN), and fuzzy logic. The combination of these methods, namely Fuzzy Radial Basis Function Neural Network (FRBFNN), is then optimized using genetic algorithms. The prediction results show that the error based on the MAPE value for FRBFNN prediction on training data is 11.7% and the MAPE value for testing data is 9.4%. In the FRBFNN prediction that was optimized using a genetic algorithm, the MAPE value was 10.2% for training data and 8.3% for testing data.]]>
<![CDATA[K-Nearest Neighbor Berbasis Particle Swarm Optimization untuk Analisis Sentimen Terhadap Tokopedia ]]>
<![CDATA[Dicki Pajri]]>;
<![CDATA[Yuyun Umaidah]]>;
<![CDATA[Tesa Nur Padilah]]>
<![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.v6i2.2658
<![CDATA[Tokopedia is a popular marketplace used by e-commerce in Indonesia. Customers’ perception of Twitter towards Tokopedia can be used as an important source of information and can be processed into useful insights. Sentiment analysis is a solution that can be used to process the customers’ perception using K-Nearest Neighbor based on Particle Swarm Optimization. The purpose of this study is to classify customers’ perception based on positive, neutral, and negative classes. The test is carried out with four different scenarios and k values which are evaluated using a confusion matrix. Evaluation results showed the distribution of the dataset is 90:10 and the value of k = 1 is the best evaluation result, which is 88.11%. The feature selection was used for results by using Particle Swarm Optimization. The Particle Swarm Optimization used 20 iterations and 10 particles. It produced 97.9% the best evaluation accuracy, 96.17% precision, 96.62% recall, and 96.39% f-measure.]]>
<![CDATA[Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine ]]>
<![CDATA[Christ Memory Sitorus]]>;
<![CDATA[Adhi Rizal]]>;
<![CDATA[Mohamad Jajuli]]>
<![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.v6i2.2672
<![CDATA[The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF kernel and modified C values. Every C value that is changed will be used K-Fold cross-validation first to separate the testing data and training data. The specified k value is 5. The results for each trial obtained accuracy, Receiver Operating Characteristic (ROC) and Area Under the Curves (AUC), for the best that is at C = 100 while the worst at C = 0.001.]]>
<![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.]]>
