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<![CDATA[Sistem Terintegrasi untuk Mendeteksi Perubahan Lingkungan dengan Algoritma Frame Difference dan Dynamic - Adaptive Template Matching menggunakan Raspberry Pi dan Virtual Private Network (VPN) ]]>
<![CDATA[Ardianto Wibowo]]>;
<![CDATA[Muhammad Ihsan Zul]]>;
<![CDATA[M Arif Fadly Ridha]]>;
<![CDATA[M Mahrus Zain]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3598
<![CDATA[Theft of empty houses and rampant illegal parking is a problem that often occurs in the community, causing anxiety. For this reason, a solution that can provide alarms / notifications automatically if such conditions occur to registered users is needed. This research develops an integrated system that has the ability to detect changes in environmental conditions, such as the occurrence of illegal parking or the presence of strangers in the house, through monitoring of connected IP cameras. The image captured through an IP camera is processed using the Frame Difference algorithm, to further be decided whether it includes an environmental condition changes or not. Compared to conventional IP cameras or IP cameras that include motion detection features, this research system has advantages in terms of notification form flexibility and internet connection flexibility.]]>
<![CDATA[Prediksi Pendapatan Kargo Menggunakan Arsitektur Long Short Term Memory ]]>
<![CDATA[Bagas Aji Aprian]]>;
<![CDATA[Yufis Azhar]]>;
<![CDATA[Vinna Rahmayanti Setya Nastiti]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3621
<![CDATA[Indonesian air cargo transportation is currently experiencing quite significant development. One of the cargo services in Indonesia is Garuda Indonesia Cargo and has several branch offices. The existence of a revenue forecast model is expected to provide insight into a branch office. This research proposes an income prediction using the Deep Learning algorithm, Long Short Term Memory (LSTM). LSTM is used because the data to be processed is time series data. The results of testing accuracy are measured using Root Mean Squared Error (RMSE). The data in this study are income from one branch office, the Cargo Service Center (CSC) Tangerang City. Data contains collections of goods delivery transactions every day. The data goes through 4 preprocessing processes, namely subtotal, outlier detection, difference, and scaling. The results of this study show the best prediction results, namely the composition of the 90% train data and 10% test data with RMSE values of train data 641375.70 and test data 594197.70.]]>
<![CDATA[PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHNG DAN DOUBLE MOVING AVERAGE UNTUK PERAMALAN HARGA BERAS ECERAN DI KABUPATEN PAMEKASAN ]]>
<![CDATA[Indah Listiowarni]]>;
<![CDATA[Nindian Puspa Dewi]]>;
<![CDATA[Andrey Kartika Widhy Hapantenda]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3634
<![CDATA[Rice is the main carbohydrate source used by Indonesians as a staple food, so the availability and price are also a concern. The purpose of this study is to forecast monthly rice prices for 2019, while comparing 2 forecasting methods namely Double Moving Average and Double Exponential Smoothing to get the best forecasting results of rice prices. The data used in this study is retail rice prices from January 2011 to March 2019. Based on the tests conducted, the Double Moving Average method is better with the MAPE value reaching 0.582542%, and the MSE value reaching 6349.25 using the time order 3. Average monthly retail price forecasts for 2019 using the DMA method of Rp.12,169, -]]>
<![CDATA[Sistem Informasi Geografis berbasis Web untuk Penentuan Prioritas Pembangunan Embung ]]>
<![CDATA[Kornelius Satria Budiyanto]]>;
<![CDATA[Ike Pertiwi Windasari]]>;
<![CDATA[Yudi Eko Windarto]]>;
<![CDATA[Desyta Ulfiana]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3642
<![CDATA[Spring water is a human need for daily life. One of the benefits is to irrigate agricultural land. Agricultural land in Semarang Regency dried up every dry season in the last few years. Semarang Regency local government had identified 8 areas as the candidate of small dam construction locations for short term priority. The selection of small dam locations becomes a special issue in data processing in the Water Resources Sector which has many criteria to be aware of. The obtained data can be stored in database of GIS. Therefore, decision support system is needed to determine the best small dams priority. The method used in this system is the TOPSIS. Data that has been processed will be implemented to determine the best small dams priority in a region and displayed visually using Carto Map. System are built using the Codeigniter framework based PHP and MySQL database. In its implementation, the Rapid Application Development (RAD) method is used. Based on the calculation results obtained the first three sequences that have the largest live storage and the lowest water cost that can be prioritized are Mluweh, Lebak, and Pakis.]]>
<![CDATA[Implementasi Pembelajaran E-Learning Pada Kelas Praktikum (Studi Kasus : Universitas Madura) ]]>
<![CDATA[Nindian Puspa Dewi]]>;
<![CDATA[Nanik Winarsih]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3656
<![CDATA[E-learning is a container that handles online learning systems that can be accessed anywhere by utilizing internet access. Practicum is one of the activities to improve the understanding of theory by carrying out trials or exercises in accordance with the subjects taught, so that e-learning in question can be used as a container that can be used by practicum lecturers and students in handling the activities of prakitkum activities at the University Informatics Engineering Laboratory Madura In this research, an e-learning is made that can handle practical activities, such as managing assignments, making modules, conducting online exams, and discussions that are built using ionic and native php programming languages.]]>
<![CDATA[CNN Modelling Untuk Deteksi Wajah Berbasis Gender Menggunakan Python ]]>
<![CDATA[warnia nengsih]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3679
<![CDATA[Face detection (Face Detection) is the Utilization of Biological data (Biometrics) by identifying physical features that exist in humans. Digitalization of gender recognition as a technology to recognize human gender by distinguishing the faces of women and the faces of men based on the Extraction features. The existence of this system can be applied implementatively for automatic surveillance systems and monitoring systems or market segmentation based on demographic trends and can also be applied to restrict access to a room. This research uses Convolutional Neural Network (CNN). CNN is a type of neural network where this method can be used on image data. CNN has the ability to recognize objects in an image. In total, the dataset used has 40 attribute annotations to describe female and male images. This face detection system uses python and Keras as an open source Machine Learning library for nerve networks, developed to make the application of deep learning models. With this system provides an accuracy analysis in gender detection so that it can be developed for more implementative research. The number of images must be balanced to get good performance for modeling, each model will have a training folder, validation and test data. The number of images that are not balanced can affect the performance of the CNN model. The model is built using transfer learning from InceptionV3 where modeling can recognize gender with an accuracy of 92.6%]]>
<![CDATA[Pengembangan Sistem Informasi Repository Data Akreditasi Institut Teknologi Kalimantan ]]>
<![CDATA[Aprizal Kamran]]>;
<![CDATA[Lovinta Happy Atrinawati]]>;
<![CDATA[Tegar palyus fiqar]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3706
<![CDATA[Accreditation is an assessment activity based on predetermined standards and requirements to guarantee the quality and performance of education for every university in Indonesia. The Study Program Accreditation Instrument version 4.0 is needed to compile performance into reports. The nstrument is the Study Program Performance Report. Institut Teknologi Kalimantan (ITK), as one of the institutions, ITK preparing the Study Program Performance Report with manual method so that during the preparation of the report, there was duplication of accreditation data. Because thedata is made from more than one source, collecting accreditation data is still using email. This causes data not stored, and it is not easy to find historical data every year. Therefore, this research was conducted to develop an accreditation management information system, especially Study Program Performance Report, so that accreditation data can be archived and managed by each Study Program at ITK. This system also makes it easier for the Pusat Jaminan Mutu to monitor the performance. The software development method used in this research is the Agile methodology with ScrumMethod. The development uses the Laravel framework, which has implemented a Model, View, Controller architecture. This research resulted in an information system accreditation for the Study Program Performance Report.]]>
<![CDATA[Analisis Risiko Sistem Informasi Pada RSIA Eria Bunda menggunakan Metode FMEA ]]>
<![CDATA[puja hanifah]]>;
<![CDATA[Jarot S Suroso]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v6i2.3728
<![CDATA[Technology that is rapidly developing in the IT field is an important and complementary component in processing data and information in an organization. There are many risks that may occur that will threaten the organization. Therefore, it is necessary to carry out risk management against threats to information system security and risk assessment. There are many methods that can be used in managing and assessing risk. One of them is the FMEA (Failure Mode and Effect Analysis) method. In this study, taking a case study at RSIA Eria Bunda. As an industry engaged in the health sector, RSIA Eria Bunda needs to maintain the confidentiality of information regarding patient data, doctors, medicines, and other staff from threats that may pose risks that can harm the industry. Where the research objective is to find out how the level of risk that occurs in the information system and provide mitigation solutions to RSIA Eria Bunda. There are ten stages in risk identification and assessment using the FMEA method. So from the results of this study there is one activity with a high category, 6 activities in the medium category and nineteen activities with a low category]]>
<![CDATA[Penerapan Haar Cascade Classifier dalam Mendeteksi Wajah dan Transformasi Citra Grayscale Menggunakan OpenCV ]]>
<![CDATA[S Yulina]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 7 No. 1 (2021): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v7i1.3411
<![CDATA[Face detection applications on digital images are very necessary in the process of face recognizing. This application is widely used in various disciplines, one of them is computer vision such as biometric recognition systems, search systems, and security systems. Computer vision is a combination of artificial intelligence and machine learning. It can gain informations from image and video by using computer algorithms. Many previous studies have developed face detection applications with various algorithms with certain programming languages. The detection of an object is the most important part in computer vision. Determining an accurate face location is still a challenging task for researchers. The location of the face is the main step in computer vision to find the face part in the input image. Open Source Computer Vision Library (OpenCV) is software that allows open-source library containing supporting object detection that is easily accessed into the Java programming language. Haar cascade classifier is one of the algorithms used for object detection. This algorithm can convert an object quickly by taking the number of images in a square shape on an image. In this study, discussing the application of face detection in digital images using the Haar Cascade Classifier and the transformation of images into gray / grayscale images using the OpenCV library. The results in this study have 100% accuracy in input images that have objects in the frontal position.]]>
<![CDATA[Kompresi Citra Digital Dengan Basis Komponen Warna RGB Menggunakan Metode K-Means Clustering ]]>
<![CDATA[Arief Bramanto Wicaksono Putra]]>;
<![CDATA[Muhammad Trisna Aryuna]]>;
<![CDATA[Rheo Malani]]>
<![CDATA[ Jurnal Komputer Terapan Vol. 7 No. 1 (2021): Jurnal Komputer Terapan ]]>
Publisher : <![CDATA[Politeknik Caltex Riau ]]>
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DOI: 10.35143/jkt.v7i1.3719
<![CDATA[With the development of technology and digital media, the quality of the data used is also getting higher but the size of the data is also getting bigger and requires larger storage media. To overcome the increasing need for data storage, one way that can be used is by compressing data to save space in storage memory. In this study, the k-means clustering method will be used to compress data in the form of a digital image. By grouping the colors of an image and changing the value of the color pixels in the image based on the value of the cluster center of each cluster member. The initial centroid value which is determined at the initial stage of clustering will affect the compression results. In this study, 10 experiments were carried out, with the best image quality results obtained in the 5th experiment with an MSE value of 70.22 and a PSNR value of 29.70. While the compression quality was obtained in the 7th experiment with a compression ratio of 74.5%. The results of the measurement of image quality in the 10th experiment were also obtained with an MSE value of 73.45 and a PSNR value of 29.51, and the lowest compression quality was obtained in the third experiment with a compression yield ratio of 71.3%. The average measurement results obtained an MSE value of 71.47, a PSNR value of 29.62 and a compression ratio of 72.40%.]]>
