Articles
<![CDATA[Enhancing the capability of online teaching for elementary school teacher through interactive video making training ]]>
<![CDATA[Yuita Arum Sari]]>;
<![CDATA[Randy Cahya Wihandika]]>;
<![CDATA[Sigit Adinugroho]]>;
<![CDATA[Indriati Indriati]]>;
<![CDATA[Putra Pandu Adikara]]>
<![CDATA[ Community Empowerment Vol 7 No 7 (2022) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
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DOI: 10.31603/ce.6616
<![CDATA[The strategies and learning mechanisms that have been widely used up until now have changed as a result of the Covid-19 pandemic. Learning from home (BDR) activities are used to replace face-to-face learning activities. Effective implementation of BDR requires information technology skills, especially the use of learning support software. Thus, it is imperative that teachers receive training in the use of learning support software in order to advance their abilities to teach online effectively and efficiently. In this community service, training activities for making creative teaching materials were carried out for elementary school teachers. The creative teaching material is in the form of animation, so that it attracts the interest of students and is expected to increase the effectiveness of online learning. This community service activity begins with a pre-test, continues with the delivery of material, and ends with the provision of a post-test and questionnaire. The evaluation's findings revealed that participants' skills had improved when learning support hardware and instructional videos were introduced.]]>
<![CDATA[Peringkasan Teks Ekstraktif Kepustakaan Ilmu Komputer Bahasa Indonesia Menggunakan Metode Normalized Google Distance dan K-means ]]>
<![CDATA[Dhimas Anjar Prabowo]]>;
<![CDATA[Mochammad Ali Fauzi]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The yearly rapid increase of digital data surface a problem for a person to be able to read every information that was served. One example of its data was a textual data document, which could be in a form of research document. This problem urges for a solution that is a technique to present all of the information in a clear and concise form, and one of its solution is a text summarization technique. This research proposed a text summarization technique using Normalized Google Distance (NGD) and K-means as its extractive algorithm, with a textual data that is a research document based on computer science studies in an Indonesian language as its research object. NGD will be used as an algorithm to derive sentences that was related to its document's title, and K-means will be used as an algorithm to obtain important sentences by its several topics that occurs in the document. The experiment result showed that this research possess an average best of precision, recall, and relative utility measures scores by 0.27, 0.43, and 0.45 respectively. In the other hand, the experiment result also showed that this research possess an average of kappa measure score by 0.41 or moderate.]]>
<![CDATA[Penentuan Jumlah Karakter pada Plat Nomor Kendaraan dengan menggunakan Selective Ratio Bounding Box ]]>
<![CDATA[Juniman Arief]]>;
<![CDATA[Fitri Utaminingrum]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The system of determining the number of characters on the vehicle number plate is one of the applications required in modern times today. The first step taken is image capture using the camera, then perform image processing and segmentation on the vehicle license plate image. Then do the determination of the number of characters on the vehicle number plate by using bounding box and selective ratio bounding box. Before the process of detection or the introduction of vehicle license plate required the validity to determine the number of characters on the license plate of the vehicle in order to know the number of characters on the license plate of the vehicle so as not to be wrong in recognizing the character on the license plate of the vehicle. It is expected that the application is able to determine the number of characters on the license plate of the vehicle. Application has been tested on 15 samples of data plate number of vehicles with standard specifications of the Police of the Republic of Indonesia. Tests of 15 data samples were performed 5 times using several variations of the ratio values ​​of the threshold. From the results of the overall testing that has been done, the average level of accuracy in the use of bounding box is 54% in the whole test. In the use of selective ratio bounding box, the highest average accuracy level in the second test was 92% and the lowest accuracy level in the 4th test was 68%. While on the other test obtained the same average accuracy that is equal to 88%.]]>
<![CDATA[Prediksi Tren Kurs Dollar Dari Berita Finansial Amerika Serikat Berbahasa Indonesia Menggunakan Support Vector Machine ]]>
<![CDATA[Ade Kurniawan]]>;
<![CDATA[Putra Pandu Adikara]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[United States Dollar (USD) is the most used currency for international transaction, its daily circulation is bigger than the other currency in the world. America's financial data not only give an impact in America itself but also directly effecting the other country. The main focus of this research is to predict USD's trend from America's Financial News in Bahasa Indonesia Using Support Vector Machine Algorithm. Kernel that used in this research is polynomial degree d, the best data ratio is 80% for training data and 20% for testing data. The output generated into 2 class to weaken USD price (Down) and on the other hand to strengthen USD price (Up) to rival's currency. The best parameter combination that give best average accuracy are using under DF threshold = 15%, upper DF threshold = 85%, λ=0.1, CLR=0.01, C=1, epsilon=0.00001, maximum iteration=100 and generated average accuracy=76.66%, sensitivity=80% and specificity=73.33%.]]>
<![CDATA[Penentuan Keaslian Tanda Tangan Menggunakan Shape Feature Extraction Techniques Dengan Metode Klasifikasi K Nearest Neighbor Dan Mean Average Precision ]]>
<![CDATA[Willy Karunia Sandy]]>;
<![CDATA[Agus Wahyu Widodo]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The pace of technological development introduces the automatic identification of signature authenticity which is an important task in many activities requiring legitimate evidence. The process of authenticating signatures begins with preprocessing, which consists of gray transformation, median filter, binary transformation, cropping, and edge detection. After the process of preprocessing followed by the process of determining the extraction of form characteristics with the method of Shape Feature Extraction Techniques consisting of area, perimeter, centroid, rectangularity, eccentricity, roundness. Then classified based on training data obtained from calculations Shape Feature Extraction Techniques. After classification with K Nearest Neighbor then done calculation process Mean Average Precision to determine the authenticity of signature and percentage calculation of Mean Average Precision. In the system accuracy test results obtained 61% accuracy with the retrieval of random data for 25 data. Then obtained an accuracy of 61% accuracy with the retrieval of random data for 15 data and 58% on the retrieval of 5 data. Highest accuracy was obtained on the largest data collection with an accuracy of 61%.]]>
<![CDATA[Optimasi Pemilihan Pekerja Kasar Perumahan Pada PT. Yaguna Bangun Pratama Menggunakan Metode Analytical Hierarchy Process dan Promethee ]]>
<![CDATA[Panji Prasuci Saputra]]>;
<![CDATA[Marji Marji]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Technological advancement as a vessel for the interests of various jobs that occurred in this era, demands competent human resources. Human resources have a very important role in the interaction of capital factors, materials, methods, and machinery. One example of human resources is the building course workers. Hence, this journal examines rough building workers in PT. Yaguna Bangun Pratama. This journal tries to selectbuilding course workers who do not meet the criteria of the company (PT Yaguna Bangun Pratama). Based on these problems, it takes an application that can process the data into a system optimization of rough-labor election. The analytical hierarchy process (AHP) dan Promethee method was chosen because it was able to rank the best alternative from a number of alternatives. The test used is bu usingthe changes of 5 types of preference in the Promethee method. The results of the usual preference type and quasi preference type resulted in a match rate of 80%, whereas the linear preferences type and the linear preferences type with unlimited areas resulted in a match rate of 60%, and the type of level preference resulted in a match rate of 40%. In other words, the greater the percentage of the suitability of the system with the expert, the better the system is]]>
<![CDATA[Penentuan Posisi Pemain Sepak Bola Menggunakan Metode AHP dan TOPSIS ]]>
<![CDATA[Rezza Pratama]]>;
<![CDATA[Edy Santoso]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The position of the football player is very important for a player or team. Mistakes and less than optimal position of players can be a factor of defeat in a match. Therefore the positioning of players becomes a very important factor in the game of football. The purpose of this research is to assist the coach in determining the football player's position. In the Analytic Hierarcy Process (AHP) method, weighting criteria is calculated according to the importance of the criteria. Methods Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) is a method that uses distance as a reference for making comparisons. The selected alternatives are not based on criteria, but are compared with positive and negative ideal solutions. To benefit from each of these methods, feeding the authors applies a combination of AHP and TOPSIS methods. In this study, the AHP method is used for weighting. While TOPSIS method is used for alternative ranking. The data used for testing using 100 data ball players. The data is processed using AHP and TOPSIS calculations. The results of the scenario will be compared with the test data to obtain the accuracy value. From the comparison, obtained an accuracy of 58%. The result of recommendation of position according to original position of player equal to 58 data.]]>
<![CDATA[Deteksi Zebra Cross Pada Citra Digital Dengan Menggunakan Metode Hough Transform ]]>
<![CDATA[Fitria Indriani]]>;
<![CDATA[Fitri Utaminingrum]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[The high number of accidents that injure pedestrians while crossing is caused by motorists who are less cautious. Accidents of course undesirable can be prevented and minimized the culture of orderly traffic one by using facilities such as zebra cross. In this research, we propose the process of zebra cross detection on digital image using Hough Transform method, in order to be implemented in smart vehicle navigation system in identifying zebra cross in order to increase equality of both riders and zebra cross users. The zebra cross detection process starts from pre-processing, which consists of grayscaling process, mean filtering, dilation, and histogram equalization, for our edge detection using the next stage canny method is the image inversion which aims to change the pixels of white to black, and vice versa. Then for line detection on zebra cross using hough transform method. Based on the test, the highest accuracy value when the 100 threshold value on the first morning condition test data is 95.2%. The result of testing the variation of the structure element obtained the maximum results with the use of rectangle has the highest accuracy value of 95.2% compared with the use of other structure element form. In the result of testing edge detection sobel has the highest accuracy value of 92.8%.]]>
<![CDATA[Penerapan Evolution Strategies untuk Optimasi Travelling Salesman Problem With Time Windows pada Sistem Rekomendasi Wisata Malang Raya ]]>
<![CDATA[Cahya Chaqiqi]]>;
<![CDATA[Agus Wahyu Widodo]]>;
<![CDATA[Yuita Arum Sari]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 7 (2018): Juli 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Tourism has became one of influential sectors that plays significant role in shaping the economy of a nation. Malang Raya is one of the place in Indonesia that have an abundance of tourism potential. Malang Raya is a region consisting of three different area of administration which are Kabupaten Malang, Kota Malang, and Kota Batu. Malang Raya has a large collection of destinations and attractions for tourists. On the other hand, the diverse tourism spots to visit can rise another issue as tourists left confused in choosing the best sites and destination alternatives that suit their expectations. The selection of routes faced with limited travel time is a common optimization problem known as Travelling Salesman Problem With Time Windows (TSP-TW). Optimization problem such of TSP-TW can be solved by utilizing Evolution Strategies (ES). According to the result acquired in (pre-research) assessment, the highest fitness value of 0,0041 is reached when the sum of population is 100 and the sum of generation is 15. The results of the optimization test obtained that the application can optimize the recommendation of respondents by 5.57%.]]>
<![CDATA[Optimasi K-Nearest Neighbour Menggunakan Particle Swarm Optimization pada Sistem Pakar untuk Monitoring Pengendalian Hama pada Tanaman Jeruk ]]>
<![CDATA[Kukuh Wiliam Mahardika]]>;
<![CDATA[Yuita Arum Sari]]>;
<![CDATA[Achmad Arwan]]>
<![CDATA[ Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya ]]>
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<![CDATA[Orange in Indonesia is one of the national commodities have the potential of high competitiveness in the local economy to abroad. The production of Indonesian Orange from 2006 to 2015 has decreased. One of the causes of this decline is pests. Therefore we need a system that can identify pests in citrus plants. The PSO-KNN method is one method that can be used to solve classification problems with many features. This method is a combination of 2 methods namely K-Nearest Neighbour and Particle Swarm optimization. K-Nearest Neighbors (KNN) are used to classify pests based on similarity calculations from existing data. Particle swarm optimization (PSO) is used to perform k value optimization and feature selection on KNN dataset and then evaluate the accuracy generated on KNN. From the results of tests that have been done can be concluded that the value of the best PSO parameter iteration is 151, popsize is 25, the value of c1 is 1, the value of c2 is 1.2 and w is 0.9. There was an increase in accuracy before and after optimization that is the highest accuracy of KNN reaches 90%, and the highest accuracy of PSO-KNN reached 96.25%. Improved accuracy indicates that the PSO algorithm is able to correct the deficiency that exist in KNN.]]>
