Telematika最新文献

{"title":"Geographic Information System Design for Bridge Management in Brebes Regency","authors":"Abdulloh Badruzzaman, Yana Hendriana","doi":"10.31315/telematika.v18i3.5463","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.5463","url":null,"abstract":"Purpose: geographic information system (GIS) design to monitoring and management of bridges that have geographic references, as well as a tool for planning activity programs (maintenance, rehabilitation, strengthening or replacement) of bridges.Design/methodology/approach: waterfallFindings/result: web-based geographic information system (GIS) for bridge management in Brebes RegencyOriginality/value/state of the art: this research does not only focus on site search as the main strength of GIS but maximizes bridge inspection activities as an important part of the bridge management system as a tool for planning bridge construction and maintenance activities","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82022463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"K-Means Algorithm and Binary Search on FiBuSI","authors":"Ahmad Khuzaifi, Ratih Titi Komala Sari","doi":"10.31315/telematika.v18i3.6300","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.6300","url":null,"abstract":"Purpose: Create an application called FiBuSI (Find Business and Stock Investment) using the k-means algorithm and binary search for data search features. This application is intended for entrepreneurs and investors where they can interact with each other to build a joint business.Method: Using the RAD (Rapid Application Development) Method which focuses on system testing based on user experience related to Blackbox Testing using the Katalon Studio tools for testing functions on the FiBuSI application.Result: Based on the results of testing the FiBuSI application which focuses on the success of application functions and algorithm implementation, that each application function is successfully executed (PASSED) based on testing using the Katalon Studio tools. Meanwhile, testing the k-means algorithm (data filter) and binary search (search for letter data) was also successfully carried out by testing it directly by the user on the FiBuSI application and also using the results from the Katalon Studio tools.State of the art: Based on several studies that have been done previously related to the use of the k-means algorithm and binary search that this algorithm is carried out on 2 different features but in 1 application for business data search. In concept, the FiBuSI application focuses on bringing together entrepreneurs and investors in one platform.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87312730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimedia Mobile Application of National Heroes History Learning for Children's Character Education","authors":"Anis Susila Abadi, Pipit Febriana Dewi","doi":"10.31315/telematika.v18i3.5542","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.5542","url":null,"abstract":"Purpose: develope a multimedia application about the history of national heroes from Indonesia.Design/methodology/approach: the method used is the UCD (User Centered Design) method.Findings/result: this multimedia mobile application of national heroes history learning for children's character education has succeeded in meeting user needs.Originality/value/state of the art: a multimedia application about the history of national heroes from Indonesia.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75271895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning and Playing in Early Childhood with Augmented Reality Technology","authors":"Doni El Rezen Purba, Parasian D. P. Silitonga","doi":"10.31315/telematika.v18i3.5569","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.5569","url":null,"abstract":"Purpose: Helping the learning process in early childhood through playing and learning activities with Augmented Reality technology.Design/methodology/approach: Using Augmented Reality technology with the Iterative Rapid Paper Prototype system development methodFindings/result: Based on tests conducted on 5 types of android devices, 10 samples of early childhood participants (4-5 years) and 5 groups of objects consisting of 10 types resulted in an increase in learning ability of 33.35% which was sourced from the measurement of the correct answers that were successfully obtained. between learning methods through pictures and learning using Augmented Reality technologyOriginality/value/state of the art: In previous research, the learning model was carried out on elementary school children (aged 6 years and over) and without the implementation of Augmented Reality technology","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90539044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recurrent Neural Network With Gate Recurrent Unit For Stock Price Prediction","authors":"Afif Ilham Caniago, Wilis Kaswidjanti, Juwairiah Juwairiah","doi":"10.31315/telematika.v18i3.6650","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.6650","url":null,"abstract":"Stock price prediction is a solution to reduce the risk of loss from investing in stocks go public. Although stock prices can be analyzed by stock experts, this analysis is analytical bias. Recurrent Neural Network (RNN) is a machine learning algorithm that can predict a time series data, non-linear data and non-stationary. However, RNNs have a vanishing gradient problem when dealing with long memory dependencies. The Gate Recurrent Unit (GRU) has the ability to handle long memory dependency data. In this study, researchers will evaluate the parameters of the RNN-GRU architecture that affect predictions with MAE, RMSE, DA, and MAPE as benchmarks. The architectural parameters tested are the number of units/neurons, hidden layers (Shallow and Stacked) and input data (Chartist and TA). The best number of units/neurons is not the same in all predicted cases. The best architecture of RNN-GRU is Stacked. The best input data is TA. Stock price predictions with RNN-GRU have different performance depending on how far the model predicts and the company's liquidity. The error value in this study (MAE, RMSE, MAPE) constantly increases as the label range increases. In this study, there are six data on stock prices with different companies. Liquid companies have a lower error value than non-liquid companies.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"349 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76602719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network","authors":"Irvan Denata, Tedy Rismawan, Ikhwan Ruslianto","doi":"10.31315/telematika.v18i3.5541","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.5541","url":null,"abstract":"Orange is a type of fruit that is easily found in Sambas Regency. The types that are widely sold are Siam oranges, madu susu and susu. Each type of orange has a different quality and a different price. The price difference often results in fraud committed by traders against buyers to the detriment of the buyer. This is because differentiating types of oranges based on the appearance of the fruit does not have a standard. Therefore, in this study, a citrus fruit classification system was created based on images by implementing deep learning. The method of deep learning used in this research is Convolutional Neural Network (CNN) with AlexNet architecture. The types of oranges that will be observed are madu oranges, madu susu, and siam. The data used are 2250 images of oranges with each class totaling 750 images with a size of 227x227 pixels. The training data is 1575 images and the test data is 675 images. The training is carried out with a total of 10 epochs and each epoch will produce a model. System testing is carried out based on the model generated in the training process. Each model will be observed results in the form of accuracy which is calculated using a confusion matrix. The most optimal model was generated from training in epoch the 9th which resulted in an accuracy of 94.81%.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87292550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sistem Informasi Manajemen Notulen (E-RISALAH) Konversi Voice to Text","authors":"D. Darwanto, Nurirwan Saputra, Ari Kusuma Wardana","doi":"10.31315/telematika.v18i3.5483","DOIUrl":"https://doi.org/10.31315/telematika.v18i3.5483","url":null,"abstract":"Tujuan:Penelitian ini dilakukan untuk membantu notulis merisalahkan hasil rapat atau pertemuan dari suara menjadi tulisan. Sehingga kerja notulis lebih ringan dan menjaga kesehatan pendengaran. Perancangan/metode/pendekatan:Penelitian ini melalui beberapa tahap, yaitu perencaaan (planning), analisis (analysis), perancangan (design), dan implementasi (implementation). Hasil:Sistem Informasi Manajemen Notulen (E-RISALAH) Konversi Voice to Text berbasis website. Keaslian/state of the art:Risalah rapat adalah kegiatan mencatat atau menyalin seluruh hasil dari pertemuan. Dalam pelaksaan masih dikerjakan secara manual, dengan mendengarkan rekaman dan menyalin atau diketik secara manual, selain kurang efektif penggunaan headset dalam waktu yang lama dapat menggangu kesehatan pendengaran. Seiring perkembangan ilmu dan teknologi, maka dibuatlah sebuah sistem yang akan membantu merisalahkan hasil rapat dari suara menjadi tulisan. Dengan teknologi speech recognition dimana ini adalah sebuah kemampuan yang dimiliki oleh mesin atau aplikasi untuk mengindentifikasi kata dan frasa yang terdapat dalam bahasa lisan. Sehingga kerja notulis lebih ringan dan menjaga kesehatan pendengaran.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84090142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Group Decision Support System with the Multi-Stage Multi-Attribute Group Decision Making (MS-MAGDM) Method on the Intelligent Warehouse Management System","authors":"S. Nugroho","doi":"10.31315/TELEMATIKA.V18I2.5507.G3835","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.5507.G3835","url":null,"abstract":"Purpose: to find a solution with MS-DAGDM for the problem of different criteria used by decision maker at each stage.Design/methodology/approach: This research was conducted using literature review with a study of the theory of decision-making methods, group decisions, suplier selection processes, and factors that influence decisions in the context of warehousing and MS-MAGDM to solve the problems.Findings/result: This research find that GDSS prototypes which have four methods in making decisions. First, Analytical Hierarchy Process for weighting the division head level. Second, TOPSIS for divison head level decisions. Third, Hybrid Weight Averaging (HWA) manager level. Fourth, Time Weight Averaging (TWA) for manager level decisions.Originality/value/state of the art:The decision-making model of the GDSS system in this study combines four methods at each level of management. The section head level uses AHP for the level weighting and TOPSIS for decision making. Level managers use Hybrid Weight Averaging (HWA) weighting and Time Weight Averaging (TWA) for decisions. The combination of these methods is carried out using a Poisson distribution, for HWA and TWA operators to combine individual decisions into group decisions. Tujuan: Fokus penelitian ini adalah mencari solusi dengan MS-MAGDM untuk permasalahan perbedaan kriteria yang dipergunakan pembuat keputusan dalam setiap stage.Perancangan/metode/pendekatan: Metode yang digunakan yaitu kajian kepustakaan dengan kajian terhadap teori metode pembuatan keputusan, keputusan kelompok, proses pemilihan supplier, dan faktor yang berpengaruh pada keputusan dalam konteks pergudangan serta MS-MAGDM untuk menyelesaikan permasalahan tersebut.Hasil: Hasil penelitian ini berupa purwarupa GDSS yang memiliki 4 metode dalam pembuatan keputusan yaitu Analytical Hierarchi Process (AHP) untuk pembobotan level kepala bagian, TOPSIS untuk keputusan level kepala bagian, Hybrid Weight Averaging (HWA) pembobotan pada level manager dan Time Weight Averaging (TWA) untuk keputusan level managerKeaslian/ state of the art:Model pengambilan keputusan sistem GDSS penelitian ini menggabungkan 4 metode pada setiap tingkatan manajemen. Level kepala bagian menggunakan AHP untuk pembobotan level dan TOPSIS untuk pembuatan keputusan. Level manager menggunakan Hybrid Weight Averaging (HWA) pembobotan dan Time Weight Averaging (TWA) untuk keputusan. Penggabungan metode dilakukan menggunakan distribusi Poisson, untuk operator HWA dan TWA guna memadukan keputusan individu mejadi keputusan kelompok.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82545831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development Of Executive Information Systems Of Cirebon City Government (Case Study: Department Of Communication, Informatics And Statistics)","authors":"Muhammad Nur Hendra Alvianto, Herry Sofyan, Juwairiah Juwairiah","doi":"10.31315/TELEMATIKA.V18I2.4844","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.4844","url":null,"abstract":"Purpose: Developing an executive information system to meet the information needs of the Mayor, Deputy Mayor, Regional Secretary and the heads of SKPD within the Cirebon City Government.Design / method / approach: Using the drill down method for solving information on executive information systems and the GRAPPLE system development methodResult: The development of an executive information system in Cirebon city government has assisted the executive, consisting of mayors, deputy mayors and regional secretaries and middle executives consisting of skpd within the Cirebon city government. Cirebon city government executive information system consists of five sectors in the city of Cirebon, namely economy, health, population, education and government. The results of the validation testing are 100% and the average user acceptance testing results are 85.29%.Authenticity / state of the art: Based on previous research, this study has the same characteristics but in the development of executive information systems it has differences in objects and methods of software development.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85358634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Backpropagation with BFGS Optimizer for Covid-19 Prediction Cases in Surabaya","authors":"Z. Fitriah, Mohamad Handri Tuloli, S. Anam, Noor Hidayat, Indah Yanti, D. Mahanani","doi":"10.31315/TELEMATIKA.V18I2.5454","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.5454","url":null,"abstract":"Covid-19 is a new type of corona virus called SARS-CoV-2. One of the cities that has contributed the most to infected Covid-19 cases in Indonesia is Surabaya, East Java. Predicting the Covid-19 is the important thing to do. One of the prediction methods is Artificial Neural Network (ANN). The backpropagation algorithm is one of the ANN methods that has been successfully used in various fields. However, the performance of backpropagation is depended on the architecture and optimization method. The standard backpropagation algorithm is optimized by gradient descent method. The Broyden - Fletcher - Goldfarb - Shanno (BFGS) algorithm works faster then gradient descent. This paper was predicting the Covid-19 cases in Surabaya using backpropagation with BFGS. Several scenarios of backpropagation parameters were also tested to produce optimal performance. The proposed method gives better results with a faster convergence then the standard backpropagation algorithm for predicting the Covid-19 cases in Surabaya.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89063111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}