Pub Date : 2022-10-31DOI: 10.31315/telematika.v19i3.7979
Fadmi Rina, Anis Susila Abadi, Sholeh Huda
The loss of hearing function in deaf children causes deaf children to experience obstacles in listening to the sound of objects or sounds of language as children generally hear. Therefore, it is necessary to optimize the hearing function of deaf children. The Development of Sound and Rhythm Perception Communication (PKPBI) is a special program to practice understanding sound so that the remaining hearing of deaf children can be maximized. So far, the PKPBI learning media at the sound identification stage used by the Karnna Manohara Yogyakarta Special School teacher is the keyboard. However, the keyboard has weaknesses such as the collection of sounds on the keyboard is very limited. Another problem is the Covid 19 pandemic, PKPBI learning is less than optimal due to limited face-to-face meetings. The purpose of this research is to design a serious game as a learning medium for sound identification for deaf children that can be used in the classroom and at home. The method used to design serious sound identification games is User Centered Design (UCD). Based on the research results, the design of this serious game can be developed into a serious game application to practice sound identification in deaf children.
{"title":"Serious Game Design Of Sound Identification For Deaf Children Using The User Centered Design","authors":"Fadmi Rina, Anis Susila Abadi, Sholeh Huda","doi":"10.31315/telematika.v19i3.7979","DOIUrl":"https://doi.org/10.31315/telematika.v19i3.7979","url":null,"abstract":"The loss of hearing function in deaf children causes deaf children to experience obstacles in listening to the sound of objects or sounds of language as children generally hear. Therefore, it is necessary to optimize the hearing function of deaf children. The Development of Sound and Rhythm Perception Communication (PKPBI) is a special program to practice understanding sound so that the remaining hearing of deaf children can be maximized. So far, the PKPBI learning media at the sound identification stage used by the Karnna Manohara Yogyakarta Special School teacher is the keyboard. However, the keyboard has weaknesses such as the collection of sounds on the keyboard is very limited. Another problem is the Covid 19 pandemic, PKPBI learning is less than optimal due to limited face-to-face meetings. The purpose of this research is to design a serious game as a learning medium for sound identification for deaf children that can be used in the classroom and at home. The method used to design serious sound identification games is User Centered Design (UCD). Based on the research results, the design of this serious game can be developed into a serious game application to practice sound identification in deaf children.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88322481","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7598
Riza Prapascatama Agusdin, Naufal Nur Aidil
Objective: One of the strategies that companies can do to survive amid fierce business competition is to invest in IT. Currently all companies need to invest in IT to improve company performance better but usually the budget costs that must be incurred by companies to make IT investments are very large. Therefore, it is necessary to analyze the feasibility of IT investment. This study aims to determine how much the costs incurred and the benefits obtained after creating a Social Media Analysis information system and also to find out whether the Social Media Analysis information system development project is feasible or not.Methods: This study uses the Cost Benefit Analysis method where the method compares the components of costs and benefits which are then recommended for a policy on investment projects. The Cost Benefit Analysis method is supported by several calculation criteria such as Net Present Value (NPV), Payback Period (PP), Return On Investment (ROI), and Benefit Cost Ratio (BCR).Results: The results showed that the NPV for 5 years was Rp. 300,138,606, PP was 2 years and 11 months, ROI was 9.03%, and BCR was 1.08. From the results of this study, it can be concluded that the Social Media Analysis information system investment project is feasible to continue.
目标:企业在激烈的商业竞争中生存的策略之一是对IT进行投资。目前，所有的公司都需要在IT方面进行投资，以更好地提高公司绩效，但通常公司必须承担的IT投资预算成本非常大。因此，有必要对it投资的可行性进行分析。本研究旨在确定创建Social Media Analysis信息系统后所产生的成本和所获得的收益，并确定Social Media Analysis信息系统开发项目是否可行。方法:本研究使用成本效益分析方法，该方法比较成本和效益的组成部分，然后推荐投资项目的政策。成本效益分析方法由几个计算标准支持，如净现值(NPV)、投资回收期(PP)、投资回报率(ROI)和效益成本比(BCR)。结果:5年NPV为Rp. 300,138,606, PP为2年零11个月，ROI为9.03%，BCR为1.08。从本研究的结果，可以得出结论，社会媒体分析信息系统投资项目是可行的，可以继续。
{"title":"Feasibility Analysis of Information Technology Investment Using Cost Benefit Analysis Method","authors":"Riza Prapascatama Agusdin, Naufal Nur Aidil","doi":"10.31315/telematika.v19i2.7598","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7598","url":null,"abstract":"Objective: One of the strategies that companies can do to survive amid fierce business competition is to invest in IT. Currently all companies need to invest in IT to improve company performance better but usually the budget costs that must be incurred by companies to make IT investments are very large. Therefore, it is necessary to analyze the feasibility of IT investment. This study aims to determine how much the costs incurred and the benefits obtained after creating a Social Media Analysis information system and also to find out whether the Social Media Analysis information system development project is feasible or not.Methods: This study uses the Cost Benefit Analysis method where the method compares the components of costs and benefits which are then recommended for a policy on investment projects. The Cost Benefit Analysis method is supported by several calculation criteria such as Net Present Value (NPV), Payback Period (PP), Return On Investment (ROI), and Benefit Cost Ratio (BCR).Results: The results showed that the NPV for 5 years was Rp. 300,138,606, PP was 2 years and 11 months, ROI was 9.03%, and BCR was 1.08. From the results of this study, it can be concluded that the Social Media Analysis information system investment project is feasible to continue.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82243165","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7544
Fadly Shabir, Ahmad Irfan Abdullah, B. Asrul, Sitti Alifah Amilhusna Nur
Purpose: This research aims to provide recommendations for planting season based on predictions of rainfall, air temperature, and wind speed based on the website.Design/methodology/approach: This study implemented the Double exponential smoothing to predict rainfall, air temperature, and monthly wind speed one year in the future using past data.Findings/result: This study has succeeded in providing recommendations for planting season. Based on the results of the accuracy calculation between the prediction results and the actual data using the Mean Absolute Percetage Error (MAPE), each has a forecast error value of 30.69% for rainfall, 0.63% air temperature, and 5.89% wind speed. Originality/value/state of the art: Research related to the application of Double exponential smoothing to determine the planting period. Based on the results of the accuracy calculation between the prediction results and the actual data using Mean Absolute Percetage Error (MAPE), this has never been done in previous studies.
{"title":"Implementation Of The Double Exponential Smoothing Method In Determining The Planting Time In Strawberry Plantations","authors":"Fadly Shabir, Ahmad Irfan Abdullah, B. Asrul, Sitti Alifah Amilhusna Nur","doi":"10.31315/telematika.v19i2.7544","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7544","url":null,"abstract":"Purpose: This research aims to provide recommendations for planting season based on predictions of rainfall, air temperature, and wind speed based on the website.Design/methodology/approach: This study implemented the Double exponential smoothing to predict rainfall, air temperature, and monthly wind speed one year in the future using past data.Findings/result: This study has succeeded in providing recommendations for planting season. Based on the results of the accuracy calculation between the prediction results and the actual data using the Mean Absolute Percetage Error (MAPE), each has a forecast error value of 30.69% for rainfall, 0.63% air temperature, and 5.89% wind speed. Originality/value/state of the art: Research related to the application of Double exponential smoothing to determine the planting period. Based on the results of the accuracy calculation between the prediction results and the actual data using Mean Absolute Percetage Error (MAPE), this has never been done in previous studies.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83258193","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7601
Bagus Muhammad Akbar, Ahmad Taufiq Akbar, Rochmat Husaini
Tujuan:Banyak negara di dunia telah berusaha mengendalikan dampak pandemi COVID-19 melalui penggunaan vaksin. vaksin sinovac merupakan salah satu vaksin populer yang telah digunakan di beberapa negara termasuk Indonesia. Sejak hadirnya vaksin sinovac, persepsi masyarakat baik di lapangan maupun di media sosial semakin muncul antara setuju dan tidak setuju dengan vaksin tersebut. Persepsi masyarakat dunia di media sosial dapat dianalisis untuk mengetahui kategori sentimen dan tingkat emosional masyarakat terhadap penerimaan vaksin Sinovac.Perancangan/metode/pendekatan:Analisis dapat dilakukan melalui data mining yang menggunakan algoritma Naive Bayes untuk menghitung probabilitas dan statistik sehingga setiap opini dapat diklasifikasikan dalam kategori sentimen positif, negatif, atau netral. Dalam penelitian ini, sumber analisis data adalah persepsi publik yang mengandung kata kunci “sinovac” dari twitter. Pengujian menggunakan sentimen, sentimen, dan library syuzhet menunjukkan bahwa sentimen positif lebih tinggi daripada negatif dan netral. Sentimen negatif paling dipengaruhi oleh tingkat emosional kesedihan dan kemarahan. Sedangkan sentimen positif sangat dipengaruhi oleh kategori senang dan emosi campur aduk. Kategori emosi campuran lebih sesuai dengan sentimen positif.Hasil:Klasifikasi emosi terhadap data tweet dalam penelitian ini menunjukkan bahwa kategori emosi kegembiraan, dan campuran memiliki persentase tertinggi yang mengandung polaritas sentimen positif. Berdasarkan penelitian ini, kata kunci sinovac cenderung memunculkan sentimen positif. Polaritas mempengaruhi emosi, namun tidak sebaliknya. Karena terlihat bahwa nilai akurasi pada klasifikasi polaritas (dengan kedua library) telah meningkat ketika fitur emosi tidak diikutkan. Sedangkan nilai akurasi pada klasifikasi emosi justru meningkat ketika fitur polaritas diikutkan.Keaslian/ state of the art:Metode Naive Bayes (library setiment) dan metode Valence Shifter (library sentimentr) yang digunakan dalam analisis sentimen pada penelitian ini menunjukkan bahwa sentimen positif lebih tinggi daripada netral dan negatif. Hasil persentase sentimen positif oleh metode Valence Shifter lebih rendah daripada metode Naive Bayes. Pada metode Valence Shifter cenderung menghasilkan agregat yang lebih kecil antara hasil persentase sentimen positif dibanding netral dan negatif.
{"title":"Analysis of Sentiments and Emotions about Sinovac Vaccine Using Naive Bayes","authors":"Bagus Muhammad Akbar, Ahmad Taufiq Akbar, Rochmat Husaini","doi":"10.31315/telematika.v19i2.7601","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7601","url":null,"abstract":"Tujuan:Banyak negara di dunia telah berusaha mengendalikan dampak pandemi COVID-19 melalui penggunaan vaksin. vaksin sinovac merupakan salah satu vaksin populer yang telah digunakan di beberapa negara termasuk Indonesia. Sejak hadirnya vaksin sinovac, persepsi masyarakat baik di lapangan maupun di media sosial semakin muncul antara setuju dan tidak setuju dengan vaksin tersebut. Persepsi masyarakat dunia di media sosial dapat dianalisis untuk mengetahui kategori sentimen dan tingkat emosional masyarakat terhadap penerimaan vaksin Sinovac.Perancangan/metode/pendekatan:Analisis dapat dilakukan melalui data mining yang menggunakan algoritma Naive Bayes untuk menghitung probabilitas dan statistik sehingga setiap opini dapat diklasifikasikan dalam kategori sentimen positif, negatif, atau netral. Dalam penelitian ini, sumber analisis data adalah persepsi publik yang mengandung kata kunci “sinovac” dari twitter. Pengujian menggunakan sentimen, sentimen, dan library syuzhet menunjukkan bahwa sentimen positif lebih tinggi daripada negatif dan netral. Sentimen negatif paling dipengaruhi oleh tingkat emosional kesedihan dan kemarahan. Sedangkan sentimen positif sangat dipengaruhi oleh kategori senang dan emosi campur aduk. Kategori emosi campuran lebih sesuai dengan sentimen positif.Hasil:Klasifikasi emosi terhadap data tweet dalam penelitian ini menunjukkan bahwa kategori emosi kegembiraan, dan campuran memiliki persentase tertinggi yang mengandung polaritas sentimen positif. Berdasarkan penelitian ini, kata kunci sinovac cenderung memunculkan sentimen positif. Polaritas mempengaruhi emosi, namun tidak sebaliknya. Karena terlihat bahwa nilai akurasi pada klasifikasi polaritas (dengan kedua library) telah meningkat ketika fitur emosi tidak diikutkan. Sedangkan nilai akurasi pada klasifikasi emosi justru meningkat ketika fitur polaritas diikutkan.Keaslian/ state of the art:Metode Naive Bayes (library setiment) dan metode Valence Shifter (library sentimentr) yang digunakan dalam analisis sentimen pada penelitian ini menunjukkan bahwa sentimen positif lebih tinggi daripada netral dan negatif. Hasil persentase sentimen positif oleh metode Valence Shifter lebih rendah daripada metode Naive Bayes. Pada metode Valence Shifter cenderung menghasilkan agregat yang lebih kecil antara hasil persentase sentimen positif dibanding netral dan negatif.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85917699","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7363
I. N. T. A. Putra, Ketut Sepdyana Kartini, K. Winatha
The lack of interest in student learning is due to the learning media used are less attractive and effective to understand the material. This study aims to implement mobile-based interactive learning media regarding OOAD material. The media feasibility test uses a blackbox testing scenario and the analysis uses the Gutman scale technique. From the test results, it was found that the percentage of blackbox testing was 100% and the functional requirements test by the resource persons obtained a percentage of 100%. Based on the results of these studies, it can be explained that this learning media is very good and has been feasible to be implemented.
{"title":"Implementation Of Mobile-Based OOAD Interactive Learning Media","authors":"I. N. T. A. Putra, Ketut Sepdyana Kartini, K. Winatha","doi":"10.31315/telematika.v19i2.7363","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7363","url":null,"abstract":"The lack of interest in student learning is due to the learning media used are less attractive and effective to understand the material. This study aims to implement mobile-based interactive learning media regarding OOAD material. The media feasibility test uses a blackbox testing scenario and the analysis uses the Gutman scale technique. From the test results, it was found that the percentage of blackbox testing was 100% and the functional requirements test by the resource persons obtained a percentage of 100%. Based on the results of these studies, it can be explained that this learning media is very good and has been feasible to be implemented.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87380209","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7044
I. P. K. Udayana, Ni Putu Eka Kherismawati, I. Sudipa
Online lectures are mandatory to deal with the implementation of education during the COVID-19 pandemic. This significant change certainly creates a different experience for students. Regarding online learning, several public health experts and ophthalmologists say that residual radiation from electronic screens is causing an epidemic of eye fatigue. Research on smart classrooms actually appeared several years ago, but in reality it has not been implemented according to the planned concept. The current smart classroom research environment only uses outdated methods, which make the computer system incongruent (such as decision trees in video feeds) or only to the level of empirical studies or blueprints, which are not much help for other academic footing or reference materials. to students. This study aims to build an intelligent system that can evaluate students' attention during online classes, use teaching videos as learning feeds and input for predictions and also use advanced algorithms in several computational domains, namely face segmentation, landmarking, PERCLOS observations, Yawning and decision analysis using Ensemble Regression Trees to detect students' sleepiness, which is expected to patch up the shortcomings of the PERCLOS algorithm and the problems found in the single regression tree-based implementation. Based on the results of the tests that have been carried out, the system developed has been able to observe sleepy objects in learning videos with an accuracy of 80% so that later it can be a lesson for teachers why there are students who are sleepy during online classes either because of uninteresting material or other reasons.
{"title":"Detection of Student Drowsiness Using Ensemble Regression Trees in Online Learning During a COVID-19 Pandemic","authors":"I. P. K. Udayana, Ni Putu Eka Kherismawati, I. Sudipa","doi":"10.31315/telematika.v19i2.7044","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7044","url":null,"abstract":"Online lectures are mandatory to deal with the implementation of education during the COVID-19 pandemic. This significant change certainly creates a different experience for students. Regarding online learning, several public health experts and ophthalmologists say that residual radiation from electronic screens is causing an epidemic of eye fatigue. Research on smart classrooms actually appeared several years ago, but in reality it has not been implemented according to the planned concept. The current smart classroom research environment only uses outdated methods, which make the computer system incongruent (such as decision trees in video feeds) or only to the level of empirical studies or blueprints, which are not much help for other academic footing or reference materials. to students. This study aims to build an intelligent system that can evaluate students' attention during online classes, use teaching videos as learning feeds and input for predictions and also use advanced algorithms in several computational domains, namely face segmentation, landmarking, PERCLOS observations, Yawning and decision analysis using Ensemble Regression Trees to detect students' sleepiness, which is expected to patch up the shortcomings of the PERCLOS algorithm and the problems found in the single regression tree-based implementation. Based on the results of the tests that have been carried out, the system developed has been able to observe sleepy objects in learning videos with an accuracy of 80% so that later it can be a lesson for teachers why there are students who are sleepy during online classes either because of uninteresting material or other reasons.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80459470","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.6460
Arif Riyandi, Tony Widodo, Shofwatul Uyun
Objective: Automatic identification is carried out with the help of a tool that can take an image of road conditions and automatically distinguish the types of road damage, the location of road damage in the image and calculate the level of road damage according to the type of road damage.Design/method/approach: Identification of damaged roads usually uses manual RCI system which requires high cost. In this study, a comparison framework is proposed to determine the performance of the image pre-processing model on the image classification algorithm.Results: Based on 733 image data classified using the CNN method from 4 models of pre-processing stages, it can be concluded that training from grayscale images produces the best level of accuracy with a training accuracy value of 88% and validation accuracy reaching 99%.Authenticity/state of the art: Testing of 4 pre-processing models against the classification algorithm used as a comparison resulted in the best algorithm/method for managing road images.
{"title":"Analysis of System Development Methodology with Comparison of Payroll Information System Software Model Using Waterfall Development Model, Rapid Application Development (RAD) Model and Agile Model","authors":"Arif Riyandi, Tony Widodo, Shofwatul Uyun","doi":"10.31315/telematika.v19i2.6460","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.6460","url":null,"abstract":"Objective: Automatic identification is carried out with the help of a tool that can take an image of road conditions and automatically distinguish the types of road damage, the location of road damage in the image and calculate the level of road damage according to the type of road damage.Design/method/approach: Identification of damaged roads usually uses manual RCI system which requires high cost. In this study, a comparison framework is proposed to determine the performance of the image pre-processing model on the image classification algorithm.Results: Based on 733 image data classified using the CNN method from 4 models of pre-processing stages, it can be concluded that training from grayscale images produces the best level of accuracy with a training accuracy value of 88% and validation accuracy reaching 99%.Authenticity/state of the art: Testing of 4 pre-processing models against the classification algorithm used as a comparison resulted in the best algorithm/method for managing road images.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90866922","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7000
Aniek Suryanti Kusuma, K. Agustini, I. G. W. Sudatha, I. Warpala
Purpose: The use of the concept of knowledge management can manage the knowledge of the teacher or lecturer and then it can be conveyed to the studentsDesign/methodology/approach: Knowledge Management SystemFindings/result: The application of the Knowledge Management System at the INSTIKI LMS was able to increase student learning satisfaction. The results of the questionnaire assessment show that student learning satisfaction increases after implementing INSTIKI e-learning, the average value of studentOriginality/value/state of the art: Implementation of Knowledge Management System on INSTIKI campus
{"title":"Knowledge Management In Instiki E-Learning To Increase Student Learning Satisfaction","authors":"Aniek Suryanti Kusuma, K. Agustini, I. G. W. Sudatha, I. Warpala","doi":"10.31315/telematika.v19i2.7000","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7000","url":null,"abstract":"Purpose: The use of the concept of knowledge management can manage the knowledge of the teacher or lecturer and then it can be conveyed to the studentsDesign/methodology/approach: Knowledge Management SystemFindings/result: The application of the Knowledge Management System at the INSTIKI LMS was able to increase student learning satisfaction. The results of the questionnaire assessment show that student learning satisfaction increases after implementing INSTIKI e-learning, the average value of studentOriginality/value/state of the art: Implementation of Knowledge Management System on INSTIKI campus","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75948146","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}
Pub Date : 2022-06-30DOI: 10.31315/telematika.v19i2.7246
I. M. Asana, Gede Aldhi Pradana, I. Handika, Santi Ika Murpratiwi
The Covid-19 has been an epidemic that has taken the world by storm since the beginning of 2020. This Covid-19 outbreak can spread easily through the air. Because Covid-19 can transmit easily, the government implements new behavior based on an adaption to develop a clean and healthy lifestyle which is often called the new normal. One way to live the new normal is to wear a mask when leaving the house. To help increase public awareness in using masks, numerous technology- based studies have been carried out. This article explain an application using the python programming language that applies digital image processing in terms of detecting the use of masks using Deep Learning with the Convolutional Neural Network (CNN) method to classify data that has been labeled using the supervised learning method. In designing this CNN architectural model, a total of 2110 images of people wearing and without wearing masks will be used, this dataset will be divided into 2 parts, with a rate of 8020, where 80 of the dataset will be used as training data, 20 is used as validation data. In testing the model by taking a total of 100 images with a 5050 ratio between face images using masks and not using masks tested using a confusion matrix, it produces 97% of an accuracy rate, 100% of precision rate, and 94% of recall in recognizing facial images that use masks and don't use masks
{"title":"Mask Detection System Using Convolutional Neural Network Method on Surveillance Camera","authors":"I. M. Asana, Gede Aldhi Pradana, I. Handika, Santi Ika Murpratiwi","doi":"10.31315/telematika.v19i2.7246","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7246","url":null,"abstract":"The Covid-19 has been an epidemic that has taken the world by storm since the beginning of 2020. This Covid-19 outbreak can spread easily through the air. Because Covid-19 can transmit easily, the government implements new behavior based on an adaption to develop a clean and healthy lifestyle which is often called the new normal. One way to live the new normal is to wear a mask when leaving the house. To help increase public awareness in using masks, numerous technology- based studies have been carried out. This article explain an application using the python programming language that applies digital image processing in terms of detecting the use of masks using Deep Learning with the Convolutional Neural Network (CNN) method to classify data that has been labeled using the supervised learning method. In designing this CNN architectural model, a total of 2110 images of people wearing and without wearing masks will be used, this dataset will be divided into 2 parts, with a rate of 8020, where 80 of the dataset will be used as training data, 20 is used as validation data. In testing the model by taking a total of 100 images with a 5050 ratio between face images using masks and not using masks tested using a confusion matrix, it produces 97% of an accuracy rate, 100% of precision rate, and 94% of recall in recognizing facial images that use masks and don't use masks ","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80738591","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}
In this study, the authors designed an algorithm based on deep learning that can automatically classify plastic waste according to Resin Identification Codes (RIC). The proposed algorithm is built through several stages as follows. In the first stage, image acquisition of plastic waste is carried out, which is the input of the designed algorithm. The acquired plastic waste image must display the resin code of the plastic waste to be classified. Furthermore, the acquired image is divided into two sets, namely training and testing sets. The training set contains images of plastic waste used in the training phase of the deep learning architecture DenseNet-121 to identify the resin code of each plastic waste image and classify it into the appropriate class. The training phase is run for 100 epochs, and at each epoch, the cross-entropy loss function is calculated, which expresses the performance of the deep learning architectures in classifying plastic waste images. In the next stage, a trained deep learning architecture is used to classify the plastic waste images from the test set. Classification performance in the test set is also expressed as the cross-entropy loss function value. In addition, the accuracy value has also been calculated, which shows the percentage of the number of plastic waste images successfully classified correctly to the total number of plastic waste images in the test set, which the best accuracy is equal to 85%.
{"title":"Deep-RIC: Plastic Waste Classification using Deep Learning and Resin Identification Codes (RIC)","authors":"Latifah Listyalina, Yudianingsih Yudianingsih, Adjie Wibowo Soedjono, Evrita Lusiana Utari, Dhimas Arief Dharmawan","doi":"10.31315/telematika.v19i2.7419","DOIUrl":"https://doi.org/10.31315/telematika.v19i2.7419","url":null,"abstract":"In this study, the authors designed an algorithm based on deep learning that can automatically classify plastic waste according to Resin Identification Codes (RIC). The proposed algorithm is built through several stages as follows. In the first stage, image acquisition of plastic waste is carried out, which is the input of the designed algorithm. The acquired plastic waste image must display the resin code of the plastic waste to be classified. Furthermore, the acquired image is divided into two sets, namely training and testing sets. The training set contains images of plastic waste used in the training phase of the deep learning architecture DenseNet-121 to identify the resin code of each plastic waste image and classify it into the appropriate class. The training phase is run for 100 epochs, and at each epoch, the cross-entropy loss function is calculated, which expresses the performance of the deep learning architectures in classifying plastic waste images. In the next stage, a trained deep learning architecture is used to classify the plastic waste images from the test set. Classification performance in the test set is also expressed as the cross-entropy loss function value. In addition, the accuracy value has also been calculated, which shows the percentage of the number of plastic waste images successfully classified correctly to the total number of plastic waste images in the test set, which the best accuracy is equal to 85%.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74934034","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}
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