Teknomatika: Jurnal Informatika dan Komputer
Teknomatika: Jurnal Informatika dan Komputer ISSN: 3031-0865 (Online), 1979-7656 (Print) is a free and open-access journal published by Fakultas Teknik dan Teknologi Informasi Universitas Jenderal Achmad Yani Yogyakarta, Indonesia. Teknomatika publishes scientific articles from scholars and experts worldwide related to the computer science, informatics, computer systems and information systems. This journal accommodates articles covering: Mathematics and Statistics Algorithms and Programming Intelligent System Artificial Intelligence Software Engineering Computer Architecture Distributed System Cyber Security Electronics and Embedded Systems Data and Information Management Information Systems Enterprise System All published articles will have a Digital Object Identifier (DOI). The Journal publication frequency is twice a year (sixth monthly: Maret and September).
Articles
11 Documents
Search results for
, issue
"Vol 15 No 2 (2022): TEKNOMATIKA"
:
11 Documents
clear
<![CDATA[Analisis Sentimen Berdasarkan Topik Terkait Wabah Covid-19 di Twitter Menggunakan Latent Dirichlet Allocation (LDA) dan Naive Bayes Classifier (NBC) ]]>
<![CDATA[Pangky Putra Aziztiya]]>;
<![CDATA[Muhammad Habibi]]>;
<![CDATA[Netania Indi Kusumaningtyas]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1098
<![CDATA[In 2020 WHO determined that the Corona Virus (COVID-19) was a pandemic. The global spread of the COVID-19 outbreak has made Twitter one of the most widely used tools to publish and find information. This study aims to form a modeling of topics related to the COVID-19 outbreak on the Twitter social media platform and analyze positive and negative sentiments in each topic that has been obtained by combining the two Latent Dirichlet Allocation (LDA) and Naïve Bayes Classification (NBC) methods. Beginning with modeling the topic using the Latent Dirichlet Allocation so that the topics that have been obtained will be searched for the sentiment value of each topic using the Naïve Bayes Classifier method. This study succeeded in combining the two methods with a fairly good accuracy of 89%. In topic modeling, 5 ideal topics were obtained and it can be seen that the most discussed topic is booster vaccination. The results of the classification using NBC show that the topic of booster vaccination has more negative sentiments than positive sentiments.]]>
<![CDATA[PURWARUPA ALAT PENDETEKSI OTOMATIS KETINGGIAN AIR UNTUK MENGATUR BUKA TUTUP PINTU AIR BERBASIS ARDUINO ]]>
<![CDATA[M Roykhul Jinan]]>;
<![CDATA[Agung Priyanto]]>;
<![CDATA[Andika Bayu Saputra]]>;
<![CDATA[Alfun Roehatul Jannah]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1102
<![CDATA[With the increasing population and the reduction of water catchment areas due to numerous development projects that disregard green open spaces, coupled with a lack of public awareness regarding the importance of water flow, many individuals continue to consciously dispose of garbage into rivers, including river areas, dams, and reservoirs. This behavior leads to river blockages, resulting in flooding in those areas and other low-lying regions. The traditional method of monitoring water levels heavily relies on human operators, which can introduce errors when interpreting water indicators. To address these challenges, this research adopts a design-oriented methodology to develop an automated system for real-time maintenance and monitoring of water levels. The study begins with a comprehensive analysis of the existing issues and the underlying processes involved. Subsequently, the research focuses on designing and implementing a prototype system using appropriate materials, system development tools, and methodologies. The proposed system consists of a water level detection device integrated with a servo-controlled dam opener. Experimental tests demonstrate the device's effectiveness in accurately detecting changes in water levels, with an average accuracy of 10 cm from the sensor point. Additionally, the servo-controlled dam opener plays a significant role in regulating water flow within the system. The proposed system aims to facilitate efficient monitoring and decision-making processes, thereby reducing the occurrence of human errors in determining water levels.]]>
<![CDATA[Analisis Sentimen Kepuasan Pelanggan Perusahaan Telekomunikasi Seluler Telkomsel di Twitter ]]>
<![CDATA[Melia Haerunnissa]]>;
<![CDATA[Agung Priyanto]]>;
<![CDATA[Choerun Asnawi]]>;
<![CDATA[Nafisa Alfi Sa'diya]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1117
<![CDATA[Telkomsel, the largest operator in Indonesia with the most users, collects a significant amount of tweet data on Twitter, containing both positive and negative feedback about their internet service. Analyzing this data can provide valuable insights and accurate information about Telkomsel's internet services based on user tweets, retweets, and comments. The aim is to build a sentiment analysis model to extract relevant information from Telkomsel users' tweets on Twitter, serving as feedback for service evaluation and an educational tool for users. The sentiment analysis process involves data retrieval, preprocessing, training, testing, classification, and visualization using Python programming with the Flask framework. Analysis of customer satisfaction sentiment reveals that Telkomsel has a negative sentiment, with an accuracy of 81.7% for training data and 84% for testing data. The sentiment analysis model was built using the Naive Bayes Classification method.]]>
<![CDATA[Perancangan Survival Horror Game Out Of Sight Menggunakan Unity 3D Engine ]]>
<![CDATA[Septian Mahendra Dewantoro]]>;
<![CDATA[Kartikadyota Kusumaningtyas]]>;
<![CDATA[Andika Bayu Saputra]]>;
<![CDATA[Nurul Fatimah]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1122
<![CDATA[A game is the result of the creative expression of a game maker that has an element of entertainment, can be played and has an end goal. In horror games, it is still divided into several more types of games, one of which is a survival horror game that requires players to live against enemy attacks that come their way. This game was created to enrich horror-themed domestically made games. Designing games using the unity 3D engine with the process of applying the MDLC (Media Develpoment Life Cycle) model. A method suitable for game design, starting with the preparation, design, collection of materials, testing and distribution. Out Of Sight game has gameplay with a first-person perspective combined with the survival horror genre. Made with the C# programming language this game is able to run perfectly with various features. There is also a unique feature, namely slowmotion which serves to slow down time to make it easier for players to shoot enemies. This survival horror game has been successfully created and completed. This game conducts testing using black box testing where all features function normally except for the key that serves to open the door. Based on respondents 18.5% said that the door lock had a bug. This game was created to enrich horror-themed domestically made games.]]>
<![CDATA[PERBANDINGAN METODE DECISION TREE DAN NAIVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN PENGGUNA LAYANAN PT PERUSAHAAN LISTRIK NEGARA (PLN) ]]>
<![CDATA[ABIYOGA BAGUS MUSTRIYANTO]]>;
<![CDATA[Muhammad Habibi]]>;
<![CDATA[Dayat Subekti]]>;
<![CDATA[Fajar Syahruddin]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1131
<![CDATA[Background : PLN is a state-owned company that is tasked with supplying electricity to all regions of Indonesia which certainly cannot be separated from the various obstacles experienced, to find out public sentiment on the services that have been provided, an analysis is carried out to determine public sentiment. The results of these sentiments are created in the dashboard using the Flask framework by comparing the Naive Bayes and Decision tree methods. To create a sentiment analysis dashboard for PT. PLN and make a research analysis model using a comparison of the Naive Bayes Classification and Decision tree methods. The method used in this research is Naive Bayes and Decision tree. The data obtained with a total of 40,745 Tweet data taken in the period 1 May 2022 - 4 June 2022 with the keyword "PLN". Making a dashboard that displays the results of the analysis where there is a menu to display the data and each analysis process. The use of 900 training data and 300 testing data resulted in the Naive Bayes method getting an accuracy of 83% on the training data and 80% for the Testing data, while the Decision tree method got an accuracy of 77% on the Training data and 56% on the Testing data. The analysis obtained for the method in this study also shows that the Naive Bayes method is better for classifying large amounts of data than the Decision tree. The sentiment generated by the highest number is negative, with most of the Tweets being complaints about the response to complaints and handling of damage reported by the public.]]>
<![CDATA[Analisis Sentimen Opini Masyarakat Tentang Penggunaan Aplikasi Bimbingan Belajar Online di Masa Pandemi Covid-19 Menggunakan Metode Support Vector Machine (SVM) ]]>
<![CDATA[Albet Gunawan]]>;
<![CDATA[Andika Bayu Saputra]]>;
<![CDATA[M. Abu Amar Al Badawi]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1132
<![CDATA[Distance learning has emerged as a response to the Covid-19 pandemic, providing students with a new approach to learning. Online learning platforms, utilizing information technology, have become essential in connecting students and teachers. Online tutoring applications offer valuable supplementary educational materials, with various features to support the learning process. Analyzing sentiment on Twitter regarding these online tutoring applications is crucial in determining the best options for students. This study aims to develop an analytical model using the Support Vector Machine (SVM) for online tutoring applications during the Covid-19 pandemic. The research focuses on analyzing positive and negative sentiments within Twitter data, utilizing the Support Vector Machine (SVM) method. The training phase involved 800 manually labeled tweets, consisting of 400 positive and 400 negative sentiments. For testing, 23,511 labeled data points were used. The training data achieved an accuracy of 91.81%. The research successfully achieved an accuracy rate of 90.62% for training and 91% for testing.]]>
<![CDATA[PURWARUPA ALAT PENDETEKSI OTOMATIS KETINGGIAN AIR UNTUK MENGATUR BUKA TUTUP PINTU AIR BERBASIS ARDUINO ]]>
<![CDATA[M Roykhul Jinan]]>;
<![CDATA[Agung Priyanto]]>;
<![CDATA[Andika Bayu Saputra]]>;
<![CDATA[Alfun Roehatul Jannah]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1102
<![CDATA[With the increasing population and the reduction of water catchment areas due to numerous development projects that disregard green open spaces, coupled with a lack of public awareness regarding the importance of water flow, many individuals continue to consciously dispose of garbage into rivers, including river areas, dams, and reservoirs. This behavior leads to river blockages, resulting in flooding in those areas and other low-lying regions. The traditional method of monitoring water levels heavily relies on human operators, which can introduce errors when interpreting water indicators. To address these challenges, this research adopts a design-oriented methodology to develop an automated system for real-time maintenance and monitoring of water levels. The study begins with a comprehensive analysis of the existing issues and the underlying processes involved. Subsequently, the research focuses on designing and implementing a prototype system using appropriate materials, system development tools, and methodologies. The proposed system consists of a water level detection device integrated with a servo-controlled dam opener. Experimental tests demonstrate the device's effectiveness in accurately detecting changes in water levels, with an average accuracy of 10 cm from the sensor point. Additionally, the servo-controlled dam opener plays a significant role in regulating water flow within the system. The proposed system aims to facilitate efficient monitoring and decision-making processes, thereby reducing the occurrence of human errors in determining water levels.]]>
<![CDATA[Analisis Sentimen Kepuasan Pelanggan Perusahaan Telekomunikasi Seluler Telkomsel di Twitter ]]>
<![CDATA[Haerunnissa , Melia]]>;
<![CDATA[Priyanto, Agung]]>;
<![CDATA[Asnawi, Choerun]]>;
<![CDATA[Alfi Sa'diya, Nafisa]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1117
<![CDATA[Telkomsel, the largest operator in Indonesia with the most users, collects a significant amount of tweet data on Twitter, containing both positive and negative feedback about their internet service. Analyzing this data can provide valuable insights and accurate information about Telkomsel's internet services based on user tweets, retweets, and comments. The aim is to build a sentiment analysis model to extract relevant information from Telkomsel users' tweets on Twitter, serving as feedback for service evaluation and an educational tool for users. The sentiment analysis process involves data retrieval, preprocessing, training, testing, classification, and visualization using Python programming with the Flask framework. Analysis of customer satisfaction sentiment reveals that Telkomsel has a negative sentiment, with an accuracy of 81.7% for training data and 84% for testing data. The sentiment analysis model was built using the Naive Bayes Classification method.]]>
<![CDATA[Perancangan Survival Horror Game Out Of Sight Menggunakan Unity 3D Engine ]]>
<![CDATA[Mahendra Dewantoro, Septian]]>;
<![CDATA[Kusumaningtyas, Kartikadyota]]>;
<![CDATA[Bayu Saputra, Andika]]>;
<![CDATA[Fatimah, Nurul]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1122
<![CDATA[A game is the result of the creative expression of a game maker that has an element of entertainment, can be played and has an end goal. In horror games, it is still divided into several more types of games, one of which is a survival horror game that requires players to live against enemy attacks that come their way. This game was created to enrich horror-themed domestically made games. Designing games using the unity 3D engine with the process of applying the MDLC (Media Develpoment Life Cycle) model. A method suitable for game design, starting with the preparation, design, collection of materials, testing and distribution. Out Of Sight game has gameplay with a first-person perspective combined with the survival horror genre. Made with the C# programming language this game is able to run perfectly with various features. There is also a unique feature, namely slowmotion which serves to slow down time to make it easier for players to shoot enemies. This survival horror game has been successfully created and completed. This game conducts testing using black box testing where all features function normally except for the key that serves to open the door. Based on respondents 18.5% said that the door lock had a bug. This game was created to enrich horror-themed domestically made games.]]>
<![CDATA[PERBANDINGAN METODE DECISION TREE DAN NAIVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN PENGGUNA LAYANAN PT PERUSAHAAN LISTRIK NEGARA (PLN) ]]>
<![CDATA[BAGUS MUSTRIYANTO, ABIYOGA]]>;
<![CDATA[Muhammad Habibi]]>;
<![CDATA[Subekti, Dayat]]>;
<![CDATA[Syahruddin, Fajar]]>
<![CDATA[ Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA ]]>
Publisher : <![CDATA[Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30989/teknomatika.v15i2.1131
<![CDATA[Background : PLN is a state-owned company that is tasked with supplying electricity to all regions of Indonesia which certainly cannot be separated from the various obstacles experienced, to find out public sentiment on the services that have been provided, an analysis is carried out to determine public sentiment. The results of these sentiments are created in the dashboard using the Flask framework by comparing the Naive Bayes and Decision tree methods. To create a sentiment analysis dashboard for PT. PLN and make a research analysis model using a comparison of the Naive Bayes Classification and Decision tree methods. The method used in this research is Naive Bayes and Decision tree. The data obtained with a total of 40,745 Tweet data taken in the period 1 May 2022 - 4 June 2022 with the keyword "PLN". Making a dashboard that displays the results of the analysis where there is a menu to display the data and each analysis process. The use of 900 training data and 300 testing data resulted in the Naive Bayes method getting an accuracy of 83% on the training data and 80% for the Testing data, while the Decision tree method got an accuracy of 77% on the Training data and 56% on the Testing data. The analysis obtained for the method in this study also shows that the Naive Bayes method is better for classifying large amounts of data than the Decision tree. The sentiment generated by the highest number is negative, with most of the Tweets being complaints about the response to complaints and handling of damage reported by the public.]]>
