Jurnal Komtika (Komputasi dan Informatika)
Aims Jurnal Komtika (Komputasi dan Informatika) is a scientific journal published by the Faculty of Engineering, Universitas Muhammadiyah Magelang and is Accredited by the Ministry for Research, Technology, and Higher Education (RISTEKDIKTI)(No:200/M/KPT/2020). It is a medium for researchers, academics, and practitioners interested in Computer Science and wish to channel their thoughts and findings. Our concept of Informatics includes technologies of information and communication as well as results of research, critical, and comprehensive scientific study which are relevant and current issues covered by the journals. Jurnal Komtika publishes regular research articles. We encourage researchers to publish their theoretical and empirical results in as much detail as possible. For theoretical papers, full details of proofs must be provided so that the results can be checked; for experimental papers, full experimental details must be given so that the results can be reproduced. Additionally, electronic files or software regarding the full details of the calculations, experimental procedure, etc., can be deposited along with the publication as “Supplementary Material”. Scope Jurnal Komputasi dan Informatika (Komtika) focuses on various issues, but not limited in the field of: Software Development: Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model Mathematics of Computing: Discrete mathematics, Mathematical software, Information theory Theory of computation: Model of computation, Computational complexity Human Computer Interaction: Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility, User Interface Study, User Experience Study Applied Computing: E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management. Machine Learning: upervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning Graphics: Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling Information System: Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval
Articles
144 Documents
<![CDATA[Deteksi Ikan Segar Secara Realtime dengan YOLOv4 menggunakan Metode Convolutional Neural Network ]]>
<![CDATA[Gunawan, Chichi Rizka]]>;
<![CDATA[Nurdin, Nurdin]]>;
<![CDATA[Fajriana, Fajriana]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i1.8986
<![CDATA[Ikan merupakan komoditas mudah rusak yang memerlukan penanganan segera setelah dikeluarkan dari laut. Untuk ikan segar dapat dilihat jika tidak diberikan pengolahan khusus yang tepat, kualitas ikan akan menurun dengan hitungan jam. Setiap orang ingin membeli ikan yang halal, aman, sehat, dan berkualitas tinggi. Selain itu juga perlu mengetahui perbedaan ikan yang segar dan tidak segar, terkadang ada pedagang nakal, ikan yang tidak segar masih dijual. Sehingga produk menjadi tidak aman saat dikonsumsi dan dapat merugikan konsumen. Untuk mengetahui akurasi dan performansi algoritma pendeteksi kesegaran ikan di Yolov4 menggunakan metode convolutional neural network (CNN), penelitian ini membuat sistem pendeteksi ikan segar secara realtime. Seiring waktu, orang mengembangkan pengetahuan dan teknologi untuk mendukung dan memfasilitasi pekerjaan mereka. Penelitian ini menggunakan 118 data citra untuk pelatihan dan 13 data citra untuk pengujian, dengan pelatihan berlangsung selama 6000 epoch. Proses YOLOv4-CNN adalah hasil dari data yang telah dideteksi oleh YOLOv4 akan diklasifikasi modelnya oleh CNN dimana sebelumnya citra akan di resize sehingga seluruh data citra memiliki ukuran yang sama untuk memudahkan proses konvolusi, dilanjutkan dengan fungsi aktivasi, pooling layer, fully connected layer dan diakhiri dengan proses klasifikasi objek. Kemudian hasil klasifikasi akan diimplementasikan kembali pada YOLOv4 untuk mengetahui pendeteksian ikan segar telah terdeteksi dengan baik atau tidak. Hasil dari pendeteksian kesegaran ikan menggunakan algoritma YOLOv4-CNN dapat dinilai bekerja dengan baik. Pengujian sistem pada Yolov4-CNN memperoleh MAP sebesar 93.75%, dengan presisi 1.00%, recall 0.93%, f-Score 0.96% dan juga rata-rata nilai IoU sebesar 74.17%.]]>
<![CDATA[Analysis of WIUM Online Education Management System User Satisfaction Using PIECES Framework ]]>
<![CDATA[Bangun, Winda Ebina Br]]>;
<![CDATA[Sihotang, Jay Idoan]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i1.9036
<![CDATA[WIOEM is a web and android application designed to quickly share information with users and help solve problems at the Salemba Adventist College. This information system is used by users for various purposes that are able to support the performance of teachers and parents in improving services to students. The various functions in this information system will affect user satisfaction or dissatisfaction with the system. So that there is a need for improvement and development of the system in the future. WIOEM information system analysis was carried out to determine the advantages and disadvantages that exist in the system based on the PIECES method. The purpose of this research is to get an overall picture of system performance, information, level of economic value, security, efficiency, and system services. From each aspect that is analyzed will be used as a recommendation for improvement of the WIOEM information system. The method used in this research is descriptive quantitative. The data collection technique uses a questionnaire distributed through the Google Forms platform. The results of this study indicate that the WIOEM system is in the good category, with an average total satisfaction level of 4.18. With each Performance indicator achieving a value of (4.31), Information (4.23), Economic (4.04), Control (4.11), Efficiency (4.25) and Service achieving a value of (4.17).]]>
<![CDATA[Pengenalan Pakaian Adat Aceh Berbasis Augmented Reality Menggunakan Metode Speed Up Robust Featured (SURF) ]]>
<![CDATA[gunawan, chicha rizka]]>;
<![CDATA[Nurdin, Nurdin]]>;
<![CDATA[Fajriana, Fajriana]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.9124
<![CDATA[Aceh Province, especially Langsa City, has a tourist attraction, namely the Langsa City Forest Park House (RTH). One of the most interesting rides in Langsa City Forest Park is Rumoh Aceh. Based on the results of visits and interviews by Rumoh Aceh officers, the large number of visitors from outside Aceh with one officer sometimes made it difficult for the officers to explain the information available about Rumoh Aceh, especially Acehnese traditional clothes, which were only displayed from a printed image, and provided no other information about these traditional clothes, so that many visitors did not know the diversity of designs and motifs of traditional clothes in Aceh. So, a medium was formed that could display Acehnese traditional clothing. The media uses augmented reality technology so that users can add virtual objects to the real environment to make it easier to use. This application uses the Speed Up Robust Featured (SURF) algorithm, which can process marker tracking quickly so that it can obtain better tracking speed times. The shortest distance from the marker to the camera that can show 3D objects is 20 cm, whereas the farthest distance that cannot show 3D objects is 100 cm. The best distance at which a marker can be detected is 20–80 cm. The best average detection time is 0.00049 s, and the average speed obtained is 1261.22 m/s at a distance of 60 cm. The Speed Up Robust Featured (SURF) algorithm can be used in the Augmented Reality-based Aceh Traditional Clothing Recognition application.]]>
<![CDATA[Analyzing the Effectiveness of Collaborative Filtering and Content-Based Filtering Methods in Anime Recommendation Systems ]]>
<![CDATA[Putri, Helmy Dianty]]>;
<![CDATA[Faisal, Muhammad]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.9219
<![CDATA[In the current digital era where content consumption via streaming platforms is increasing, the need for accurate recommendation systems is becoming increasingly important, especially in the animation industry. This research focuses on implementing a recommendation system that can help viewers easily navigate the abundance of content. By comparing collaborative filtering and content-based filtering methods, this research attempts to find the optimal approach for providing anime recommendations. From the results of A/B testing and further analysis, it was found that Collaborative Filtering was effective in providing recommendations based on similar interests between users. On the other hand, content-based filtering offers the advantage of personalizing recommendations based on content characteristics. Additionally, integrating these techniques into mobile applications will enrich the user experience, allowing them to receive recommendations more quickly and interactively. With these findings, this research contributes to the development of more intuitive and responsive recommendation systems, driving the growth of the anime streaming industry by increasing user satisfaction and retention.]]>
<![CDATA[Evaluation of Maturity Level and Recommendations for Improvement of Software Testing Process Based on Test Maturity Model Integration (TMMi): A Case Study ]]>
<![CDATA[Prastiti, Rizdiani Tri]]>;
<![CDATA[Hidayanto, Achmad Nizar]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.9628
<![CDATA[XYZ company as one of the companies that provides Over-The-Top (OTT) services has problems related to defects that pass into the production environment caused by an ineffective testing process. This has an impact on user satisfaction as indicated by various user complaints when using the application. It is necessary to evaluate the maturity level which shows the ability to perform software testing and what recommendations can be given to improve the software testing process. Test Maturity Model Integration (TMMi) as a model for improving the software testing process has been widely known to improve the testing process and positively impact product quality. XYZ company, which is looking to improve its software testing process, uses the TMMi model as a reference to determine the maturity level of the testing process and provide best practices for the testing process. The assessment was conducted using the TMMi Assessment Method Application Requirement (TAMAR) and information was collected using the Delphi method. The assessment is carried out in the process area at the maturity level of level 2 and produces a rating value of P (Partially Achieved) so that the maturity level of the XYZ company software testing process is level 1 (Initial). Recommendations are prepared based on specific practices in the Test Planning and Test Environment process areas that still have weaknesses that must be improved to reach maturity level 2 (Managed). The process of preparing recommendations is assisted by the Deming cycle which is then validated with stakeholders whether these recommendations can be implemented according to the needs of XYZ company to improve the testing process.]]>
<![CDATA[Penerapan Metode Convolutional Neural Networks pada Pengenalan Gender Manusia berdasarkan Foto Tampak Depan ]]>
<![CDATA[Muhammad, Aqil]]>;
<![CDATA[Pratiwi, Dian]]>;
<![CDATA[Salim, Agus]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.9937
<![CDATA[Recognition is one of the many problems encountered today, this problem has several ways to be solved. This research used Convolutional Neural Networks (CNN), which is a deep neural networks method as a means of face recognition, which has been proven to be widely used in face classification, using a dataset of male and female facial photos totaling 27,167 photos, of which 17,678 are male and 9,489 are male. woman. To avoid unbalanced data processing, the researchers disguised the photos of women and men so that the total photos used for the training amounted to 18,978 photos. Besides that, the researcher also added dropout as a test parameter. The author uses python to implement gender differences in the images in the data that has been prepared. For the preparation of the Convolutional Neural Networks model architecture the authors use several layers. Then the data will be trained before being tested with new data that has been prepared where the new data for testing is divided into two datasets to see if there are differences in accuracy results. What distinguishes the two datasets is the position of the photo and the background of the photo. Of the two existing datasets, the first dataset produces an average of 73.33%, while the second dataset produces the highest 84.34%.]]>
<![CDATA[Monitoring dan Klasifikasi Kualitas Air Kolam Ikan Gurami Berbasis Internet of Things Menggunakan Metode Naive Bayes ]]>
<![CDATA[Kristiyanto, Arip]]>;
<![CDATA[Fikriah, Fari Katul]]>;
<![CDATA[Inkiriwang, Rully]]>;
<![CDATA[Andriansah, Zulfi]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.10200
<![CDATA[Ministry of Marine Affairs and Fisheries (KKP) noted that Indonesia produced 56,539 tons of gourami fish in the second quarter of 2022 High market demand and economical selling prices encourage farmers to cultivate gourami fish. In cultivating gourami fish there are several obstacles, for example, disease caused by poor water quality. Water quality is the main parameter in the success of gourami fish farming. This research aims to develop a water quality monitoring system based on the Internet of Things. The system prototype uses a temperature sensor (DS18B20), Ph sensor (dfrobot SEN0161), turbidity sensor (dfrobot SEN0189), flowmeter, and ultrasonic sensor (JSN-SR04) as input. The Arduino Mega R3 microcontroller is the processor and the Oled module (SSD1306) is the output. Thingboard is a cloud server that functions as sensor data monitoring. Temperature sensor testing results (DS18B20) average error 0.48%, Ph(dfrobot SEN0161) sensor testing average error 0.64%, ultrasonic sensor testing (JSN-SR04) average error 7.83%, testing Turbidity sensors can measure the level of water turbidity. Next, the water quality parameter data is processed using the Naïve Bayes algorithm method for classifying the water quality of gourami ponds. The results of this classification obtained an accuracy of 99.94% a Kappa Statistics value of 0.9989 and a Mean Absolute Error of 0.0003]]>
<![CDATA[Implementasi Metode The Unified Process Pada Mobile Application Monitoring Gizi Bayi Dibawah Dua Tahun ]]>
<![CDATA[Hartawan, Muhammad Syarif]]>;
<![CDATA[Nursyanti, Reni]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.10422
<![CDATA[This study is based on the trend of the nutritional status of Indonesian toddlers which is still unfavorable. In 2021, 24.4% of children under five were stunted, 7.1% were wasted, 17.0% were underweight and 3.5% were overweight. Although in 2022 stunting and overweight decreased to 21.6% and 3.5%, wasting and underweight increased to 7.7% and 17.1%. In addition, there are still many children whose daily needs are not fulfilled, therefore the application of The Unified Process method is used to build a nutrition monitoring application for infants under two years or abbreviated as Baduta. Furthermore, the purpose of the study is the development of a clown child nutrition monitoring application which is expected to help parents, especially those who have babies under two years old, so that they can monitor the development of their child's nutritional intake every day. based on the results of black box testing, it can be concluded that this application is good enough and informative based on providing nutritional information for baduta.]]>
<![CDATA[Analisa Pengukuran Tingkat Kepuasan Pengguna Aplikasi Daytrans Dengan Kerangka Kerja Pieces Framework ]]>
<![CDATA[Purba, Antonius]]>;
<![CDATA[Sihotang, Jay Idoan]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.10432
<![CDATA[Daytrans is a company that is developing and operates in the field of land transportation services and sending goods between provinces. Based on the results of the author's observations, it was found that no analysis of Daytrans Bandung consumer satisfaction, especially Dipatiukur, had ever been carried out. The main objective of this research is to analyze and measure the level of satisfaction with system service quality. The PIECES method was used to analyze the data in this research. And the Slovin formula was used to determine the sample size of 100 respondents. This number is obtained from measuring the tolerable error limit of 10%. The final results of the questionnaire calculation using the Likert scale and variables in the PIECES Framework produced an average score of 4.47. So that in general service users feel satisfied with the Daytrans Application system services.]]>
<![CDATA[Analysis of User Experience on the MyPertamina Application using User Experience Questionnaire Method ]]>
<![CDATA[Ramadhan, Muhammad Gilang]]>;
<![CDATA[Oktadini, Nabila Rizky]]>;
<![CDATA[Putra, Pacu]]>;
<![CDATA[Sevtiyuni, Putri Eka]]>;
<![CDATA[Meiriza, Allsela]]>
<![CDATA[ Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Magelang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.31603/komtika.v7i2.10467
<![CDATA[MyPertamina is an application launched by PT Pertamina (Persero) in 2017. Although the MyPertamina application has many programs that should be able to facilitate the community but unfortunately the rating of this application is still bad, namely 3.3 on a scale of 5 on Google PlayStore from hundreds of thousands of user reviews both android and ios this application has negative reviews. Thus user experience (UX) analysis is needed. User Experience Questionnaire (UEQ) is the right method in this research. Because this method interacts directly with what users feel when operating the MyPertamina application. There are 6 variables namely attractiveness, stimulation, and novelty that have a value (above average) except perspicuity and dependability (below average), and there is an average scale of three aspects and a value of Pragmatic Quality 1.11 (positive), Hedonic Quality 0.93 (positive), and Attractiveness 1.21 (positive).]]>
