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INDONESIA
JURNAL MEDIA INFORMATIKA BUDIDARMA
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 26145278     EISSN : 25488368     DOI : http://dx.doi.org/10.30865/mib.v3i1.1060
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Decission Support System, Expert System, Informatics tecnique, Information System, Cryptography, Networking, Security, Computer Science, Image Processing, Artificial Inteligence, Steganography etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,182 Documents
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Sistem Pendukung Keputusan Penilaian Kinerja Guru Selama Pembelajaran Daring menggunakan Metode Vikor Sedihati Kayan Lumbangaol; Erna Budhiarti Nababan; Maya Silvi Lydia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3798

Abstract

Teacher is a profession that has important role for the progress of education literacy, primarily in this current digitalization era that implements the online learning system. Therefore, the assesment of teacher’s performance during online learning is needed to find the advantages and disadvantages of each teacher, with an aim to get an evaluation that can be utilized to fix or improve the teacher’s performance. This study proposes a decision support system that applies the Vikor method as a solution to get the result of teacher’s performance assessment during online learning and make it easier for the decision makers. By using 4 research criteria and 5 alternatives, this research shows that A5 on behalf of Kayan Marbun with a value of 0.5025 is chosen as the teacher with the best performance.
Analisis Sentimen terhadap Peluang Kerja di Indonesia selama Masa Pandemi COVID-19 dengan Metode Klasifikasi Naive Bayes Mohammad Aldinugroho Abdullah; Deni Mahdiana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3972

Abstract

The impact of the COVID-19 pandemic is very broad, one of which is in the business sector. This has resulted in an impact on job opportunities during the COVID-19 pandemic in Indonesia. This study aims to conduct in-depth learning related to job opportunities in Indonesia during the COVID-19 pandemic using the Naive Bayes model. The data source used comes from Twitter. The results of this study indicate that the largest AUC score falls to the Random Forest model (79.40%), but for more accurate precision falls to the Naive Bayes model (87.88%). In addition, there is a confusion matrix which shows that the Naive Bayes model trial is running well.
Pengukuran Pengalaman Pengguna Aplikasi Platform Pembelajaran dan Konferensi Video Menggunakan Framework UEQ+ Angela Angela; Fandi Halim; Chatrine Sylvia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3878

Abstract

This study aims to measure and evaluate the user experience of the Microsoft Teams application as a learning and video conferencing platform using the UEQ+ framework, which is the development of the UEQ method. Through the UEQ+ framework, questionnaires can be designed by customizing user experience variables according to the application to be measured, thus the research results are expected to be more accurate and relevant. The scale of user experience measured in this questionnaire includes: efficiency, perspicuity, dependability, trust, usefulness, intuitive use, trustworthiness of content, quality of content, and clarity. After the questionnaires were distributed, 149 data were obtained which could be processed using data processing tools which being provided by UEQ+ called Data Analysis Tools. In conclusion, respondents have positive impression of Microsoft Teams as a video conferencing application and learning platform. The most important scale that represents the quality of Microsoft Teams is usefulness, clarity, trustworthiness of content, and quality of content.
Penerapan Metode Simple Additivie Weighting Untuk Mengefektifkan Penilaian Kinerja Karyawan Neni Mulyani; Jeperson Hutahaean
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3103

Abstract

In this study, the application of the Simple Additive Weighting (SAW) method is needed to generate preference values for 16 employee data which is used as an alternative employee performance appraisal. The application of the SAW method in this case also aims to improve the results of decisions made by managers. So that in one period of performance appraisal of employees at the end of the year the manager can make decisions on employees who have very good performance (0.80 – 1.00) to employees who have poor performance. The results of the preference value from the calculation using the SAW method will be made in the form of an assessment range, so that based on the value of the range the manager can provide an employee performance assessment.
Phrase Based Statistical Machine Translation Javanese-Indonesian Aufa Eka Putri Lesatari; Arie Ardiyanti; Ibnu Asror
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2812

Abstract

This research aims to produce a statistical machine translation that can be implemented to perform Javanese-Indonesian translation and to know the influence of the main data sources of statistical machine translation namely parallel corpus and monolingual corpus on the quality of Javanese-Indonesian statistical machine translation. The testing was carried out by gradually adding the quantity of parallel corpus and monolingual corpus to seven configurations of Javanese-Indonesian statistical machine translation. All machine translation configuration experiments were tested with test data totaling 500 lines of Javanese sentences. Results from machine translation are evaluated automatically using Bilingual Evaluation Understudy (BLEU). Test results in seven configurations showed an increase in the evaluation value of the translation machine after the quantity of parallel corpus and monolingual corpus was added. The quantity of parallel corpus in configurations 1 and 2 increased by 3,6%, configurations 2 and 3 increased by 8,23%, configurations 3 and 7 increased by 14,92%. Additional monolingual corpus quantity in configurations 4 and 5 increased BLEU score by 0,18%, configurations 5 and 6 increased by 0,06%, configurations 6 and 7 increased by 0,24%. The test results showed that the quantity of parallel corpus and monolingual corpus could increase the evaluation value of statistical machine translation Javanese-Indonesian, but the quantity of parallel corpus had a greater influence than the quantity of monolingual corpus
Implementasi Recurrent Neural Network dalam Memprediksi Kepadatan Restoran Berbasis LSTM Annisa Farhah; Anggunmeka Luhur Prasasti; Marisa W Paryasto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2916

Abstract

In this modern era, restaurants are becoming very popular, especially in big cities. However, this can lead to density or queues of visitors at a restaurant, which should be avoided during the current Covid-19 pandemic. So that accurate information that can predict the density of restaurant will be very useful. In predicting the density of restaurants, data processing on the number of visitors obtained from one of the restaurants is carried out using artificial intelligence in the form of LSTM (Long Short Term Memory) RNN (Recurrent Neural Network). The results of the research on Recurrent Neural Network based on LSTM (Long Short Term Memory) at the best learning rate parameter of 0.001 and a maximum epoch of 2000 resulted in an MSE value of 0.00000278 on the training data and 0.0069 on the test data
Rancang Bangun Sistem Informasi Manajemen Aset “SIMA+” Berbasis User Centred Design (UCD) Murdiaty Murdiaty; Angela Angela; Cindy Aprilia; Nuraina Nuraina
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2663

Abstract

Assets consist of immovable and movable objects, both tangible and intangible assets, which are included in the fortune of an agency, organization, business entity or individual. The wrong management of assets will be bad for the company so that it is resulting in losses. Utilization of assets cannot be carried out optimally because they are not clearly identified, making it difficult to know which assets can still be used or when it is time to replace them and when it is time to carry out maintenance. Assets really need to be managed and utilized properly in order to maintain high asset value and to achieve optimal use and utilization of assets so as to provide more benefits for the company. The purpose of this research is to develop an asset management information system called SIMA+. The system development methodology used is prototyping. The system design uses Microsoft Visual Studio 2015 with the programming language Visual Basic, the system output design uses Crystal Report, and data storage uses Microsoft SQL Server 2014. The result of this paper is a system that is expected to have the ability to maintain asset value, asset service life, and asset security, minimizing asset utilization transaction errors and can adjust asset data recorded in the system and physical asset data.
Metode Seleksi Fitur Untuk Klasifikasi Sentimen Menggunakan Algoritma Naive Bayes: Sebuah Literature Review Fitria Septianingrum; Agung Susilo Yuda Irawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.2983

Abstract

In the era of the industrial revolution 4.0 as it is today, where the internet is a necessity for people to live their daily lives. The high intensity of internet use in the community, it causes the distribution of information in it to spread widely and quickly. The rapid distribution of information on the internet is also in line with the growing growth of digital data, so that the public opinions contained therein become important things. Because, from this digital data, it can be processed with sentiment analysis in order to obtain useful information about issues that are developing in the community or to find out public opinion on a company's product. The number of studies related to sentiment analysis that applies the Naive Bayes algorithm to solve the problem, so researchers are interested in conducting research on the use of feature selection for the algorithm. Therefore, this research was conducted to determine what feature selection is the most optimal when combined with the Naive Bayes algorithm using the Systematic Literature Review (SLR) research method. The results of this study concluded that the most optimal feature selection method when combined with the Naive Bayes algorithm is the Particle Swarm Optimization (PSO) method with an average accuracy value of 89.08%.
Pengembangan Learning Management System (LMS) dengan Menerapkan Video Based Learning dan Gamification Dalam Meningkatkan Motivasi dan Keterlibatan Mahasiswa Paradise Paradise; Merlinda Wibowo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3087

Abstract

The learning model used greatly affects the learning process in the Covid-19 pandemic era. The online learning that has been passed in this one year has caused boredom. The learning process is too monotonous, the teacher's intonation is less varied, and not easy to interact directly with friends and teachers. Therefore, to achieve an effective and maximum learning process, the researcher proposes using video-based learning and gamification methods to increase deeper understanding of the material, motivation in learning, and student involvement in the learning process through the Learning Management System (LMS). The material presented will be transformed into more interactive and interesting videos such as simple animated videos, tutorial videos, podcast videos, and others. This research aims to provide positive benefits for students to be more active in discussing and collaborating and enthusiastic in doing all learning activities. The test to measure the level of motivation and involvement can be carried out in three stages, namely with pre-test and post-test, T-test and analytical data from student access to the LMS according to the indicators involved in this study such as video completion, total video, total comments, total badges, and completion of the game level. This study result indicates a positive influence from the application of video-based learning and gamification methods on LMS to increase student motivation and engagement.
Penerapan Metode Data Mining Pada Point of Sale Berbasis Web Menggunakan Algoritma Apriori Adam Firmansyah; M Iwan Wahyudin; Ben Rahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3085

Abstract

To be able to understand which products have been purchased by customers, it is done by describing the habits when customers buy. Use association rules to detect items purchased at the same time. This study uses an a priori algorithm to determine the association rules when buying goods. The results of the study and analyzing the data obtained a statement that using the a priori algorithm to select the combined itemset using a minimum support of 25% and a minimum confidence of 100%, found the association rule, namely, if the customer buys at the same time. Buying goods has the highest value of support and trust. Likewise with the support value of 25%, the confidence value is 100%. In this way, if a customer buys an item, the probability that the customer buys the item is 100%

Page 58 of 119 | Total Record : 1182

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