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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Merlinda Wibowo
Institut Teknologi Telkom Purwokerto, Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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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.
Rancangan dan Evaluasi Usability Pada Aplikasi Website Media Pembelajaran Cyberbullying Menggunakan Metode Gamifikasi Abimanyu Manusakerti; Merlinda Wibowo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4627

Abstract

Cyberbullying is bullying act carried out on social media to cause harm the victim. The potential for cyberbullying is very high among teenagers. This is due to high frequency of using social media, increasing cases of cyberbullying, and the lack of education about cyberbullying. Cyberbullying education has been carried out using digital literacy educational activities using digital literacy only focus on introducing and preventing cyberbullying in the form of seminars or lectures. However, using this method has many shortcomings, for example someone easily forgets the material presented and gives less space to develop creativity. Interactive education is needed to increase motivation to learn about cyberbullying. The gamification method is very suitable to be applied in learning because the gamification method involves game elements such as points, leaderboards, badges, challenges, and achievements. In addition to using the gamification methods, visual media such as images and animation video is also suitable for learning. This research utilizes gamification-based learning and visual media to deliver the material as a prototype application for cyberbullying learning sites. This research aims to provide education and increase motivation to learn about more interactively cyberbullying. This research involved 42 respondents with a range of ages between 12-21 years. This research will use System Usability Scale (SUS) to evaluate the prototype's usability. The usability score from the prototype in this research is 77.8, meaning the prototype is acceptable to use.
Implementasi Metode Convolutional Neural Network untuk Klasifikasi Breast Cancer pada Citra Histopatologi Muhammad Afrizal Amrustian; Merlinda Wibowo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5194

Abstract

Breast cancer is a tumor that manifests as an abnormal lump in the breast of a woman. The occurrence of breast cancer in women can be triggered by genetic and lifestyle factors. According to Global Cancer Statistics, 600,000 out of 2.3 million occurrences of breast cancer result in death. The death rate from breast cancer in Indonesia is likewise relatively high, reaching 17% for every 100,000 female inhabitants. Using histological pictures to diagnose breast cancer is one technique. The patient will capture an image of her breast cells, which will be examined and diagnosed by medical personnel. Even if histopathological scans are utilized as a baseline for the detection of breast cancer, the death rate associated with this disease remains rather high. One of the causes for the high mortality rate associated with breast cancer is the late detection of the disease, which results in patients being treated when the disease is in a severe state, and sometimes a misdiagnosis. The authors propose creating a breast cancer classification model utilizing the convolutional neural network (CNN) method in order to address the described issues. The study's findings indicate that CNN can classify breast cancer patients with an accuracy of 85 percent. Moreover, by calculating the loss function, the constructed model prevents overfitting.
Perbandingan Metode Klasifikasi Untuk Deteksi Stress Pada Mahasiswa di Perguruan Tinggi Merlinda Wibowo; Muh. Rizieq Fazlulrahman Djafar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5182

Abstract

The outbreak of the COVID-19 pandemic is increasingly affecting the high level of stress in humans. Stress due to this pandemic has also occurred, especially for students. This stress is caused by students spending too much time studying online. Using student data can act as a tool to identify student stress by processing it through various machine-learning methods. This method can extract information and find patterns and information from the data. Classification techniques are used as data groupings based on mapping data into sample data. This study used several classification methods: Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Neural Network, Random Tree, Random Forest, and K Nearest Neighbor (KNN). These methods were successfully compared to determine which is the best for detecting stress precisely and accurately based on the classification performance results of each method. Random Tree and Decision tree were chosen as the best methods for the results of this performance comparison with an 80:20 split reaching up to 100%.
Analisis Sentimen Terhadap Isu Resesi Tahun 2023 di Indonesia menggunakan Metode Naïve Bayes Naufal Fakhri Zakaria; Merlinda Wibowo; Novanda Alim Setya Nugraha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6386

Abstract

Recession is a phenomenon in which the real GDP (gross domestic product) decreases for two consecutive quarters, meaning that economic activities such as distribution, investment, consumption, production will decrease, causing a domino effect that is detrimental to various parties, one of which is layoffs (termination of employment). The recession was initiated by the weakening of the global economy which had an impact on the domestic economy and countries in the world. The stronger the dependence of a country's economy on the global economy, the faster a recession will occur in that country. Indonesian President Joko Widodo predicts that in 2023 Indonesia will be a dark year due to the economic and energy crisis due to COVID-19 and the war between Russia and Ukraine Therefore a sentiment analysis is needed to see public opinion regarding the issue of the 2023 recession in Indonesia. The method used in this study is the Naïve Bayes classification method. Naïve Bayes is a classification algorithm that is widely used in Data Mining or Text Mining. This study aims to search for negative, positive, and neutral comments and to find out the accuracy of the Naïve Bayes method. Sentiment analysis was obtained by means of data cleaning, labeling, TF-IDF, split, Naïve Bayes classification, and evaluation. It is hoped that after making sentiment analysis using the Naïve Bayes method, negative, positive and neutral comments will be obtained and the accuracy of Naïve Bayes will reach 70%.
Co-Authors Abimanyu Manusakerti Muh. Rizieq Fazlulrahman Djafar Muhammad Afrizal Amrustian Naufal Fakhri Zakaria Novanda Alim Setya Nugraha Paradise, Paradise