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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Semesta Teknika Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Electrical Technology UMY Indonesian Journal of Information System Jurnal Surya Masyarakat Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat Scientific Journal of Informatics
Slamet Riyadi
Universitas Muhammadiyah Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Medicine & Pharmacology Public Health Social Sciences

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Classification of Student Understanding on Covid-19 Booster Vaccine Using Machine Learning Cahya Damarjati; Slamet Riyadi; Ricki Irawan
Emerging Information Science and Technology Vol 3, No 2 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v3i2.18680

Abstract

The outbreak of COVID-19 has been declared a global pandemic by the World Health Organization (WHO). Developing a vaccine is one of the best ways to reduce the virus's impact. Nevertheless, the development of virus mutations produces new variants that diminish the efficacy of the previous vaccine. Booster doses of the Covid-19 vaccine is still a matter of debate among the public, particularly among students, as evidenced by the low rate of booster vaccinations in the community, which is a result of a lack of knowledge about booster vaccines. The purpose of this study is to assess the level of understanding among Universitas Muhammadiyah Yogyakarta (UMY) students regarding booster vaccinations, with the results subsequently serving as a factor or strategy for future government booster vaccination policy decisions. ANN and SVM algorithms could be used to predict the level of understanding of booster vaccinations among UMY students. However, the maximum level of precision in classifying the level of comprehension is not yet known. To determine which of the two methods, kernel and k-fold, provided the maximum level of accuracy, a comparative study was conducted between them. The research was conducted by disseminating questionnaires containing assessments of booster vaccinations to a total of 2095 respondents. Using randomized sampling type, this study yielded an accuracy of 88.45% for the ANN method and 89.93% for the SVM method in each scenario. In addition, the authors conduct feature efficiency, which aims to reduce the time and cost associated with data computation.
Evaluating the Hybrid Multi-Protocol Label Switching (MPLS) on the Enhanced Interior Gateway Routing Protocol (EIGRP) Ridho Novradinata; Slamet Riyadi; Ronald Adrian
Emerging Information Science and Technology Vol 3, No 2 (2022): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v3i2.16865

Abstract

The development of technology and communication is expanding rapidly. In this case, the internet has become a vital necessity in globalization. Technological innovations are required to create a seamless, fast, and secure communication system. This research aims to evaluate the Enhanced Interior Gateway Routing Protocol (EIGRP) implementation by applying the Multiprotocol Label Switching (MPLS) technology. This study adhered to several stages in the Network Development Life Cycle (NDLC) method. The results of the two technology combinations, EIGRP and MPLS, demonstrated MPLS network simulation testing in several dynamic routing systems: EIGRP and OSPF, identified through the Quality of Service (QoS) value. It revealed that the best performance was EIGRP with a throughput of 2152.5 bps, delay of 335.6 ms, and jitter of 411 ms. Furthermore, MPLS and EIGRP network redundancy was better applied in the mesh topology with a multi or backup link than in the linear topology with a single link.
Development and Testing of a Mathematics Learning Application Arif Ahmad Fadlil; Dwijoko Purbohadi; Slamet Riyadi
Emerging Information Science and Technology Vol 4, No 1 (2023): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v4i1.18886

Abstract

Distance learning, or online learning, is essential for both teachers and students as they work to create a system of education to function during a pandemic. Therefore, this research aims to develop a CAI media-based learning application for teaching fractions in mathematics. The survey results conducted at SDN 1 Sukerejo Boyolali revealed that students encountered the most trouble with fractions. Hence, their comprehension of the mathematics learning application was evaluated. The pre-test and post-test results of 30 students in both the control and experiment classes demonstrated that the application was easier to use than direct or traditional learning methods.
Peningkatan Ketrampilan Guru SD dalam Pembuatan Video Pembelajaran dengan Menggunakan Telepon Cerdas Slamet Riyadi; Erwan Sudiwijaya; Apriliya Kurnianti; Arif Bintoro Johan
Jurnal Surya Masyarakat Vol 6, No 1 (2023): November 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.1.2023.104-110

Abstract

Learning in digital era demands innovation of learning materials where video is one of the attractive materials. Unfortunately, many teachers don't have skills to make learning videos, including some teachers at the Muhammadiyah Elementary School in Godean. Therefore, this program aims to improve the skills of partners in making learning videos. The main equipment used is a cell phone which is owned by all teachers so there is no need to procure additional equipment. The steps carried out are preparation, implementation and evaluation. Preparations have been made by discussing with partners about the training to be held. The training was completed on April 9, 2022 at the UMY Information Technology Laboratory, attended by 37 teachers from three Muhammadiyah elementary schools in Godean District. Participants received theory and immediately practiced making videos using their respective cell phones. Training evaluation was carried out using pre and post tests as well as observation. This program has succeeded in increasing teachers' knowledge and skills in making learning videos with a significant increase in pre-test and post-test results of 54.7%.
Optimization of the VGG Deep Learning Model Performance for Covid-19 Detection Using CT-Scan Images Slamet Riyadi; Cahya Damarjati; Siti Khotimah; Asnor Juraiza Ishak
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3598

Abstract

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) causes a pneumonia-like disease known as Coronavirus Disease 2019 (COVID-19). The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is the current standard for detecting COVID-19. However, CT scans can be applied for radiological inspection to detect infections in their earliest lung stages. Machine learning, specifically deep learning, can potentially speed up the evaluation of CT scan diagnoses of COVID-19. To date, no studies have been discovered that employ SGD, Adamax, or AdaGrad optimization methods with deep learning VGG model variants for COVID-19 detection in CT scan images with datasets comprising 2,038 images. This study aims to assess and compare the performance of various optimization methods for detecting COVID-19 utilizing variations of the VGG-16 and VGG-19 models based on CT scan images. Results from performance optimization comparison tests employing two VGG deep learning models were obtained, demonstrating the influence of optimization methods on model performance. The Adamax optimization method applied to the VGG-16 model performance achieved an average accuracy of 94.11% in COVID-19 detection using CT scan images, while the Adamax optimization method applied to the VGG-19 model performance achieved an average accuracy of 93.77%.
Modified Convolutional Neural Network for Sentiment Classification: A Case Study on The Indonesian Electoral Commission Slamet Riyadi; Naufal Gita Mahardika; Cahya Damarjati; Suzaimah Ramli
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4929

Abstract

Purpose: This study aims to analyze public sentiment towards the Indonesian Electoral Commission (KPU) performance and evaluate a modified Convolutional Neural Network (CNN) model effectiveness in sentiment analysis. Methods: This research employs several methods to achieve its objectives. First, data collection was conducted using web crawling techniques to gather public opinions on the performance of the Indonesian Electoral Commission for the 2024 elections, with a specific focus on platform X. A total of 5,782 data points were collected and underwent preprocessing before sentiment analysis was performed. This study uses the CNN method due to its exceptional ability to recognize patterns and features in text data through its convolutional layers. CNN is highly effective in sentiment analysis tasks because of its ability to capture local context and spatial features from text data, which is crucial for understanding the nuances of sentiment in comments. The modified CNN model was then trained and evaluated using a labeled dataset, where each comment was classified into positive, negative, or neutral sentiment categories. Modifying the CNN model involved adjusting its architecture and parameters, as well as adding layers such as batch normalization and dropout to optimize its performance. The effectiveness of the modified CNN model was assessed based on metrics such as classification accuracy, precision, recall, and F1 score. Through this methodological approach, the research aims to gain insights into public sentiment towards the KPU performance in the 2024 elections and to evaluate the effectiveness of the modified CNN model in sentiment analysis. Result: The research revealed several significant findings. Firstly, most comments expressed concerns regarding performance aspects of KPU’s, including transparency, fairness, and integrity. Neutral sentiment dominated the discourse, with approximately 23.66% of comments conveying dissatisfaction or skepticism towards KPU's handling of the elections. Additionally, sentiments expressed on social media platform X mirrored those found across other platforms, indicating a consistent perception of KPU performance among users. Furthermore, the evaluation of the modified CNN model demonstrated a substantial improvement in accuracy, achieving an impressive 93% accuracy rate compared to the pre-modification model's accuracy of 77%. These results suggest that the modifications made to the CNN model effectively enhanced its performance in sentiment analysis tasks related to KPU performance during the 2024 elections. These findings contribute to a deeper understanding of public sentiment toward KPU performance and underscore the importance of leveraging advanced technology, such as modified CNN models, for sentiment analysis. Novelty: This study contributes novelty in several ways. Firstly, it provides insights into public sentiment towards the performance of the KPU during the 2024 General Elections, which is crucial for understanding the perception of democracy in Indonesia. Second, the study employs a mixed-methods approach, combining web crawling techniques for data collection and a modified CNN model for sentiment analysis, which offers a comprehensive and advanced methodology for analyzing sentiments on social media platforms. Thirdly, the evaluation of the modified CNN model demonstrates a significant improvement in accuracy, indicating the approach's efficacy in analyzing sentiments related to KPU performance. This study offers valuable contributions to academic research and practical applications in sentiment analysis, particularly in democratic processes and institutional performance evaluation.
Co-Authors Abdul Eriawan Nahar Abyiyansyah Meidy Laksono Agus Jamal Amelia Mutiara Ayu Ratiwi Anna NN Chamim Aprilia Kurnianti Arif Ahmad Fadlil Arif Bintoro Johan Asnor Juraiza Ishak Asroni Asroni Badrahini Masajeng Respati Cahya Damarjati Cut Maya Putri Audilla Dwijoko Purbohadi Dyah Mutiarin Fadhan Anwarodin Fauri Hakim Haris Setyawan Indira Prabasari Kamarul Hawari Ghazali Kurnianti, Apriliya Laila Ma’rifatul Azizah Lusiana Ning Saputri Milou Derks Muhammad Aldi Joko Satria Perdana Muhammad Habil Muhammad Rizki Frebian Nadiyah Maharty Ali Nafi A Utama Nafi Ananda Utama Nasrul Saefullah Naufal Gita Mahardika Nisrina Akbar Rizky Putri Nurfahmi Nurfahmi Rammadhany Rammadhany Ricki Irawan Ridho Novradinata Rohmana Zulfa Bakhtiar Ronald Adrian Ronald Adrian Sakir Ridho Wijaya Sartika Puspita Siti Khotimah Sofiani, Erma Sudiwijaya, Erwan Suparman Suwadi Suwadi Suzaimah Ramli Tony K Hariadi Yunita Lestari Zulfan Fadholi