cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Slamet Riyadi
Contact Email
eist@umy.ac.id
Phone
-
Journal Mail Official
eist@umy.ac.id
Editorial Address
Department of Information Technology Faculty of Engineering, Universitas Muhammadiyah Yogyakarta F3 Building, 2nd Floor Brawijaya Street, Tamantirto, Kasihan, Bantul, Yogyakarta 55183 Indonesia
Location
Kab. bantul,
Daerah istimewa yogyakarta
INDONESIA
Emerging Information Science and Technology
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27226042     EISSN : 27226050     DOI : https://doi.org/10.18196/eist
Core Subject : Science,
Computer Science & IT
Emerging Information Science and Technology is a double-blind peer-reviewed journal which publishes high quality and state-of-the-art research articles in the area of information science and technology. The articles in this journal cover from theoretical, technical, empirical, and practical research. It is also an interdisciplinary journal that interested in both works from the boundaries of subdisciplines in Information Science and Technology and from the boundaries between Information Science and Technology with other disciplines. EIST is an Open Access Journal to advance sharing science and technology. People have rights to read, download, copy, distribute, print and use with proper acknowledgment and citation. There is no publication fees for authors.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 141 Documents
 11   12   13   14   15 
Improving YOLO Object Detection Performance on Single-Board Computer using Virtual Machine Haq, Muhamad Amirul; Huy, Le Nam Quoc; Fahriani, Nuniek
Emerging Information Science and Technology Vol. 5 No. 1 (2024): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i1.22486

Abstract

Single-board computers have gained popularity in the recent decade, largely due to the immense advancements in deep learning. Deep learning involves complex computational processes that are beyond the capabilities of regular microcontrollers, thus necessitating the use of single-board computers. However, single-board computers are primarily designed to operate efficiently in low-power environments. Therefore, optimization is crucial for running deep learning algorithms effectively on single-board computers. In this work, we explore the impact of utilizing the DeepStream framework to run deep learning algorithms, specifically the YOLO algorithm, on NVIDIA Jetson single-board computers. The DeepStream framework can be executed in virtual machines, notably Docker, to improve the performance and portability of the model. Additionally, deploying the Docker virtual machine from removable disks can further enhance its portability and even increase the algorithm's speed. Our benchmarks indicate that real-time streaming of the YOLO algorithm can operate up to 8.5 times faster when deployed from a Docker virtual machine.
Discrete Curvelet Transform Feature Extraction for Mangosteen Fruit Surface Damage Detection Utama, Nafi Ananda; Triyani, Wahyu Indah; Riyadi, Slamet; Damarjati, Cahya
Emerging Information Science and Technology Vol. 5 No. 1 (2024): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i1.22602

Abstract

Mangosteen (Garcinia mangostana L) is one of the commodities of Indonesian fruit and is used as an export primadona that became the basis of Indonesia to increase the currency of the country. The quality of the fruit can be seen from the surface, whether there is damage or not. The sorting that the farmers have been doing all this time is still using the conventional way, that is, with the sense of sight. This conventional method seems to be less effective because it takes a lot of energy, takes a long time, and there are different perceptions between farmers. To solve this problem, a method of surface quality extraction of mango fruit will be developed based on image processing. The initial stage of image processing is with the image size equation then the image is converted to grayscale mode, then a discrete curvelet transformation is performed. The next stage is the extraction of mean, energy, entropy, standard deviation, variance, sum, correlation, contrast, and homogeneity. The result of the subsequent feature extraction is used to enter a value at the classification stage. From some of these extractions it will be known which extraction has the highest accuracy value. The method of classification used is Linear Discriminant Analysis (LDA) with the method of K-Fold Cross Validation which in this study is divided into 4-fold cross validation. After testing on 120 images, the highest value of accuracy is with extraction of standard characteristics deviation of 91.7% and variance of 88.4%.
Community perspective analysis of Yogyakarta special region using K-means algorithm Berlina, Laila Indah; Aesyi, Ulfi Saidata; Kharisma, Kharisma
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24729

Abstract

This study explores community perspectives on Yogyakarta, a culturally rich region in Indonesia known as "Jogja Istimewa," "Student City," and "City of Tourism." Given the potential challenges faced by the region, the research employs the K-Means Algorithm to analyze opinions gathered from Twitter, offering a novel alternative to traditional surveys. Using a data crawling method, relevant tweets about Yogyakarta were collected and processed through preprocessing and TF-IDF to enhance word significance. The findings reveal diverse community views regarding job opportunities, culture, tourism, religious activities, stakeholder involvement, and security. The application of K-Means clustering effectively highlights the multifaceted perspectives of Yogyakarta's residents, providing valuable insights for understanding the region’s socio-cultural dynamics. 
Classification of Duration in Global Terorism using ResNet Adriansyah, Adinda Nurhayati; Riyadi, Slamet
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24756

Abstract

Terrorism is a global threat that affects the political, economic, and social stability of many countries. The number of victims killed is based on the duration of the terrorism incident. This study uses the Residual Network (ResNet) model to classify terrorism incidents based on the duration of the incident (less than 24 hours and more than 24 hours) using the Global Terrorism Database (GTD) dataset. The GTD data used covers terrorism incidents from 1970 to 2017, with a total of 181,691. After preprocessing the data by converting categorical features to numeric and removing missing values, the data was divided into training, validation, and test sets with a composition of 70%, 15%, and 15%. The results show that the ResNet model is able to achieve a validation accuracy of 99.61% and a validation loss value of 0.0183. These findings show that the ResNet model is effective in classifying the duration of terrorism incidents and has the potential to be used in the development of better terrorism prevention systems.
The Influence of Developing an Understanding of Basic Programming Concepts Through Educational Games as A Learning Method For Elementary School Students. Kurnianti, Apriliya; Praditia, Rizky Nanda; Qodri, Krisna Nuresa
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24781

Abstract

Integrating engaging learning methods into the educational landscape has become increasingly crucial for enhancing student understanding and engagement. This study investigates the impact of educational games as a learning method for improving elementary school students' comprehension of basic programming concepts. The research hypothesizes that educational games can significantly enhance the understanding of programming concepts by making abstract ideas more tangible and engaging through interactive and enjoyable learning environments. Using a quasi-experimental design, the study involved two groups of elementary school students: the experimental group, which utilized an educational game designed for teaching introductory programming concepts, and the control group, which received traditional instruction. Both groups underwent assessments of their understanding of basic programming concepts before and after the intervention through standardized tests. The results demonstrated a statistically significant improvement in the scores of students in the experimental group compared to those in the control group. This suggests that educational games not only aid in better understanding complex subjects such as programming and increase students' motivation and engagement levels. The findings of this study contribute to educational practices by illustrating how educational games can effectively support the introduction and teaching of programming at an early age within the elementary educational system. Furthermore, the study highlights the potential of integrating such tools into standard curricula to enhance student learning across various subjects. The study urges future research to explore the long-term impacts of educational games on learning outcomes and how these tools can be further integrated into the educational system
Mobile Surveillance System using Unmanned Aerial Vehicle for Aerial Imagery Haq, Muhamad Amirul
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24837

Abstract

Crowd counting plays a vital role in public safety, particularly during riot scenarios where understanding crowd dynamics is crucial for effective decision-making and risk mitigation. Accurate crowd estimation in such environments enables authorities to monitor the situation in real time, allocate resources efficiently, and prevent potential escalations. However, counting individuals in a riot scenario presents unique challenges due to the chaotic nature of the scene, varying crowd densities, and obstructions caused by movement and environmental factors. Traditional methods struggle to provide reliable results in these conditions, necessitating advanced solutions. This study explores the implementation of CSRNet (Congested Scene Recognition Network), a state-of-the-art deep learning model, to address crowd counting in challenging environments characterized as "images in the wild." CSRNet’s ability to leverage dilated convolutions allows it to effectively capture contextual information and handle high crowd densities without sacrificing spatial resolution. We evaluate the model’s performance on diverse datasets, including aerial imagery and real-world riot scenarios, focusing on its adaptability to dynamic, unstructured environments. The results demonstrate the potential of CSRNet to provide accurate crowd density estimates under adverse conditions, offering critical insights for public safety applications. By addressing the technical challenges of implementing CSRNet in these contexts, this study contributes to the advancement of deep learning-based crowd counting, emphasizing its significance in real-world scenarios such as riots and other high-stakes events. Future work aims to further enhance the model's robustness and applicability to diverse operational settings.
Application for Recording Marriage Events at KUA: Design and Implementation Using Visual Studio 2012 and MySQL Siska, Sri Tria; Suhery, Lilik; Hariyadi, Hariyadi; Ananda, Suci Rizki
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24846

Abstract

 In the management of data recording marriage events at the KUA (the Office of Religious Affairs) is still manual and has not utilized information technology to the fullest. The process of inputting, managing and storing data on recording marriage events only utilizes Microsoft Office applications and there is even some data that is written manually so that the process of presenting reports for recording marriage events is still relatively slow and inefficient, there are often errors and loss of data which results in the KUA having to search for data again. To overcome this problem, an application design is carried out that can speed up the process of recording marriage events at the KUA. Design and Implementation was using Visual Studio 2012 and MySQL as a database processing place. The application designed to support the process of recording marriage events more effectively and efficiently. 
Integration of Human Organisational Technological Factors and Information Quality Metrics for Assessing Hospital Information System Performance Rumambi, Frendy Rocky; Prasetya, Didik Dwi; Patmanthara, Syaad
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.29172

Abstract

Digital transformation in health care necessitates HISs that can efficiently integrate people, organisations, and technology to deliver best performance in service delivery. This paper proposes a HIS performance evaluation model solution based on humanorganisation– technology fit (HOT-Fit) and information quality metrics (IQM) concept to evaluate system success technically. This model modifies the HOT-Fit model by incorporating an information quality metrics dimension with four constructs: accuracy, completeness, timeliness, and relevance. Research Methodology: The study follows quantitative approach and the survey questionnaires were filled by 150 active SIRS users from which the data was analysed using SEM. The findings suggest that the technology quality and information quality constructs significantly influence both user satisfaction and system use. Information quality is established as a mediating variable in the relationship between technological factors and user satisfaction. From a practical perspective, this integrated model has implications for understanding that achieving SIRS success depends not only on how well humans, organisations, and technology fit together but on the quality of the data and information produced. This research is a step towards the advancement of data metric based information system evaluation specifically in the domain of Health Infor matics, and also serves as a conceptual framework for enhancing HIS governance in Indonesia.
Understanding Burnout Experiences in Social Media Discourse: Evidence from YouTube User Comments Putri, Nisrina Akbar Rizky; Ardiansyah, Ardiansyah; Widyastuti, Erma; Azizah, Laila Ma'rifatul
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.31343

Abstract

Burnout has become an important psychological concern that is increasingly discussed through social media, providing valuable textual data for understanding public experiences of emotional exhaustion, workplace pressure, and coping. This study analyzes sentiment in burnout related YouTube comments using DistilIndoBERT and examines the contribution of back translation to classification performance. The initial dataset consisted of 2,931 comments collected from 5 YouTube videos published between 2021 and 2025 was subjected to a data quality audit that removed exact duplicates, promotional content, spam, and nonmeaningful comments, resulting in 2,829 relevant records. Sentiment labels were assigned through a semi automated process and reviewed by the researchers into positive, neutral, and negative categories. The dataset was divided using stratified sampling into 70% training data, 15% validation data, and 15% test data. Back translation was applied exclusively to the positive and neutral classes in the training set to prevent data leakage, expanding the training data from 1,980 to 3,003 records. Negative sentiment was dominant, accounting for 1,430 comments or 50.55%, followed by neutral sentiment with 846 comments or 29.90% and positive sentiment with 553 comments or 19.55%. DistilIndoBERT achieved 82.4% accuracy, 81.9% macro precision, 81.5% macro recall, and 81.6% macro F1 score on the original dataset. After augmentation, the respective scores increased to 87.1%, 86.8%, 86.2%, and 86.4%. These observed improvements demonstrate the potential of training focused back translation to strengthen DistilIndoBERT classification of burnout discourse.
Real-Time YOLOv8-Based Semantic Segmentation and Mask-to-Polygon Preprocessing Pipeline for Surgical Scene Delineation in Laparoscopic Cholecystectomy Messakh, Billy Daniel; Tahalele, Paul L
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.31491

Abstract

Clear semantic definition of laparoscopic video images is an important prerequisite for the development of context-sensitive computer-assisted surgery systems. Although the CholecSeg8k dataset contains pixel-wise annotations of 13 anatomical structures, making real-time predictions while retaining localization accuracy of the object boundaries is a difficult task in surgical informatics. The present study tries to solve these problems through the use of the lightweight one-stage YOLOv8m-seg neural network model along with an automatic pre-processing pipeline, which uses contour-filtering techniques to transform color-coded masks into normalized polygonal annotations. After the fine-tuning procedure, which included multi-scale geometric transformations such as random spatial rotations and scaling, the system performance was validated on a specific validation set. Mask mean average precision (mAP50) was measured at 0.955 and 0.793 (mAP50-95). The class-based metrics had values above 0.98 for the classes’ liver, abdominal wall, and grasper, while amorphous tissues such as blood and connective tissue were the most difficult to classify due to their higher morphological ambiguity. In the computational analysis, an average inference time of 13.2 milliseconds was recorded on a consumer-level NVIDIA RTX 3060 GPU, thus achieving a rate of 75 frames per second. From the results recorded, it is evident that anchor-free single-stage frameworks perform best in balancing latency and segmentation precision as opposed to multi-stage frameworks

Page 14 of 15 | Total Record : 141

 11   12   13   14   15 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol. 7 No. 1 (2026): May Vol. 6 No. 2 (2025) Vol. 6 No. 1 (2025) Vol. 5 No. 2 (2024): November Vol 5, No 1 (2024): May Vol. 5 No. 1 (2024): May Vol. 4 No. 2 (2023): November Vol 4, No 2 (2023): November Vol 4, No 1 (2023): May Vol. 4 No. 1 (2023): May Vol. 3 No. 2 (2022): November Vol 3, No 2 (2022): November Vol 3, No 1 (2022): May Vol. 3 No. 1 (2022): May Vol. 2 No. 2: November 2021 Vol 2, No 2: November 2021 Vol 2, No 1: May 2021 Vol. 2 No. 1: May 2021 Vol 1, No 4: November 2020 Vol. 1 No. 4: November 2020 Vol 1, No 3: August 2020 Vol. 1 No. 3: August 2020 Vol. 1 No. 2: May 2020 Vol 1, No 2: May 2020 Vol 1, No 1: February 2020 Vol. 1 No. 1: February 2020 More Issue