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Slamet Riyadi
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Department of Information Technology Faculty of Engineering, Universitas Muhammadiyah Yogyakarta F3 Building, 2nd Floor Brawijaya Street, Tamantirto, Kasihan, Bantul, Yogyakarta 55183 Indonesia
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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 7 Documents
 1 
Search results for , issue "Vol 5, No 1 (2024): May" : 7 Documents clear
Bandwidth Management using Per Connection Queue and Queue Tree: A Case Study on a High School Network Prasetyo, Eko; Santoso, Tossy; Riyadi, Slamet; ., Asroni
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.v5i1.22376

Abstract

This research aims to enhance the efficiency of bandwidth utilization at MTs Muhammadiyah Tawangsari using the PCQ and Queue Tree bandwidth management methods on a MikroTik router. Through this approach, an analysis of bandwidth requirements, network topology design, and implementation were conducted. Post-implementation measurement results show a remarkable improvement in average user download speeds: 17.3 Mbps in administration rooms, 10 Mbps in classrooms, and 18.8 Mbps in faculty rooms. These results indicate a significant improvement in tailored bandwidth distribution that meets the specific needs of each network area and ensures that all users receive equal bandwidth usage. This leads to more evenly distributed and efficient network performance.
Partial Adaptive Multi-Level Block Truncation Coding (Ambtc) Of Spinal X-Ray Image For Efficient Compression 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.v5i1.22409

Abstract

This study aims to explore various adaptations of the AMBTC compression model applied to lumbar spine radiographic images, focusing on minimizing image size while preserving essential information. The approach involves adjusting several technical aspects of the AMBTC model, including the number of blocks, block size, and compression rate. The quality of the compressed images is assessed using image quality metrics such as PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Squared Error). The findings indicate that a modified AMBTC compression model can significantly enhance the quality of lumbar spine radiographic images, evidenced by increased PSNR values, while substantially reducing the file size without compromising crucial image details
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.
Study On Implementation Of Watergate Control System From Manual To Automatic Based Arduino Nano ATmega328 Syafrudin, Ahmad; Jeckson, Jeckson; Afrida, Yenni
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.v5i1.22424

Abstract

The system for processing river water into clean water by operating sluice gates which are controlled manually by operators often causes problems (human error), so a system must be created that is capable of controlling sluice gates automatically. The turbidity sensor detects water turbidity which is then accepted by the Arduino nano as the basis for controlling the water gate. In this research, data collection techniques were carried out using interviews with resource persons (operators), observation and literature study. After the automatic sluice control system based on the Arduino nano ATmega328 was implemented, the results obtained were: When the water conditions are muddy and murky, relay one is "on", relay two is "off" (the sluice gate is closed), while when the water conditions are normal and clear then the relay two "on" relays and one "off" relay (open floodgate). In this way, we no longer just have to rely on the operator to control the sluice gate, so that the water treatment process can be more optimal and efficient
Cognitive Remediation Game of Selecting Tools Independently for Mentally Disordered Patients Isnanda, Reza Giga; Yosa, Muhammad Ferdy; Setiawan, Asep
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.v5i1.22335

Abstract

Cognitive impairment is one of the negative effects of schizophrenia. To treat this, cognitive remediation could serve as an option to improve the cognitive performance of the sufferers. In this line, Doctor Herdaetha developed a computer-based game module to assist psychiatrists in performing cognitive remediation treatment during the recovery period of patients. However, patients could not utilize the game module without the assistance of psychiatrists, making it ineffective and inefficient. Given these considerations, a mobile Selecting Tool game was created as an independent cognitive remediation medium for schizophrenia patients during their recovery period. The game was created using a waterfall model and involved multiple review and discussion sessions with psychiatrists. The results revealed that the game was well-functioning and could be played independently.
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%.
Analysis of Cross Validation on Classification of Mangosteen Maturity Stages using Support Vector Machine Prabasari, Indira; Zuhri, Afrizal; Riyadi, Slamet; Hariadi, Tony K; Utama, Nafi Ananda
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.v5i1.22359

Abstract

This study explores the efficacy of the Support Vector Machine (SVM) method in classifying mangosteen fruit images based on six ripeness levels. Employing SVM enables nonlinear data classification and simultaneous utilization of multiple feature extractions, resulting in enhanced accuracy. Analysis reveals that models integrating three feature extractions outperform those with only two. With ample training data and optimized parameters, SVM achieves detection accuracy exceeding 90%. However, algorithmic enhancements are necessary to compute RGB color index values for all pixels on mangosteen skin surfaces, possibly through circular-shaped windows approximating the fruit's contour. Moreover, comparative assessments of RGB color system calculations against alternative systems such as HSI are crucial for selecting the most suitable color model in alignment with human perception.

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