Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Comparison between Power Dissipation and Propagation Delay on 6T SRAM Cell Design Using GDI Logic with Transmission Gate VMSA and Voltage Divider

2023-10-14T02:50:13+00:00

The rapid evolution of the semiconductor industry has witnessed shrinking portable and mobile devices alongside an increasing demand for extended battery life. Addressing the critical challenges of speed and battery life in digital devices, this paper investigated the effectiveness of innovative low-power design techniques. Focusing on the Gate Diffusion Input (GDI) approach, a recent advancement in the field, a comprehensive analysis revealed its significant potential for reducing power consumption in digital circuits. Additionally, a comparative analysis was conducted to evaluate the performance of conventional 6T GDI SRAM cells and their Modified 6T GDI SRAM with Voltage Divider, considering the influence of Sense Amplifiers. Simulation data demonstrated that Modified 6T SRAM designs, particularly the Voltage Divider and TGVMSA variants, achieved significantly lower power dissipation and delay despite having a larger cell area. Remarkably, the proposed design substantially improved power dissipation and propagation delay, achieving 1.3 ps, and 889.41mV at 1.8V shows that the suggested design enhances power dissipation and propagation delay. These findings suggest that the proposed design offers a promising strategy for enhancing power efficiency and performance in digital devices, thereby mitigating the limitations of battery life and speed in the modern technological landscape.

Thorax X-ray Image Segmentation Technique Using Four Variants of Thresholding Algorithm

2023-09-11T17:47:45+00:00

Pneumonia is a respiratory infection caused by bacteria, viruses or fungi, and has been recognized as a fairly common and threatening disease. When diagnosing this disease, doctors usually also use thorax X-ray images. Nowadays, diagnosing pneumonia has been made possible with the help of machine learning technology. Doctors or medical personnel in locations where there are no pulmonary specialists or experts can be assisted by this technology. Machine learning algorithms are used to process digital images that have passed the pre-processing and segmentation stages. This paper offers a solution to segmentation technique of thorax X-ray digital image using a combination of four thresholding algorithms. This combination aims to find the best CNN model with segmentation techniques in the form of the most suitable thresholding algorithm. The result of this research is four different data sets. The thresholding algorithms used include binary, thresh binary inv, thresh to zero, thresh tozero inv with a threshold value of 150. The data used in this research is a thorax X-ray image dataset, as many as 5,856 images acquired from the Kaggle repository data. The program code in this research uses the Python programming language in the Anaconda environment. This research has resulted in a comparison of the accuracy values obtained using 4 variants between thres_binary thresholding algorithm and thres_binary_inv. The thres_tozero obtained 95% of accuracy while thres_tozero_inv obtained 94% of accuracy.

Entropy-Based Feature Extraction and K-Nearest Neighbors for Bearing Fault Detection

2023-11-20T00:12:20+00:00

Bearing failures in rotating machines can lead to significant operational challenges, causing up to 45-55% of engine failures and severely impacting performance and productivity. Timely detection of bearing anomalies is crucial to prevent machine failures and associated downtime. Therefore, an approach for early bearing failure detection using entropy-based machine learning is proposed and evaluated while combined with a classifier based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Entropy-based feature extraction should be able to effectively capture the intricate patterns and variations present in the vibration signals, providing a comprehensive representation of the underlying dynamics. The results of the classification carried out by KNN-Entropy have an accuracy value of 98%, while the SVM-Entropy model has an accuracy of 96%. Hence, the Entropy-based feature extraction giving the best accuracy when it is coupled with KNN.

Mental Health Prediction Model on Social Media Data Using CNN-BiLSTM

2023-11-19T05:21:19+00:00

Social media has transformed into a global platform for expression and interaction where users can share photos, images, and videos. The rapid development and widespread use of social media afford the opportunity to analyze the construction of social life in societies and communities. As a result of alterations in lifestyle during the COVID-19 pandemic, mental health disorders increased. Mental health is a complex disease involving numerous individual, socioeconomic, and clinical variables. Natural language processing and analysis methods are required to address this complexity. The classification of mental health-related texts, which can serve as early warnings and early diagnoses, is facilitated by analytical and natural language processing techniques. In this investigation, a CNN-BiLSTM model was utilized, which was aided by a FastText-based word weighting method. The utilized data set consists of texts on mental health with labels such as borderline personality disorder (BPD), anxiety, depression, bipolar, mentalillness, schizophrenia, and poison. There are 35000 training records and 6108 test records. The data will undergo a data cleansing procedure, which will include lower text stages, number removal, reading mark removal, and stopword removal. Modeling with CNN-BiLSTM and FastText weighting yielded an F1-Score and accuracy of 85% and 85%, respectively. In comparison to the Bi-LSTM model, the F1-Score and accuracy were both 83%.

BGP Dynamic Routing Protocol: A QoS Analysis for TCP and UDP

2023-11-13T03:40:57+00:00

The Border Gateway Protocol (BGP) is commonly used for TCP and UDP services, but it poses challenges in terms of Quality of Service (QoS) analysis. Parameters like throughput, packet loss, delay, and jitter are crucial for assessing network service quality. This study aims to analyze the performance and influence of the BGP routing protocol on TCP and UDP services using QoS parameters. The research used a GNS3 network simulation to conduct multiple packet transmission tests for TCP and UDP protocols, lasting 15, 30, and 60 seconds; and monitored using Wireshark. For TCP services, the average QoS index value is 3.75, categorizing the quality as "Good". The tested network topology and routing configuration exhibit reliable performance, providing good throughput, low packet loss rates, minimal delays, and stable jitter. Similarly, UDP services demonstrate “Good” performance with an average QoS index of 3.75. The BGP routing protocol in the tested network topology ensures high-quality service with good delivery speed, low packet loss rate, minimal delay, and stable jitter. Overall, the study concludes that the BGP routing protocol effectively provides satisfactory QoS for TCP and UDP services. This research contributes to understanding network performance and optimizing routing protocols for improved telecommunications services. The findings highlight the significance of routing protocols in facilitating efficient data transmission on the Internet, reinforcing the importance of QoS analysis for enhancing service quality.

Ant Colony Optimization for Efficient Distance and Time Optimization in Swarm Drone Formation

2023-09-25T06:46:24+00:00

One of the challenges in swarm drone formation is achieving fast and effective formation with optimal distances. In this paper, we propose a swarm drone formation approach utilizing Ant Colony Optimization (ACO) for achieving it. We conducted simulations involving the formation of three or more drones, aiming to identify the best formation based on distance, acceleration, and time criteria. Simulation results demonstrate that formation time is significantly reduced when employing ACO optimization compared to non-optimized methods. Additionally, the optimized formations exhibit shorter inter-drone distances compared to non-optimized formations. By implementing this approach, swarm drone formations can be rapidly established with minimized distances, resulting in substantial battery savings. The simulation encompassed various patterns formed by 3, 5, 10, 15, 20, and 25 drones. The findings indicate that the approach can reduce formation time by varying degrees, ranging from 12% to 51%, across 66% of the conducted experiments, notably for patterns created with a substantial drone count. The degree of diversity observed among the proposed solutions reached 60%, with minimal variances of less than 1% for each.

Website Quality Analysis Using Modified Webqual Method and Importance Performance Analysis on SITU TAK Website

2023-12-01T12:36:25+00:00

Technological developments affect information services, such as website. Information services on the website make it easy to convey information widely. Therefore, the quality of the website can affect the information services. This research assessed one of Telkom University's academic service websites, namely SITU Student Activities Transcript (SITU TAK). The purpose of this study was to measure the quality of the website, user satisfaction, and the factors that can increase the user satisfaction. This study employed Webqual 4.0 method as the indicator and Importance-Performance Analysis (IPA) for grouping the factors based on the quadrant of IPA. Before grouping the data, the data first passed the validity test, reliability test, and gap analysis between user perceptions and expectations. Therefore, it can strengthen the conclusions and recommendations resulted from this study. After conducting this study, the final results were obtained, which stated that SITU TAK website still did not meet the expectations of its users. This can be seen in the results of the gap analysis calculation with a value of -0.63, which means that the level of importance or expectations of the users is still higher than the performance of the website.

Exploring the Impact of Octalysis Gamification in Japanese M-learning Using the Technology Acceptance Model

2023-11-19T05:16:48+00:00

Indonesia is a country with the second highest number of Japanese language learners in the world. However, with the main language of Indonesia being derived from the roman alphabets, it makes Indonesian students hard to get used to learning Japanese alphabets, especially Kanji. This study aims to develop a gamified mobile learning application following the Octalysis gamification framework, and assess its impact in garnering student’s acceptance to enhance their Japanese Kanji learning experience. This study was conducted quantitatively using the Technology Acceptance Model, and analyzed through the Structural Equation Model. The data were collected via questionnaires from 194 members of the local Japanese learning community. The variables analyzed in this research are Perceived Usefulness, Perceived Ease of Use, Attitude Towards Using, and Behavioral Intention. All variables are tested for validity and reliability using SPSS Statistics, and structural equation model analysis using SPSS AMOS. The results showed positive significant correlations between Perceived Usefulness and Attitude Towards Using, Perceived Ease of Use and Attitude Towards Using, and Attitude Towards Using and Behavioral Intention. The result also noted a negative correlation between Perceived Usefulness and Behavioral Intention. Each variable contributes to the acceptance of the gamified mobile learning application with a strong emphasis on Perceived Ease of Use, and a mild emphasis on Perceived Usefulness.

The Citizens' Satisfaction on Service Quality of Mobile Government (Case Study : Wargaku Surabaya Application)

2023-11-13T03:30:02+00:00

This research aims to analyze the citizens’ satisfaction with the service quality of a mobile Government application named WargaKu Surabaya, by using the Mobile Government Service Quality (SQ-mGov) measurement involving four indicators, namely connectivity, interactivity, authenticity, and understandability. The research method used was quantitative using SmartPLS version 0.3 software with primary data sources in the form of questionnaires totaling 100 respondents. This research is interesting because it discusses citizens’ satisfaction with the quality of government services using parameters for measuring the quality of mobile government services. The research results show that the connectivity and understandability influence the citizens’ satisfaction with the service quality of WargaKu Surabaya application. However, the interactivity and authenticity do not affect the citizens’ satisfaction on WargaKu Surabaya application. The practical implications of this research can be used as an input for the government to improve the quality of mobile government services, particularly WargaKu Surabaya application, with the hope that as the service quality mobile Government increases, the citizens’ satisfaction will also improve.

Optimized Support Vector Machine with Particle Swarm Optimization to Improve the Accuracy Amazon Sentiment Analysis Classification

2023-11-13T03:35:05+00:00

Text mining is a valuable technique that empowers users to gain a deeper understanding of existing textual data, ultimately allowing them to make more informed decisions. One important application of text mining is in the field of sentiment analysis, which has gained significant traction among companies aiming to understand how customers perceive their products and services. In response to this growing need, various research efforts have been made to improve the accuracy of sentiment analysis classification models. The purpose of this article is to discuss a specific approach using the Support Vector Machine (SVM) algorithm, which is often used in machine learning for text classification tasks and then combined with the application of Particle Swarm Optimization (PSO), which optimizes the SVM model parameters to achieve the best classification results. This dynamic combination not only improves accuracy but also enhances the model's ability to efficiently handle large amounts of text data to achieve better results. The research findings highlight the effectiveness of this approach. The application of the SVM algorithm with PSO resulted in an outstanding accuracy performance of 94.92%. The substantial increase in accuracy compared to previous studies shows the promising potential of this methodology. This proves that the SVM algorithm model approach with Particle Swarm Optimization provides good performance.