JURNAL INFOTEL

Performance comparison of cache replacement algorithms onvarious internet traffic

2023-03-24T03:58:32+00:00

Internet users tend to skip and look for alternative websites if they have slow response times. For cloud network managers, implementing a caching strategy on the edge network can help lighten the workload of databases and application servers. The caching strategy is carried out by storing frequently accessed data objects in cache memory. Through this strategy, the speed of access to the same data becomes faster. Cache replacement is the main mechanism of the caching strategy. There are seven cache replacement algorithms with good performance that can be used, namely LRU, LFU, LFUDA, GDS, GDSF, SIZE, and FIFO. The algorithm is developed uniquely according to the internet traffic patterns encountered. Therefore, a particular cache replacement algorithm cannot be superior to other algorithms. This paper presents a performance comparison simulation of the seven cache replacement algorithms on various internet traffic extracted from the public IRcache dataset. The results of this study indicate that the hit ratio performance is strongly influenced by cache size, cacheable and unique requests. The smaller the unique request that occurs, the greater the hit ratio performance obtained. The LRU algorithm shows an excellent hit ratio performance to perform cache replacement work under normal internet conditions. However, when the access impulse phenomenon occurs, the GDSF algorithm is superior in obtaining hit ratios with limited cache memory capacity. The simulation results show that GDSF reaches a 50.75% hit ratio while LRU is only 49.17% when access anomalies occur.

Qur’an tilawatil examination system

2023-03-24T04:00:44+00:00

A group Decision Support System (GDSS) is used when a decision system has many stakeholders providing recommendations in a system. One of them is the Tilawatil of the Qur'an for students in the religious field test. The assessment consists of several raters. The purpose of this study is to apply the SMART (Simple Multi-Attribute Rating Technique) and Borda methods in calculating the results of the Tilawatil Qur'an test based on a decision support system. The SMART method is used in assessing the results of the Tilawatil Qur'an test and the Borda method is used in optimizing the overall results of the assessors on the SMART method. The results of the study were based on the SMART value accuracy test manually with the system yielding 95%. After that, the results of the optimization test of the Borda method were calculated by calculating the average value of the SMART method with optimal ranking results.

The combination of color-texture features and machine learning for detecting Dayak beads

2023-03-24T04:02:26+00:00

Dayak is one of the tribes in East Kalimantan, Indonesia, which has a lot of cultural wealth. Beads craft is one of the Dayak traditional cultures made using various materials with distinctive motifs. The Dayak beads have many different motifs and color combinations. Hence not everyone can distinguish between the bead motif of Dayak and non-Dayak easily. This study aims to develop a bead detection method to differentiate between the bead types of Dayak and non-Dayak. The main processes required include preprocessing, feature extraction, and classification. The features were extracted based on color and texture. Experiments were carried out using several machine learning approaches. The highest results were achieved using the combination of color and texture features with the implementation of K-Nearest Neighbor (KNN) methods as indicated by the parameters precision, recall, and accuracy achieved of 92%, 92%, and 92.2% using Cross-Validation with a K-Fold value of 10.

Application of the k-means clustering method and simple linear regression to new student admissions as a promotion method

2023-03-24T04:04:07+00:00

At private label universities in Indonesia, new students are still the main thing in terms of achieving university operational income. This study intends to group the data of ITTelkom Surabaya students by utilizing the data mining process using the k-means clustering method, then the results of the clustering are forecasted using simple linear regression to be able to predict the achievement of new students as the effect variable and year as the causative variable. The results of this study consist of 5 variables, namely student province, student study program, income of student parents, student parent work and student ethnicity, each of which consists of 4 clusters, then each cluster is predicted for achievement 3 the coming year 2022,2023,2024. It can be concluded that the highest combination of student/parent student profiles was obtained from East Java province, information systems study program, parents' income of 5-10 million per month, the occupation of other parents and the ethnicity of students from Java. The highest forecasting results are found in the income variable of students' parents in cluster 3 with predictions of 1292 students in 2024. It is hoped that with clustering and forecasting based on this research, ITTelkom Surabaya can make the right decision as a basis for decision making to determine strategy in promoting the campus.

Prediction of flood events in the city of Bandar Lampung using the artificial neural network

2023-03-24T04:05:23+00:00

The city of Bandar Lampung is currently experiencing seasonal flooding which occurs almost every year, resulting in significant losses. Floods recorded by BNPB in the last 10 years there were 16 incidents of flooding in the Bandar Lampung area. More than 14,000 people suffered, more than 500 people had to be evacuated, more than 900 houses were damaged, and 4 public facilities were damaged. To study the pattern of flood events in the past, the Artificial Neural Network Backpropagation learning method will be used which will utilize its non-linear variable learning abilities. The configuration settings for the Artificial Neural Network were carried out experimentally without any basis for assigning values, especially for the parameters of the number of hidden layers, number of neurons, and epochs used in training and variable testing. The results obtained from this study are the results of training and testing of datasets that have been carried out by ANN backpropagation are able to properly study patterns of flood events and also non-flood events in the dataset, this is evidenced by the results of high model configuration accuracy and also the results of predictive tables that able to describe actual conditions, setting the configuration model experimentally is able to produce an accuracy value of 90-100%, an average training correlation value of 0.96 and an average test correlation value of 0.89, and an average error value of 0.0089 out of 20 model configuration, and the flood prediction table are made based on the 1 best configuration with a training and testing accuracy rate of 100% with an error value of 0.00134, namely configuration model 20, the prediction table uses an average air temperature of 27˚C with 80% humidity. The prediction table is able to produce excellent flood potential results which are able to represent flood events as well as non-flood events based on the results of the dataset learning.

Assessment decisions of independent learning activities using SMART–FCM method

2023-03-24T04:06:31+00:00

Sekolah Tinggi Ilmu Komputer (STIKOM) PGRI Banyuwangi implemented the Independent Learning - Independent Campus (MBKM) activity for two semesters. The results of student assessments for MBKM activities for one semester are influenced by the results of daily and weekly logbook monitoring, monitoring and evaluation assessments, and assessments of supervisors, examiners, and work partners. Assessments that are less objective cause many students to get good grades even though the implementation of MBKM activities is not well. The Simple Multi-Attribute Rating Technique (SMART) method is used to produce student eligibility group data and a more objective assessment. The results of the SMART calculations are combined with the Fuzzy C-Means (FCM) algorithm so that the results of grouping student data are more appropriate based on the similarities and characteristics of the members. Silhouette Coefficient is used to compare the grouping results. The results obtained that the use of SMART-FCM is better than the SMART results because it has a Silhouette Coefficient value close to 1 of 0.31187.

Analysis of Lampung Provincial Social Service website using PIECES framework

2023-03-23T13:06:21+00:00

The official website of the Social Office Lampung Provincial contains news content, routine activities, and information on public services. The website has been changed from an internal point of view but does not yet know how satisfaction is based on user perceptions. As a result, users no longer want to visit the Social Office website, which is one of the problems, so it is necessary to build a measurement model for the classification of website user problems using the Framework Pieces. Previously, the reliability and validity test of the questionnaire was taken from 338 samples of respondents, then entered the data analysis stage by assessing user characteristics based on the Likert scale and class intervals. Framework Pieces succeed to classify the problems of 338 samples in the range of good and poor. For the Performance domain with a score of 3,62 (Good), the Informations or Data domain with a score of 2,57 (Poor), Economics domain with a score of 3,35 (Moderate), Control or Security domain with a score of 3,61 (Good), Efficiency domain with a score of 3,36 (Moderate), finally for Service domain with a score of 2,60 (Poor).

Automatic temperature detector to mitigate the spread of COVID-19

2023-03-24T00:20:08+00:00

The COVID-19 causes wide impact in business operation. The enterprise must mitigate the risk of COVID-19 spread in its environment. The monitoring of body temperature for employees can be applied as a method to prevent COVID-19 spread. However, the monitoring system must consider several factors such as contactless system, accountable, and simple. The integration between IR temperature sensor and attendance system based on ESP32 is able to provide those need. The use of proximity, IR, and RFID sensor is affordable to detect body temperature properly within 10 cm. The proposed system provides notification if user gets fever or suspect of COVID-19 by detecting the body temperature. The accuracy of sensor is adequate. It is based on the comparison testing between proposed system with body thermometer where the testing is performed 30 times for each condition. In order to deduce the comparison result, this study uses analysis of variance method. The analysis produces F-critical (4,006) greater than F-value (0,022) where it means that the proposed system and body thermometer have similar testing result. It is shown good accuracy for the proposed system.

Identifying customer preferences on two competitive startupproducts: An analysis of sentiment expressions and textmining from Twitter data

2023-03-24T03:57:20+00:00

Startups have great potential to grow and scale up their business quickly; moreover, they have an essential role in the growth of the country and the global economy. However, with the high risk of failure, startup success needs to be supported and concerned. The success of startups depends on market needs and expectations, which are currently highly uncertain, dynamic, and chaotic. Thus, it is necessary to identify and monitor customer preferences for startup products/services. This research identifies the customer preferences of two competitive food delivery startups that have been successful, namely Go Food and Grab Food. With increasing customer opinions on social media, Twitter data can be used to explore customer needs and preferences. However, social media data like Twitter tend to be unstructured, informal, and noisy, so data mining mechanisms are needed. Using sentiment analysis and text mining methods, this study explores and compares customer preferences for successful startup products, which has yet to be done in previous studies. The sentiment analysis results show the dominance of positive customer opinions and expressions of the products/services offered. Furthermore, customer product aspects reviewed positively and negatively by customers were analyzed more deeply using text mining to find the strength and weaknesses of these two businesses. The method and analysis of this paper help monitor customer opinions in real-time, both related to their satisfaction and complaints. Finally, the research results have been validated by comparing sentiment analysis classifications using machine learning and manual analysis by experts, which show an accuracy of 85% and 86% in Go Food and Grab Food reviews.

Room cleaning robot movement using A* algorithm and imperfect maze

2023-03-24T03:53:02+00:00

Cleanliness is a mandatory requirement to help prevent virus spread. The cleaning process can be done automatically by humans or robotic devices. If a robot does this process, it is a must that the robot is able to explore the room autonomously. The robot movement in room tracking should reach all points without obstructions and return to its initial position. This study simulated the movement of a room explorer robot using the imperfect maze method, as well as searching a room that has not been explored using the A* algorithm. The A* algorithm was also used to find the shortest path to reach the initial place of the robot when the room exploration was completed. The results of the simulation showed that the imperfect maze could be used to explore the room well, and A* algorithm is quite optimal to be used for searching both the unexplored room and the path to return to its initial position

Classification of tea plantation using orthomosaics stitching maps from aerial images based on CNN

2023-03-25T09:13:40+00:00

In Indonesia, Tea is an important economic crop that is widely grown, and in many countries, accurate mapping of tea plantations is essential for the operation, management, and monitoring of the growth and development of the tea industry. We propose a classification of tea plantations using orthomosaics from aerial images based on the Convolutional Neural Network (CNN) which identifies the condition of the tea plantations with the parameters observed, namely the condition of the tea leaves, estimated yields achieved, and monitoring of treeless areas caused by tree death. In this study, we took a sample of 20 hectares. We classify images based on maps generated by drones in previous studies. Image segmentation is performed to maintain image objects, while an enhanced CNN model is used to extract deep image features. To get complete results, this study uses UAV (Unmanned Aerial Vehicle) imagery as the basis for the map, which is then combined or stacked into one image. The results of the images that are used as maps undergo image classification, where the information contained in the map is mapped and divided according to its type. The area of the tea plantations sampled is 20 ha, and the threshold for the image captured by the UAV is 5% of the total area captured, which is around 1 ha. If the image created by the UAV has an error of more than 5%, then the image does not meet the classification requirements. We determine this margin of error based on the performance of the drone camera capture when capturing Fig. 2, and the resolution used is 4096 x 2160 for each image captured by the drone. We conclude that the proposed method for mapping tea plantations using ultra-high resolution remote sensing imagery is effective and has great potential for mapping tea plantations in areas such as the development of drone aerial photography methods for tea plantations based on image classification for forecasting. tea plantations Image stitching can be used to improve the monitoring of tea plantations and predict harvest time using a classification process. The tea garden map has 5 types of information categorized by harvest time, medium leaf tea, milled tea, tea, and old tea. The success of image recognition shows the error matrix data by testing 123 random points spread over the map, of which 113 random points were identified with an average accuracy of 91.87%, this value is of course very good and exceeds the specified threshold of 75%. When using this method, an error occurs that the colors of similar pixels cannot be distinguished, resulting in an incorrect detection. In addition, the image stitching method using the orthomosaics method has succeeded in performing image stitching and can be well applied to classification using the CNN approach.

Energy consumption analysis of DSR reactive routing protocol on mobile ad-hoc network

2023-03-24T22:25:42+00:00

Mobile ad-hoc network is a connection between mobile devices that uses wireless media. Mobile devices on the network hereinafter referred to a nodes. This network does not have an administrative center so each node on the network in addition to functioning as a sender and receiver of data information also functions as a router that will look for route information from the sender to the receiver. The topology of an ad-hoc network is always changing because the nodes move dynamically. The topology changes resulted in the repetition of route information searches. The process of finding route information requires a routing protocol. The routing protocol-enabled nodes must maintain the energy usage in the route-finding mechanism. Choosing the right routing protocol can be a solution to make energy use by nodes more efficient, especially in ad-hoc networks. In this study, a routing protocol in the reactive category is used, namely DSR (Dynamic Source Routing). This study aims to determine the performance of energy consumption, remaining energy, and PDR with scenarios of increasing node movement speed and network area. Based on the research results, it is known that the DSR routing protocol can handle changes in the speed of node movement and network area related to energy consumption and remaining energy. This is evidenced by the results of research showing that with faster node movements and wider areas, less energy is required. Meanwhile, regarding the success of packet delivery, the DSR routing protocol cannot handle changes in the speed of node movement and network area. This is evidenced by the results of the packet delivery ratio measurement which shows that with faster node movements and wider areas, many packets are not successfully received.

Can PhET simulate basic electronics circuits for undergraduate students?

2023-03-25T09:17:14+00:00

PhET is one of the most powerful and impressive simulator innovations, widely used in the STEM-based learning process. Based on literature reviews, students are allowed to independently practice their skills and understanding of the material concept using this tool. PheT involves students in process competencies comprehensively and also provides a highly interactive virtual environment for STEM materials, including basic electronics, a sub-category of physics. This tool can also be easily accessed online at https://phet.colorado.edu/ or offline with a note that the user should download and install the application on a PC. An interesting question regarding this education tool is, "can PhET support basic electronics learning in Higher Education (HE)?" Numerous preliminary studies have not answered this question, which is associated with the technical aspect of the tool, because they only focused on the pedagogical aspect. Therefore, this research aims to fill this gap by exploring the capability of PhET in simulating basic electronic circuits that were commonly studied by students in HE, including Kirchoff Current Law (Kirchoof I), Kirchoff Voltage Law (Kirchoff II), Voltage Divider, Series/Parallel Resistors, Wheatstone Bridge, and Star – Delta Resistors. These circuits are simulated in two PhET products, namely, online (1.2.7) and offline (3.20) versions, with numerous setups used to compare their performances to the theoretical calculations. Finally, the answers were obtained clearly from the experimental results in the simulation environment.

The effect of power spectral density on the electroencephalography of autistic children based on the welch periodogram method

2023-03-25T09:20:59+00:00

Autism spectrum disorder (ASD) is a serious mental disorder affecting social behavior. Some children also face intellectual delay. In people with ASD, the signals detected have abnormalities compared to normal people. This can be a reference in diagnosing the disorder with electroencephalography (EEG). This study will analyze the effect of Power spectral density (PSD) on the EEG of autistic children and also compare it with the PSD value on the EEG of normal children using the Welch Periodogram method approach. In the preprocessing stage, the Independent Component Analysis (ICA) method will be applied to remove artifacts, and a Finite Impulse Response (FIR) filter to reduce noise in the EEG signal. The study results indicate differences in the PSD values obtained in the autistic and normal EEG signals. The PSD value obtained in the autistic EEG signal is higher than the normal EEG signal in all frequency sub-bands. From the study results, the highest PSD value obtained by the autistic EEG signal is in the delta sub-band, which is 54.06 dB/Hz, while the normal EEG signal is only 33.14 dB/Hz at the same frequency sub-band. And in the Alpha and Beta sub-bands, the normal EEG signal increases the PSD value, while in the autistic EEG signal, the PSD value decreases in the Alpha and Beta sub-bands. In addition, FIR and ICA methods can also reduce noise and artifacts contained in autistic and normal EEG signals.

Back Matter

2023-03-01T03:14:46+00:00

Back Matter Februari 2023