Scientific Journal of Informatics最新文献

{"title":"Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients","authors":"Yusi Yustikasari, H. Mubarok, Rianto Rianto","doi":"10.15294/sji.v9i2.32369","DOIUrl":"https://doi.org/10.15294/sji.v9i2.32369","url":null,"abstract":"Purpose: The implementation of this manual system is considered less accurate in obtaining the results of social assistance recipients. From these problems to overcome this problem, systematic calculations are needed. In processing data, a model is needed that can explain the data with its application, so a machine learning model is made that can help process the data.Methods: This study's classification of non-cash food social assistance receipts uses the K-Nearest Neighbor and Adaptive Boosting algorithms. This study will compare the performance of the two algorithms.Result: The results obtained for Adaptive Boosting are the best classification results with a maximum accuracy of 100% and produce a high AUC value of 1.0. In comparison, the ROC curve for the K-Nearest Neighbor algorithm produces an accuracy of 96% with an AUC value of 0.94.Novelty: ROC curves in the two algorithms are good classification results because the two graphs cross above the diagonal line and produce an AUC value included in the Excellent classification.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67039360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform","authors":"P. Andono, C. A. Sari","doi":"10.15294/sji.v9i2.34173","DOIUrl":"https://doi.org/10.15294/sji.v9i2.34173","url":null,"abstract":"Purpose: Image is an imitation of everything that can be materialized, and digital images are taken using a machine. Although digital image capture uses machines, digital images are not free from interference. Image restoration is needed to restore the quality of the damaged image.Methods: Bi-directional Akamatsu Transform is proven to have an effective performance in reducing blur in images. Meanwhile, Discrete Wavelet Transform has been widely used in digital image processing research. We had been investigated the image restoration method by combining Bi-directional Akamatsu Transform and Discrete Wavelet Transform. Bi-directional Akamatsu Transform applied in Low-Low (LL) sub-band is the Discrete Wavelet Transform decomposition image most similar to the original image before decomposing. In this study, there are still shortcomings, including the determination of the values of N, up_enh, and down_enh, which are still manual. Manually setting the three values makes the Bi-directional Akamatsu Transform method not get the best results. With the use of machine learning methods can get better restoration results. Further testing is also needed for a more diverse and robust blur. The image data has a resolution of 256x256, 512x512, and 1024x1024. The image will be directly converted to a grey-scale image. The converted image will be given an attack model: average blur, gaussian blur, and motion blur. The image that has been attacked will apply two restoration methods: the proposed method and the Bi-direction Akatamatsu Transform. These two restoration images will then be compared using PSNR.Result: The average PSNR value from the restoration of the proposed method is 0.1446 higher than the average PSNR value from the restoration of the Bi-directional Akamatsu Transform method. When we compare it with the average PSNR value of the Akamatsu Transform restoration method, the average PSNR of the proposed method is 0.2084.Value: The combination of DWT and akamatsu transform results produce good PSNR values even though they have gone through the blurring method in image restoration.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48562969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Travel Time Estimation Using Support Vector Regression on Model with 8 Features","authors":"R. Kosasih, Iffatul Mardhiyah","doi":"10.15294/sji.v9i2.37215","DOIUrl":"https://doi.org/10.15294/sji.v9i2.37215","url":null,"abstract":"Purpose: In travelling, we need to predict travel time so that itinerary is as expected. This paper proposes Support Vector Regression (SVR) to build a prediction model. In this case, we will estimate travel time in the Bali area. We propose to use a regression model with 8 features, i.e., time, weather, route, wind speed, day, precipitation, temperature and humidity information.Methods: In this study, we collect real-time data from Global Positioning System (GPS) and weather applications. We divide our data into two types: training dataset consisting of 177 data and testing dataset comprising 51 data. The Support Vector Regression (SVR) method is used in the training stage to build a model representing data. To validate the model, error measurements were carried out by calculating the values of R2, Accuracy, MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and Accuracy.Result: From the research results, the model obtained is the SVR model with parameters γ=0.125, ε=0.1 and C = 1000, which has a value of R2= 0.9860528612283006. Later, we predict travel times on testing data using the SVR model that has been obtained. Based on the result of the research, our model has a 0.8008 MAE (Mean Absolute Error), 1.2817 RMSE (Root Mean Square Error) and 95.3369% Accuracy.Novelty: In this study, we use 8 features to estimate travel time in the Bali area. Furthermore, we will compare the KNN regression method (previous studies) with Support Vector Regression (SVR) (proposed method) on a model with 8 features to estimate travel time.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42989516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Clustering Approach for Mapping Dengue Contingency Plan","authors":"Farida Amila Husna, D. Purwitasari, Bayu Adjie Sidharta, Drigo Alexander Sihombing, A. Fahmi, M. Purnomo","doi":"10.15294/sji.v9i2.36885","DOIUrl":"https://doi.org/10.15294/sji.v9i2.36885","url":null,"abstract":"Purpose: The dengue epidemic has an increasing number of sufferers and spreading areas along with increased mobility and population density. Therefore, it is necessary to control and prevent Dengue Hemorrhagic Fever (DHF) by mapping a DHF contingency plan. However, mapping a dengue contingency plan is not easy because clinical and managerial issues, vector control, preventive measures, and surveillance must be considered. This work introduces a cluster-based dengue contingency planning method by grouping patient cases according to their environment and demographics, then mapping out a plan and selecting the appropriate plan for each area.Methods: We used clustering with silhouette scoring to select features, the best cluster formation, the best clustering method, and cluster severity. Cluster severity is carried out by levelling the attributes of the average value to low, medium, high, and extreme, which are related to the plans each region sets for village type and season type.Result: In five years of data (2016-2020) ±15K cases from Semarang City, Indonesia, feature selection results show that environmental and demography group features have the biggest silhouette score. With these features, it is found that K-Means has a high silhouette score compared to DBSCAN and agglomerative with three optimum numbers of clusters. K-Means also successfully mapped the cluster severity and assigned the cluster to a suitable contingency policy.Novelty: Most of the research on DHF cases is about predicting DHF cases and measuring the risk of DHF occurrence. There are not many studies that discuss the policy recommendations for dengue control.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46267734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Development of Chicken Coop Automatic Remote Visual Monitoring System","authors":"S. Wahjuni, Suryo Hamukti Sanjiwo, W. Wulandari, Auriza Rahmad Akbar","doi":"10.15294/sji.v9i2.34630","DOIUrl":"https://doi.org/10.15294/sji.v9i2.34630","url":null,"abstract":"Purpose: A remote visual monitoring system will be very helpful for chicken farmers to monitor their cages, that usually located away from their houses. This system needs adequate bandwidth in transmitting the video over the internet, which is usually very limited in urban areas. The main goal of this research is to develop an automatic chicken coop remote monitoring system and define the optimum video resolution to be transmitted. Methods: We used an 8 MP Raspberry Pi camera V2 to record the video and send the results to Google Drive by utilizing the GDrive API. Furthermore, a live streaming video from the chicken coop is accessible through a simple HTTP web page utilizing ngrok as a tunneling software so that the live streaming video can be publicly accessed from anywhere using a web browser. Three video resolutions of 640x480, 800x600, 1024x768 with 15 and 30 framerates were used in our experiments. Each scenario has a duration of five minutes and takes 12 times.Result: The experiment results showed, resolutions that provide a stable video recording and streaming are 640x480 and 800x600. The resulting system succeeded in performing live streaming along with the process of data acquisition. Value: The Google Drive infrastructure is used because of its popularity and convenience by people with limited digital literacy such as smallholder chicken farmers. Furthermore, the video produced by this system can be used in supporting research of chicken behavior pattern identification to build a system notification of an emergency situation in the cage.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47213996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination of Cross Stage Partial Network and GhostNet with Spatial Pyramid Pooling on Yolov4 for Detection of Acute Lymphoblastic Leukemia Subtypes in Multi-Cell Blood Microscopic Image","authors":"T. Mustaqim, C. Fatichah, N. Suciati","doi":"10.15294/sji.v9i2.37350","DOIUrl":"https://doi.org/10.15294/sji.v9i2.37350","url":null,"abstract":"Purpose: Acute Lymphoblastic Leukemia (ALL) Detection with microscopic blood images can use a deep learning-based object detection model to localize and classify ALL cell subtypes. Previous studies only performed single cell-based detection objects or binary classification with leukemia and normal classes. Detection of ALL subtypes is crucial to support early diagnosis and treatment. Therefore, an object detection model is needed to detect ALL subtypes in multi-cell blood microscopic images.Methods: This study focuses on detecting the ALL subtype using YOLOV4 with a modified neck using Cross Stage Partial Network (CSPNet) and GhostNet. CSPNet is combined with Spatial Pyramid Pooling (SPP) to become SPPCSP to get various features map before the YOLOv4 final layer. Ghostnet was used to reduce the computation time of the modified YOLOV4 neck.Result: Experimental results show that YOLOv4 SPPCSP outperformed the recall value of 14.6%, the value of mAP@.5 0.8%, and reduced the computation time by 4.7 ms compared to the original YOLOv4.Novelty: The combination of CSPNet and GhostNet for YOLOV4 neck modification can increase the variety of features map and reduce computing time compared to the Original YOLOv4.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41915174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Analysis of Classification Algorithms for Cyberbullying Crime Detection: An Experimental Study of Twitter Social Media in Indonesia","authors":"A. Muzakir, Hadi Syaputra, F. Panjaitan","doi":"10.15294/sji.v9i2.35149","DOIUrl":"https://doi.org/10.15294/sji.v9i2.35149","url":null,"abstract":"Purpose: This research aims to identify content that contains cyberbullying on Twitter. We also conducted a comparative study of several classification algorithms, namely NB, DT, LR, and SVM. The dataset we use comes from Twitter data which is then manually labeled and validated by language experts. This study used 1065 data with a label distribution, namely 638 data with a non-bullying label and 427 with a bullying label.Methods: The weighting process for each word uses the bag of word (BOW) method, which uses three weighting features. The three-word vector weighting features used include unigram, bigram, and trigram. The experiment was conducted with two scenarios, namely testing to find the best accuracy value with the three features. The following scenario looks at the overall comparison of the algorithm's performance against all the features used.Result: The experimental results show that for the measurement of accuracy weighting based on features and algorithms, the SVM classification algorithm outperformed other algorithms with a percentage of 76%. Then for the weighting based on the average recall, the DT classification algorithm outperformed the other algorithms by an average of 76%. Another test for measuring overall performance (F-measure) based on accuracy and precision, the SVM classification algorithm, managed to outperform other algorithms with an F-measure of 82%.Value: Based on several experiments conducted, the SVM classification algorithm can detect words containing cyberbullying on social media.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41359072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Traditional and Software-Defined Networking on Performance of Computer Network","authors":"Isiaka Babatunde Sadiku, W. Ajayi, Wilson Sakpere, Temilola John-Dewole, R. A. Badru","doi":"10.15294/sji.v9i2.31315","DOIUrl":"https://doi.org/10.15294/sji.v9i2.31315","url":null,"abstract":"Purpose: Computer networks and the Internet are changing the way we communicate, learn, work, and even play. Conventional computer networks are not smart enough towards processes that contribute to improving online control transaction of services and demand for unlimited communication services. Hence, computer networking has to go smart.Methods: This paper explores the effect of different computer networking types - traditional computer networking (D0) and Software-Defined Networking (D1). The paper combined traditional computer networking (D0) with Software-Defined Network (D2) running applications (A1, A2, A3, A4 and A5) with the host sending 5 packets (P1, P2, P3, P4 and P5) across the networks emulated using Mininet network emulation to observe various performance parameters on the network.Result: It was observed that Application A1 recorded the highest bandwidth, throughput and latency. The least bandwidth, throughput and latency were observed in A4. The result showed that below 80% of the IPv4 packet size (65,507 bytes) of running application, the higher the bandwidth the higher the throughput. Also, the lower the latency the more statistically similar the jitter experienced. Packet P1 has the highest bandwidth and throughput usage with high latency. The results indicate that the higher the bandwidth and throughput, the higher the latency observed in the packet sent across the network. Traditional computer networking (D1) recorded the highest bandwidth and throughput with the highest jitter. The correlation result showed that the jitter decreases with increasing bandwidth and throughput.Novelty: This study provides information on traditional computer networking and Software-Defined Networking. The result validates studies that observed significant F-value and stability in the SDN application-awareness experiment.  ","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49020249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CO and PM10 Prediction Model based on Air Quality Index Considering Meteorological Factors in DKI Jakarta using LSTM","authors":"Emanuella M C Wattimena, Annisa Annisa, I. S. Sitanggang","doi":"10.15294/sji.v9i2.33791","DOIUrl":"https://doi.org/10.15294/sji.v9i2.33791","url":null,"abstract":"Purpose: This study aimed to make CO and PM10 prediction models in DKI Jakarta using Long Short-Term Memory (LSTM) with and without meteorological variables, consisting of wind speed, solar radiation, air humidity, and air temperature to see how far these variables affect the model.Methods: The method chosen in this study is LSTM recurrent neural network as one of the best algorithms that perform better in predicting time series. The LSTM models in this study were used to compare the performance between modeling using meteorological factors and without meteorological factors.Result: The results show that the use of meteorological predictors in the CO prediction model has no effect on the model used, but the use of meteorological predictors influences the PM10 prediction model. The prediction model with meteorological predictors produces a smaller RMSE and stronger correlation coefficient than modeling without using meteorological predictors.Novelty: In this paper, a comparison between the prediction model of CO and PM10 has been conducted with two scenarios, modeling with meteorological factors and modeling without meteorological factors. After the comparative analysis was done, it was found that the meteorological variables do not affect the CO index in 5 air quality monitoring stations in DKI Jakarta. It can be said that the level of CO pollutants tends to be influenced by factors other than meteorological factors.  ","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49201733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement Of Image Quality Using Convolutional Neural Networks Method","authors":"A. Nugroho, Ipung Permadi, Muhammad Faturrahim","doi":"10.15294/sji.v9i1.30892","DOIUrl":"https://doi.org/10.15294/sji.v9i1.30892","url":null,"abstract":"Abstract. Purpose: This desire for high resolution stems from two main application areas, namely improving pictorial information for human interpretation and assisting automatic machine perception in representing images or videos. Image resolution describes the detail contained in an image, the higher the resolution, the more detail there is. The resolution of a digital image can be classified into various types, namely pixel resolution, spatial resolution, temporal resolution, and radiometric resolution. In this context, we are interested in spatial resolution.Methods: Elements of a digital image consist of a collection of small images called pixels. Spatial resolution refers to the pixel density of an image and is measured in pixels per unit area. A quality digital image is determined by the size of the resolution it has. A low resolution or low-resolution is a drawback of a digital image because the information contained in the image means little compared to a high-resolution image.Result: Therefore, in this study, a digital image processing program was created in the form of Image Super-Resolution with the Convolutional Neural Network method to utilize low-resolution images to produce high-resolution images. With a fairly short training process, namely 6050 datasets with 100 CNN epochs, the average PSNR image is 5% higher.Novelty: Image quality can be improved by changing the parameters in the CNN method so that image quality can be improved.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48357673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}