2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)最新文献

{"title":"Performance Evaluation of Visible Light Communication System Design in Indoor Scenario","authors":"M. I. Nashiruddin, Berlian Nurfadhilah, Brian Pamukti, M. Nugraha","doi":"10.1109/ISRITI54043.2021.9702814","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702814","url":null,"abstract":"The successor of the fifth-generation (5G) cellular technology is the sixth-generation (6G) wireless communication network. It is one of the most anticipated technologies because the 6G network operates at considerably higher frequencies, has far greater capacity, and has much-reduced latency than 5G networks now do. Visible Light Communication (VLC) is a new technology in Optical Wireless Communication (OWC) that has the potential to become the communication medium for a 6G network through the visible light from Light Emitting Diode (LED) that can be implemented for indoor and outdoor communication systems. We have done simulations with the assumption that communication occurs indoors and uses the line of sight channel model. We also consider a high bit rate of up to 1 Gbps to meet future needs. For indoor VLC systems, the LED placement technique is very influential on the coverage of a room, and this research proposes designing VLC systems in a closed indoor room, measuring $5times 5times 3 mathrm{m}^{3}$. The computer simulation obtained the highest received power at −18 dBm with the best coverage link communication 1.88 m2 and receiver propagation distance to the transmitter at 2.397 m.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114933446","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":"Optimization Placement of SVC and TCSC in Power Transmission Network 150 kV SUMBAGUT using Artificial Bee Colony Algorithm","authors":"Y. Siregar, Popy Naomi Agustina, Z. Pane","doi":"10.1109/ISRITI54043.2021.9702832","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702832","url":null,"abstract":"Voltage profile and minimizing power losses are the most challenging part of the power system. Flexible AC transmission system (FACTS) devices support sustaining and advancing voltage profiles and minimizing power losses. But, choosing the suitable FACTS device and its optimal placement in the network is a matter of concern. This paper presents an Artificial Bee Colony (ABC) Algorithm to find the optimal location and sizing parameters of the FACTS device in the transmission network. The FACTS device implemented in this paper is SVC, TCSC, and combination SVC-TCSC. SVC, TCSC, and combination SVC-TCSC are compared to determine the system's optimal placement for improving voltage profile and minimizing power losses. The transmission network 150 kV SUMBAGUT is used for this purpose. The Artificial Bee Colony (ABC) Algorithm shows that the optimal location for SVC is in bus 61 (TELE), which can improve voltage profile 6.14% and minimize power losses 5.89 MW. The optimal location for TCSC is in line 45 (TBING-KTNJG) can improve voltage profile 6.09% and minimize power losses 28.51 MW. Combination of SVC-TCSC can improve voltage profile 6.19% and minimize power losses 33.26 MW","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116307438","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":"Automatic Personality Prediction using Deep Learning Based on Social Media Profile Picture and Posts","authors":"Nicholaus Hendrik Jeremy, George Christian, Muhammad Fadil Kamal, Derwin Suhartono, Kristien Margi Suryaningrum","doi":"10.1109/ISRITI54043.2021.9702873","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702873","url":null,"abstract":"Uploaded contents by social media users are affected by their personality, for example the profile photo they used and the posts they published. In this research, we create an automatic prediction for Twitter users' personalities through their photo profile and their tweets, comparing the result from using either of the feature and both of them. 1290 Twitter users that had taken MBTI test from 16personalities were used as the dataset. Facial feature from profile photo is obtained by using the face detection model that is combined with smile detection such that not only can we obtain the feature of the face, but also their expressions. As for the color, the feature is obtained by their color composition, which is hue, saturation, and value. For tweets, features are obtained by using a pre-trained word vector. Our result shows that image features can predict personality better than text feature and the combination of text and image features. Based on our result, we also found that a single profile picture is capable of reliably predicting personality.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"371 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122774416","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":"Region Proposal Convolutional Neural Network with augmentation to identifying Cassava leaf disease","authors":"Budi dwi Satoto, M. Syarief, B. K. Khotimah","doi":"10.1109/ISRITI54043.2021.9702829","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702829","url":null,"abstract":"This article describes a new idea in recognizing cassava plant disease patterns based on the damage that occurs to the leaves. Classification using an image processing approach is used to solve these problems. The aim is to improve the classification results that have been carried out by previous researchers. There are four classes of observed disease and one class of Normal. Based on the image resources of cassava leaves, sometimes there are background colors that are almost the same or close to the color of the object being sought, so a solution with the right region contour method is needed. The proposed region uses the Convex Hull approach. The results showed that better accuracy values were obtained by using a Convolutional Neural Network with a region. The addition of the proposed region clarifies the area observed in cassava leaves. The proposed Convolutional neural network method can recognize patterns well in the previous architecture and also in the Custom Layer. The addition of the regional proposed method increases the classification accuracy indicator by an average of 99.01%. Evaluation of the effectiveness of this method was confirmed by calculating the average MSE 0.0080, RMSE 0.0935, and MAE 0.0063 with an average training computation time of about 7 minutes 50 seconds.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122536551","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":"Detection pests system for Local Mayas Rice Plant East Kalimantan using Dempster Shafer","authors":"N. Puspitasari, H. Hamdani, H. R. Hatta, Anindita Septiarini, M. Wati, Sumaini","doi":"10.1109/ISRITI54043.2021.9702801","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702801","url":null,"abstract":"Mayas rice is a variety that contributes to staple foods because it has a significant contribution to food consumption and has high genetic diversity. However, the production of Mayas rice plant is lower than other types of rice due to pest attacks and the lack of knowledge of society to identify the types of pests that hit it. Applying the Dempster-Shafer method into an expert system can be a solution for the community to find out the types of the bug found in Mayas rice plants. The data used in this study came from experts with 32 symptoms and 10 types of pests. This study shows the percentage level of possible types of pests found in Mayas rice plant based on the calculation results of the Dempster-Shafer method. The test results on the expert system are 80%, indicating that the expert system is accurate to use in detecting kinds of pests on it.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"121 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131318837","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":"Sentiment Analysis in Indonesian on Jakarta Culinary as A Recommender System","authors":"Boby Siswanto","doi":"10.1109/ISRITI54043.2021.9702772","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702772","url":null,"abstract":"In text mining, there is a technique to determine whether the opinion statement is positive or negative, namely sentiment analysis. The positive opinion indicates that it can be used as a reference for recommendations. According to a survey conducted by YouGov of the diet habit which correlates with culinary, Jakarta is one of the healthiest cities in the world according to a study conducted by YouGov of diet habit. Diet has a strong correlation with culinary. Culinary is related to food which is one of the basic human needs. Sentiment analysis in this study processes the Jakarta culinary dataset from Twitter using the Sastrawi library to compare Twitter's sentiments on Jakarta's cuisine with the results of the YouGov survey.y. The results obtained are that Jakarta culinary's recommendations are worth looking for and visiting based on this study's 53% positive sentiment. This result follows the YouGov survey on Jakarta city's dietary habits, proving that Jakarta is one of the healthiest cities in the world.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"2 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130612782","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":"Capacity Analysis of Non-Orthogonal Multiple Access (NOMA) Network over Rayleigh Fading Channel with Dynamic Power Allocation and Imperfect SIC","authors":"Rummi Sirait, G. Wibisono","doi":"10.1109/ISRITI54043.2021.9702840","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702840","url":null,"abstract":"Non-Orthogonal Multiple Access (NOMA) is a multiple access scheme that can increase channel capacity and spectral efficiency by using superposition coding (SC) on the transmitter and successive interference cancellation (SIC) to detect multiuser on the receiver. This paper investigates the effect of imperfect SIC and dynamic power allocation on NOMA channel capacity. Based on the simulation results, it is shown that the sum capacity of NOMA schemes in the imperfect SIC with dynamic power allocation is better than the sum capacity of orthogonal multiple access (OMA) schemes. The sum capacity of NOMA users with dynamic power allocation is better than fixed power allocation. In imperfect SIC, with a transmit power value of 3 0 dBm and the value of the residual interference level is 0.005, the channel capacity is 9.04 bps/Hz. While the residual interference level is 0.02, the channel capacity is 7.07 bps/Hz.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125395776","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 handwriting of Image Segmentation Using the K-Means Clustering Algorithm with Contrast Stretching and Histogram Equalization","authors":"Muhammad Munsarif, E. Noersasongko, P. Andono, A. Soeleman, Pujiono, Muljono","doi":"10.1109/ISRITI54043.2021.9702800","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702800","url":null,"abstract":"An analysis of handwritten documents is a scientific technique to understand a writer's personality using handwriting scratches and patterns. Graphologists have identified human characters using visual observations. The identification process requires a long time because the observations are conducted comprehensively and examining one by one of the letters or words of the overall handwriting. Therefore, we need a system that automatically identifies the characters of human personalities based on handwriting, requires a shorter period, and provides objectivity. Handwriting image processing to identify human characters has been developed in various fields, such as education, medicine, psychology, and criminology. In image processing, segmentation is an important stage to separate an object from its background. On the other hand, the k-means clustering algorithm is an algorithm to classify some cluster regions based on certain characteristics. Therefore, it can be implemented at the segmentation stage of handwriting images. This research started with data acquisition. The data employed constituted scans of handwriting obtained from graphologists. Then, the image quality improvement employed contrast stretching and histogram equalization. The next step was image segmentation using the k-means clustering algorithm. Segmentation was conducted by varying k values to gain the best segmentation results. The evaluation was conducted by comparing the results of segmentation images with the results of reference images. The reference images were obtained from segmentation images using Otsu's thresholding method. Otsu's method (1979) has been widely applied in various research on segmentation and produced good accuracy. Therefore, this study applied the image segmentation results with Otsu's method as a reference. The results showed that (1) the highest evaluation indicator was in the segmentation results without pre-processing, and (2) the k value was = 2 with the average accuracy of 100%, the average sensitivity of 100%, and the average specificity of 100%.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126579404","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":"Firebase Authentication Cloud Service for RESTful API Security on Employee Presence System","authors":"Luthfan Hadi Pramono, Yohanes Krisna Yana Javista","doi":"10.1109/ISRITI54043.2021.9702776","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702776","url":null,"abstract":"Authentication is essential in identifying users to access or use the system. One application of the Authentication process is the Presence System. The old Presence System at Amigo Company is prone to misuse and data manipulation, so it is necessary to develop a new Presence System based on Smartphones. Data communication between Back-end and Front-end architecture in Presence System using RESTful API. This study aims to implement security on the RESTful API by using JSON Web Token generated by Firebase Authentication Cloud Service. The results of the study indicate that the user cannot manipulate data or use the identity of another user because of Firebase Authentication security and the strict data verification process.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123185836","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":"Implementation of Chatbot on University Website Using RASA Framework","authors":"Liana Fauzia, R. B. Hadiprakoso, Girinoto","doi":"10.1109/ISRITI54043.2021.9702821","DOIUrl":"https://doi.org/10.1109/ISRITI54043.2021.9702821","url":null,"abstract":"Chatbots are increasingly being utilized to help human performance to boost communication ease and provide better and faster services. A chatbot was built to make it easier to give better and more conveniently available information services 24 hours a day, seven days a week. The suggested chatbot design is constructed using the Rasa framework and is based on the Dual Intent and Entity Transformer (DIET). DIET is a multi-tasking transformer architecture that is both advanced and lightweight. The chatbot will be implemented on the “Politeknik Siber dan Sandi Negara” website, focusing on addressing questions about new student admittance. The chatbot is built with Docker and put as a Chat Widget on the website. The dataset utilized to train the algorithm combines past chat data and data from university social media. The built-in chatbot is evaluated using accuracy metrics and F1 scores. Model evaluation metrics and functionality tests are used in the evaluation. Testing with evaluation results in a precision value of 0.99. a recall value of 1.0, and an F1 score of 0.99. While the functioning test percentage is 90.625 %.","PeriodicalId":156265,"journal":{"name":"2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841775","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}