Jurnal Ilmiah Teknik Elektro Komputer dan Informatika最新文献_第8页

Master Plan for Electricity Distribution Networks Based on Micro-Spatial Projection of Energy Demand 基于能源需求微空间预测的配电网总体规划

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2022-01-18 DOI: 10.26555/jiteki.v7i3.22244

A. Senen, Christine Widyastuti, Oktaria Handayani

{"title":"Master Plan for Electricity Distribution Networks Based on Micro-Spatial Projection of Energy Demand","authors":"A. Senen, Christine Widyastuti, Oktaria Handayani","doi":"10.26555/jiteki.v7i3.22244","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22244","url":null,"abstract":"Received November 14, 2021 Revised December 02, 2021 Accepted January 18, 2022 The existing method of the Master plan for electricity distribution networks is sectoral and macro-based, which means it is unable to show load centers in micro-grids. The inaccurate and bias results lead to the failure of determining the capacity of transformers, the total of transformers, and the locations of distribution substations, and thus it will complicate the master planning of the distribution network. Therefore, a micro-spatial-based method in electricity master planning is needed, as it will generate more accurate forecasting, energy projection and estimate the numbers of load centers at each grid based on the geographical structure. The research contribution is to produce a master planning of distribution network that will help in determining transformer capacity, the placement of substations and distribution substations, evaluation, and orientation of electricity distribution system development to a smaller area. The results of the load growth become the basis for determining the capacity and the total of transformers in the area. The methodology developed in this research has analyzed the transformer rating, transformer capacity, total of transformers, and the location of transformer with growing energy demand in the smaller range. The results can be developed into the design planning of distribution network systems with better accuracy.","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133777597","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}

引用次数: 1

Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis 基于机器学习的音乐类型分类与预处理特征分析

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2022-01-18 DOI: 10.26555/jiteki.v7i3.22327

M. Islam, M. Hasan, M. Rahim, A. Hasan, Mohammad Mynuddin, Imran Khandokar, Md Jabbarul Islam

引用次数: 3

Mental Health Helper: Intelligent Mobile Apps in the Pandemic Era 心理健康助手:大流行时代的智能移动应用

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2022-01-10 DOI: 10.26555/jiteki.v7i3.22012

Anggunmeka Luhur Prasasti, Iftita Rahmi, Syarifah Faisa Nurahmani, Ashri Dinimaharawati

引用次数: 0

The Development of Real-Time Mobile Garbage Detection Using Deep Learning 基于深度学习的实时移动垃圾检测的开发

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2022-01-01 DOI: 10.26555/jiteki.v7i3.22295

H. I. K. Fathurrahman, A. Ma’arif, Li-yi Chin

{"title":"The Development of Real-Time Mobile Garbage Detection Using Deep Learning","authors":"H. I. K. Fathurrahman, A. Ma’arif, Li-yi Chin","doi":"10.26555/jiteki.v7i3.22295","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22295","url":null,"abstract":"Received November 24, 2021 Revised December 30, 2021 Accepted January 02, 2022 The problem of garbage in the world is a serious issue that must be solved. Good garbage management is a must for now and in the future. Good garbage management is accompanied by a system of classification and sorting of garbage types. This study aims to create a mobile-based application that can select the type of garbage and enter the garbage data into a database. The database used is a Google SpreadSheet that will accommodate data from the output issued by the garbage detection mobile application. The image data used in this study amounted to 10108 images and was divided into six different garbage classes. This study uses a deep learning platform called densenet121 with an accuracy rate of 99.6% to train the image data. DenseNet121 has been modified and added an optimization based on a genetic algorithm. The genetic algorithm applied in the optimization uses four generations. The model resulting from the training of the two approaches is converted into a model that mobile applications can access. The mobile application based on a deep learning model accommodates the detection data of the type of garbage, the level of detection accuracy, and the GPS location of the garbage. In the final experiment of the mobile application, the delay time in sending data was very fast, which was less than one second (0.86s).","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114460299","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}

引用次数: 4

Design and Prototyping of Electronic Load Controller for Pico Hydropower System 微型水电系统电子负荷控制器的设计与样机

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2021-12-21 DOI: 10.26555/jiteki.v7i3.22271

S. Praptodiyono, H. Maghfiroh, M. Nizam, C. Hermanu, A. Wibowo

{"title":"Design and Prototyping of Electronic Load Controller for Pico Hydropower System","authors":"S. Praptodiyono, H. Maghfiroh, M. Nizam, C. Hermanu, A. Wibowo","doi":"10.26555/jiteki.v7i3.22271","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22271","url":null,"abstract":"Received November 19, 2021 Revised Desember 04, 2021 Accepted Desember 24, 2021 A hydroelectric power plant is an electrical energy generator that utilizes water energy to drive a water turbine coupled to a generator. The main problem in hydroelectric power plants is the frequency and voltage fluctuations in the generator due to fluctuations in consumer loads. The purpose of this research is to make a prototype of the Electronic Load Controller (ELC) system at the Pico Hydropower Plant. The main part of ELC is the frequency sensor and gating system. The first part is made by a Zero Crossing Detector, which detects the generator frequency. The gating system was developed with TRIAC. The method used is the addition of a complement load which is controlled by delaying the TRIAC. Load control is intended to maintain the stability of the electrical energy produced by the generator. The PID algorithm is used in frequency control. The results of the frequency sensor accuracy test are 99.78%, and the precision is 99.99%. The ELC system can adjust the frequency automatically by setting the firing delay on the TRIAC to distribute unused power by consumer loads to complementary loads so that the load used remains stable. The ELC is tested with increasing and decreasing load. The proposed ELC gives a stable frequency at 50Hz. Whereas at the first test, the mean voltage is 183V, and in the second test is 182.17V.","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241409","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}

引用次数: 4

Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters 基于参数优化支持向量回归的主食价格预测模型

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2021-12-20 DOI: 10.26555/jiteki.v7i3.22010

Mungki Astiningrum, V. N. Wijayaningrum, Ika Kusumaning Putri

{"title":"Forecasting Model of Staple Food Prices Using Support Vector Regression with Optimized Parameters","authors":"Mungki Astiningrum, V. N. Wijayaningrum, Ika Kusumaning Putri","doi":"10.26555/jiteki.v7i3.22010","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22010","url":null,"abstract":"Received October 20, 2021 Revised November 28, 2021 Accepted December 21, 2021 The large number of Indonesians who consume rice as their primary food makes rice price a benchmark for determining the other staple food prices. The instability of rice prices due to climate change or other uncontrollable factors makes it difficult for Indonesians to estimate the rice prices, especially for the poor. This study proposes the usage of the Improved Crow Search Algorithm (ICSA) to optimize the Support Vector Regression (SVR) parameter in building a regression model to predict the price of staple foods. The forecasting process is carried out based on time series data of 11 staples for four years. The proposed ICSA optimizes the six parameters used in the SVR to form a regression model, consisting of lambda, epsilon, sigma, learning rate, soft margin constant, and the number of iterations. Algorithm performance is measured using MAPE and NRMSE by comparing the actual price of staple foods and forecasting results to get the error rate. With this parameter optimization mechanism, the forecasting results given are good enough with a small error value, in the form of MAPE of 17.081 and NRMSE of 1.594. A MAPE value between 10 and 20 indicates that the forecasting result is acceptable, while an NRMSE value of less than 10 indicates that the forecasting accuracy is excellent. The improvised technique on Crow Search Algorithm is proven to improve the performance of Support Vector Regression in forecasting the price of staple foods.","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717882","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}

引用次数: 0

Comparing Machine Learning and Human Judge in SATU Indonesia Awarding Processes 机器学习与人类裁判在SATU印尼评奖过程中的比较

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2021-12-20 DOI: 10.26555/jiteki.v7i3.22201

Onno W. Purbo

{"title":"Comparing Machine Learning and Human Judge in SATU Indonesia Awarding Processes","authors":"Onno W. Purbo","doi":"10.26555/jiteki.v7i3.22201","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22201","url":null,"abstract":"Received November 09, 2021 Revised November 25, 2021 Accepted December 23, 2021 For more than ten years, SATU Indonesia Awards, with PT. Astra International Tbk's support is given to inspiring young Indonesians. Every year, more than 10,000 nominations must be short-listed to 90 nominations within one week with five (5) assessment parameters. The research contributions are (1) creating a machine learning mechanism for the awarding process from ten years of the SATU Indonesia Awards nomination archive, (2) creating two (2) models of training data for the five (5) assessed parameters, namely motivation, obstacle, outcome, outreach, and sustainability, and (3) compare machine learning prediction with 2021 judge's assessment. TEMPO Data and Analysis Center (PDAT) extracts the corpus training data from ten years' SATU Indonesia Awards data in six months. The corpus training data contains nomination texts with Judges' scores on motivation, obstacle, outcome, outreach, and sustainability. Two (2) corpus training data and two models were generated with, namely, (1) the average Judges' parameter value per instance and (2) the Judges' smallest value and stored in two (2) corpus of 1220 instances each. The classification model was generated by Random Forest, which has the slightest error among the classification algorithms tested. The first model aims to predict the nomination assessment parameters. The second model is to detect the outlier in the incoming nominees for extraordinary nominees. The machine learning predictions were compared and found to be similar to the 2021 judge's assessment in the awarding processes at SATU Indonesia Awards. The average Judges' pre-final 2021 nominees' scores are compared to the Random Forest's predictions and found to be reasonably similar, with a small RMSE error around 1.1 to 1.6 for all assessment parameters. The smallest RMSE was obtained in the Sustainability parameter. The Obstacle parameter was found to have the largest RMSE.","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125617016","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}

引用次数: 0

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model 基于Feyn Qlattice机器学习模型的卒中特征联动检测

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2021-12-20 DOI: 10.26555/jiteki.v7i3.22237

P. Purwono, A. Ma’arif, Iis Setiawan Mangku Negara, Wahyu Rahmaniar, Jihad Rahmawan

{"title":"Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model","authors":"P. Purwono, A. Ma’arif, Iis Setiawan Mangku Negara, Wahyu Rahmaniar, Jihad Rahmawan","doi":"10.26555/jiteki.v7i3.22237","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22237","url":null,"abstract":"Received November 13, 2021 Revised December 14, 2021 Accepted December 21, 2021 Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121497214","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}

引用次数: 9

Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization 基于Naïve贝叶斯方法和粒子群优化的在家办公策略情感分析

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2021-12-20 DOI: 10.26555/jiteki.v7i3.22080

Rista Azizah Arilya, Yufis Azhar, Didih Rizki Chandranegara

{"title":"Sentiment Analysis on Work from Home Policy Using Naïve Bayes Method and Particle Swarm Optimization","authors":"Rista Azizah Arilya, Yufis Azhar, Didih Rizki Chandranegara","doi":"10.26555/jiteki.v7i3.22080","DOIUrl":"https://doi.org/10.26555/jiteki.v7i3.22080","url":null,"abstract":"Received October 28, 2021 Revised November 14, 2021 Accepted December 10, 2021 At the beginning of 2020, the world was shocked by the coronavirus, which spread rapidly in various countries, one of which was Indonesia. So that the government implemented the Work from Home policy to suppress the spread of Covid-19. This has resulted in many people writing their opinions on the Twitter social media platform and reaping many pros and cons of the community from all aspects. The data source used in this study came from tweets with keywords related to work from home. Several previous studies in this field have not implemented feature selection for sentiment analysis, although the method used is not optimal. So that the contribution in this study is to classify public opinion into positive and negative using sentiment analysis and implement PSO for feature selection and Naïve Bayes for classifiers in building sentiment analysis models. The results showed that the best accuracy was 81% in the classification using Naive Bayes and 86% in the classification using naive Bayes based on PSO through a comparison of 90% training data and 10% test data. With the addition of an accuracy of 5%, it can be concluded that the use of the Particle Swarm Optimization algorithm as a feature selection can help the classification process so that the results obtained are more effective than before.","PeriodicalId":244902,"journal":{"name":"Jurnal Ilmiah Teknik Elektro Komputer dan Informatika","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124211246","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}

引用次数: 2

Secure Key Exchange Against Man-in-the-Middle Attack: Modified Diffie-Hellman Protocol 针对中间人攻击的安全密钥交换:改进的Diffie-Hellman协议

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Pub Date : 2021-12-12 DOI: 10.26555/jiteki.v7i3.22210

Mostefa Kara, A. Laouid, M. AlShaikh, A. Bounceur, M. Hammoudeh

引用次数: 6