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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 389 Documents
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ENERGY EFFICIENT SLEEP WAKEUP SCHEDULING METHOD FOR P-COVERAGE AND Q-CONNECTIVITY MODEL IN TARGET BASED WIRELESS SENSOR NETWORKS Rosyadi, Fuad Dary; Anggoro, Radityo
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 1, January 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i3.a1088

Abstract

Energy limitations are the problem that gets the most attention in the term of Wireless Sensor Networks (WSN). Sleep wakeup scheduling method is one of the most efficient techniques to increase sensor node operational time on WSN. However, in the target-based WSN environment with p-coverage and q-connectivity models, the use of wake-up scheduling has to consider the constraints on the number of connectivity on the sensor and coverage on the target. Genetic Algorithm is a solution to the problem of sleep-wake scheduling with multi-objective problems. This study proposes a new method of sleep wakeup scheduling based on Genetic Algorithm for energy efficiency in target-based WSN with p-coverage and q-connectivity models. This new method uses the sensor range, connectivity range and energy as an objective function of the fitness function in the Genetic Algorithm. With the presence of energy as a factor of the objective function can increase energy efficiency in target-based WSN with p-coverage and q-connectivity models.
K-MEANS AND XGBOOST FOR CUSTOMER ELECTRICITY ACCOUNT PAYMENT BEHAVIOR ANALYSIS (CASE STUDY: PLN ULP PANAKKUKANG) Nugraha, Raditya Hari; Purwitasari, Diana; Raharjo, Agus Budi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1132

Abstract

Revenue Acceleration from electricity account receivables is one of the energy companies' efforts to maintain cash flow so that they can carry out operational activities and carry out investment activities to develop company assets. Factors that influence electricity bill payment behavior include the location of consumers, the amount of the bill, payment point facilities located around consumers' homes, the use of digital technology as a media of payment, as well as consumer awareness and understanding regarding the time limit for paying electricity bills. Therefore, it is necessary to conduct an analysis so that the company can determine a special strategy for customers who have the potential to be in arrears in electricity bills. To get the characteristic of electricity bill payments, several previous studies have used various classification methods of machine learning such as random forest, nave bayes, SVM, CART, etc. to get the best accuracy. In this research, to increase the accuracy of the model, author using the cluster method with the k-means technique and combining it with the eXtreme Gradient Boosting (XGBOOST) classification method based on data on the characteristics of consumer electricity bill payments. In this study also used hyperparameter adjustment with hillclimbing, random search, and bayesian techniques to increase the accuracy of the model. The model simulation carried out in this thesis gives the result that the combination of the k-means cluster with the XGBoost classification and by adjusting the bayesian technique hyperparameters has a much better model accuracy rate with a value of 89.27% and an Area Under Curve (AUC) value of 0.92 when compared to gradient boosting method with an accuracy rate of only 74.76% and an AUC value of 0.75. Based on the simulation results on ULP Panakkukang customer data, it was found that the subsidy category customer group and customers who often experience power outages have a tendency to be in arrears on electricity bills.
LOAD FORECASTING FOR DAILY LOAD OPERATIONAL PLAN USING LSTM (CASE STUDY: SOUTH SULAWESI SUB SYSTEM) Raharjo, Agus Budi; Wakhid, Muhammad Abdul; Purwitasari, Diana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1138

Abstract

The electrical load required in an electricity sub-system changes every day. Electric power operators must be able to generate and distribute electricity according to consumer needs. In the Sulawesi sub-system, the power plants used are still dominated by fossil fuel generators, so that in their operations, fuel requirements need to be given serious attention. Planning a good daily electricity consumption is needed so that the fuel cost becomes optimal. In the current condition, the load forecasting for the Daily Load Operation Plan (ROH) is still based on Expert Judgment, which is different for each forecaster. With a fairly large error tolerance limit of 4%. We need a load forecasting instrument capable of better error tolerance. Forecasting methods such as ARIMA, SARIMA and ARIMAX have been used for many years. In recent years, several artificial intelligence techniques such as Neural Network and machine learning have been developed for time series analysis. And recently, more accurate forecasting results are shown by Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) compared to traditional forecasting methods. Long Short Term Memory (LSTM) is a model of RNN that uses past data (Long Term) to predict current data (Short Term). Electric load in Sulawesi subsystem used as data training after normalized using min-max normalization. The LSTM model is made with different data input. Forecasting  performance of each model is then evaluated based on the RMSE and MAPE values. Of the several data input models, forecasting models with daily data input show better performance than other scenarios. The MAPE and RMSE values obtained were 2.384% and 33.95, respectively.
APPLICATION OF GRAPH THEORY AND WELCH-POWELL METHOD AT TRAFFIC LIGHT REGULATION Sulistiani, Diah Ayu; Ais, Chalawatul; Fanani, Aris
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1096

Abstract

Traffic lights are state-owned infrastructure facilities that are used to mark vehicles that must stop alternately from various directions. Traffic lights are often found at crossstreets, such as at the traffic lights on Jl. Margerejo with a long duration of red light and short green light. This study aims to obtain a traffic flow graph at the intersection of 4 Jalan Demak-Dupak Surabaya. Optimization of traffic light duration settings is very necessary on this street, because the long duration of the red light while the short duration of the green light causes the accumulation of vehicles at the intersection of Jalan Demak-Dupak Surabaya. In this study, the duration of the new traffic light was obtained, namely on Jl. Demak (North) red light 112.5 seconds and green light 37.5 seconds. For Jl. Dupak red light 84 seconds and green light 28 seconds. Jl. Demak (South) red light for 135 seconds and green light for 45 seconds. And for the axle Jl. Surabaya-Gresik red light for 84 seconds and green light for 28 seconds. The level of effectiveness of the green light is obtained by a value of 21.77% and the level of effectiveness of the red light is 6.62%.
OFFLOADING DECISION SELECTION METHOD FOR ENERGY EFFICIENCY AND LOW LATENCY IN HETEROGENE SIMUATION ENVIRONMENTS Rhosady, Achmadaniar Anindya; Anggoro, Raditya
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1089

Abstract

Mobile Cloud Computing (MCC) is a technology that can overcome the problems of high computing and limited resources owned by mobile devices. However, in practice, MCC has a very long transmission distance from the mobile device, resulting in a large latency. Mobile Edge Computing (MEC) is a technology that exists to overcome this problem. However, new problems arise from the presence of this MEC.One of the problems that arise is the selection of offloading decisions from mobile devices. Several studies consider energy efficiency / large latency or both in determining offloading decisions. However, there are not many studies that consider the movement of mobile devices in determining offloading decisions. Even though the movement of mobile devices is also very influential on latency because tasks need to be migrated to another edge server when a mobile device has moved. Several studies that have addressed this issue apply the solution to smaller, less heterogeneous simulation environments.This study used a new method of offloading decision-making that pays attention to the movement of mobile devices in a heterogeneous environment. This proposed method uses Black Widow Optimization in solving the problem of decision selection when offloading. From the simulation results, the performance of the proposed method is better than the comparison method in terms of the amount of energy consumption and delay latency. 
EVALUATION OF THE SUCCESSFUL APPLICATION OF MOVEAPS AT PT. PIXEL RESEARCH Fryonanda, Harfebi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1097

Abstract

Evaluation of success in IS is an important aspect that must be done to develop information systems. Over time, the paradigm of evaluating the success of information systems continues to change according to the objectives, context, and impact of information technology. The information system evaluation models that can be used include the DeLone and McLean Information Systems Success Model (DM IS Model), Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, and others. Each model has a different purpose, so the evaluator must choose the model that suits his needs. Moveaps application is an application developed by PT. Pixel Research. The Moveaps application is used to support every research project and online research needs. The evaluation of these systems adopts the DM IS Model. The model adopted in this study used all the variables in the DM IS Model and added intrinsic motivation. Thus, the variables become 8 variables consisting of 1) Information quality, 2) System quality, 3) Service quality, 4) Intrinsic motivation, 5) Perceived interaction, 6) Usage, 7) User satisfaction, and 8) Net impact. Previously identified variables, then a model of the relationship between variables was developed. The relationship between variables produces 16 hypotheses. This hypothesis is then compiled into several questions that are arranged in the form of a questionnaire. Questionnaires were distributed to 41 Moveaps Users. The results of 16 hypotheses found five hypotheses that have a positive effect, namely: information quality has a positive effect on service quality, system quality has a positive effect on information quality, system quality has a positive effect on service quality, user satisfaction has a positive effect on net benefits, and usage has a positive effect on benefits. Clean. While 11 hypotheses have no effect.
IMPROVING ROBUSTNESS OF FACE EXPRESSION RECOGNITION USING MULTI-CHANNEL LOCAL BINARY PATTERN AND NEURAL NETWORK Bimantara, Andaru Kharisma; Suciati, Nanik
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1150

Abstract

ABSTRACTFacial Expression Recognition (FER) is a subset of Artificial Intelligence (AI) that relates to human non-verbal communication. The development of Convolutional Neural Network (CNN) based FER is subject to noise, mainly because of the usage of RGB Original Image as training data. Many research explored texture feature methods which noise resistant, such as Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), which mainly worked on grayscale images. Multi-Channel Local Binary Pattern (MCLBP) is derived from LBP which analyzes texture on color images.This research aims to develop FER using MCLBP as a method of hand-crafted texture feature and NN as a classification method. The combination of MCLBP and Neural Network (NN) is expected more robust to noise. First, preprocessing is applied to the facial image for contrasting with Adaptive Gamma Correction Weighted Distribution (AGCWD). Next, the facial image is converted to MCLBP images. Then MCLBP images are converted to vectors as a NN architecture training data with 5 Fully Connected layers. Batch Normalization and Rectified Linear Unit (ReLu) activation are used in every Fully Connected layer. At the last Fully Connected Layer, ReLu activation was replaced with SoftMax activation. This NN uses Stochastic Gradient Descend (SGD) optimizer with a learning rate of 0.005.Performance testing was held by comparing the epoch required to reach F1-score 1 and F1-Score from many scenarios in FER with LBP + NN with 140 × 190 image size, LBP + NN with 70 × 85 image size, and MCLBP + NN with 70 × 85 image size approaches. From all scenarios we have tried, the best method is MCLBP with F1-Score =1 in 22 epochs. The method of hand-crafted texture feature with NN can increase the desirable FER performances.                                                                                       Keywords: Local Binary Pattern, Multi-Channel LBP, Neural Network, Face Expression Recognition, Gamma Correction
KUBERNETES CLUSTER MANAGEMENT FOR CLOUD COMPUTING PLATFORM: A SYSTEMATIC LITERATURE REVIEW Huda, Aris Nurul; Kusumawardani, Sri Suning
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 20, No. 2, July 2022
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v20i2.a1103

Abstract

Kubernetes is designed to automate the deployment, scaling, and operation of containerized applications. With the scalability feature of Kubernetes technology, container automation processes can be implemented according to the number of concurrent users accessing them. Therefore, this research focuses on how Kubernetes as cluster management is implemented on several cloud computing platforms. Standard literature review method employing a manual search for several journals and conference proceedings. From 15 relevant studies, 5 addressed Kubernetes performance and scalability. Seven literature review addressed Kubernetes deployments. Two articles addressed Kubernetes comparison and the rest is addressed Kubernetes in IoT. Regarding the cloud computing cluster management challenges that must be overcome using Kubernetes: it is necessary to ensure that all configuration and management required for Docker containers are successfully set up on on-premises systems before deploying to the cloud or on-premises. Data from Kubernetes deployments can be leveraged to support capacity planning and design Kubernetes-based elastic applications.
IMPLEMENTATION OF ANIMAL FACE’S RECOGNITION BY CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM Permadi, Dimas Fanny Hebrasianto; Abdullah, Moch Zawaruddin
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 1, January 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i1.a1131

Abstract

The purpose of this research is to apply the Convolutional Neural Network (CNN) method in the field of Computer Vision. The CNN algorithm is a combination of Neural Network and Multilayer Perceptron which uses a convolution approach to extract features. The CNN technique was used to identify an animal dataset that has 16,130 images divided into three categories: Cats, Dogs and Wild. This study aims to recognize facial images of animals belonging to the category of Cats, Dogs or Wild Animals which resemble the derivatives of cats or dogs such as Lions, Tigers, Hyenas, Wolves and so on. Comparing to learning rate and epoch, the results are 10e-4 and 60 respectively. Utilizing random images from the datasets, learning rate and epoch may achieve an accuracy of about 97.22% or 116.33 out of 120 images. When using images taken outside of the datasets, the accuracy may be as high as 77.78% or 93.33 out of 120 images.
INFORMATION SYSTEM USING WEB-BASED RAPID APPLICATION DEVELOPMENT METHOD Wirabangsa, Rangga Satria; Ratnasari, Dwi; Wiriasto, Giri Wahyu
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 1, January 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i1.a1169

Abstract

The service of recording and borrowing goods at the Teacher Learning Center agency is currently still being carried out manually. parties who will borrow goods must come to the teacher learning center directly and record borrowing data and item data is still done manually, whereas at this time it has entered the era of the industrial revolution 4.0, of course, everything will have efficiency and very fast access. With this problem, of course, the right solution is to have a system that can borrow and record goods based on a website. The process of creating and designing a website uses the Rapid Application Development model, which is a method that starts with the requirements planning stage, RAD design workshop (work with users, build system) and implementation. Testing on the application uses alpha and beta testing, alpha testing uses blackbox testing while for beta testing uses the mean opinion score (MOS). Alpha testing obtained the website running according to what was expected to be successful while beta testing obtained a measurement result score with 10 respondents, namely 88.60% based on alpha testing and beta testing, it was concluded that this website was able to meet and support the needs of users and agencies.

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