JURNAL INFOTEL 2021-06-04T12:55:40+00:00 Danny Kurnianto Open Journal Systems <h2>About Jurnal INFOTEL</h2> <table border="0"> <tbody> <tr> <td><img src="" alt="telecommunication journal" width="180" height="250"></td> <td align="justify" valign="top"> <div style="background-color: #ebfeec; border: 1px solid #bae481; border-radius: 5px; text-align: justify; padding: 10px; font-family: sans-serif; font-size: 14px; margin-left: 10px;">Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of <strong>informatics, telecommunication, and electronics</strong>. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August and November. <strong>Starting in 2018, Jurnal INFOTEL uses English as the primary language..</strong></div> </td> </tr> </tbody> </table> <h4><strong><span style="text-decoration: underline; color: #ff0000;">Call for Editors and Reviewers</span></strong></h4> <div style="text-align: justify;"> <p>We invite researchers, scientists, and practitioners to become editors or reviewers in Jurnal INFOTEL. If you are interested, please send us information about yourself, such as your full name, education and degree, affiliation, Scopus ID, orcid ID or other researcher ID, and the area of expertise. Send your biodata via the link below.</p> <p><a href=""><strong>Register for Editor and Reviewer</strong></a><br>Thank you.</p> <h4><strong>Important For Authors</strong></h4> <p>Reminder for all the authors, you are expected to submit papers that:<br> 1. are original and have not been submitted to any other publication.<br> 2. have at least 15 references with 80% of scientific Journals.<br> 3. use references published on the last 5 years.<br> 4. structured using IMRaD format.<br> 5. use template specified by Jurnal INFOTEL.<br> 6. use reference manager e.g. Mendeley or others when managing the references.<br>Thank you</p> <p>&nbsp;</p> </div> Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features 2021-06-04T11:31:54+00:00 Heriyanto Heriyanto Tenia Wahyuningrum Gita Fadila Fitriana <p>This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames that have the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% compared to other frames.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control 2021-06-04T11:32:50+00:00 Maya Ameliasari Aji Gautama Putrada Rizka Reza Pahlevi <p>Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, there are several parameters that affect the performance of the SVM model and need to be evaluated. This study aims to evaluate the parameters in building an SVM model for hand gesture detection in smart lighting control. In this study, 8 gestures were defined to turn on and off 4 different lights, then the data were collected through a smartwatch that has an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The results from the first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71% respectively. Furthermore, the second set of gestures has an accuracy that is lower than the first set of gestures, which is 64%. Results show that the lower the number of features the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods and a distinctive set of gesture selection is required for a model with good performance.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture 2021-06-04T12:55:40+00:00 Nor Kumalasari Caecar Pratiwi Yunendah Nur Fu'adah Edwar Edwar <p>This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## Classification Based on Configuration Objects by Using Procrustes Analysis 2021-06-04T11:34:48+00:00 Ridho Ananda Agi Prasetiadi <p>Classification is one of the data mining topics that will predict an object to go into a certain group. The prediction process can be performed by using similarity measures, classification trees, or regression. On the other hand, Procrustes that refers to a technique of matching two configurations has been implemented for outlier detection. Based on the result, Procrustes has a potential to tackle the misclassification problem when the outliers are assumed as the misclassified object. Therefore, Procrustes classification algorithm (PrCA) and Procrustes nearest neighbor classification algorithm (PNNCA) were proposed in this paper. The results of those algorithms had been compared to the classical classification algorithms namely k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), AdaBoost (AB), Random Forest (RF), Logistic Regression (LR), and Ridge Regression (RR). The data used were iris, cancer, liver, seeds, and wine dataset. The minimum and maximum accuracy values obtained by the PrCA algorithm were 0.610 and 0.925, while the PNNCA were 0.610 and 0.963. PrCA was generally better than k-NN, SVM, and AB. Meanwhile, PNNCA was generally better than k-NN, SVM, AB, and RF. Based on the results, PrCA and PNNCA certainly deserve to be proposed as a new approach in classification process.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## A New Method of Artificial to Reach the Optimization Problems without the Violated Constraints 2021-06-04T11:35:28+00:00 Jangkung Raharjo Hermagasantos Zein Adi Soeprijanto Kharisma Bani Adam <p>There are some problems in optimization that cannot be derived mathematically. Various methods have been developed to solve the optimization problem with various functional forms, whether differentiated or not, to overcome the problem, which are known as artificial methods such as artificial neural networks, particle swarm optimization, and genetic algorithms. In the literature, it is said that there is an artificial method that frequently falls to the minimum local solution. The local minimum results are proof that the artificial method is not accurate. This paper proposes the Large to Small Area Technique for power system optimization,&nbsp; which works based on reducing feasible areas. This method can work accurately, which that never violates all constraints in reaching the optimal point. However, to conclude that this method is superior to others, logical arguments and tests with mathematical simulations are needed. This proposed method has been tested with 24 target points using ten functions consisting of a quadratic function and a first-order function. The results showed that this method has an average accuracy of 99.97% and an average computation time of 62 seconds. The proposed technique can be an alternative in solving the economic dispatch problem in the power system.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## Requirements Engineering of Village Innovation Application Using Goal-Oriented Requirements Engineering (GORE) 2021-06-04T11:35:58+00:00 Condro Kartiko Ariq Cahya Wardhana Wahyu Andi Saputra <p>The delay in the absorption of village funds from the central government to the village government is due to the village government's difficulty in preparing village development innovation programs. The innovation tradition will grow if the cycle of transformation of knowledge and acceptable practices from one village to another, especially villages with similar conditions and problems, can run smoothly. In order for the process of exchanging knowledge and experiences between villages to run smoothly, it is necessary to codify best practices in a structured, documented, and disseminated manner. This research aims to design an application that functions as a medium for sharing knowledge about the use of village funds through government innovation narratives. The application is expected to become a reference for villages to carry out innovative practices by conducting replication studies and replicating acceptable practices that other villages have done. Therefore, it is necessary to have a system requirements elicitation method that can explore the village's requirements in sharing knowledge so that the resulting system is of high quality and by the objectives of being developed. There are several Goal-Oriented Requirements Engineering (GORE) methods used, such as Knowledge Acquisition in Automated Specification (KAOS) and requirements engineering based on business processes. In this research, the elicitation activity of a village innovation system using the KAOS method was demonstrated. Then the results were stated in the Goal Tree Model (GTM). Model building begins with discussions with the manager of the village innovation program to produce goals. The goals are then broken down into several sub-goals using the KAOS method. The KAOS method is used for the requirements elicitation process resulting in functional and non-functional requirements. This research is the elicitation of the requirement for the village innovation system so that it can demonstrate the initial steps in determining the requirements of the village innovation system before carrying out the design process and the system creation process. The results of this requirement elicitation can be used further in the software engineering process to produce quality and appropriate village innovation applications.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## Design and Implementation of Smart Parking System Using Location Based Service (LBS) and Gamification Based On Internet Of Things (IoT) 2021-06-04T11:47:06+00:00 Alam Nasyrah Muharram Nasution Rendy Munadi Sussi Sussi <p>Information on the number of available parking slot capacity, information on trip routes to the location of the destination parking area and motivation in choosing a parking area location are parameters that can help two-wheeled vehicle users in choosing the right parking area location. The three parameters that determine the accuracy of selecting a parking area location are implemented in an Internet of Things (IoT) based smart parking system which can provide information about the maximum number of slot capacities and the number of available slot capacities at the parking area, as well as Two-wheeled riders are given information about which route to take to the destination parking area by utilizing the Location Based Service (LBS). These two features are then supported by the application of the gamification method that can motivate two-wheeled riders to choose the right parking area location. The smart parking system tested with considered Quality of Service (QoS) parameter and black box testing. The results of testing the smart parking system produce QoS performance on the Line of Sight (LOS) test, with an average delay is 71.66 ms, average jitter is 107.59 ms and throughput is 23 kbps. Meanwhile, in the non-LOS test, the average delay is 132.88 ms, the average jitter is 200.84 ms and the throughput is 12 kbps. Overall system performance obtained the percentage of reliability is 99.65 % and availability is 99.65 %. In black box testing, LBS and gamification methods can be implemented according to application requirements specifications.</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement## Object Position Estimation based on Dual Sight Perspective Configuration 2021-06-04T11:36:49+00:00 Dion Setiawan Maulana Ifdhil Hanafi Indra Riyanto Akhmad Musafa <p>Development of the coordination system requires dataset, because the dataset could provide information around the system that can be used by the coordination system to make decisions. The capability to process and display data related positions of objects around the robots is necessary. This paper provides a method to predict an object’s position. This method is based on the idea of the Indoor Positioning System (IPS) and object position estimation with multi-camera system (i.e. stereo vision). This method needs two input data to estimate the ball position: input image, and the robot’s relative position. The approach adopts simple and easy calculation technics: trigonometry, angle rotations, and linear function. This method was tested on a ROS and Gazebo simulation platform. The experimental result shows that this configuration could estimate the object’s position with Mean Squared Error was 0.383 meters and R squared distance calibration value is 0.9932, which implies that this system worked very well at estimating an object’s position.</p> <p>&nbsp;</p> 2021-05-30T00:00:00+00:00 ##submission.copyrightStatement##