IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Articles
10 Documents
Search results for
, issue
"Vol 17, No 2 (2023): April"
:
10 Documents
clear
<![CDATA[Siamese-Network Based Signature Verification using Self Supervised Learning ]]>
<![CDATA[Muhammad Fawwaz Mayda]]>;
<![CDATA[Aina Musdholifah]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.74627
<![CDATA[The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes. The frequent use of signatures does not mean a procedure without loopholes, but we must remain vigilant against signature falsification carried out with various motives behind it. Therefore, in this study, a signature verification system was developed that could prevent the falsification of signatures in public documents by using digital imagery of existing signatures. This study used neural networks with siamese network-based architectures that also empower self-supervised learning techniques to improve accuracy in the realm of limited data. The final evaluation of the machine learning method used gets a maximum accuracy of 83% and this result is better than the machine learning model that does not involve self-supervised learning methods.]]>
<![CDATA[The Tweetology of New and Renewable Energy in Indonesia ]]>
<![CDATA[Ariana Yunita]]>;
<![CDATA[Sara Florensia Telaumbanua]]>;
<![CDATA[Ade Irawan]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.81397
<![CDATA[The amount of unstructured data is increasing annually, which is promising forgaining insights. Twitter, a platform producing unstructured data, is currently one of the mostpopular media platforms used for conducting research on a topic's trend. This study attempts toanalyze the topic of New and Renewable Energy (NRE) in Indonesia. The purpose of this studyis to gain insights into the NRE topic trend over the last ten years by modeling the topicsdiscussed on Twitter and examining the location distribution of users who post tweets about thetopic. Accordingly, this study employed descriptive analysis, geocoding analysis, and topicmodeling. The results of descriptive analysis show that the development of NRE has acceleratedin recent years, particularly in 2021. Geocoding analysis reveals that the distribution of peoplewho engage in NRE posting activities is dominated by DKI Jakarta province. Topic modelingyielding two topics that were discussed the most by Indonesians over a 10-year period. The twotopics are related to government policies that support the development of NRE and electricity,which is Indonesia's focus in NRE. This study highlights the importance of analyzing theTweetology of NRE.]]>
<![CDATA[Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation ]]>
<![CDATA[Wawan Gunawan]]>;
<![CDATA[Nurul Latifah]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.81521
<![CDATA[A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.]]>
<![CDATA[World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model ]]>
<![CDATA[Stanislaus Jiwandana Pinasthika]]>;
<![CDATA[Dzikri Rahadian Fudholi]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.82280
<![CDATA[Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. This prediction model is also a brief example to overcome prediction problem using limited dataset. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction.]]>
<![CDATA[Inverse Kinematic Algorithm with Newton-Raphson Method iteration to Control Robot Position and Orientation based on R programming language ]]>
<![CDATA[Budiman Nasution]]>;
<![CDATA[Lulut Alfaris]]>;
<![CDATA[Ruben Cornelius Siagian]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.82781
<![CDATA[Â The homogeneous transform program is a function used to calculate the homogeneous transformation matrix at a specific position and orientation of a three-link manipulator. The homogeneous transformation matrix is a 4x4 matrix used to represent the position and orientation of an object in three-dimensional space. In the program, the rotation matrix R is calculated using the Euler formula and stored in a 4x4 matrix along with the position coordinates. The Jacobian matrix function calculates the Jacobian matrix at a specific position and orientation of a three-link manipulator using the homogeneous transformation matrix. The Euler formula used in the program is based on the rotation matrices for rotations around the x, y, and z-axes. The output of these functions can be useful for future research in developing advanced manipulators with improved accuracy and flexibility. Research gaps in exploring the limitations of these functions in real-world applications, particularly in scenarios involving complex manipulator configurations and environmental factors.]]>
<![CDATA[Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network ]]>
<![CDATA[Mabrouka Abuhmida]]>;
<![CDATA[Daniel Milner]]>;
<![CDATA[Jiping Bai]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.82912
<![CDATA[Impact actions, such as a zone directly affected by conflict and warfare, can negatively impact the structural integrity of concrete structures. Even indirect impact actions can make structures unsafe, creating subsurface defects in concrete. However, the result of indirect impact actions is often undetected because of the time required and expert knowledge needed to assess the structure. Yet, there are no techniques currently available to assess the usability and the safety of a concrete structure rapidly and with no expert knowledge.. This paper presents a combination of thermal imaging and artificial intelligence (AI) to enable a novel, contactless, autonomous, and fast technique for detecting hidden defects in concrete structures. In this paper, a ResNet50 model was trained on simulated data of subsurface defected and defect-free concrete blocks to test if it is possible to classify between the two. The model developed achieved a validation accuracy of 99.93%. Because of the success of this model, a laboratory experiment was conducted by compressing concrete blocks and recording the process using a thermal camera to create a dataset of concrete blocks with and without subsurface cracks. This dataset was used to train a new model with the same architecture and hyper-parameters as the initial model and achieved a validation accuracy of 100%. This investigation proves it is possible for AI to detect subsurface cracks and hidden defects by classifying the thermal images of concrete surfaces.]]>
<![CDATA[The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation ]]>
<![CDATA[Anita Desiani]]>;
<![CDATA[Yuli Andriani]]>;
<![CDATA[Irmeilyana Irmeilyana]]>;
<![CDATA[Rifkie Primartha]]>;
<![CDATA[Muhammad Arhami]]>;
<![CDATA[Dwi Fitrianti]]>;
<![CDATA[Henny Nur Syafitri]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.82948
<![CDATA[One of the datasets used to classify heart disease is UCI dataset. unfortunately, the dataset contains missing data. Backpropagation is an easy and fast method, but it is very dependent on input data so if there is missing data, it can reduce the performance of the backpropagation. One of the techniques used to handle missing data is feature selection. This study compares ReliefF and C4.5 algorithm in feature selection. The purpose of the study is to find way in overcoming missing data by feature selection to improve backpropagation performance in the heart disease classification. The results of these algorithms are applied to the classification by Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are above 82%. The performance results of of C4.5 and backpropagation are 80.54% on average for accuracy, recall and precision. Based on the results it can be concluded the ReliefF gives better performance on backpropagation than C4.5. ReliefF is also able to handle missing data by performing feature selection to improve the performance of the backpropagation method for heart disease classification compared to C4.5. Although the C4.5 algorithm is able to provide increased performance on backpropagation, C4.5 is not appropriate to be used as a feature selection method for handling missing data.]]>
<![CDATA[Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara ]]>
<![CDATA[Mutammimul Ula]]>;
<![CDATA[Gita Perdinanta]]>;
<![CDATA[Rahmad Hidayat]]>;
<![CDATA[Ilham Sahputra]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.83195
<![CDATA[PT. Perkebunan Nusantara 1 is engaged in oil palm production with a total land area of 1,144 Ha. The formulation of this research can determine productive land clusters based on land area, number of trees, number of stages, and palm oil production. Methodological steps include plantation area data and oil palm production data. This study can compare the C-means and K-means groups. As for predictions using the Backpropagation Neural Network (BPNN) algorithm and Fuzzy time series for production results. The results of grouping Cot girek palm oil production data for the 2019-2022 period from January to December were 1,365,530, while in 2022 it reached 1,768,720. The analysis used a land grouping method of 1,144 hectares, which resulted in 800.4 hectares of productive land and 343.6 hectares of less effective land. The results of the C-menas clustering model are more than K-meas with shorter iterations while for predictions it has an accuracy rate of 90.77%. As a comparison, the level of accuracy of the fuzzy time series is 81.27%. The results of this study can be used as recommendations for companies in the analysis of productive land grouping analysis and forecast results from these lands.]]>
<![CDATA[Applying Data Mining to Classify Customer Satisfaction using C4.5 Algorithm Decision Tree ]]>
<![CDATA[J. Prayoga]]>;
<![CDATA[Zelvi Gustiana]]>;
<![CDATA[Sabrina Aulia Rahmah]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.83535
<![CDATA[Tight business competition demands business actors to make responsive, timely decisions to survive the uncertainty. Food business, especially cafes, has emerged as one of the most popular business types recently. One cafe concept that draws most customers' interest is modern concepts, friendly service, and affordable prices. Finn Coffee is one of the cafes providing a range of foods and beverages, especially coffee-based beverages. Customer satisfaction defines one's feelings when comparing performance. It denotes customer's responses to their satisfied needs. The term satisfaction itself is described as one's happy expression after receiving a quality product with affordable price and satisfying quality. The present study aimed to analyze cafe customer satisfaction using the C4.5 algorithm with predetermined criteria. Customer satisfaction was classified using C4.5. The algorithm displays the level of customer satisfaction based on the customers' response to the Google form distributed by the cafe employees/owner.]]>
<![CDATA[Simulation Technique in Determining Student Attendance Using The Monte Carlo Method ]]>
<![CDATA[Klara Bonita Madao]]>;
<![CDATA[I Gusti Ayu Ngurah Kade Sukiastini]]>;
<![CDATA[Engelina Prisca Kalensun]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.22146/ijccs.83891
<![CDATA[In lectures, attendance is one of the assessment points that play an important role in determining a student's graduation. When a student is in the upper semester their attendance rate at lectures starts to decrease. The attendance prediction simulation is an estimate of the calculation of student attendance in lectures. This type of research is quantitative research using data collection techniques using observation and documentation study. In the process of analysis, the observed data were attendance data of 5th-semester computer engineering study program students and a sample of 83 people as research subjects. The stages of the monte carlo simulation are used: Determining variable frequency; Calculating cumulative probabilities; Determining random number intervals; Creating a simulation to determine student attendance; Generating random numbers; Make a simulation of the experimental circuit. The simulation is carried out by comparing and entering random numbers that have been generated into a comparison simulation of attendance and absence data for 5th-semester computer engineering study program students at the STMIK Agamua Wamena Papua Campus, starting from October 3 to October 31, 2022. Based on a series of experimental data that has The simulation results obtained predicted attendance and absence of computer engineering study program students at the STMIK Agamua Wamena campus from November 7 to December 19, 2022 with an average attendance of above 50%.]]>
