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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 476 Documents
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Inverse Kinematic Algorithm with Newton-Raphson Method iteration to Control Robot Position and Orientation based on R programming language Budiman Nasution; Lulut Alfaris; Ruben Cornelius Siagian
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.82781

Abstract

 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.
Concrete Subsurface Crack Detection Using Thermal Imaging in a Deep Neural Network Mabrouka Abuhmida; Daniel Milner; Jiping Bai
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.82912

Abstract

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.
The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation Anita Desiani; Yuli Andriani; Irmeilyana Irmeilyana; Rifkie Primartha; Muhammad Arhami; Dwi Fitrianti; Henny Nur Syafitri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.82948

Abstract

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.
Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara Mutammimul Ula; Gita Perdinanta; Rahmad Hidayat; Ilham Sahputra
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.83195

Abstract

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.
Applying Data Mining to Classify Customer Satisfaction using C4.5 Algorithm Decision Tree J. Prayoga; Zelvi Gustiana; Sabrina Aulia Rahmah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.83535

Abstract

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.
Simulation Technique in Determining Student Attendance Using The Monte Carlo Method Klara Bonita Madao; I Gusti Ayu Ngurah Kade Sukiastini; Engelina Prisca Kalensun
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 2 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.83891

Abstract

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%.
Audio-Visual CNN using Transfer Learning for TV Commercial Break Detection Muhammad Zha'farudin Pudya Wardana; Moh. Edi Wibowo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76058

Abstract

The TV commercial detection problem is a hard challenge due to the variety of programs and TV channels. The usage of deep learning methods to solve this problem has shown good results. However, it takes a long time with many training epochs to get high accuracy.    This research uses transfer learning techniques to reduce training time and limits the number of training epochs to 20. From video data, the audio feature is extracted with Mel-spectrogram representation, and the visual features are picked from a video frame. The datasets were gathered by recording programs from various TV channels in Indonesia. Pre-trained CNN models such as MobileNetV2, InceptionV3, and DenseNet169 are re-trained and are used to detect commercials at the shot level. We do post-processing to cluster the shots into segments of commercials and non-commercials.    The best result is shown by Audio-Visual CNN using transfer learning with an accuracy of 93.26% with only 20 training epochs. It is faster and better than the CNN model without using transfer learning with an accuracy of 88.17% and 77 training epochs. The result by adding post-processing increases the accuracy of Audio-Visual CNN using transfer learning to 96.42%.
Error Action Recognition on Playing The Erhu Musical Instrument Using Hybrid Classification Method with 3D-CNN and LSTM Aditya Permana; Timothy K. Shih; Aina Musdholifah; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76555

Abstract

Erhu is a stringed instrument originating from China. In playing this instrument, there are rules on how to position the player's body and hold the instrument correctly. Therefore, a system is needed that can detect every movement of the Erhu player. This study will discuss action recognition on video using the 3DCNN and LSTM methods. The 3D Convolutional Neural Network method is a method that has a CNN base. To improve the ability to capture every information stored in every movement, combining an LSTM layer in the 3D-CNN model is necessary. LSTM is capable of handling the vanishing gradient problem faced by RNN. This research uses RGB video as a dataset, and there are three main parts in preprocessing and feature extraction. The three main parts are the body, erhu pole, and bow. To perform preprocessing and feature extraction, this study uses a body landmark to perform preprocessing and feature extraction on the body segment. In contrast, the erhu and bow segments use the Hough Lines algorithm. Furthermore, for the classification process, we propose two algorithms, namely, traditional algorithm and deep learning algorithm. These two-classification algorithms will produce an error message output from every movement of the erhu player.
Smart GreenGrocer: Automatic Vegetable Type Classification Using the CNN Algorithm Raden Bagus Muhammad AdryanPutra Adhy Wijaya; Delfia Nur Anrianti Putri; Dzikri Rahadian Fudholi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.82377

Abstract

In the food industry, separating vegetables is done by visually trained professionals. However, because it takes plenty of time to sort a large number of different types of vegetables, human errors might arise at any time, and using human resources is not always effective. Thus, automation is needed to minimize process time and errors. Computer vision helps reduce the need for human resources by automatizing the classification. Vegetables come in various colors and shapes; thus, vegetable classification becomes a challenging multiclass classification due to intraspecies variety and interspecies similarity of these main distinguishing characteristics. Consequently, much research is made to automatically discover effective methods to group each type of vegetable using computers. To answer this challenge, we proposed a solution utilizing deep learning with a Convolutional Neural Network (CNN) to perform multi-label classification on some types of vegetables. We experimented with the modification of batch size and optimizer type. In the training process, the learning rate is 0.01, and it adapts on arrival in the local minimum for result optimization. This classification is performed on 15 types of vegetables and produces 98.1% accuracy on testing data with 25 minutes and 45 seconds of training time.
Autism Spectrum Disorder (ASD) Identification Using Feature-Based Machine Learning Classification Model Anton Novianto; Mila Desi Anasanti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.83585

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that impairs the development of behaviors, communication, and learning abilities. Early detection of ASD helps patients to get beter training to communicate and interact with others. In this study, we identified ASD and non-ASD individuals using machine learning (ML) approaches. We used Gaussian naive Bayes (NB), k-nearest neighbors (KNN), random forest (RF), logistic regression (LR), Gaussian naive Bayes (NB), support vector machine (SVM) with linear basis function and decision tree (DT). We preprocessed the data using the imputation methods, namely linear regression, Mice forest, and Missforest. We selected the important features using the Simultaneous perturbation feature selection and ranking (SpFSR) technique from all 21 ASD features of three datasets combined (N=1,100 individuals) from University California Irvine (UCI) repository. We evaluated the performance of the method's discrimination, calibration, and clinical utility using a stratified 10-fold cross-validation method. We achieved the highest accuracy possible by using SVM with selected the most important 10 features. We observed the integration of imputation using linear regression, SpFSR and SVM as the most effective models, with an accuracy rate of 100% outperformed the previous studies in ASD prediciton

Page 37 of 48 | Total Record : 476

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