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Agus Harjoko
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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 466 Documents
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An Electrocardiogram Signal Preprocessing Strategy in the LSTM Algorithm for Biometric Recognition Rahayu, Fenny Winda; Faisal, Mohammad Reza; Nugrahadi, Dodon Turianto; Nugroho, Radityo Adi; Muliadi, Muliadi; Redjeki, Sri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.93895

Abstract

Electrocardiogram (ECG) signals are a very important tool for clinical diagnosis and can be used as a new biometric modality. The aim of this research is to determine the results of ECG signal processing using RNN methods such as the Long Short Term Memory (LSTM) algorithm by utilizing several preprocessing techniques. In this study, the ECG signal itself was previously tested by carrying out the LSTM classification process without preprocessing, and the results obtained were 0% accurate, so preprocessing was needed. The preprocessing methods tested with the LSTM classification method are Adjacent Segmentation and R Peak Segmentation to find out which preprocessing techniques greatly influence LSTM classification accuracy. The experimental results were that LSTM classification with R Peak Segmentation preprocessing obtained the highest accuracy on the two data used, namely filtered and raw data, with 80.7% and 78.95%, respectively. Meanwhile, the accuracy obtained from LSTM classification when using Adjacent Segmentation preprocessing is not good. This research compares LSTM accuracy from each preprocessing stage to determine which combination has the best results in the ECG data classification process. This research also offers new insights into the preprocessing stages that can be carried out on ECG data.
Comparing text classification algorithms with n-grams for mediation prediction Lewu, Retzi Y.; Kusrini, Kusrini; Yaqin, Ainul
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.93929

Abstract

Tingkat keberhasilan mediasi perkara perdata di pengadilan negeri dari tahun ke tahun sangat rendah dan menyebabkan penumpukan perkara yang harus ditangani dengan persidangan. Sementara itu, pendaftaran perkara baru dengan klasifikasi perkara serupa terus bermunculan dan wajib dimediasi. Penelitian ini dilakukan dengan memanfaatkan data mediasi perkara terdahulu sebagai dataset untuk memprediksi hasil mediasi perkara baru. Ketika n-gram digunakan pada dataset yang telah di-preprocessing, hanya ditemukan nilai pada unigram (n=1). Pada penerapan model menggunakan algoritma machine learning, dihasilkan akurasi yang sama sebesar 0.6875 pada Algoritma Naïve Bayes, Logistic Regression dan Support Vector Machine (SVM), sedangkan algoritma Decision tree menghasilkan akurasi paling rendah sebesar 0,375. Rendahnya nilai dikarenakan Decision Tree lebih cenderung overfit untuk digunakan dengan teks berbahasa Indonesia. Pola kalimat formal pada dokumen mediasi berbahasa Indonesia tidak memenuhi unsur – unsur kata majemuk, imbuhan, variasi susunan kata, dan semantik leksikal. Untuk penelitian selanjutnya direkomendasikan penggunaan algoritma klasifikasi lain, pemanfaataannya pada dokumen – dokumen lain seperti putusan pengadilan, penentuan rangking mediator berdasarkan keberhasilan mediasi serta implementasi model pada aplikasi e-mediasi yang terintegrasi dengan sistem informasi manajemen perkara
Developments and Trends in Indonesian Tourism Technology Using Bibliometric Analysis Ayulya, Agisti Mutiara; Syaifullah, Syaifullah; Hamzah, Muhammad Lutfi; Ahsyar, Tengku Khairil; Saputra, Eki
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94190

Abstract

Information technology has changed society, services, and the tourism sector has attracted many research and publications. Even though previous research aims to show an understanding of tourism technology factors, there is still little to discuss the technology factors of Indonesian tourism. Discussing scientific publications about tourism technology in Indonesia can provide a deeper understanding of the development of information technology in the Indonesian tourism sector by providing solutions. This research aims to analyze developments and trends in tourism technology factors in Indonesia from 2014 to 2023 with bibliometric analysis from R Studio and using 113 Scopus indexed articles. The methodology includes planning, keyword identification, Scopus data searches, bibliometrics, developments and trends in Indonesian tourism technology. The results of this research show an increase in publications from year to year, in annual citations there are fluctuations, the number of articles published varies with the position of Sustainability (Switzerland) being ranked first with 25 published articles, Indonesia is the country that publishes the most articles and the frequency has increasing, Indonesia has also become a top keyword, and in tourism technology trends there are two clusters within the basic themes, namely tourism and West Java, which are the direction for further research
Optimal Feature Selection in Diabetes Classification Using the MLP Algorithm Jogo Samodro, Maulana Muhamammad; Biddinika, Muhammad Kunta; Fadlil, Abdul
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94575

Abstract

In 2021, approximately 531 million people worldwide were affected by diabetes, with 90% diagnosed as type 2. Diabetes often coexists as a comorbidity with other conditions such as kidney and heart disease. The research aims to employ machine learning for diabetes classification, with the Multilayer Perceptron (MLP) algorithm being a key component in the early detection process. The experiments utilized data from the UCI database of Sylhet hospitals, featuring 16 attributes and 2 classes indicating positive and negative diabetes cases. Performance testing using the MLP algorithm involved varying the number of neurons in the hidden layer. The research architecture is denoted as n:p:m, where n represents 16 neurons based on the attributes, m signifies 2 neurons based on the number of classes, and p undergoes variations. The machine learning tool employed in this research is Weka. Within the Weka tool, MLP offers types of hidden layer neuron configurations: 'a', 't', 'i', and 'o'. The test results, conducted with 520 training data and testing on the same dataset, yielded accuracies of 98.85%, 98.85%, 99.42%, and 98.46% for types 'a', 't', 'i', and 'o', respectively.
Optimizing Clustering Models Using Principle Component Analysis for Car Customers Savira, Agnes Riska
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94744

Abstract

 In the competitive business world, companies strategically utilize customer data to achieve goals, requiring a comprehensive understanding of various customer traits, behaviors and needs. Customer segmentation, an important strategy, requires grouping individuals based on various characteristics. The K-Means algorithm is widely used for customer data grouping connectivity because of its ease of implementation in Machine Learning. However, challenges arise in high-dimensional data, prompting the need for dimensionality reduction. Principal Component Analysis (PCA) is emerging as an effective method for data communication while minimizing information loss. Previous research emphasizes the success of PCA in improving analysis and clustering efficiency. This research contributes by integrating PCA into K-Means clustering to analyze customer segments in a car company. This empowers companies to attract new customers, implement targeted marketing, understand customer-company relationships, and increase expected profitability. PCA, which preserves 75% of the variation with 3 principal components, precedes the implementation of K-Means after normalization. Evaluation using the Elbow and Silhouette Score Method identified eight optimal clusters. The post-PCA K-Means model with optimal cluster selection produces a Silhouette Score of 0.7789. 
Object Detection Based on You Look Only Once Version 8 for Real-Time Applications Santiago, Gede Agus; Sugiartawan, Putu; Ardriani, Ni Nengah Dita
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94843

Abstract

This research focus to involves human detection in crowded situations, especially in the lecturer's room. The lecturer's room is very vulnerable because it can be accessed by anyone with only one entry and exit to the lecturer's room, so it would be perfect to place this Yolo camera in front of the lecturer's room so that incoming and outgoing activities can be monitored during work days on campus. The main challenge is how the system can distinguish individuals in dense crowds and identify their relative locations to each other. In this context, it is necessary to find a solution that can overcome the uncertainty of recognizing individuals in a group and accurately understand the location and distance between them. One proposed solution is to use the YOLO algorithm on video recordings to detect human objects in the lecturer's room during working hours. This research introduces the YOLOv8 model, a real-time detection system with high speed and accuracy in detecting and classifying objects in video recordings. YOLOv8 can accurately detect object movement, making it an efficient real-time framework for dealing with complex objects. This research experiment involved using eight different smartphone devices to collect datasets. Using various smartphone devices aims to test object detection performance under various shooting conditions, including variations in image quality, lighting, shooting angle, and camera resolution. The research results show that using multiple smartphone devices in dataset collection can improve the robustness and accuracy of object detection models. By integrating datasets from various sources and shooting conditions, the YOLOv8 model was successfully trained to better recognize objects in different situations, even in campus environments that often have challenges such as weather variations and lighting fluctuations. The test results show an accuracy rate of 93.33% in human object detection
Integrating Learning Management System and Clasification Learning Media Based On Two Dimension Animation Suarya Putra, I Nyoman Agus; Sustiawati, Ni Luh; Udayana, Anak Agung Gde Bagus; Suardana, I Wayan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.94981

Abstract

This research aims to combine the advantages of LMS technology with the potential of two-dimensional animated learning media in an educational context. This research explores the basic concept of a Learning Management System (LMS) and its advantages in providing a structured platform for delivering learning material, student-teacher interaction, and evaluating learning progress. LMS offers broad accessibility, student progress tracking, and the ability to facilitate collaborative learning. The research focus shifted to the potential of two-dimensional animation-based classification learning media. Two-dimensional animation offers interesting and engaging visualizations and can help students understand complex concepts better. In this context, this research integrates the visual power of animation with the learning structure provided by the LMS. This integration is expected to increase student engagement, facilitate a deeper understanding of concepts, and increase information retention. In addition, this combination is also expected to improve the overall quality of teaching by enabling the use of more innovative and interactive teaching methods. This research aims to contribute to our understanding of how learning technology can be integrated effectively to improve student's learning experiences. Thus, it is hoped that the results of this research can provide valuable insight into the development of better learning practices.
Optimizing Coral Fish Detection: Faster R-CNN, SSD MobileNet, YOLOv5 Comparison Santoso, Syifa Afnani; Jaya, Indra; Priandana, Karlisa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.95011

Abstract

This study underscores the critical role of accurate Chaetodontidae fish abundance observations, particularly in assessing coral reef health. By integrating deep learning algorithms (Faster R-CNN, SSD-MobileNet, and YOLOv5) into Autonomous Underwater Vehicles (AUVs), the research aims to expedite fish identification in aquatic environments. Evaluating the algorithms, YOLOv5 emerges with the highest accuracy, followed by Faster R-CNN and SSD-MobileNet. Despite this, SSD-MobileNet showcases superior computational speed with a mean average precision (mAP) of around 92.21% and a framerate of about 1.24 fps. Furthermore, employing the Coral USB Accelerator enhances computational speed on the Raspberry Pi 4, enabling real-time detection capabilities. This study incorporates centroid tracking, facilitating accurate counting by assigning unique IDs to identified objects per class. Ultimately, the real-time implementation of the system achieves 87.18% accuracy and 87.54% precision at 30 fps, empowering AUVs to conduct real-time fish detection and tracking, thereby significantly contributing to underwater research and conservation efforts.
Significant Wave Height Forecasting using Long-Short Term Memory (LSTM) in Seribu Island Waters Khatimah, Husnul; Jaya, Indra; Atmadipoera, Agus Saleh
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.95044

Abstract

Wind waves are natural phenomena primarily generated by the wind. Information about wave height and period is highly crucial in various marine fields such as coastal engineering, fisheries, and maritime transportation. However, accurately predicting wave height remains a challenge due to the stochastic nature of ocean waves themselves. Several approaches to predicting wave height have been developed, including numerical models and machine learning methods, such as the Long-Short Term Memory (LSTM) algorithm, which has currently garnered significant attention from researchers. The objective of this research is to develop a forecast model for wind wave height using the LSTM algorithm in Seibu Island Waters, DKI Jakarta. The ERA5 dataset comprises zonal and meridional wind components and significant wave height, along with wind measurement data using the Automatic Weather System (AWS) instrument, are used to train and test to train and test the LSTM model. The research results show that the LSTM model can predict significant wave height effectively. Predictions using the ERA5 significant height dataset are observed to be closer to field data, with RMSE, MAE, and MAPE values of 0.1535 m, 0.1181 m, and 37.11% respectively. Thus, the model evaluation results indicate good performance, with relatively low RMSE and MAE values, and a good MAPE value. The highest accuracy in significant wave height prediction is found for forecasts one week (7 days) ahead
STUDENT VIRTUAL CLASS ATTENDANCE BASED ON FACE RECOGNITION USING CNN MODEL Nursantika, Dian; Piantari, Erna; Huseani, Dwi Fitria Al; Husaeni, Dwi Novia Al; Urwah, Mushfani Ainul
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.95824

Abstract

Attendance records are an important tool that can be used to include and broadcast member participation in an activity, including the learning process. In online learning classrooms, the process of recording attendance becomes challenging to do manually, thus an automatic attendance recording system is needed. The authentication process is important in developing an existing recording system to guarantee the correctness of the recorded data. In this research, a face authentication system was built to create a system for recording online class attendance to help integrate participant activities and participation in online class learning. The face recognition approach uses a Convolutional Neural Network (CNN) model specifically designed to automate student attendance in virtual classes. Student image data is taken from virtual classroom sessions and used to train a CNN model. This model can recognize and verify student identity in various lighting conditions and head positions. This research consists of several stages, namely data collection, artificial neural networks, use of facial recognition, dataset application stage, and facial recognition in video frames. The experimental results showed that there were 11193 samples studied and of these 11193 samples the distribution was even, namely 6.7%. In addition, the model performance results show an accuracy of 76.28%.

Page 41 of 47 | Total Record : 466

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