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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 10 Documents
 1 
Search results for , issue "Vol 18, No 3 (2024): July" : 10 Documents clear
Smart Product Recommendations in Web E-Commerce: Leveraging Apriori Algorithm for Market Basket Analysis Hendra, Hendra; Hermawan, Aditiya; Edy, Edy
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.89075

Abstract

 The world of online commerce is becoming increasingly competitive, and to succeed in this field, it is not enough to showcase products to potential buyers. It is crucial to offer various products and keep product recommendations up-to-date, especially for customers who buy multiple items. To address this challenge, an intelligent system is needed that can automatically generate trending product recommendations based on sales data. In this research, the Market Basket Analysis (MBA) method analyzes consumer transaction data and identifies products often purchased together. The apriori algorithm is applied to generate association rules, and the Lift Ratio parameter is used to evaluate the strength of these rules. This research is implemented on an e-commerce website, and the generated association rules will be applied to provide automatic product recommendations based on recent sales trends. The results show that the automatic product recommendation system developed for the e-commerce website significantly helps users enhance their online shopping experience. Using the Lift Ratio parameter in validating association rules provides strong evidence of the relevance and accuracy of the generated product recommendations, which can increase customer satisfaction and sales potential.
Analysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests Kumoro, Rabbani Nur; Fattima, Audrey Shafira; Susatyo, William Hilmy; Fudholi, Dzikri Rahadian
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.90202

Abstract

Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.
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
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%.
Gaussian Blur Filter Effect Analysis on Facial Detection Accuracy Using Viola Jones Method Saryanto, Saryanto; Andriyani, Widyastuti
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.96017

Abstract

Human face detection is one of the most studied topics in computer vision. The purpose of facial detection is to find out whether or not a face is present in an image. Blur can be caused by many things, such as motion that occurs when the camera takes a picture or the use of a camera that is not focused when taking a picture. For facial recognition, blur becomes difficult to get information about an object, get a description about it, or identify a face in the image. The more blur a picture, the more difficult it is to identify it. This research applies the Viola-Jones relative method for facial detection with a high degree of accuracy and fast computation. This study analyzed the influence of a gaussian blur filter by calculating how much radius an object has been given a gausian blur filter so that it can no longer be identified as an object, and also looking for the minimum PSNR value that is still acceptable in the object detection process. The minimum PSNR value for the image is 16.6 dB, and the minimum PSR value before the face can no longer be detected is 17.84 dB.
APPLICATION OF DATA MINING USING THE C4.5 ALGORITHM AND THE K-NEAREST NEIGHBOR (KNN) Nurmayanti, Nurmayanti; Supriyanto, Supriyanto; Parida, Merri; Sartika, Sartika
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.96515

Abstract

Direct cash assistance is a governmental or social institution intervention that provides financial aid directly to individuals or families in need. To streamline this process, a system is necessary to convert data into predictive information regarding eligibility for direct cash assistance. This research utilizes the C4.5 algorithm and the K-Nearest Neighbor algorithm for predicting eligibility based on factors such as housing status, employment, income, and eligibility status. Using the C4.5 algorithm, Microsoft Excel calculations identified 238 individuals as eligible and predicted 62 as ineligible who were eligible, out of a total of 300 recipients. The accuracy rate from RapidMiner calculations was 93.00%. Regarding the K-Nearest Neighbor method, Microsoft Excel calculations identified 226 eligible and 74 ineligible recipients out of 300. RapidMiner analysis showed an accuracy rate of 76.55% for the 226 eligible recipients and 98.23% for the 74 ineligible recipients.
Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques Mastrika Giri, Gst Ayu Vida; Radhitya, Made Leo
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.96665

Abstract

 Electroencephalogram (EEG) records brain activity as electrical currents to discern emotions. As interest in human-computer emotional connections rises, reliable and implementable emotion recognition algorithms are essential. This study classifies EEG waves using machine and deep learning. A four-channel Muse EEG headband recorded neutral, negative, and positive emotions for the publicly available Feeling Emotions EEG dataset. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized for deep learning, while SVM, K-NN, and MLP were used for machine learning. The models were assessed for accuracy, precision, recall, and F1-Score. SVM, K-NN, and MLP have accuracy scores of 0.98, 0.95, and 0.97. Deep learning methods CNN, LSTM, and GRU had 0.98, 0.82, and 0.97 accuracy. SVM and CNN surpassed other approaches in accuracy, precision, recall, and F1-Score. The research shows that machine learning and deep learning can classify EEG signals to identify emotions. High accuracy results, especially from SVM and CNN, suggest these models could be used in emotion-aware human-computer interaction systems. This study adds to EEG-based emotion classification research by revealing model selection and parameter tweaking strategies for better categorization.
Transformation of State Civil Apparatus Learning Task Administration Services through the J-SiLAKON Application Fu’adah, Washiqotul; Hidayat, Rofiq
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.98298

Abstract

 The emergence of the Covid-19  pandemic and the progress of industry 4.0. The developments encourage the government to digitize public administration services. The Jember Regency Personnel and Human Resources Development Agency (BKSDM) therefore launched a web-based application named J-SiLAKON (Jember Online Personnel Services System) for study assignments to increase effectiveness and efficiency in providing services, and to encourage good governance. This research examines the effectiveness of the transformation of ASN learning task administration services through J-SiLAKON at the Jember Regency Education Office. By Using a qualitative case study approach, data were collected through semi-structured interviews, passive observation, and primary documentation involving J-SiLAKON operators and staff from the Teachers and Education Personnel Division. The analysis of data involved condensation, presentation, and conclusion drawing, with triangulation to ensure data validity. Results showed that J-SiLAKON is easy to learn, control, understand, and use, thus improving users' proficiency in performing their duties. It provided significant benefits, including faster work, improved employee performance, increased productivity, and more effective administrative services. Overall, the J-SiLAKON application improves the efficiency, productivity and transparency of ASN study assignment administration services at the Education Office, meeting user expectations and improving the performance of the Education office.
Machine Translation Indonesian Bengkulu Malay Using Neural Machine Translation-LSTM Miranda, Bella Okta Sari; Yuliansyah, Herman; Biddinika, Muhammad Kunta
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.98384

Abstract

The machine translator is an application in Natural Language Processing (NLP) that focuses on translating between languages. Several previous research have used Statistical Machine Translation (SMT) with a parallel corpus of Indonesian and Bengkulu Malay totaling 3000 data points. However, SMT performs poorly when confronted with limited data and infrequent language pairs. Therefore, this study aims to build a machine translation model from Indonesian to Bengkulu Malay using an NMT approach with Long Short-Term Memory (LSTM), and to create a parallel corpus of 5261 data pairs between Indonesian and Bengkulu Malay. The research was conducted in three stages: data collection, data preprocessing, training and modeling, and evaluation. The performance of the machine translator was evaluated using the Bilingual Evaluation Understudy (BLEU). The evaluation results show that this model achieved the highest average score of 0.6016332 on BLEU-1 and the lowest average score of 0.3680788 on BLEU-4. These results indicate that considering the natural linguistic structural differences between Indonesian and Bengkulu Malay can be suggested as the best solution for translating from Indonesian to Bengkulu Malay.
Hyperparameter Optimization Techniques for CNN-Based Cyber Security Attack Classification Adnyana, I Gede; Sugiartawan, Putu; Hartawan, I Nyoman Buda
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.98427

Abstract

Abstract The proliferation of cyber security attacks necessitates advanced and efficient detection methods. This study explores the application of Convolutional Neural Networks (CNNs) for classifying cyber security attacks using a comprehensive dataset containing various attack types and network traffic features. Emphasizing the role of hyperparameter optimization (HPO) techniques, this research aims to enhance the CNN model's performance in accurately detecting and classifying cyber attacks. Traditional machine learning approaches often need to catch up in capturing the complex patterns in such data, whereas CNNs excel in automatically extracting hierarchical features. Using the provided dataset, which includes attributes such as packet length, source and destination ports, protocol, and traffic type, we implemented various (HPO) techniques, including Grid Search, Random Search, and Bayesian Optimization, to identify the optimal CNN configurations. Our optimized CNN model significantly improved classification result. to baseline models without hyperparameter tuning. The results underline the importance of HPO in developing robust CNN models for cybersecurity applications. This study provides a practical framework for leveraging deep learning and optimization techniques to enhance cyber defense mechanisms, paving the way for future advancements in the field.

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