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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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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Predicting Price and Risk ICBP Stocks Using GRU and VaR Ryan Dana, Alvin; Trimono, Trimono; Idhom, Mohammad
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.101974

Abstract

The economy plays a vital role in maintaining a country’s stability and progress, where stock investments serve as a primary financial instrument to enhance societal welfare. In Indonesia, interest in stock investments, especially in the essential food sector, continues to grow due to its long-term profit potential. This study combines stock price prediction with risk analysis using a Gated Recurrent Unit (GRU) model and Value at Risk (VaR) calculation based on historical simulation. The GRU model is selected for stock price prediction due to its ability to capture complex, fluctuating patterns and adapt to market changes, while VaR is used to measure potential maximum loss at a 95% confidence level. The findings indicate a potential loss of IDR 65.785, demonstrating that this approach can provide a risk estimate by combining future predicted prices with historical data. Thus, this approach offers guidance for investors in understanding potential profits and risks in stock assets. The integration of GRU-based predictions and historical simulation VaR is expected to support more informative and prudent investment decision-making, particularly in facing the dynamic and risky stock market conditions.
Utilizing Machine Learning for Pattern Recognition of Wayang Kamasan in Efforts to Digitize Traditional Balinese Art Ariningsih, Kadek Ayu; Lasiyo, Lasiyo; Ariani, Iva; Putu Sugiartawan, Putu
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.102313

Abstract

The extinction of local cultural identities gives rise to profound inquiries concerning the conservative approach that may be adopted by a range of stakeholders. The ongoing process of globalization continues to drive technological innovation, while local cultural knowledge is increasingly marginalized. Conversely, an affirmative attitude towards the preservation of local culture is positively correlated with knowledge of local culture. This study focuses on Wayang Kamasan culture and employs a machine learning-based approach to reintroduce Wayang Kamasan in the context of a global community. The research employs a combination of qualitative and experimental quantitative methods. The former is used to gain an in-depth understanding of the socio-cultural aspects of Wayang Kamasan, while the latter are employed to assess the effectiveness of machine learning methods. The findings demonstrate that the machine learning approach to classifying Wayang Kamasan is an effective method for preserving Balinese culture. By accurately classifying the visual identity of Wayang Kamasan, it is possible to digitally document it, thereby facilitating the preservation of Balinese local culture. Pattern recognition through classification enables the preservation of this cultural heritage in digital form while also supporting the recognition of Balinese wayang.  
Comparison of Artificial Intelligence Methods for Tuberculosis Detection Using X-Ray Images Udayana, I Putu Agus Eka Darma; Prawira, I Made Karang Satria; Tika, I Gede Bagus Arya Merta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.102601

Abstract

Penyakit tuberkulosis (TB), yang disebabkan oleh bakteri Mycobacterium tuberculosis, merupakan penyakit menular yang sangat berbahaya. Di Indonesia, TB adalah penyakit menular paling mematikan setelah COVID-19 dan menempati urutan ke-13 sebagai penyebab kematian global. Deteksi dini TB sangat penting untuk meningkatkan peluang kesembuhan, namun keterbatasan jumlah ahli radiologi menjadi tantangan utama. Teknologi deep learning, khususnya Convolutional Neural Network (CNN), mejadi solusi efektif untuk masalah ini. Oleh karena itu, pada penelitian ini akan membandingkan dua arsitektur CNN, yaitu AlexNet dan VGG-19, dalam mendeteksi TB pada citra rontgen paru-paru, dengan penerapan metode perbaikan kualitas citra, seperti Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), dan Gamma Correction. Dataset yang digunakan diperoleh dari Kaggle dan mencakup citra rontgen paru-paru normal serta TB. Evaluasi performa dilakukan berdasarkan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa VGG-19 dengan CLAHE memberikan performa terbaik dengan akurasi 93.5%, presisi 98.88%, recall 88%, dan F1-score 93.12%. VGG-19 dengan Gamma Correction juga menunjukkan hasil yang sangat baik dengan akurasi 91%, presisi 97.67%, recall 84%, dan F1-score 90.32%. Temuan ini menggarisbawahi efektivitas kombinasi CNN dan metode pemrosesan citra dalam meningkatkan deteksi TB.
LOOK ALIKE-SOUND ALIKE PREDICTION AS A TOOL FOR PATIENT SAFETY anggiratih, endang
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103210

Abstract

Report from the WHO that one of the highest causes of medication errors is Look Alike – Sound Alike (LASA) drugs, leading to errors in receiving information about the drugs, which of course will affect patient safety. Efforts to reduce medication errors have been widely implemented, such as conducting medication training, managing medications, and storing and labeling medications. However, all of that leads to human error, so the utilization of technology is needed to address this issue. The technology expected to help reduce medication errors is the utilization of artificial intelligence (AI). AI is designed for automation processes and systems that can learn independently, allowing the causes of medication errors such as LASA to be learned by the system and predicted automatically. Deep learning is a part of AI that works by providing solutions accurately and automatically. The Recurrent Neural Networks (RNN) algorithm is one of the deep learning methods that has been proven accurate in predictions based on previous research studies. In this study, LASA predictions were made using RNN with the aim of serving as an aid to reduce medication errors, thereby ensuring patient safety. The accuracy achieved is 99% for training and 81% for testing.
Enhancing Neural Collaborative Filtering with Metadata for Book Recommender System Sedyo Mukti, Putri Ayu; Baizal, Z. K. A.
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103611

Abstract

Book recommender systems often face the challenges of information overload and item cold start due to the dynamics of the evolving book market. This paper proposes Feature Enhanced Neural Collaborative Filtering (FENCF), which is a novel method that combines the interaction between users and items with genre metadata information to address the item cold start problem and improve the accuracy of rating predictions. The uniqueness of FENCF lies in the preprocessing of metadata genres, which is different from typical book recommendation research. Experiments with the Amazon book dataset show the contribution of FENCF, which outperforms NCF by reducing RMSE by 4.04% and MAE by 2.73%. In addition, FENCF is also better able to cope with item cold start, with lower MAE across all testing data scenarios. The advantages of FENCF in improving rating accuracy and overcoming item cold start on complex data are very relevant to the actual condition of book sales in e-commerce, which is dynamic. In real-world applications, FENCF can accurately recommend old and new books according to each user's preference. This not only encourages users to stay with the e-commerce platform in the long run but also has the potential to increase the conversion rate of sales.
Sentiment Analysis of X Platform on Viral 'Fufufafa' Account Issue in Indonesia Using SVM Suryanto, Suryanto; Andriyani, Widyastuti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.104158

Abstract

In this study, we conducted a comprehensive sentiment analysis of users on the social media platform X concerning the viral controversy surrounding the KasKus account known as “Fufufafa.” This issue attracted widespread attention and sparked varied reactions within the online community. To gain insights into public opinion on the topic, we utilized the Support Vector Machine (SVM) method, a widely recognized machine learning algorithm for classification tasks. The data for this research was gathered from various posts, comments, and public discussions on platform X, which were pre-processed to filter out irrelevant information, such as spam, unrelated topics, and non-informative content. After cleaning the data, user sentiments were categorized into three primary classes: positive, negative, and neutral. The SVM model was then trained and tested using a labeled dataset to accurately predict user sentiments based on the textual content of their interactions.
Optimization of Gradient Boosting Method for Predicting Narcissistic Personality Disorder (NPD) in Employees Solichin, Achmad; Pramudita, Bagas; Painem, Painem; Pradiptha, Anindya Putri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103551

Abstract

Narcissistic Personality Disorder (NPD) is a serious challenge in modern workplace environments; however, early detection and appropriate intervention remain unmet needs. This research aims to address the issue by proposing an intelligent system model based on machine learning, utilizing the Gradient Boosting method to predict NPD. The Gradient Boosting method was chosen for its ability to handle complex data and gradually improve prediction performance. This model is integrated with employee data, including a range of psychological, behavioral, and demographic variables relevant to NPD. The primary contribution of this research is the development of a predictive model that can assist organizations in identifying and providing early intervention to employees at risk of developing NPD. In doing so, it is expected to reduce the negative impact of NPD on the workplace, such as interpersonal conflicts and decreased productivity. The study shows significant results in the model's classification performance after applying Recursive Feature Elimination (RFE) to optimize the Gradient Boosting method. The accuracy rate reached 82%, an improvement from the previous 79% achieved using the Gradient Boosting Classifier. This indicates that the RFE-Gradient Boosting model has greater potential in classifying employees who genuinely have narcissistic personality disorder versus those who do not.
SMOTE-SVM for Handling Imbalanced Data in Obesity Classification Biddinika, Muhammad Kunta; Yuliansyah, Herman; Soyusiawaty, Dewi; Razak, Farhan Radhiansyah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103994

Abstract

 Obesity is a significant health issue associated with various chronic diseases, making its early classification critical for effective interventions. This study investigates the performance of Support Vector Machine (SVM) models with Radial Basis Function (RBF) and Linear kernels on imbalanced obesity datasets. To address data imbalance, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS) were applied. The results reveal that balancing techniques significantly enhance classification performance, with the Linear model achieving the highest accuracy of 96.54% when balanced using SMOTE. However, limitations include reduced recall for minority classes and potential overfitting risks. These findings underscore the importance of balancing techniques in health data classification and offer insights for further optimizing model performance. The study highlights the need for advanced data balancing strategies to improve predictive accuracy and equity across all classes.
Personality Classification of Myers Briggs Type Indicators (MBTI) Using BERT and Machine Learning Sihabuddin, Agus; Ekapratiwi, Dian
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.104126

Abstract

Personality classification using textual data from social media or online forums is a complex task due to the unstructured text and the multifaceted nature of personality. While the Myers-Briggs Type Indicator (MBTI) provides a comprehensive framework, adapting it to media data and handling diverse linguistic patterns requires effective algorithms. The psychological basis of MBTI is intricate, especially when using complex methods like deep learning, which can be challenging.      This study classifies personality types based on each individual's behavior on an online forum by observing the linguistic patterns of posted textual data using the SVM, Random Forest, BERT, and Word2Vec algorithms. The SVM and Random Forest algorithms are traditional machine learning algorithms known for their capabilities and effectiveness in text classification. Meanwhile, BERT and Word2Vec identify semantic relationships and contextual information from textual data. In addition, the IndoBERT model will be used for the BERT model because this study focuses on the classification of Indonesian language texts.Testing was carried out using textual data from posts on the PersonalityCafe forum. The test results showed that the combination of the SVM and IndoBERT models outperformed other models with an accuracy rate of 82% and an F1 score of 75%.
Two-Step Iris Recognition Verification Using 2D Gabor Wavelet and Domain-Specific Binarized Statistical Image Features Mulyana, Sri; Wibowo, Moh. Edi; Kurniawan, Arie
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 2 (2025): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.104157

Abstract

The Iris is one of the most reliable biometric features due to its complex textural properties. However, using coloured contact lenses renders the iris unreliable in iris recognition systems. Colored contact lenses are one of the spoofing methods in biometrics that can conceal a person's identity. To prevent spoofing, a two-step verification process is needed in the iris recognition system. The first verification step is to detect colored contact lenses, while the second is to recognize or match a person's identity. The feature extraction methods used are Domain Specific Binarized Statistical Image Features (DSBSIF) and Gabor Wavelet. The method for detecting contact lenses is Support Vector Machine (SVM), and matching is performed using Hamming Distance (HD). This study conducted experiments using single features, feature fusion, and hybrid feature extraction methods combining DSBSIF and Gabor Wavelet for two-step iris recognition verification. The results indicate that the hybrid feature extraction method of DSBSIF and Gabor Wavelet achieved the highest accuracy of 99.95% for the first verification and 95.40% for the second verification. These results are 0.02 and 0.31 percentage points better, respectively than previous methods in the first and second verifications.

Page 44 of 47 | Total Record : 466

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