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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 19, No 1 (2025): January" : 10 Documents clear
Exploring the Relationship between Artificial Intelligence and Business Performance Lutfiani, Ninda; Sembiring, Irwan; Setyawan, Iwan; Setiawan, Adi; Rahardja, Untung; Sulistio, Sulistio
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.86697

Abstract

The integration of Artificial Intelligence (AI) into business operations has garnered significant attention due to its potential impact on business performance. However, the relationship between AI adoption and business performance remains not fully understood. This article comprehensively analyzes this relationship through three key aspects: the acceptance and implementation of AI within organizations, the impact of AI on various dimensions of business performance, and the potential challenges associated with AI adoption. In this study, we employ SmartPLS as an analytical tool to evaluate the relationships between identified factors and the impact of AI adoption on business performance. Our findings reveal that several factors influence the adoption and implementation of AI, including data availability, organizational culture, leadership support, technical expertise, and ethical considerations. Moreover, AI adoption significantly influences business performance metrics such as productivity, efficiency, revenue, and customer satisfaction. Nonetheless, challenges arising from AI adoption, including shifts in job roles, data privacy, and security concerns, also require substantial attention. In conclusion, successful AI adoption and implementation necessitate careful consideration of organizational, technical, and ethical factors. This research provides valuable insights for business leaders and researchers seeking a deeper understanding of the relationship between Artificial Intelligence and business performance.
Financial Forecast Optimization with Ensemble Models and Error Analysis Hari Purwidiantoro, Moch; Aini, Afifah Nur; Agustin, Tinuk
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.100689

Abstract

 This study proposes an error mitigation model applied to the financial sector in higher education, aiming to improve the prediction accuracy in a linear regression model used to monitor and manage campus finances. By analyzing the error distribution of the original model, an additional model is developed to reduce the impact of errors on identified sensitive areas. These two models are then combined into one ensemble model, which is able to reduce the standard residual error (RSE) by up to 7%. The use of this ensemble model has proven effective in improving the accuracy of the results compared to a single model. A case study using university financial data, including parameters such as operating costs, revenues, and budget allocations, shows that error mitigation can provide significant improvements in campus financial management, especially in terms of budget planning and expenditure prediction. This study opens up opportunities for wider application in the higher education sector that requires more accurate and efficient financial management
Obstacles Detection in Underwater Environment Using ROV Based on Convolutional Neural Network Asri, Purwidi; Widiarti, Yuning; Purwanti, Endang Pudji; Wismawati, Endah; Arifin, M. Firman Tsany
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.101698

Abstract

Pada saat RoV berada dibawah air tidak sedikit obstacle yang dijumpai dan berpengaruh terhadap  kinerja dan keselamatan body ROV itu sendiri. Obyek yang tertangkap kamera ROV seringkali sulit untuk diidentifikasi dan dideteksi karena besarnya noise bawah air. Selain itu, sifat air yang membiaskan cahaya dan tingkat kejernihan air turut berpengaruh terhadap kualitas gambar yang dihasilkan. Untuk membantu dalam mengidentifikasi obyek yang ada di bawah air, maka pada penelitian ini proses identifikasi dilakukan dengan menggunakan Convolutional Neural Networks (CNN). CNN mengekstraksi fitur penting dari gambar melalui beberapa lapisan konvolusi. Setiap lapisan konvolusi menggunakan filter untuk mendeteksi pola seperti tepi, sudut, atau tekstur dari gambar input. Pada tahap akhir, fitur-fitur yang sudah diproses ini dihubungkan ke lapisan fully-connected yang bertindak sebagai pengklasifikasi. CNN kemudian memetakan fitur-fitur tersebut ke dalam kelas-kelas tertentu , misalnya objek seperti botol, tiang kayu, rantai, dan propeller. Dari pengujian secara real-time sistem berhasil menunjukkan performansi yang baik dengan akurasi validasi sebesar 99.25% dan akurasi klasifikasi real-time sebesar 85%. Hasil klasifikasi selanjutnya menentukan pergerakan thruster ROV.
Sentiment Analysis Mobile JKN Reviews Using SMOTE Based LSTM Tamami, Ghufron; Triyanto, Wiwit Agus; Muzid, Syafiul
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.101910

Abstract

The JKN Mobile application plays an important role in providing easy and fast access to health services for JKN-KIS users. However, user reviews indicate dissatisfaction with several aspects of the application, such as login issues and OTP codes, which can affect the overall user experience. Another challenge faced is class imbalance in the review dataset, which can affect the performance of sentiment analysis. This study uses Long Short-Term Memory (LSTM) combined with Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Review data was collected from Google Play Store and Kaggle, then preprocessed including lemmatization, tokenization, and padding. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that LSTM with SMOTE achieved 88% accuracy, 90% precision, 88% recall, and 89% F1-score. SMOTE successfully improved performance in the minority class although there was a slight decrease in accuracy compared to the model without SMOTE. Word cloud visualization reveals positive sentiments regarding the ease of use of the application, while negative sentiments indicate areas that need improvement. This study emphasizes the importance of handling imbalanced datasets to produce more accurate sentiment analysis.
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.

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