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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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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Systematic Review of High Interaction Honeypots for Microsoft SQL Server Jazadi, Faiz Unisa; Mujiyatna, I Gede
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.104450

Abstract

This systematic review aims to dive into high interaction honeypots for Microsoft SQL Server. Topics covered include various honeypot environments (bare-metal, virtual machine, container) and monitoring methods (network-based, VMM-based, honeypot-based) to understand how to effectively monitor encrypted communications. The main focus is to compare different data monitoring techniques for high-interaction honeypots, especially considering the challenges posed by encrypted protocols such as TDS used by Microsoft SQL Server. This research identifies limitations in current research and proposes the use of encrypted MITM proxies as a potential solution. Ultimately, this research highlights the need for further research in this area due to the limited existing literature on high interaction honeypots for Microsoft SQL Server.
The Impact of Data Augmentation Techniques on Improving Speech Recognition Performance for English in Indonesian Children Based on Wav2Vec 2.0 Maskur, Maimunah; Zahra, Amalia
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.104646

Abstract

Early childhood education is a crucial phase in shaping children's character and language skills. This study develops an Automatic Speech Recognition (ASR) model to recognize the speech of Indonesian children speaking English. The process begins with collecting and processing a dataset of children's speech recordings, which is then expanded using data augmentation techniques to enhance pronunciation variations. The pre-trained ASR Wav2Vec 2.0 model is fine-tuned with both the original and augmented datasets. Evaluation using Word Error Rate (WER) and Character Error Rate (CER) shows a significant accuracy improvement, with WER decreasing from 53% to 45% and CER from 33% to 27%, reflecting a performance increase of approximately 15%. Further analysis reveals pronunciation errors in phonemes such as /ð/, /θ/, /r/, /v/, /z/, and /ʃ/, which are uncommon in the Indonesian language, manifesting as substitutions, omissions, or additions in words like "three," "that," "rabbit," "very," and "zebra." These findings highlight the need for targeted phoneme training, audio-based approaches with ASR feedback, and the listen-and-repeat technique in English language instruction for children.Keywords— Early childhood education, Automatic Speech Recognition, Augmentation, Character Error Rate, Word Error Rate
Exploring the Impact of Back-Translation on BERT's Performance in Sentiment Analysis of Code-Mixed Language Data Setiono, Nisrina Hanifa; Sari, Yunita
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.104757

Abstract

Social media, particularly Twitter, has become a key platform for communication and opinion-sharing, where code mixing, the blending of multiple languages in a single sentence, is common. In Indonesia, Indonesian-English code mixing is widely used, especially in urban areas. However, sentiment analysis on code-mixed text poses challenges in natural language processing (NLP) due to the informal nature of the data and the limitations of models trained on formal text. This study applies back translation to address these challenges and optimize BERT-based sentiment analysis. The method is tested on the INDONGLISH dataset, consisting of 5,067 labeled tweets. Results show that applying back translation directly to raw tweets yields better performance by preserving original meaning, improving model accuracy. However, when back translation follows monolingual translation, accuracy declines due to semantic distortions. Repeated translation modifies sentence structure and sentiment labels, reducing reliability. These findings indicate that each additional translation step risks decreasing sentiment analysis accuracy, particularly for code-mixed datasets, which are highly sensitive to linguistic shifts. Back translation proves to be an effective approach for formalizing data while maintaining contextual integrity, enhancing sentiment analysis performance on code-mixed text
Prediction Sentiment Analysis Grab Reviews Using SVM Linear Based Streamlit Hidayat, Muhammad Taufiq; Arifin, Muhammad; Muzid, Syafiul
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.104924

Abstract

Advances in digital technology have accelerated the transformation of online transportation services, intensifying competition and driving innovations to enhance service quality. As a leading platform in Indonesia, Grab faces various challenges, including driver service quality, payment systems, and application stability, as reflected in user reviews on Google Play Store. This study aims to gain strategic insights by evaluating a linear kernel-based Support Vector Machine (SVM) model integrated into the Streamlit platform to predict the sentiment of Grab user reviews. Data were collected via web scraping and processed using tokenization, stopword removal, and stemming techniques to improve model accuracy. The model was implemented on an interactive Streamlit website featuring two main functionalities: sentiment prediction and plot visualization. The sentiment prediction feature presents sentiment distribution, performance metrics, a confusion matrix, and a classification report, while the visualization feature displays interactive word clouds, bar charts, and pie charts. Model evaluation reveals an accuracy of 83% in the Streamlit environment. These findings are expected to contribute to developers and stakeholders in enhancing Grab services and advancing more effective sentiment prediction methods.
Multi-Domain Sentiment Analysis on Ibu Kota Nusantara (IKN) Tweets Using CNN-LSTM Prasastio, Fahmi Reza; Winarko, Edi
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.104925

Abstract

The construction of Ibu Kota Nusantara (IKN) is a national project aimed at relocating Indonesia’s capital from Jakarta to East Kalimantan. This project has sparked various public opinions, which are widely expressed through social media platforms such as Twitter. Sentiment analysis of these opinions is crucial for understanding public perception of the IKN project. However, previous sentiment analysis studies have often overlooked domain variations in the analyzed data, such as economy, environment, and politics, each of which has distinct linguistic characteristics. This study aims to develop a multi-domain sentiment analysis model by comparing three main methods: CNN-LSTM, CNN, and LSTM. The multi-domain model is designed to address the differences in characteristics across domains and enhance the model’s ability to capture more complex sentiment patterns. The results indicate that multi-domain models outperform single-domain models, as they improve classification performance by leveraging information from multiple domains. CNN-LSTM proved to be the best model, achieving the most balanced Accuracy and F1-Score across various scenarios. The use of Keyword Embedding also significantly enhances model performance, particularly benefiting LSTM, which initially had the lowest performance.
Preprocessing Algorithm for K-Means Anomaly Detection on Payment Logs Alamsyah, Reka; Rokhman, Nur
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.105290

Abstract

The payment aggregator system with the single settlement feature enhances transaction efficiency. However, this also poses risks of cyberattacks and system errors. These risks can lead to abnormal events or anomalies. The middleware service records transaction activities in the form of logs. Log data can be analyzed for anomaly detection resulting from cyberattacks or system errors.K-Means clustering is less effective in detecting anomalies in log data because transaction log data is often unstructured, inconsistent, and has varying feature scales.This study develops a preprocessing algorithm to improve data quality before clustering. Transaction log data from July to December 2023 is used, with preprocessing stages including normalization, standardization, and Principal Component Analysis (PCA). K-Means is applied with K-Means++ initialization, and the number of clusters is determined using the kneedle algorithm. The results show that standardization improves segmentation, and PCA enhances anomaly detection effectiveness.
Classifying Heart Disease through Fusion of Multi-Source Datasets: Integration of Feature Selection and Explainable Machine Learning Techniques Aprianto, Kasiful; Anasanti, Mila Desi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.92395

Abstract

This study delves into heart disease classification through integrated feature selection and machine learning methodologies, utilizing three datasets comprising 4,728 participants and 11 features, with 4.27% missing data. Employing machine learning, we used XGBoost to achieve 0.95 accuracy for one feature, while Random Forest (RF) demonstrated accuracies of 0.92 and 0.99 for the remaining two features. Comparing 11 classification models, RF and XGBoost classified heart disease with 0.97 and 0.99 accuracy, respectively, using all available features. Applying Feature Elimination with Simultaneous Perturbation Feature Selection and Ranking (SpFSR) revealed that RF attained 0.99 accuracy by selecting only four features (cholesterol level, age, resting electrocardiographic measurements, and maximum heart rate), while XGBoost dropped to 0.91. Constructing an RF model with four features enhanced interpretability without compromising accuracy. Explainable Machine Learning (XAI) techniques, including Permutation Importance and SHAP Summary Plot analyses, gauged feature impact on heart disease prediction. The resting electrocardiographic measurements feature held the highest value (0.40 ± 0.01), followed by maximum heart rate (0.32 ± 0.01), cholesterol level (0.28 ± 0.01), and age (0.26 ± 0.005). These results underscore the significance of each feature in diagnosing heart disease via machine learning.
DEVELOPMENT OF CHATBOT FOR PRE-DIAGNOSIS AND RECOMMENDATION OF ANXIETY DISORDER USING DIET AND SENTENCE TRANSFORMER MODELS Winarko, Edi; Suryanti, Angel Berta Desi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.95900

Abstract

 Previous research on chatbots for pre-diagnosis and recommendation of anxiety disorders has been limited to therapy aids.  Comparing NLU DIET and LogisticRegressionClassifier models, this chatbot system calculates anxiety levels using GAD-7, DASS, and STAIT/STAIS-5 methods along with Sentence Transformer (SBERT) for semantic similarity.Intent classification testing yielded 95% accuracy for NLU DIETClassifier and 99% for LogisticRegressionClassifier. The Dialog Model achieved 68% accuracy with TEDPolicy. Testing involved 35 randomly selected respondents, including students and workers. From their interactions, the SBERT recommendation model scored 30% MAP, 26% for the Indobert base and paraphrase-multilingual-MiniLM-L12-v2 models.The average satisfaction and performance rating for the chatbot system was 3.7 out of 5. This research addresses the need for a prototype chatbot for pre-diagnosis and recommendation of anxiety disorders, with the best NLU model being LogisticRegressionClassifier at 99% accuracy and the dialog model at 68%. However, the recommendation system still has a low MAP due to the use of non-valid clinical data as references, suggesting room for improvement in future research.
Breast Cancer Classification Based on Mammogram Images Using CNN Method with NASNet Mobile Model Pramesti, Diah Devi; Farida, Yuniar; Novitasari, Dian Candra Rini; Wibowo, Achmad Teguh
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.98187

Abstract

In Indonesia, the type of cancer that contributes to the highest death rate is breast cancer, so there is a great need for early examination, clinical examination, and screening, which includes mammography. Mammography is currently the most effective method for detecting early-stage breast cancer. This study aims to classify breast cancer cells based on mammogram images. The method used in this research is CNN (Convolutional Neural Network) with the NASNet Mobile model for classifying three classes: normal, benign, and malignant. The CNN method can learn various input attributes powerfully so that CNN can obtain more detailed data characteristics and has better detection capabilities. This research obtained the most optimal model based on the percentage of accuracy, sensitivity, and specificity values of 99.67%, 98.78%, and 99.35%, respectively. This research can be used to help radiologists as considerations in making breast cancer diagnosis decisions.
Enhancing Image Classification Performance Using Multi CNN Feature Fusion Method Hamda, Hizbullah; Wibowo, Moh Edi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 3 (2025): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.98531

Abstract

This research aims to overcome general challenges in the field of image pattern recognition using a convolutional neural network (CNN), which is still faced with the complexity and limitations of image data. Achieving high accuracy is essential because it significantly influences the effectiveness and success of numerous areas. Although deep learning technology, especially CNNs, offers the potential to improve accuracy, it is still limited to the 70–80% range for achieving the expected level of accuracy. In this research, a fusion method was developed that combines pre-trained models using concatenation techniques to increase accuracy. By utilizing pre-trained models such as ResNet50, VGG16, and MobileNet-v2, which were then adapted to various datasets and cross-validation techniques, researchers managed to achieve significant improvements in accuracy. The results of this study show an improvement in the accuracy of the Fusion Multi-CNN model for various datasets. On the fashion dataset, MNIST managed to achieve an accuracy of 0.87840, while on CIFAR-10 and Oxford-102, the accuracy was 0.81260 and 0.84004, respectively.

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