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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 2 (2025): April" : 10 Documents clear
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.
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.

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