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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 3 (2025): July" : 10 Documents clear
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.
DESICION SUPPORT SYSTEM OF LAND SUITABILITY FOR CORN SEED VARIETIES Mulyana, Sri; Syahputra, Rizky Yurdan
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.105285

Abstract

Decision making in selecting suitable agricultural land is a key factor for the success of corn cultivation. The selection of agricultural land is still largely based on the experience of farmers, which lacks a strong analytical foundation, this can lead to a decrease in production as the evidenced in 2023, the dry corn kernel production decline by 12,5%  compared to the previous year. This research develops a Decision Support System (DSS) to analyze land suitability for corn varieties by using the Analytical Hierarchy Process (AHP) method to calculate the priority weights of each evaluation criterion, and the Profile Matching (PM) method to rank agricultural. The research uses data from 22 sub-districts in Blitar Regency as alternatives and 5 types of corn varieties as ideal profiles. The ranking results of this research indicate that the best agricultural land for varieties V1, V2, V3, and V4 is in Sanankulon Sub-district, while for variety V5, it is in Doko Sub-district. The validity test results showed a “Strong” coefficient, and the reliability test yielded a Cronbach's alpha of 0.8019, indicating a "Good" level of consistency.
Support Vector Machine for Accurate Classification of Diabetes Risk Levels Sugiartawan, Putu; Wardani, Ni Wayan; Pradhana, Anak Agung Surya; Batubulan, Kadek Suarjuna; Kotama, I Nyoman Darma
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.107740

Abstract

This research explores the application of Support Vector Machines (SVM) for accurately classifying diabetes risk levels based on a publicly available dataset containing 768 instances and 9 attributes, including glucose levels, BMI, blood pressure, and insulin levels. The model's systematic development process involved data preprocessing, feature selection, and hyperparameter optimization to ensure robust performance. Results indicate an overall accuracy of 76%, with high precision and recall for the non-diabetic risk class, but relatively lower performance for the diabetic risk class, highlighting the challenges posed by class imbalance and overlapping data features. To address these issues, future research should incorporate advanced resampling techniques, refined feature engineering, and alternative machine learning models like Random Forest or XGBoost. This research underscores the potential of SVM as a valuable tool for early diabetes detection, offering healthcare professionals a reliable means to identify at-risk individuals and personalize intervention strategies. By bridging theoretical advancements and practical applications, the research contributes to enhancing predictive analytics in medical diagnostics, paving the way for improved patient outcomes and efficient public health management
Prostate Cancer Detection Using Gradient Boosting Machines Effectively MusliminB, Muslimin; Karim, Syafei; Nurhuda, Asep
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.107742

Abstract

Prostate cancer remains a leading cause of cancer-related deaths among men globally, emphasizing the critical need for accurate diagnostic tools. This study investigates the application of Gradient Boosting Machines (GBMs) for prostate cancer detection using a dataset with key tumor characteristics such as radius, texture, area, and symmetry. Data preprocessing included normalization, missing value handling, and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. The GBM model demonstrated an accuracy of 75%, with high precision (82%) and recall (88%) for malignant cases, underscoring its potential as a reliable diagnostic tool. However, the model's performance for benign cases was limited by severe class imbalance, reflected in a precision of 33% and recall of 25%. Interpretability was enhanced using SHAP values, identifying key predictors like tumor perimeter and compactness. While GBMs show promise in prostate cancer diagnostics, future research should incorporate multimodal data, advanced balancing techniques, and rigorous validation frameworks to enhance generalizability and fairness. This study highlights the value of machine learning in healthcare, contributing to improved diagnostic accuracy and patient outcomes.
Implementation of Chi-Square Feature Selection for Parkinson’s Disease Classification Using LightGBM Ahdyani, Annisa Salsabila; Budiman, Irwan; Kartini, Dwi; Farmadi, Andi; Mazdadi, Muhammad Itqan
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.107881

Abstract

Penyakit Parkinson merupakan penyakit yang disebabkan oleh kerusakan sel saraf otak dan termasuk penyakit yang jumlah kasusnya meningkat pesat di dunia. Salah satu cara yang dapat dilakukan untuk mencegah meningkatnya kasus penyakit Parkinson adalah dengan melakukan diagnosis melalui metode klasifikasi dengan pendekatan pembelajaran algoritmik. Penelitian ini mengimplementasikan teknik Chi-Square untuk pendekatan pemilihan fitur yang relevan dengan algoritma Light Gradient Boosting Machine (LightGBM) dalam klasifikasi penyakit Parkinson. Pemilihan fitur Chi-Square bertujuan untuk mengurangi fitur yang kurang relevan sehingga dapat meningkatkan hasil kinerja model. Selain itu, metode SMOTE diterapkan untuk menangani ketidakseimbangan data dan penyetelan hiperparameter guna menentukan kombinasi parameter yang optimal. Pengujian dilakukan terhadap sepuluh variasi jumlah fitur, dengan hasil terbaik diperoleh dengan menggunakan 200 fitur yang menghasilkan akurasi sebesar 96,05%. Dengan menggunakan metode Chi-Square, kinerja model LightGBM meningkat dibandingkan dengan kinerja tanpa pemilihan fitur. Penerapan kombinasi metode ini dapat meningkatkan kinerja model klasifikasi secara signifikan dan berpotensi untuk diterapkan dalam sistem pendukung diagnosis penyakit Parkinson.
Court Decision Prediction Model Using Natural Language Processing and Random Forest Nasution, Nasution; Suprapto, Suprapto
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.108377

Abstract

The increasing number of criminal cases in Indonesia, which reached 288,472 in 2023, or rose by 15% from the previous year, has created a substantial workload for judicial professionals. This situation highlights the urgent need for artificial intelligence–based decision support systems to accelerate and improve the quality of legal decision-making. This study proposes a court decision prediction approach using the Random Forest algorithm combined with Natural Language Processing (NLP) techniques. The dataset consists of 21,630 court decisions from the Supreme Court of Indonesia, originally in PDF format and converted into XML. The research procedure includes text preprocessing, feature construction using Word2Vec and Fast Text, and Random Forest classification. Unlike previous studies employing LSTM, BiLSTM, and CNN methods with accuracy ranging from 49.14% to 77.32%, the proposed approach delivers better performance. Experimental results show that the model achieves a prediction accuracy of up to 63%-81% for Penalty Categories classification and up to 65%-80% for long punishment regression. These findings demonstrate the significant potential of applying NLP and Random Forest to develop predictive systems in Indonesian legal document analysis.
Deep Learning for Automatic Assessment and Feedback in LMS-Based Education Kusuma, Aniek Suryanti; Ekayana, Anak Agung Gde; Dwi Utami Putra, Desak Made
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.109333

Abstract

Learning Management Systems (LMS) play a critical role in modern education by organizing content, facilitating communication, and supporting student assessment. However, most current LMS platforms depend on manual grading and generalized feedback, which can be inefficient and lack personalization. This research enhances LMS capabilities by integrating deep learning techniques—specifically Natural Language Processing (NLP)—to automate assessment and deliver personalized feedback. The system analyzes student input, such as written assignments and discussion forum posts, to evaluate performance and generate real-time, adaptive feedback. A modular framework was developed using a Bidirectional LSTM-based architecture trained on sequence data with regression objectives. The model was evaluated using the Mean Squared Error (MSE) metric. The results show that the model performs reasonably well, with predictions closely aligned to actual values in most cases, although its performance decreases slightly at the distribution extremes. Visualization via scatter plots further confirms the model's ability to capture context and structure in textual input. These findings demonstrate the model's feasibility in educational environments and its potential to reduce instructor workload while improving the quality of feedback. Future work will consider integrating attention mechanisms and multilingual capabilities for broader applicability.

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