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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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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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.
Predicting Resale Prices using Random Forests with Fine-Tuning Hyperparameters Widjaja, Herman; Perdana, Nanda; Wasito, Ito
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103967

Abstract

The accurate prediction of housing prices is essential for informed decision-making by purchasers, sellers, and policymakers in dynamic real estate markets. This study investigates the application of machine learning models—Random Forest, XGBoost, Decision Tree, and LightGBM—to predict resale flat prices in Singapore. It provides valuable insights into the use of machine learning in housing markets, particularly for datasets with similar size, complexity, and data types. The objectives are to develop predictive regression models for property prices and to analyze and compare the performance of these models. Key contributions include the development of tools to objectively estimate suitable property prices and the advancement of price prediction research through an extensive comparison of machine learning models. While previous studies have demonstrated the predictive capabilities of these models, this research focuses on the impact of hyperparameter tuning on the performance of the Random Forest model. By systematically optimizing parameters such as max_depth, n_estimators, and n_jobs, computation time was reduced by over 93% (from 865 seconds to 50 seconds) with minimal loss in accuracy. With proper hyperparameter tuning, Random Forest achieved the best performance in terms of MAE score (26.555), outperforming XGBoost (27.552), Decision Tree (28.832), and LightGBM (29.752).
Classifying Indonesian Hoax News Titles with SVM, XGBoost, and BiLSTM Trisna, I Nyoman Prayana; Putra, I Made Wiraharja Jaya; Vihikan, Wayan Oger
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.106608

Abstract

This study investigates the automated detection of hoaxes related to President Jokowi in Indonesian news by analyzing only news titles, aiming for efficient detection and reduced traffic to harmful websites. We compared the performance of traditional (SVM, XGBoost) and deep learning (BiLSTM) algorithms, with and without Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in a dataset scraped from trusted news sources (CNN Indonesia, Detik News) and a fact-checking platform (turnbackhoax.id). The results indicate that BiLSTM generally outperformed SVM and XGBoost, demonstrating the potential of deep learning for this task. However, applying SMOTE negatively impacted BiLSTM's performance, suggesting overfitting. Notably, precision consistently exceeded recall across all models, indicating high reliability in identifying hoaxes but a potential for missing a significant number of actual hoaxes. This highlights a trade-off between avoiding false positives and ensuring comprehensive detection. The findings also suggest that language-specific characteristics influence algorithm effectiveness. This research contributes to developing efficient and accurate tools for combating misinformation in the Indonesian online environment, emphasizing the importance of title-based analysis and careful consideration on data balancing.
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Anniswa, Iqbal Ramadhan; JAUHARIS SAPUTRA, Wahyu Syaifullah; Idhom, Mohammad; Rizaldy Pratama, Alfan; Susrama Mas Diyasa, I Gede
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
Optimization of Multimodal Deep Learning for Depression Detection Hermawan, Aditiya; Daniawan, Benny; Edy, Edy; Nathaniel, Joese
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111407

Abstract

Depression is a complex and often underdiagnosed mental health condition that manifests through subtle verbal, acoustic, and behavioral cues. Traditional unimodal detection systems struggle to capture the full spectrum of depressive symptoms, often leading to inaccurate or incomplete assessments. This study proposes a multimodal deep learning framework that integrates textual, audio, and visual modalities to improve the robustness and reliability of automatic depression detection, achieving an overall classification accuracy of 74%. The approach prioritizes privacy and interpretability by using facial keypoints and gaze direction rather than raw video frames, and applies attention mechanisms to align and fuse features across modalities. Each modality is processed through dedicated neural architectures tailored to its data type, and their outputs are combined within a fusion model that learns to capture cross-modal emotional patterns. Experimental results demonstrate that the proposed multimodal system significantly outperforms its unimodal counterparts in terms of classification performance. The visual modality was found to contribute most strongly to detection accuracy, as confirmed by ablation analysis. These findings highlight the value of multimodal integration in capturing complex psychological signals and support the development of intelligent, non-invasive screening tools for use in digital mental health applications.

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