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Agus Harjoko
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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 506 Documents
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OPTIMIZING MACHINE LEARNING PIPELINE DESIGN THROUGH PROGRAMMING PARADIGM SELECTION Kusjani, Adi; Andriyani, Widyastuti; Kristomo, Domy
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112691

Abstract

This study investigates the impact of programming paradigm selection on the efficiency and sustainability of machine learning (ML) pipeline design. A case study was conducted using an agricultural IoT dataset for crop yield prediction, where four paradigms imperative, functional, object-oriented (OOP), and declarative were implemented to construct modular, maintainable, and reproducible pipelines. Each paradigm was evaluated through five key metrics: development time, debugging time, modularity, reproducibility, and maintainability. Experimental data were analyzed using descriptive statistics and visualized with boxplots and radar charts to identify performance differences. The results demonstrate that the functional paradigm achieved superior performance in data preprocessing with high reproducibility (95%), OOP produced the highest modularity (5.0/5), while the declarative paradigm exhibited the best reproducibility (98%) and deployment efficiency. In contrast, the imperative paradigm enabled faster prototyping but lacked long-term stability. Integrating paradigms in a multi-paradigm design reduced development time by 30.3%, debugging effort by 41.2%, and improved modularity and reproducibility by 41.6% and 21%, respectively. These findings highlight that no single paradigm is universally optimal; instead, a multi-paradigm approach provides a more efficient, maintainable, and production-ready ML pipeline framework adaptable to industrial-scale implementations.
Optimization of Electric Vehicle Charging Stations Recommendation for Intercity Travel in Bali Using K-NN Algorithm Theijer, Jessica; Tanamal, Rinabi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.113372

Abstract

The problem of range anxiety among electric vehicle (EV) users is caused by the uneven distribution of Public Charging Stations (Indonesian: Stasiun Pengisian Kendaraan Listrik Umum, or SPKLU) in the Bali region often occurs. Currently, existing navigation applications provide SPKLU locations, but still lack route-based, battery-aware and vehicle connector type recommendations.To address this limitation, an SPKLU recommendation system was developed using the K-Nearest Neighbors (KNN) algorithm, specifically designed for intercity travel across Bali Island. The proposed method applies a two-stage filtering mechanism: Geodesic Distance for initial candidate selection, followed by the Google Maps Directions API for route-accurate distance validation. The research data were obtained through manual collection from the PLN Mobile application, containing geographic coordinate locations and connector type information. User inputs parameters include origin, destination, current EV range, maximum travel capacity, and vehicle connector type.Experimental results show that the system can provide accurate SPKLU suggestions aligned with planned routes and optimal charging intervals. The findings indicate that the proposed model is lightweight, adaptive, and effective in supporting EV users, thereby reducing range anxiety while contributing to the promotion of sustainable transportation in Indonesia.
An Explainable Stacked Ensemble Learning Model for Predicting On-Time Doctoral Graduation Using Institutional Academic Data Biasa, I Wayan Eka
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.115875

Abstract

Kelulusan tepat waktu mahasiswa program doktoral merupakan indikator utama kinerja akademik dan tata kelola perguruan tinggi, namun hingga kini masih sulit diprediksi secara akurat dan objektif. Banyak institusi yang belum memiliki sistem berbasis data untuk mengidentifikasi mahasiswa yang berpotensi mengalami keterlambatan penyelesaian studi. Oleh karena itu, penelitian ini bertujuan mengembangkan model prediksi tepat waktu yang akurat dan dapat dijelaskan dengan memanfaatkan pendekatan pembelajaran ansambel bertumpuk dan kecerdasan buatan yang dapat dijelaskan. Data penelitian berasal dari rekam akademik mahasiswa doktoral Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar yang meliputi kinerja akademik, tahapan penelitian, intensitas bimbingan, dan status publikasi, dengan pembagian data pelatihan dan pengujian yang diproses menggunakan Google Colab. Model dibangun dengan menggabungkan Random Forest, Gradient Boosting, dan Extreme Gradient Boosting melalui skema stacking, serta dianalisis menggunakan SHapley Additive exPlanations (SHAP) untuk menjelaskan kontribusi setiap variabel. Hasil penelitian menunjukkan bahwa model ensemble yang diusulkan memiliki akurasi dan stabilitas yang lebih tinggi dibandingkan model tunggal, dengan faktor kinerja akademik awal, bimbingan disertasi, dan publikasi sebagai penentu utama izin tepat waktu. Temuan ini penting sebagai dasar pengambilan keputusan perancang dan intervensi akademik yang lebih tepat sasaran.
Evaluating Latent Emotional Structures through Unsupervised Semantic Text Clustering Edy, Edy; Junaedi, Junaedi; Hermawan, Aditiya; Kurnia, Yusuf; Maranto, Ardiane Rossi Kurniawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.116764

Abstract

Emotion analysis in textual data is an important topic in natural language processing, as emotions play a crucial role in understanding public opinion, psychological states, and dynamics of digital interaction. However, most existing studies rely heavily on supervised classification approaches based on predefined emotion labels, which may overlook latent semantic structures and emotional overlap inherent in natural language. This study aims to evaluate latent emotional structures in text using an unsupervised semantic clustering approach. The proposed method involves text preprocessing, feature representation using Term Frequency–Inverse Document Frequency (TF–IDF), dimensionality reduction through Singular Value Decomposition (SVD), and clustering using K-Means and Hierarchical Agglomerative algorithms. Both internal and post-hoc external evaluation metrics are employed to assess cluster quality and examine their correspondence with available emotion labels. The results indicate that K-Means clustering produces more compact and interpretable clusters than the hierarchical approach, while both methods reveal substantial emotional overlap across clusters. These findings suggest that emotional expressions in text exhibit a continuous semantic structure rather than discrete categorical boundaries. This study highlights the importance of unsupervised semantic clustering as an analytical tool for gaining deeper insight into latent emotional patterns in textual data.
Clustering High School Students’ Career Interests Using K-Means with Multi-Metric Validation latifah, noor; fatia, imelda annas; adiyono, soni
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117507

Abstract

Understanding students' career interests is essential for supporting effective career guidance programs in schools. However, identifying patterns of career interest among students is often challenging due to the diversity of motivational, cognitive, and planning-related factors. This study aims to analyze the segmentation of high school students' career interests using clustering techniques based on questionnaire data. This study uses the K-means algorithm run in conjunction with the Elbow Method to find the most appropriatenumber of clusters. The data preparation stages included cleaning the data, performing normalization using the Min-Max scaling method, and reducing the number of variables using principal component analysis (PCA) to facilitate visualization and initial analysis. In addition, cluster validity was evaluated using several internal validation indices, namely the silhouette score, Davies-Bouldin Index, and Calinski-Harabasz Index. The experimental results show that the data can be grouped into three clusters representing different levels of career interest characteristics among students. The identified clusters reveal variations in motivation, career planning clarity, and expectations for future careers. These findings provide useful insights for school counselors in designing targeted career guidance strategies.
Designing a Website-Based Internet Billing System with WhatsApp Payment Notifications Zulfannisa, Izzatul Husna; Wijayanti, Esti; Evanita, Evanita
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117555

Abstract

PT Muria Global Network, penyedia layanan internet, masih menggunakan sistem penagihan manual, yang memperlambat proses penagihan, pencatatan transaksi, dan penyampaian informasi pembayaran, meningkatkan risiko kesalahan, dan mempersulit rekapitulasi laporan keuangan. Studi ini bertujuan untuk merancang dan membangun sistem penagihan layanan internet berbasis web yang terintegrasi dengan WhatsApp sebagai media pemberitahuan pembayaran. Metode yang digunakan meliputi analisis kebutuhan, desain sistem, implementasi, dan pengujian fungsional. Sistem yang dikembangkan menyediakan fitur manajemen data pelanggan, penagihan otomatis, pencatatan status pembayaran, pelaporan keuangan terpusat, dan penyampaian pemberitahuan melalui WhatsApp Web dengan pesan yang terformat. Hasil menunjukkan bahwa sistem mampu mempercepat proses administrasi, meningkatkan akurasi pencatatan, dan menyederhanakan penyampaian informasi kepada pelanggan. Integrasi pemberitahuan membantu meningkatkan ketepatan waktu pembayaran dan efisiensi kerja administrasi. Dengan demikian, sistem yang diusulkan dapat menjadi solusi efektif untuk meningkatkan kualitas manajemen penagihan pada penyedia layanan internet.
Predicting Resale Prices using Random Forests with Fine-Tuning Hyperparameters Herman Widjaja; Nanda Perdana; Ito Wasito
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 I Nyoman Prayana Trisna; I Made Wiraharja Jaya Putra; Wayan Oger Vihikan
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 Iqbal Ramadhan Anniswa; Wahyu Syaifullah JAUHARIS SAPUTRA; Mohammad Idhom; Alfan Rizaldy Pratama; I Gede Susrama Mas Diyasa
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 Aditiya Hermawan; Benny Daniawan; Edy Edy; Joese Nathaniel
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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