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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 10 Documents
 1 
Search results for , issue "Vol 20, No 2 (2026): April" : 10 Documents clear
Website Optimization by Relocating Third-Party Script Using Web Worker M.Kom, Nur Rokhman; Andreanor, Muhammad Pazrin
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.108581

Abstract

The use of third-party scripts such as Google Analytics, Meta Pixel, and Twitter Widget has become standard in modern website development. On a website, the main thread is the main line that handles the display process and user interaction in the browser. However, third-party scripts often overload the main thread and slow down page load time, degrading user experience. This study aims to optimize website performance by accelerating page load time, maintaining user interactivity, and improving resource efficiency. This research proposes an optimization method by moving the execution of third-party scripts to the Web Worker. By applying Script Offloading and Isolation, Inter-thread Communication, and Proxy Pattern methods, this system is able to safely move the execution to the background without affecting user interface interaction. The results of relocating third-party scripts to Web Workers on three types of websites (Blog, Company Profile, and E-commerce) showed an increase in performance score of up to 13.3% and a reduction in blocking time of up to 51.8%, although accompanied by an increase in memory of 1-4%. These findings indicate that using Web Worker significantly improves website performance with minimal negative impact on resource consumption.
Hybrid Recommendation System Based on Implicit Feedback with Collaborative Filtering and Gradient Boosting Kurniawan, Hendra; Zahra, Amalia
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.112368

Abstract

Recommendation systems are essential components in video streaming services as they assist users in selecting relevant content in line with the increasing availability of large-scale content. However, most recommendation systems still rely on explicit feedback data such as ratings, which are often unavailable on many platforms. This study aims to develop a hybrid recommendation system based on implicit feedback by constructing an interaction score derived from user behavior as a substitute for ratings. The proposed model integrates collaborative filtering methods (matrix factorization and k-nearest neighbor) with the CatBoost gradient boosting decision tree algorithm. The evaluation was conducted using empirical data from a video streaming service, with performance measured using root mean squared error (RMSE) and mean absolute error (MAE). The results indicate that the hybrid model achieves lower RMSE and MAE values compared to individual models. These findings confirm that the hybrid approach is effective in improving recommendation accuracy while also contributing to enhanced user experience quality in video streaming platforms without explicit rating data.
BATINARA: Hybrid LLM-BERT-ML Chatbot for Safe Mental Health Support Walangitan, Jeanette; Ujianto, Erik Iman Heri
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.112432

Abstract

This study addresses the pressing need for safe and personalized digital mental health support by mitigating the inherent risk of Large Language Models (LLMs) generating unsafe or unethical responses during high-risk psychological crises. We developed BATINARA, a chatbot system based on a Neuro-Symbolic Hybrid framework. This architecture integrates a Predictive Module (IndoBERT for crisis detection, Random Forest for multi-label emotion classification) with a Generative LLM Module (OpenAI API). Ethical control is enforced by the Dynamic Context Integration Logic (D-CIL), which utilizes clinical rules to uphold the Principle of Nonmaleficence. Key results demonstrate the system’s ability to: (1) Enforce safety protocols through the automatic override of LLM responses when suicidal ideation is detected (Recall IndoBERT 0.9977). (2) Achieve high contextual accuracy in multi-label emotion detection (F1 = 0.94), which supports dynamic personalization via Dynamic Prompt Modulation based on specific therapeutic styles and user PHQ-9/GAD-7 clinical scores. (3) Enhance interaction transparency through the real-time visualization of detected emotions. This Neuro-Symbolic hybrid approach proves effective in mitigating clinical risks associated with generative AI, resulting in adaptive and ethically sound therapeutic interactions.
Preliminary Evaluation of an LLM-Based Annotation Framework for Medical NER in Bahasa Indonesia Ningtyas, Annisa Maulida; Salim, Marko Ferdian; Syairaji, M; Santoso, Dian Budi
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.112690

Abstract

Keterbatasan data beranotasi berkualitas menghambat pengembangan model Named Entity Recognition (NER) medis untuk Bahasa Indonesia. Penelitian ini menyajikan desain evaluasi untuk framework anotasi semi-otomatis berbasis Large Language Models (LLMs) untuk terminologi medis Indonesia. Framework mengintegrasikan pre-anotasi LLM dengan validasi manusia melalui web interface. Evaluasi dirancang dari dua aspek: reliabilitas teknis melalui self-consistency analysis dan pengalaman pengguna melalui System Usability Scale (SUS). Data uji berupa 50 dokumen forum kesehatan online dengan lima kategori entitas: penyakit, gejala, pengobatan, obat, dan anatomi. Metodologi evaluasi yang diusulkan bertujuan mengukur konsistensi prediksi LLM, distribusi confidence level, dan usability platform untuk anotator non-pakar. Hasil evaluasi akan melaporkan Cohen's Kappa untuk self-consistency, skor SUS untuk usability, dan acceptance rate untuk kualitas pre-anotasi. Framework lengkap dan dataset hasil anotasi akan dilaporkan dalam publikasi terpisah. 
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
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.

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