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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 486 Documents
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Sentiment Classification of MyTelkomsel Reviews Using SVM and Logistic Regression Adinata, Rijal Bagus; Supriyono, Supriyono; Fithri, Diana Laily
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.110409

Abstract

The development of digital technology has encouraged increased user participation in expressing opinions through review platforms, such as the Google Play Store. MyTelkomsel's application, a digital service from Indonesia's leading telecommunications provider, has received various responses, from appreciation to complaints related to app performance and customer service. This study aims to evaluate sentiment in user reviews using Support Vector Machine (SVM) and Logistic Regression algorithms. Data was collected from the Google Play Store and underwent a series of pre-processing stages, including data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The feature extraction process uses the TF-IDF approach, while model performance evaluation is based on accuracy, precision, recall, F1-score, and Area Under Curve (AUC) metrics. The results showed that the performance of both models was relatively balanced, but SVM exhibited an advantage in recall for positive sentiment (82%), accuracy (93.36%), and AUC (0.9680). Logistic Regression excels in precision (99%) in the positive class. WordCloud visualization illustrates consistency of dominant words in each sentiment class, reflecting the model's ability to identify patterns in user opinion. These findings are expected to contribute to the improvement of digital services based on user input.
Leaf Disease Detection Model in Gayo Coffee Plantations Using Deep Learning Hidayat, Rahmad
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.110529

Abstract

Coffee is one of the most important tropical plantation commodities, significantly supporting the economy of the Gayo Highlands. Attacks of various diseases can significantly reduce the productivity and quality of Gayo coffee. This study developed a leaf disease detection model in coffee plants using the Convolutional Neural Network (CNN) method. The model developed in this study used two datasets. The first dataset, the Gayo Coffee Leaf Disease (PDKG), comprises 900 images of healthy and diseased leaves collected from Gayo coffee plantations. The acquired images in the PDKG dataset were then preprocessed to improve their image quality. The results of model training and testing on the PDKG dataset showed an accuracy of 0.91. On the public Coffee Leaf Diseases (CLD) dataset, the model achieved an accuracy of 0.95, representing a 7.1% increase compared to previous studies. The resulting model can help local coffee farmers in the Gayo Highlands detect leaf diseases early and manage plant health more efficiently and accurately. 
Developing an NLP-Powered Chatbot Application for MSME Legal Literacy Hidayatulloh, Syarif; Utami, Lilyani Asri; Rachmi, Hilda
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111460

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the national economy but often face legal challenges due to limited literacy regarding regulations and lack of access to information. This study aims to develop a legal literacy chatbot application for MSMEs based on Natural Language Processing (NLP) using the Rapid Application Development (RAD) method. The development process was carried out iteratively by involving users to ensure the system meets their needs. System evaluation included Black Box testing, usability testing using the System Usability Scale, relevance testing, and performance testing. The Black Box results showed that all functions ran 100% successfully. Usability testing involving 24 respondents obtained an average SUS score of 71.98, which exceeded the standard threshold of 68, indicating that the application is acceptable and easy to use. Relevance testing showed a high level of answer suitability, while performance testing with GTmetrix produced a Performance score of 87%, Structure score of 92%, a fully loaded time of 2.1 seconds, and a total page size of 0.98 MB. These findings highlight that the chatbot application can provide legal information quickly, accurately, and practically, as well as has the potential to improve the legal literacy of MSME actors.
Virtual Reality-Based Counseling Innovation for Depression Therapy in the Patient Healing Process Jiwa Permana, Agus Aan Aan Jiwa
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111768

Abstract

Depression is a mental disorder that significantly affects patients’ quality of life and psychological well-being, necessitating innovative therapeutic approaches. This study focuses on the development of Virtual Reality (VR)-based Counseling Innovation, as VR technology offers immersive and interactive experiences that enhance patient engagement and support the healing process. The research employs the Rapid Application Development (RAD) method, which includes requirement identification, prototype design, system construction, and implementation. The VR therapy model incorporates a calming night environment with natural sounds and a daytime environment accompanied by instrumental music to facilitate relaxation and emotional recovery. Testing was conducted with 12 respondents to evaluate system usability and user interface/user experience (UI/UX), yielding a System Usability Scale (SUS) score of 82.08, indicating high acceptance. The results demonstrate that the VR counseling model provides an enjoyable and effective experience that supports patients’ emotional healing. These findings suggest that VR-based counseling can serve as an innovative alternative for depression therapy, offering a personalized, interactive, and adaptive approach. Furthermore, the study provides a foundation for integrating digital technologies into modern psychotherapy practices, highlighting the potential of VR to enhance therapeutic engagement and promote mental health recovery in a controlled, immersive environment.
Interpretable Rule-Based Clinical Decision Support for Early Screening Of Heart Disease using C4.5 Decision Trees Andriyani, Widyastuti; Wiyanti, Dian Tri; Nugroho, Daniel C.A.
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112701

Abstract

This study develops an interpretable rule-based clinical decision support system for early screening of heart disease presence by integrating the C4.5 decision tree algorithm with a rule-based reasoning mechanism. The proposed approach is intended to assist clinicians in obtaining rapid, transparent preliminary indications from clinical data, particularly in settings that require lightweight and auditable solutions. The dataset was obtained from the UCI Heart Disease Repository and comprises 299 patient records, evaluated using a 70% training and 30% testing split in RapidMiner. Experimental results show that the C4.5 model achieves an accuracy of 86.52% and produces clinically interpretable IF–THEN rules, enabling traceable reasoning and decision auditing. Although C4.5 is a classical learning algorithm, it remains relevant for clinical decision support due to its auditability, low computational cost, and ease of deployment in resource-constrained environments. The developed system is expected to support early screening/triage and data-driven clinical decision-making, contributing to the advancement of medical decision support systems (MDSS).
Hybrid Manhattan Distance-Certainty Factor for Early Cardiovascular Diagnosis in a Hospital Tomatala, Michel
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.114611

Abstract

Cardiovascular disease drives high mortality and operational strain in public hospitals, underscoring the need for tools that standardize rapid early decisions. We evaluated a hybrid expert system that integrates Manhattan Distance (MD) for case-based similarity with a Certainty Factor (CF) framework for rule-based evidence aggregation. Using a locally curated knowledge base (110 cases, 69 symptoms, 13 conditions) and a 26-case hold-out against specialist references, the system retrieves nearest cases via MD on symptom vectors and then computes per-diagnosis confidence with CF. The system achieved 23/26 exact matches (accuracy 88.46%), with confidence values spanning 71.90–99.99% higher when nearest-case patterns and rules converged and moderated in ambiguous presentations (e.g., suspected aneurysm). Outputs were interpretable and suitable for 15–30-minute consultations, supporting consistent triage where specialist capacity is limited. These findings suggest a practical pathway to improve timeliness and reduce variability. Future work should pursue multi-site validation, knowledge-based expansion for atypical phenotypes, and governance for safe, equitable deployment.
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. 

Page 48 of 49 | Total Record : 486

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