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JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Kristen Maranatha
ISSN : 24432210     EISSN : 24432229     DOI : https://doi.org/10.28932/jutisi
Core Subject : Science,
Computer Science & IT
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 7 Documents
 1 
Search results for , issue "Vol 11 No 3 (2025): JuTISI" : 7 Documents clear
Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen dengan Bayesian Optimization Yudianto, Muhammad Resa Arif; Zakariah, Masduki; Rozam, Nadhir Fachrul; Rahman, Dzul Fadli; Sari, Tika Novita; Mustofa, Zaenal
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11524

Abstract

This study examines the effect of Bayesian Optimization in improving the performance, computational efficiency, and sustainability of Aspect-Based Sentiment Analysis models using Support Vector Machine (SVM). A dataset consisting of 988 customer reviews about Borobudur Temple, classified into six dimensions: Attractiveness, Facilities, Accessibility, Visual Image, Price, and Human Resources is used to compare two scenarios, namely Baseline SVM and SVM enhanced with Bayesian Optimization (BO). Important metrics used include accuracy, computational duration, energy usage, and carbon emissions. The results show that BO significantly improves accuracy, especially on difficult aspects such as Facilities (from 0.7294 to 0.8682) and Price (from 0.8047 to 0.9576). The most complicated aspect, namely visual image due to the very minimal number of datasets (unbalanced), achieved an increase in accuracy from 0.6729 to 0.72. In addition, BO reduces training time, especially for resource-intensive tasks such as the visual image aspect, reducing training time from 13.04 seconds to 9.4 seconds. Substantial reductions in energy consumption and CO₂ emissions are seen in line with sustainable machine learning principles. The hyperparameter adaptability of SVM, with linear kernels performing well in simpler tasks, while polynomial and sigmoid kernels improve performance for more complex parts. BO substantially alleviates the limitations of Baseline SVM, offering a robust, efficient, and environmentally friendly solution for ABSA. Future research can explore more enhancements for complex tasks to improve performance and efficiency.
Penerapan Metodologi Terpadu pada Rekayasa Proses Akademik Universitas Gantini, Tiur; Adelia, Adelia; Siboro, Sinta Marito; Sandroto, Indah Victoria
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11801

Abstract

Rapid technological developments in the era of globalization require universities to continuously improve their academic systems. When academic systems undergo renewal, related business processes must also be reengineered to align with ongoing activities. This study aims to analyze the effectiveness of integrating four methods—Value Stream Mapping (VSM), Voice of Customer (VOC), Voice of Business (VOB), and Pick Chart—in reengineering academic business processes at a university in Bandung. The research methodology consists of three phases: problem formulation, theoretical review, and business process reengineering design. Data was collected through interviews with stakeholders, including academic staff, lecturers, and students, as well as direct observation of 14 (fourteen) Standard Operating Procedures. The VSM method was used to map the current process and identify problems, VOC captured customer complaints, VOB measured institutional-level problems, and Pick Chart prioritized solutions based on difficulty and impact of implementation. The results of the study show that this integrated framework effectively identifies inefficiencies and proposes targeted improvements. For the grade revision process, the re-engineered business process reduced processing time from approximately one week to 55 (fifty-five) minutes, while eliminating paper waste through the implementation of digital forms. This study concludes that the integration of VSM, VOC, VOB, and Pick Chart provides a comprehensive and systematic framework for re-engineering academic business processes that can be replicated by other higher education institutions.
Implementasi Certainty Factor dalam Sistem Pakar untuk Mendiagnosis Penyakit pada Kelapa Sawit Fathinah, Nadiva Azro; Suryani, Suryani; Desiani, Anita
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11886

Abstract

Diseases in palm oil plants are one of the main causes of palm oil production not being maximized, and can even result in crop failure. Farmers need to know the symptoms that occur in oil palm plants in order to diagnose and overcome the diseases that infect the palm oil plants. A system for early detection of disease in palm oil plants is needed in order to prevent a decrease in productivity. An approach that can be used for early diagnosis is an expert system. Expert systems not only provide a diagnosis, but also offer an explanation of the type of disease as well as practical and accurate treatment recommendations. This research applies one of the methods of the certainty factor method to an expert system that combines several symptoms to determine how likely a diagnosis is. This expert system involves 22 symptoms to diagnose six diseases in palm oil plants. The accuracy rate obtained from the application of the expert system with the certainty factor method in diagnosing diseases of oil palm plants based on data from five users shows a result of 100%. This shows that the expert system with the certainty factor method is accurate and can be applied to early detection of diseases that attack palm oil plants.
Implementasi Retrieval Augmented Generation dalam Sistem Chatbot Dermatologi Berbasis Website Kharisma, Ivana Lucia; Hidayat, Muhammad Syarif; Somantri, Somantri; Kamdan, Kamdan
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.12258

Abstract

Indonesia’s tropical climate, poor sanitation, and limited access to medical services especially in remote areas are key factors contributing to the high prevalence of skin diseases. Direct access to dermatologists remains difficult for many people. This study aims to develop a dermatological consultation Chatbot using a Retrieval Augmented Generation (RAG) approach, leveraging the LangChain framework, the LLaMA model, and the Qdrant vector database. The dataset includes 30 types of skin diseases sourced from the National Library of Medicine. The preprocessing stage involved whitespace normalization, removal of special characters, and handling of missing values to ensure data consistency before vectorization. Evaluation results showed high scores for Faithfulness (0.9429) and LLMContextRecall (0.9600), indicating that the responses were relevant and aligned with the source documents. However, a relatively low Precision score (0.4720) suggests a need for improved information accuracy. The Chatbot is integrated with the Chainlit platform, offering an interactive user interface that supports login, conversation history, and user feedback features to facilitate system development based on user input. The system demonstrated fast retrieval times (0.08–0.29 seconds), though answer generation remains slow due to CPU infrastructure limitations (255–283 seconds). Future improvements should focus on enhancing answer accuracy, optimizing the model's performance, enriching the medical reference dataset, and adding automated medical validation features to ensure the reliability of consultations. Therefore, this Chatbot system is expected to serve as a cost-effective and efficient alternative for providing initial information on skin conditions to individuals with limited access to healthcare services.
Analisis Efektivitas Fusi Fitur Multimodal dalam Klasifikasi Citra Daun Herbal Riyandi, Riki; Sumarsono, Sumarsono
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.12262

Abstract

This study aims to evaluate the performance of leaf image classification models based on feature fusion strategies that integrate shape, texture, and semantic representations. Three feature extraction methods were employed: Histogram of Oriented Gradients (HOG) for shape, Gabor Filter for texture, and Convolutional Neural Network (CNN) using MobileNetV2 for semantic features. Each feature type was tested using three classification algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Experimental results show that CNN features consistently outperformed the others, achieving the highest accuracy and F1-score, with a peak accuracy of 91.0% using CNN+SVM. In contrast, HOG and Gabor features resulted in significantly lower performance. Feature fusion—such as HOG+CNN and HOG+Gabor+CNN—did not improve performance and instead caused a notable decline, primarily due to the high dimensionality of HOG features, leading to the curse of dimensionality. Confusion matrix and ROC curve analyses confirmed that the CNN-based model achieved high inter-class separability, while models with fused features produced near-random predictions in several classes. These findings suggest that feature fusion does not inherently lead to better classification performance, particularly when dimensional imbalance is not addressed. The study recommends the use of single semantic features extracted from CNN for efficient and accurate leaf image classification, while also encouraging future research into adaptive fusion strategies such as feature weighting or multimodal integration.
Analisis Ulasan Daring Menggunakan Metode Density-Based Spatial Clustering of Applications with Noise Hartono, Edwin; Fibriani, Charitas
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.12363

Abstract

This study applies a density-based clustering method to analyze user perceptions based on reviews on Google Maps. The focus of this research lies in processing dynamic, unlabeled review data to address managers' needs in understanding public sentiment. A total of 399 data sets were collected through Apify, then the data were processed through cleaning, normalization, and stemming stages. Text representation was performed by weighting word frequencies across documents, while WordCloud visualization was utilized to identify dominant words reflecting positive perceptions to help understand the context before the clustering process. The Density-Based Spatial Clustering of Applications with Noise method was applied to form review clusters. The analysis results show that this method is able to group reviews into clusters based on content similarity, although some data were identified as noise. These findings provide useful insights in understanding public perception, thus aiding in strategic decision-making. With the right parameter selection, this method can be an effective approach for further public review sentiment analysis.
Komputasi Numerik Berkinerja Tinggi dari Integral Multidimensi Menggunakan Sampel Acak Widjaja, Andreas; Gautama, Tjatur Kandaga; Sujadi, Sendy Ferdian; Wijaya, Bernadus Indra
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.12999

Abstract

This study examines the use of high-performance computing to carry out multidimensional integral calculation based on stochastic techniques, particularly in the context of Monte Carlo integration. Considering that traditional methods are facing extreme difficulty especially in high-dimension when encountered with "dimensionality curse", random sampling technique to estimate integral values is used. This technique is superior in many aspects, for example in terms of scalability and flexibility, even in complex and irregular domains. In particular, the work concentrates on the case of calculating the volume of a multi-dimensional sphere using random sampling or Monte Carlo techniques. It also introduces a framework that employs the Graphics Processing Unit (GPU) to carry out these computations more effectively. Using dimensionalities from 2 to 24, the work compares both accuracy and computation time of the method. The results show that the random sampling method attains high accuracy in the computation of π which is used as a benchmark. The computational model is implemented in CUDA C/C++, and it takes advantage of GPU parallelism to process large sample sizes as well as execute calculations efficiently. Here it is shown in general that Monte Carlo integration is a viable approach to high-dimensional problems when combined with very rapid GPU parallelism.

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