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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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Improving Low-Light Face Recognition using DeepFace Embedding and Multi-Layer Perceptron Kurniadi, Dede; Fernando, Erick; Fauziyah, Asyifa; Mulyani, Asri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6797

Abstract

Facial recognition systems often struggle under extreme lighting conditions, which distort facial features and reduce recognition accuracy. This study introduces a novel integration of DeepFace embeddings with a lightweight Multi-Layer Perceptron (MLP) classifier tailored to improve facial recognition under extreme lighting conditions. This combination has not been explored in previous studies and offers a compact alternative to conventional CNN-based methods. The Labeled Faces in the Wild (LFW) dataset was augmented using rotation, flipping, and lighting variations, and further enhanced with CLAHE for improved contrast under poor illumination. The resulting 128-dimensional DeepFace embeddings were classified using a four-layer MLP with LeakyReLU activation, Batch Normalization, and Dropout. The model was evaluated across three data-splitting schemes (70:30, 80:20, and 90:10), with the 80:20 configuration achieving the highest accuracy of 95.16%. Compared to the baseline CNN, the proposed method demonstrated superior robustness to illumination variations. This makes the proposed model suitable for real-time applications such as biometric authentication and AI-based surveillance systems.
Addressing Class Imbalance in Oil Palm Disease and Micronutrient Deficiency Detection Using Meta-Learned Transfer Metric Learning Hartono, Hartono; Ongko, Erianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6857

Abstract

Class imbalance is a major challenge in oil palm disease and nutrient deficiency detection, where healthy samples dominate while diseased or deficient cases are underrepresented, often leading to biased models with high false-negative rates. To address this issue, this study proposes MetaTMLDA (Meta-Learned Transfer Metric Learning with Distribution Alignment), a hybrid framework that combines Transfer Metric Learning (TML) with MW-FixMatch. TML learns discriminative and domain-invariant features, while MW-FixMatch employs a meta-learned weighting mechanism to adaptively reweight samples, improving sensitivity to minority classes and enhancing robustness against pseudo-label noise. Experiments on four public datasets—Ganoderma Disease Detection, Palm Oil Leaf Disease, and Leaf Nutrient Detection for Boron and Magnesium—demonstrated that the proposed method consistently outperforms TML-DA, MW-FixMatch, SMOTE, Random Undersampling, and Biased SVM. On the smaller datasets (Ganoderma and Palm Oil Leaf Disease), MetaTMLDA achieved accuracy of 0.976, precision 0.951, recall 0.915, Cohen’s Kappa 0.912, and macro F1-score 0.933 for Ganoderma, and accuracy of 0.980, precision 0.972, recall 0.957, Kappa 0.911, and macro F1-score 0.964 for Palm Oil Leaf Disease. On the larger datasets (Boron and Magnesium), the model reached near-perfect accuracy of 0.995, with precision up to 0.967, recall up to 0.973, Kappa above 0.919, and macro F1-scores up to 0.969, highlighting its robustness and balanced predictive performance. These findings confirm that MetaTMLDA effectively addresses both class imbalance and domain shift, providing a scalable solution for precision agriculture through earlier and more reliable detection of oil palm health issues.
Computer Vision-Based Information System for Early Detection of Elderly Patient Falls using YOLOv12 Triyanto, Wiwit Agus; Fernando Candra Yulianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6858

Abstract

Falls in elderly patients are a significant public health problem due to their high frequency and potential to cause serious injury or even death. Traditional fall detection systems often rely on wearable sensors, which can be intrusive and uncomfortable for long-term monitoring. This study proposes a non-intrusive computer vision-based information system for early fall detection using the YOLOv12 (You Only Look Once version 12) object detection model. The system integrates real-time video processing with a lightweight convolutional neural network architecture to detect falls in indoor care settings. A dataset of 10,793 annotated images, including simulated fall scenarios and daily activities, was used to train and validate the proposed model. The proposed system achieved a Mean Average Precision (mAP) of 90.60%, demonstrating robust performance under various lighting conditions and camera angles when compared with the YOLOv8, YOLOv11, and YOLO-NAS models. This study contributes to the development of intelligent healthcare systems that improve the safety and quality of life of elderly patients through proactive monitoring and rapid response capabilities.
Improving Classification Performance on Imbalanced Stroke Datasets Using Oversampling Techniques Innuddin, Muhammad; Hairani; Jauhari, M. Thonthowi; Mardedi, Lalu Zazuli Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6859

Abstract

Stroke is the second leading cause of death worldwide and a major factor in long-term disability. Although early detection based on machine learning continues to be developed, it still faces challenges in the form of data imbalance, which can reduce classification performance. This study aimed to evaluate the effectiveness of several oversampling techniques, such as SMOTE, Borderline-SMOTE, and SVM-SMOTE, in improving the performance of stroke classification models on imbalanced data. The methods used included the application of three oversampling techniques, namely SMOTE, Borderline-SMOTE, and SVM-SMOTE, to balance the data distribution. Furthermore, Random Forest and XGBoost algorithms were used as classification models to identify stroke cases. The results of this study show that the use of oversampling techniques significantly improves model performance, especially in MCC and AUC metrics, compared to models without oversampling. Borderline-SMOTE provides the best results, with the highest accuracy of 96.45% on Random Forest and 96.41% on XGBoost, as well as MCC and AUC values that are consistently superior to other techniques. Furthermore, this study highlights that the use of Borderline-SMOTE significantly enhances model performance, as evidenced by an increase in MCC of 87.51% and an AUC of 45.40% in Random Forest, along with an increase in MCC of 76.52% and an AUC of 41.81% in XGBoost. These findings confirm that Borderline-SMOTE is an effective approach for dealing with data imbalance and improving the model's ability to detect minority classes in stroke classification.
Modeling and Deploying RESTful Services with SOMF-Based SOA: A Case Study in the Credit Guarantee Industry Ramdhani, Amrid; Nilo Legowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6867

Abstract

The integration of electronic systems across financial institutions poses significant challenges, particularly when legacy architectures rely on siloed, point-to-point connections. This often leads to what is commonly known as "spaghetti integration," where changes in one system can trigger unintended disruptions in others. This study addresses such integration issues within the Kredit Usaha Rakyat (KUR) credit guarantee service of an Indonesian credit guarantee institution by implementing a Service-Oriented Architecture (SOA) approach, guided by the Service-Oriented Modeling Framework (SOMF). This study aims to improve system performance, scalability, and regulatory adaptability through a structured, multi-phase methodology based on SOMF: conceptualization, discovery and analysis, business integration, logical design, and logical architecture. Data for the study were drawn from system documentation, national regulatory requirements (e.g., Coordinating Minister Regulation No. 1/2023), and the evaluation of service interactions via RESTful APIs using lightweight JSON formatting. These findings demonstrate that the adoption of SOA with SOMF enables the development of modular, interoperable, and adaptable services. This approach reduces redundant processes, enhances real-time data flow, and strengthens integration between the guarantee institution and its partner banks. The resulting system aligns with modern digital governance requirements and provides a sustainable foundation for future growth and compliance.
Sentiment Classification on Indodax Using Term Frequency, FastText, and Neural Attention Models Hartama, Dedy; Riski, Ginanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6871

Abstract

The rapid growth of mobile-based investment platforms such as Indodax has triggered a surge in user-generated reviews that reflect public perception and sentiment. This study aimed to develop and evaluate sentiment classification models that can accurately classify Indonesian user reviews on the Indodax app into negative, neutral, and positive sentiments. A dataset of 11,000 reviews was collected via web scraping from the Google Play Store. Reviews were preprocessed, labeled using a lexicon-based unsupervised method, and balanced using oversampling. Two models were built: a Bidirectional LSTM (BiLSTM) with attention mechanism using FastText embeddings, and a Feedforward Neural Network (FFNN) using a hybrid feature vector combining TF-IDF and FastText. The evaluation was performed using accuracy, classification report, confusion matrix, and PCA visualization. The FFNN model outperformed the BiLSTM-Attention model with an accuracy of 97.07% compared to 96.00%. Both models demonstrated strong performance in classifying three sentiment classes, though the FFNN showed better separation in PCA space and higher macro-average metrics. This study demonstrates the effectiveness of combining statistical and semantic feature representations for sentiment classification in Indonesian text. The proposed approach is particularly valuable for low-resource languages and informal user-generated content.
Empowering Low-Resource Languages: Javanese Machine Translation Sulistyo, Danang Arbian; Aji Prasetya Wibawa; Wayan Firdaus Mahmudy; Fadhli Almu’iini Ahda; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6887

Abstract

This study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese.
Interdisciplinary Analysis of Machine Learning Applications: Focus on Intent Classification Khouya, Nabila; Retbi, Asmaâ; Bennani, Samir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6899

Abstract

Given the rapid growth of machine learning publications on platforms such as arXiv, there is a need for systematic approaches to understand their objectives and contributions. This study aimed to analyze scientific intentions across domains, identify research trends, and evaluate the impact of external contextual enrichment on automatic intent classification. We perform a cross-domain comparison of research objectives, methodological designs, and application scenarios in machine learning publications, focusing on computer science and biology. We propose IntentBERT-Wiki, an enhanced BERT model enriched with contextual knowledge from Wikipedia, designed for intent classification in scientific documents. Our dataset comprises annotated sentences extracted from arXiv articles, categorized according to established rhetorical role taxonomies. The model’s performance is evaluated using standard classification metrics and compared to a baseline BERT model. Experimental results show that IntentBERT-Wiki achieves F1-scores of 95.9% in computer science and 87.4% in biology, with corresponding accuracies of 96.5% and 91.4%, outperforming the baseline. These findings demonstrate that Wikipedia-based contextual enrichment can significantly improve intent classification accuracy, enhance the organization of academic discourse, and facilitate cross-domain knowledge transfers. This study contributes to the understanding of how machine learning research is framed across disciplines and provides a scalable framework for scientific content analysis.
Comparing Data Preprocessing Strategy on T5 Architecture to Classify ICD-10 Diagnosis Lanang Wijayakusuma, I Gusti Ngurah; Sudarma, Made; Darma Putra, I Ketut Gede; Sudana, Oka; Astutik, Dian
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6919

Abstract

Manual ICD-10 coding in healthcare systems remains time-consuming, error-prone, and inefficient, particularly in resource-constrained settings. This study investigates the effect of various preprocessing strategies on the performance of the Text-to-Text Transfer Transformer (T5) model for primary diagnosis classification using structured clinical data. A total of 7,263 clinical records were collected from two high-density regions in Bali (Badung and Gianyar) between January 2023 and March 2024, then converted into descriptive text prompts for model training. Four experimental scenarios combined variations of input features and label configurations, comparing T5 with Oversampling against T5 with Easy Data Augmentation (EDA) plus Oversampling. Results showed that T5 with Random Oversampling consistently outperformed the EDA-based configuration across all scenarios, with performance gaps ranging from 8% to 19%. Scenario 4, which excluded body system features and the semantically overlapping E860 label, achieved the highest balance, reaching 84.7% accuracy, 85.1% precision, 84.7% recall, and 84.3% F1-score. Conversely, the EDA-based approach reduced training time by up to 72%, indicating a clear trade-off between performance and efficiency. Both configurations frequently misclassified semantically similar codes within the same ICD-10 categories, underscoring the difficulty of distinguishing clinically related diagnoses. Overall, the results suggest that careful selection of preprocessing strategies can enhance transformer-based medical text classification, while striking a balance between model performance and training efficiency. This work may serve as an initial reference for developing more efficient semi-automated medical coding systems in the Indonesian regional healthcare context.
Student-Generated User Story Quality: A Study on Practitioner and ChatGPT Evaluation Zul, Muhammad Ihsan; Yasin, Suhaila Mohd.; Sahid, Dadang Syarif Sihabudin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6950

Abstract

Evaluating the quality of student-generated user stories is important in software engineering education, but only a limited number of industry practitioners can assist. The integration of generative AI can facilitate this process. To do so, the INVEST quality evaluation framework is widely recognized for assessing user story quality; however, prior research has not explored its use in conjunction with generative AI. This study investigated ChatGPT's ability to evaluate user stories using the INVEST framework. This study compares two ChatGPT-based evaluation approaches with those of experienced practitioners, focusing on student-generated user stories. Discrepancies between ChatGPT and practitioner evaluations were measured using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Statistical significance was tested using the Mann-Whitney U Test. The results indicate that ChatGPT’s 1st approach yielded lower discrepancies than practitioner evaluations. Moreover, significance testing showed no statistically significant differences between the ChatGPT and practitioner results for the two INVEST criteria- Independent and Estimable. These findings suggest that the 1st approach can assist in the evaluation process, although practitioners must ensure comprehensive and accurate evaluations. ChatGPT can provide preliminary evaluations in educational contexts, enabling students to receive formative feedback and allowing educators to streamline evaluation processes. Although practitioner validation is still required, their role may shift toward verifying AI-generated results, thus reducing the overall workload and accelerating quality evaluation

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