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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 1,090 Documents
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Optimizing Automated Essay Scoring with Lightweight Large Language Models and Validated Rubrics Prayitno; Fahima Choirun Nabila; Mohammad Khambali; Afandi Nur Aziz Thohari; Karisma Trinanda Putra; Viqi Ardaniah; Jurianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7012

Abstract

Manual grading of English as a Foreign Language (EFL) essays often leads to inconsistent scores among educators, despite the use of rubrics. While traditional Automated Essay Scoring (AES) systems offer speed, they often fail due to high computational cost, reliance on extensive datasets, and an inability to capture holistic writing qualities such as creativity and humanistic expression. This study addresses these issues by introducing AESCORE, a novel, lightweight, and cost-effective AES framework. Our methodology centers on integrating validated rubric criteria (identified via VOSviewer analysis) with open-source Large Language Models (LLMs), specifically emphasizing a human-centered approach. We evaluated AESCORE across 100 EFL essays using several prompting techniques, including few-shot and multi-trait specialization. The system achieved its most robust performance and high scoring consistency (Quadratic Weighted Kappa QWK = 0.6660) using the DeepSeek-R1 8B LLM with few-shot prompting. AESCORE represents a significant contribution by demonstrating that sophisticated, pedagogically-aligned writing assessment and generative feedback can be achieved with accessible AI, offering a reliable alternative for improving productive writing skills in higher education.
Nonlinear Modeling of Agricultural and Environmental SDG Indicators in ASEAN Using Extreme Learning Machine Algorithms Ita Arfyanti; Muhammad Ibnu Sa'ad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7041

Abstract

This study presents a data-driven approach for modeling Sustainable Development Goal (SDG) indicators in ASEAN countries using the Extreme Learning Machine (ELM) algorithm. Focusing on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 15 (Life on Land), we utilized FAOSTAT datasets from 2020 to 2024 to forecast key indicators such as undernourishment, water use efficiency, and forest area. ELM, known for its rapid learning speed and capability to model nonlinear relationships, outperformed baseline models Linear Regression and Support Vector Machine (SVM) in terms of R² score, RMSE, and MAE. Specifically, ELM achieved R² values exceeding 0.93, with up to 54% RMSE reduction compared to linear models. The model successfully captured national development trends, including deforestation in Indonesia and Cambodia, water stability in Brunei, and varied progress in sustainable agriculture across the region. This study underscores the effectiveness of the Extreme Learning Machine (ELM) in forecasting Sustainable Development Goal (SDG) indicators and provides actionable insights to support evidence based policy planning, particularly in resource-constrained settings. The findings demonstrate that ELM’s combination of interpretability, computational efficiency, and scalability positions it as a highly valuable tool for real-time monitoring of sustainable development across Southeast Asia.
Multi-Band EEG Spectrogram Decomposition with Residual Attention Network for Enhanced Stress Classification Sza Sza Amulya Larasati; Fitra Abdurrachman Bachtiar; Budi Darma Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7225

Abstract

Stress can affect both physical and mental health, and it is important to support faster intervention. EEG can record brain activity directly, but EEG signals are complex, noisy, and difficult to handle. This study explores how EEG spectrogram decomposition can improve stress classification accuracy using deep learning models. Decomposition was accomplished by splitting a single EEG spectrogram into five distinct segments based on frequency range. Deep neural networks resembling ResNet are well-suited for spectrogram data, as the iterative feature extraction across layers facilitates the identification of hidden patterns. Incorporating an attention module before the classification layer further strengthens the model's capabilities by highlighting the most pertinent features during the training process. The baseline architecture employed in this study was ResNet-152. By incorporating a Multi-Head Attention mechanism prior to the Fully Connected layer, the modified network is denoted as RAN-152. The combination of spectrogram decomposition, ResNet, and attention has been proven to improve classification accuracy in complex EEG data. Without these three together, the accuracy obtained was only 0.5479, while the combination of the three achieved the highest accuracy of 0.9026. Evaluation of other metrics such as precision, recall, and F1-score also confirms that the attention module is good enough to highlight important features while reducing noise, thereby making classification more balanced across classes. These findings show that the combination of EEG decomposition and attention can be a promising approach for stress detection.
IoT-Based Water Quality Monitoring and Suitability Modeling for Smart Campuses Fachrul Kurniawan; Miladina Rizka Aziza; Novrindah Alvi Hasanah; Fadia Irsania Putri; Aji Prasetya Wibawa; Jehad Hammad; Yuhefizar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7360

Abstract

This study proposes an IoT-based water quality monitoring framework integrated with a continuous suitability modeling approach for smart campus applications. A total of 404 sensor observations were collected, including pH, turbidity, temperature, and Total Dissolved Solids (TDS). A continuous water suitability score ranging from 0 to 1 was constructed based on WHO drinking water standards, and Multiple Linear Regression was employed to model the relationship between water quality parameters and the suitability score. The main contribution of this study lies in the development of a lightweight analytical framework that combines continuous regression modeling with threshold-based classification to support real-time decision-making in resource-constrained environments. The dataset was divided into 90% training and 10% testing data. The results show that the proposed framework achieved a classification accuracy of 88.5% based on threshold mapping of regression outputs, with a misclassification rate of 11.5%. These findings demonstrate the effectiveness of integrating IoT-based monitoring with interpretable and computationally efficient analytical models for sustainable campus water management.
Sea Land Segmentation of East Java’s North Coast Using Landsat 9 and ResNet50 Nur Nafiiyah; Ilyas; Rifky Aisyatul Faroh; Salwa Nabilah; Nur Azizah Affandy
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7435

Abstract

Coastal regions are among the most vulnerable ecosystems due to the combined impacts of natural processes and human activities. Climate change, population growth, and coastal development accelerate shoreline dynamics, increasing the need for accurate and efficient coastal monitoring. Satellite-based remote sensing, combined with deep learning techniques, provides a promising solution for large-scale and continuous shoreline analysis. This study proposes a deep learning–based approach for coastal land–sea segmentation using the ResNet50 architecture applied to Landsat 9 OLI imagery of the North Coast of East Java, Indonesia. The dataset consists of multispectral images processed into 224×224 pixel tiles, accompanied by manually generated ground truth segmentation maps. Two optimization strategies, Adam and Stochastic Gradient Descent (SGD), are evaluated to determine the most effective optimizer for improving segmentation performance. Experimental results demonstrate that the Adam optimizer outperforms SGD across multiple training epochs, achieving the highest segmentation accuracy with mean Intersection over Union (IoU) and Dice coefficient values of 0.888 and 0.934, respectively. These findings indicate that optimizer selection significantly influences the performance of ResNet50-based coastal segmentation. The proposed approach shows strong potential for supporting automated and large-scale coastal monitoring applications using medium-resolution satellite imagery.
Improving Diagnostic Accuracy on Prescription Text Data Using SMOTE-Optimized SVM Linda Perdana Wanti; Nur Wachid Adi Prasetya; Riyadi Purwanto; Rahmat Mulyadi; Akmal Fauzan Ananta
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7441

Abstract

Disease classification based on drug prescription data plays a crucial role in helping healthcare professionals understand patient health conditions and supporting clinical decision-making. Drug prescription data actually contains a wealth of information regarding disease indications, but is generally presented in unstructured, free-text form. Furthermore, the data distribution across disease classes is often imbalanced, with some diseases receiving less data than others. This can lead to inaccurate classification models that favor disease classes with more data. This study aims to enhance the performance of disease classification based on drug prescription data by combining text mining approaches, the Synthetic Minority Oversampling Technique (SMOTE), and the Support Vector Machine (SVM) algorithm. The research process begins with text preprocessing, which includes case folding, tokenization, stopword removal, and stemming, to clean and normalize the prescription data. Next, the text data is converted into numeric features using the Term Frequency–Inverse Document Frequency (TF-IDF) method to enable processing by machine learning algorithms. To address the class imbalance issue, the SMOTE method is applied to training data by generating synthetic data for a limited number of disease classes. A classification model was then built using the SVM algorithm, known to be effective in handling high-dimensional text data. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the application of SMOTE and parameter optimization in Support Vector Machine significantly improved classification performance, with an accuracy of 92.6%, a precision of 91.8%, a recall of 93.4%, and an F1-score of 92.6%. The increased recall value in the class of patients diagnosed with diabetes indicates that the model is able to correctly identify most diabetes cases based on medical prescription data.
Comparison Of Machine Learning Algorithms For Rice Production Prediction Abdul Karim; Yuwaldi Away; Syahrial; Roslidar; Jeperson Hutahaean; William Ramdhan; Yessica Siagian
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7453

Abstract

Rice production forecasting plays an important role in supporting future agricultural planning, food supply management, and food security. Accurate yield prediction allows governments and farmers to estimate production outcomes and develop appropriate strategies to maintain stable food availability.This study addresses this gap by comparing four regression-based machine learning models: Random Forest, XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN). All models were trained and tested using the same dataset to ensure a fair evaluation. Model performance was measured using the coefficient of determination (R²). The results show that Random Forest achieved the best performance (R² = 0.963), followed by XGBoost (R² = 0.959). In contrast, SVR (R² = -0.064) and ANN (R² = -2.417) performed poorly, indicating limited predictive capability. Overall, these findings suggest that ensemble-based methods, particularly Random Forest and XGBoost, are more reliable and effective for rice production forecasting compared to SVR and ANN.
Performance Evaluation of YOLOv9, YOLOv10, and YOLOv11 for Real-Time Early Detection of Ganoderma Boninense in Oil Palm Rizky Delianngi; Ratu Mutiara Siregar; Nurliana; Muhammad Akbar Syahbana Pane; Phaklen Ehkan; Andi Prayogi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7479

Abstract

Early detection of Ganoderma boninense infection is essential to reduce yield losses in oil palm plantations. This study aims to evaluate the performance of three recent YOLO architectures, namely YOLOv9, YOLOv10, and YOLOv11, for real-time detection of early infection symptoms under natural field conditions. A dataset of 2,000 annotated RGB images was used with a 70:20:10 split for training, validation, and testing. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP50 and mAP50–95), and inference speed. The results show that YOLOv9 achieved the highest detection accuracy with an mAP50 of 0.989 and F1-score of 0.968. Meanwhile, YOLOv11 demonstrated the best computational efficiency with an inference speed of 35 FPS and processing time of 28.5 ms per frame. These findings indicate a trade-off between accuracy and speed, where YOLOv9 is suitable for accuracy-oriented applications, while YOLOv11 is more appropriate for real-time deployment in precision agriculture.
Behavioral Analysis of Semantic Similarity Metrics under Transformer-Based Representations Musthofa Galih Pradana; Nindy Irzavika; Nurhuda Maulana; Syaila Ananta Karenina; Salma Ashiila Rabbani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7490

Abstract

Knowledge extraction has several approaches such as traditional approaches that rely on lexical representation capabilities, one of which is TF-IDF whose implementation can be combined with several classic similarity metrics such as cosine similarity and dice coefficient similarity. In addition to applying the lexical representation approach, this study tries to apply it to a more modern type of representation, namely transformer-embedding-based contextual representation. The data used in this study is abstract document data of students' theses. The findings of the study show that contextual embedding changes the behavior of similarity values. The results of the analysis showed an average ranking shift of 6.70 positions. The test results showed a weak rating correlation value (Spearman = 0.22; Kendall = 0.146), and the high-ranking alignment measured with NDCG (0.97), which shows structural differences in the order of similarity between lexical and contextual representations. Other findings also show that the gap in ranking produced by the two representations used is quite far due to the difference in the mechanism and working pattern of the two representations that are far different, the selection of the type of representation must be on the characteristics of the data to be processed, if looking at the character of the text data in academic documents, the selection of contextual representations based on transformer embedding will be more suitable with contextual understanding to avoid the use of variations in words avoid plagiarism detection when applying a semantic-based representation approach.
Leveraging Sarcasm Text Detection in Meme Polarity Classification Zeva Patu Assyadid; Ema Rachmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7516

Abstract

Memes are a popular internet format and spread quickly across many social media platforms. People can express their ideas, criticisms, interests, or aversions through memes. But in some cases, other people may interpret memes differently and feel bad about it. This variation in meme interpretation is a challenge in sentiment analysis, as a meme can be judged negative or positive by different individuals. Therefore, there is a need for an automated system that can consistently predict the sentiment polarity of memes. A meme is multimodal content that could consist of visual and textual components, which is suitable for a sentiment polarity analysis study. To model a system that effectively leverages multimodal features, the model needs to understand the meme features. This study proposes a joint deep learning model—BERT and Densenet121—that concatenates text, image, and cluster features based on the extracted face encodings. To assess the context of the texts better, BERT was trained with a sarcasm dataset. The widely used ‘SemEval 2020 Task 8: Memotion Analysis dataset’ was used in this study due to its comprehensive annotation of meme-based sentiment and sarcasm, which aligns with this study’s approach. The result demonstrates that the model achieved 0.3738 Accuracy (+2.52%) and 0.3735 Weighted F1 (+1.04%), while maintaining competitive Macro F1 (0.3047). This result shows effective sarcasm adaptation on imbalanced datasets, with improved ability to detect positive and neutral sentiments and reduce sarcastic false negatives compared to the base model. This highlights the effectiveness of integrating sarcasm detection into the model framework for robust sentiment classification in memes.

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