cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yuhefizar
Contact Email
jurnal.resti@gmail.com
Phone
+628126777956
Journal Mail Official
ephi.lintau@gmail.com
Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.
Location
,
INDONESIA
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 1,145 Documents
 111   112   113   114   115 
Burned Area Segmentation Using Random Band Selection and Ensemble Encoder Annisaa Nurul Ramadhani Novelika; Laksmita Rahadianti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7370

Abstract

Wildfires are increasingly recognized as major environmental hazards that endanger ecosystems, human health, and economic stability. In practice, various monitoring efforts, such as hotspot detection, burned-area indices, and field inspections still depend on reliable spatial information to quantify fire impacts over large regions. Accurate segmentation of burned areas from satellite imagery plays a vital role in supporting post-disaster response and sustainable land management. This paper proposes a novel framework for burned area segmentation using multispectral imagery sampled by Random Subspace Band Selection (RSBS) with a U-Net architecture with an ensemble encoder. The RSBS module generates multiple 3-band subsets from Landsat-8 data, incorporating spectrally informative bands such as Near-Infrared (NIR) or Shortwave Infrared (SWIR). These subsets are used to train U-Net models with an ensemble of encoder backbones ResNet34, ResNet50, DenseNet121, MobileNetV2 and InceptionV4 for spectral and architectural diversity. The final predictions are aggregated using majority voting to enhance robustness and generalization. The framework is evaluated on a publicly available Indonesian burned area dataset encompassing diverse land cover types. Experimental results demonstrate that the proposed ensemble model achieves up to 0.7673 IoU, 0.8734 mIoU, and 0.8683 F1 Score, outperforming the best single-backbone model. These findings confirm that the proposed framework offers a scalable, accurate, and adaptable solution for wildfire damage assessment using satellite data.
Performance Enhancement of Distribution System using Grey Wolf Optimizer for Capacitor Placement and Sizing Considering Load Variations Sahat Siagian; Yoakim Simamora; Desman Jonto Sinaga; Mas Aly Afandi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7482

Abstract

This research introduces a Grey Wolf Optimizer (GWO) strategy for determining the optimal placement and sizing of shunt capacitors in radial distribution networks under different load scenarios. The task is structured as a multi-scenario optimization model aimed at reducing active power loss, voltage deviation, and annual compensation expenses, all while adhering to bus voltage and line current limitations. Unlike traditional methods that focus on capacitor allocation for a single operating condition, this approach explicitly accounts for light, normal, and heavy load conditions to ensure solutions are effective throughout daily demand fluctuations. The method is tested on the IEEE 33-bus distribution system and benchmarked against several existing metaheuristic techniques. Findings reveal that GWO consistently provides viable capacitor configurations across all load conditions and offers enhanced technical and economic outcomes. During normal load conditions, the method decreases active power loss from 202.69 kW to 131.83 kW, and under heavy load, it achieves the highest annual net savings of $97,588. In summary, the results suggest that GWO is a reliable and economical solution for reactive power compensation planning in distribution systems with variable loads.
Abstractive Dialogue Summarization using Fine-Tuning Pre-Trained Language Model BART Desty Rodiah; Hanif Syahri Ramadhani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7488

Abstract

The increasing volume of conversational data on digital platforms necessitates effective automatic summarization methods. This study investigates the use of a pre-trained BART (Bidirectional and Auto-Regressive Transformers) model for abstractive dialogue summarization on the DialogSum dataset, which consists of 14,460 English dialogues. The model is fine-tuned using a sequence-to-sequence framework with systematic hyperparameter tuning, including variations in learning rate, beam size, and training epochs. The optimal configuration is achieved using a learning rate of 2e-5, a beam size of 6, and 5 training epochs. Model performance is evaluated using ROUGE and BERTScore metrics. The experimental results show that the proposed model attains an average ROUGE-L F1-score of 0.40, indicating moderate structural similarity between generated and reference summaries, and an average BERTScore F1-score of 0.69, reflecting strong semantic alignment. These findings suggest that fine-tuned BART is effective in preserving semantic relevance in abstractive dialogue summarization, although further improvements are required to enhance lexical precision and discourse-level coherence.
Texture Analysis and Classification of Lontara Batik Using GLCM and k-Nearest Neighbor Mohammad Yazdi Pusadan; Fuad Mahfud; Chairunnisa AR. Lamasitudju; Anisa Yulandari; Sabarudin Saputra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7497

Abstract

Batik is an Indonesian cultural heritage recognized by UNESCO as a Masterpiece of the Oral and Intangible Heritage of Humanity. One distinctive regional variation is Batik Lontara from South Sulawesi, which features motifs derived from the traditional Bugis–Makassar Lontara script. However, the visual similarity between woven and non-woven (stamped) Batik Lontara makes manual identification difficult for the general public. This study proposes an image-based classification approach to distinguish woven and non-woven Batik Lontara using texture analysis and machine learning techniques. Texture features were extracted from batik images using the Gray Level Co-occurrence Matrix (GLCM), including contrast, homogeneity, energy, and entropy. The extracted features were then classified using the k-Nearest Neighbor (k-NN) algorithm with varying values of k (5, 7, and 9). The dataset consisted of Batik Lontara images captured using a smartphone camera and divided into training and testing sets using both hold-out and k-fold cross-validation schemes. Experimental results show that the highest classification accuracy of 86% was achieved using the k-NN algorithm with k = 9 under 5-fold cross-validation, while the hold-out method produced the best accuracy of 87% at k = 5. These results demonstrate that the proposed GLCM and k-NN-based approach is effective for classifying Batik Lontara types and has strong potential for supporting cultural heritage preservation through digital image processing.
Explainable Creditworthiness Scoring using a Calibrated Multimodal Framework Ankur Agarwal; Shashi Prabha; Raghav Yadav
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7507

Abstract

This research presents an explainable, calibrated multimodal framework for creditworthiness scoring. It integrates financial ratios with narrative risk disclosures by using the 823 firm SEC (Securities and Exchange Commission,) filings. In this approach we have used a class-weighted, L2-regularized logistic regression within a unified pipeline. It produces calibrated probabilities via isotonic regression. We have assessed the performance across discrimination (ROC-AUC, PR-AUC), reliability (Brier, calibration curves), operating thresholds (F1, asymmetric costs), and business diagnostics. We have also included the Decision Curve Analysis to cover the pattern of net benefit across practical cutoffs. Our implementation covers an ablation study to verify multimodal gains over financial-only and text-only baselines. The present research findings state that the LightGBM + SHAP benchmark depicts better interpretability along with improved prediction accuracy. Thus. the study provide an useful insights towards effective governance and transparent system requirements which makes this suitable for effective financial risk management.
Enhanced InceptionV3 Transfer Learning with Augmentation Strategy for Multi-Class Fruit Classification Abdul Karim; Fakhri Lambardo; Rizqi Elmuna Hidayah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7531

Abstract

Fruit variety recognition using digital images is an important component in developing automated systems for agricultural handling and food-industry processing. The task is difficult because different fruit types may present nearly identical visual patterns, while image acquisition factors such as illumination, viewing position, and image quality can reduce classification reliability. To address this issue, this study designed a deep learning model for 131 fruit classes by adapting InceptionV3 through a transfer learning scheme. The pre-trained feature extraction layers were retained without retraining, while the original output structure was replaced with task-specific layers consisting of global average pooling, a 1024-unit dense layer with ReLU activation, and a softmax classifier. The image data were standardized to 224 × 224 pixels, augmented to increase visual variation, and divided into training, validation, and testing subsets using an 80:10:10 ratio. The proposed model produced an accuracy of 99.80%, with precision, recall, and F1-score values of 0.9900. These results exceeded the performance of GoogLeNet, ResNet, and VGGNet, showing that the use of pre-trained InceptionV3 features, customized classification layers, and augmentation can improve prediction consistency and reduce classification errors. Further evaluation on unconstrained real-world images and optimization for real-time use are recommended for future development.
An Applied Evaluation of Multi-Label Hate Speech Detection for Indonesian Digital Platforms Dwija Wisnu Brata; Arif Djunaidy; Daniel Oranova Siahaan; Edio da Costa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7591

Abstract

The rapid growth of user-generated content on digital platforms has increased the difficulty of moderating hate-related expressions, particularly in linguistically diverse environments such as Indonesian social media. Automated hate speech detection systems are therefore expected to operate not only with reliable predictive behavior but also with practical efficiency under large-scale deployment conditions. This study reports an applied evaluation of Transformer-based models for multi-label hate speech detection on Indonesian digital platforms. Rather than introducing a new classification architecture, the work focuses on assessing multiple pretrained language models within a unified and reproducible evaluation framework. The analysis examines overall model behavior, per-label performance tendencies, inference efficiency, and common error patterns under realistic multi-label settings. The results indicate that IndoBERT-based models (indobenchmark/indobert-base-p1 and cahya/bert-base-indonesian) achieved the strongest predictive performance for multi-label hate speech detection, although performance differences across the evaluated Transformer models remained relatively incremental. Experimental results show that the best-performing model achieved a macro-F1 score of 0.9742 and a micro-F1 score of 0.9749, while other Transformer models demonstrated competitive performance with macro-F1 values ranging from 0.955 to 0.964. In terms of efficiency, distilled models provided faster inference (approximately 5–7 ms per sample) compared to full-size models (8–11 ms per sample), highlighting a practical trade-off between predictive performance and computational cost. These findings emphasize the importance of practical evaluation strategies and suggest that flexible model configurations are more suitable than reliance on a single high-capacity model in real-world moderation systems.
Detecting AI-Generated Text with Fine-Tuned RoBERTa: A Cross-Validated Study with Interpretability Analysis Yuhefizar Yuhefizar; Ronal Watrianthos; Dony Marzuki
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7660

Abstract

The rapid proliferation of large language models capable of generating fluent and contextually coherent text has made reliable authorship attribution both a practical and ethical necessity. Outputs from systems such as GPT-4, LLaMA 2, and Claude are increasingly indistinguishable from human-authored prose, yet current detection methodologies remain insufficient to meet this challenge. This study aims to develop and evaluate a fine-tuned RoBERTa-base binary classifier for distinguishing AI-generated text from human-authored text under stratified cross-validation conditions; assess the model's robustness to a known length-based confound through a controlled ablation study; and provide interpretable explanations of the classifier's decisions through dual token-level attribution using Gradient × Input Saliency and Attention Rollout. The model is trained and evaluated via stratified five-fold cross-validation on a balanced corpus of 6,069 samples (3,069 AI-generated and 3,000 human-authored) and benchmarked against four established baselines spanning classical, neural, and zero-shot paradigms. The classifier achieves a mean accuracy of 99.93%, a weighted F1-score of 0.9993, and a mean AUC-ROC of 1.0000 across all five folds. A length-balanced ablation produces an accuracy decline of only 0.11 percentage points, providing empirical evidence that the model has learned genuine semantic and stylistic distinctions rather than superficial length-based cues. Gradient × Input Saliency maps indicate that AI-generated texts are characterised by technically precise, domain-specific vocabulary, whereas human-authored texts are identified through discourse markers and rhetorically structured expression, with both attribution methods converging on the same high-saliency tokens.
ResNet34-Encoded U-Net with Transfer Learning for Breast Cancer Cell Segmentation Under Data Scarcity Ronal Watrianthos; Yuhefizar Yuhefizar; Rayendra Rayendra; Ervan Asri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7661

Abstract

Histopathological examination of hematoxylin and eosin (H&E)-stained tissue remains the clinical gold standard for breast cancer diagnosis; however, manual cell segmentation is labor-intensive, subjective, and prone to substantial inter-observer variability. Deep learning-based segmentation models have demonstrated considerable promise in computational pathology, yet their performance under conditions of severe data scarcity remains insufficiently characterized. This study proposes and evaluates a ResNet34-encoded U-Net trained via transfer learning as a solution to this challenge, benchmarking it against a vanilla U-Net trained from random initialization as a controlled baseline. The experimental dataset comprises 42 H&E-stained breast cancer whole-slide image patches acquired at Qingdao Central Hospital (2019–2022), partitioned into 34 training and 8 test images. To mitigate the severe class imbalance inherent in the dataset — in which cancerous cell regions constitute only 16.2% of image pixels on average — a weighted Binary Cross-Entropy and Dice composite loss function was employed alongside a differential learning rate strategy to preserve pretrained encoder representations during fine-tuning. Data augmentation was applied to the training set to improve generalization under extreme data constraint. Evaluation was conducted using the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) as primary metrics. The ResNet34-UNet achieved a DSC of 0.6071 and IoU of 0.4392, outperforming the vanilla U-Net (DSC: 0.5554, IoU: 0.4004) by 9.3% and 9.7% respectively, while converging 37.5% faster. These findings demonstrate that ImageNet-pretrained encoder features transfer effectively to H&E pathology domains even under extreme data constraints, providing a reproducible and computationally efficient baseline pipeline for automated cell segmentation in resource-limited clinical settings.
Serendipity-Aware Decision Support System Using Entropy-Weighted Hybrid GA-PSO Ahmad Kamal; Suaini Binti Sura; Lai Po Hung; Renita Astri; Johan Johan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7668

Abstract

The rapid growth of social commerce has intensified competition among online handicraft businesses, making effective store planning increasingly important. While most studies focus on consumer recommendation systems, limited research supports entrepreneurs during the early stage of store configuration. This study proposes a serendipity-aware Decision Support System (DSS) for handicraft store planning using an entropy-weighted hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO). A dataset of 105 handicraft stores in West Sumatra was encoded into 19-bit chromosomes representing materials, product types, location, and digital commerce visibility. Entropy-based weighting objectively determined attribute importance without subjective judgment. GA explored store configurations, while PSO optimized evolutionary parameters to balance preference similarity and serendipitous exploration. The proposed framework generated store configurations superior to those in the original dataset. The best solution achieved a preference similarity score of P(x)=0.9108, outperforming the best existing store (P(x)=0.8513) by 6.99%. The hybrid GA-PSO also showed stable performance across multiple runs, indicating robust convergence. This study contributes a data-driven DSS framework integrating entropy weighting, hybrid GA-PSO optimization, and serendipity-aware exploration for entrepreneurial decision support in social commerce.

Page 113 of 115 | Total Record : 1145

 111   112   113   114   115 

Filter by Year

2017 2026


Reset
Filter By Issues
All Issue Vol 10 No 4 (2026): August 2026 (in progress) Vol 10 No 3 (2026): June 2026 Vol 10 No 2 (2026): April 2026 Vol 10 No 1 (2026): February 2026 Vol 9 No 6 (2025): December 2025 Vol 9 No 5 (2025): October 2025 Vol 9 No 4 (2025): August 2025 Vol 9 No 3 (2025): June 2025 Vol 9 No 2 (2025): April 2025 Vol 9 No 1 (2025): February 2025 Vol 8 No 6 (2024): December 2024 Vol 8 No 5 (2024): October 2024 Vol 8 No 4 (2024): August 2024 Vol 8 No 3 (2024): June 2024 Vol 8 No 2 (2024): April 2024 Vol 8 No 1 (2024): February 2024 Vol 7 No 6 (2023): December 2023 Vol 7 No 5 (2023): October 2023 Vol 7 No 4 (2023): August 2023 Vol 7 No 3 (2023): Juni 2023 Vol 7 No 2 (2023): April 2023 Vol 7 No 1 (2023): February 2023 Vol 6 No 6 (2022): Desember 2022 Vol 6 No 5 (2022): Oktober 2022 Vol 6 No 4 (2022): Agustus 2022 Vol 6 No 3 (2022): Juni 2022 Vol 6 No 2 (2022): April 2022 Vol 6 No 1 (2022): Februari 2022 Vol 5 No 6 (2021): Desember 2021 Vol 5 No 5 (2021): Oktober2021 Vol 5 No 4 (2021): Agustus 2021 Vol 5 No 3 (2021): Juni 2021 Vol 5 No 2 (2021): April 2021 Vol 5 No 1 (2021): Februari 2021 Vol 4 No 6 (2020): Desember 2020 Vol 4 No 5 (2020): Oktober 2020 Vol 4 No 4 (2020): Agustus 2020 Vol 4 No 3 (2020): Juni 2020 Vol 4 No 2 (2020): April 2020 Vol 4 No 1 (2020): Februari 2020 Vol 3 No 3 (2019): Desember 2019 Vol 3 No 2 (2019): Agustus 2019 Vol 3 No 1 (2019): April 2019 Vol 2 No 3 (2018): Desember 2018 Vol 2 No 2 (2018): Agustus 2018 Vol 2 No 1 (2018): April 2018 Vol 1 No 3 (2017): Desember 2017 Vol 1 No 2 (2017): Agustus 2017 Vol 1 No 1 (2017): April 2017 More Issue