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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 1,070 Documents
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Energy-Efficient Sector-Based Routing in 3D Wireless Sensor Networks Using Midpoint-Aware Cluster Head Selection Hari, Nirwana Haidar; Ahmad Khairul Umam; Lusiana Agustien
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6875

Abstract

Energy efficiency is a key concern in three-dimensional Wireless Sensor Networks (3D WSNs), where irregular node distribution can shorten network lifetime. This study introduces a novel sector-based clustering protocol that partitions the sensing field azimuthally and selects Cluster Heads (CHs) based on residual energy and distance to each sector’s geometric midpoint. The core innovation lies in an adaptive threshold formula that incorporates both energy and spatial distance, enabling fairer CH rotation and better intra-cluster communication. This approach ensures more balanced energy depletion and improved load distribution. Simulations in a 3D environment showed that the proposed protocol outperformed LEACH-Classic, LEACH-GA, and LEACH-KMeans in FND, HND, LND, residual energy, and throughput. Notably, it improves FND by up to 59.2% compared to LEACH and increases throughput by 37.8%, confirming the benefits of azimuth-based clustering and midpoint-aware CH selection.
EBAQ: An Entropy-Based Bit Allocation Framework for Lightweight Autoencoder Models Zaidir, Zaidir; Mohammad Diqi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6925

Abstract

Autoencoder-based models have shown strong potential for anomaly detection in complex time-series data; however, they often assume equal importance across latent dimensions, resulting in inefficiencies and reduced precision. This study addresses this limitation by introducing the Entropy-Based Bit Allocation Quantizer (EBAQ), a novel quantization framework that adaptively allocates bits to each latent dimension based on its entropy, preserving more precision where information content is highest. The primary objective is to enhance representational efficiency and anomaly detection performance without increasing model complexity or computational cost. EBAQ is implemented as a plug-and-play module within a standard autoencoder architecture, requiring no retraining or architectural modification. The method was evaluated using a publicly available ECG dataset, where reconstruction-based anomaly detection was employed to assess its performance. Results show that EBAQ outperforms the standard autoencoder baseline, achieving higher accuracy (94.9%), precision (99.4%), and recall (91.4%), while also demonstrating more apparent separation between normal and anomalous data in latent space visualizations. These findings confirm that entropy-aware quantization improves both fidelity and interpretability in unsupervised anomaly detection. Overall, this work presents a theoretically grounded and practically efficient solution that bridges information theory and deep learning, offering a human-centered approach to developing more intelligent and efficient AI systems for real-world applications.
Improving Vehicle Payment Method Classification Using XGBoost with SMOTE and SHAP Interpretation Dedi Trisnawarman; Reza Mahendra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6935

Abstract

Class imbalance in vehicle payment method classification can cause predictive models to become biased toward the majority. This study aims to build a classification model for automotive consumer payment methods using Extreme Gradient Boosting (XGBoost), with class balancing handled through the Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), and model interpretability performed using SHAP (SHapley Additive Explanations). The dataset consisted of 11,011 records and 13 attributes derived from Toyota vehicle delivery order transactions. Results show that the XGBoost model without balancing achieved 67.37% accuracy but only 0.24 recall for the Cash class. After applying SMOTE, the recall for the Cash class improved to 0.58, while ADASYN produced a similar improvement at 0.59, with overall accuracy maintained at around 61–62% and a stable ROC-AUC of 0.65. Feature importance and SHAP analysis identified c_vehicle_model and c_city as the most influential factors in predicting the payment method. From a business perspective, the improved ability to detect cash customers reduces the risk of misclassification and enables dealers to better segment customer payment preference. This supports more effective marketing campaigns, sales strategies, and financing risk management. The combination of XGBoost, SMOTE, ADASYN, and SHAP has proven effective in handling imbalanced data while offering transparent interpretability of predictions, making it a practical foundation for data-driven decision-making in the automotive industry.
Leveraging Machine Learning to Predict Academic Specialization Pathways in Higher Education Tamrin, Rendy Wirawan; Wella
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6941

Abstract

This study developed a machine learning-based model to predict academic concentration selection among information systems students at Universitas Multimedia Nusantara (UMN). A survey of 125 students from the 2024 cohort revealed that 90% experienced difficulties in choosing a specialization, primarily due to limited information on course relevance, unclear academic pathways, and career uncertainty. While the survey provides a contextual background, the predictive model was trained using historical academic performance data from the 2021–2023 cohorts. The three classification algorithms, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost) were implemented following the CRISP-ML methodology. To address class imbalance in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, followed by hyperparameter tuning and feature selection. The Random Forest model demonstrated superior performance, achieving an accuracy of 78.08% on the 2021–2022 cohort data, outperforming Decision Tree and XGBoost across all experimental settings. This result highlights Random Forest's robustness in this context, particularly after the integration of SMOTE and optimization procedures. The main contribution of this study lies in the application of machine learning for academic pathway prediction in an Indonesian higher education setting, providing a data-driven decision support tool to assist students in making informed and personalized specialization choices.
HyRoBERTa: Hybrid Robustly Optimized BERT Approach Model for Sentiment and Sarcasm Detection in Post-Flood Social Media Analysis Yuliyanti, Siti; Septi Asriani, Aveny; Purwayoga, Vega; Gusnadi, Zakwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6963

Abstract

The detection problem is a crucial step in sentiment classification because it strengthens the validity and reliability of the model's interpretation of ambiguous text, especially in complex social contexts such as post-disaster public communication. Without this detection, the model is prone to significant classification errors. This study presents a hybrid approach for sentiment analysis with sarcasm detection after a flood disaster by combining the RoBERTa model with sequential deep learning architectures such as GRU, LSTM, and BiLSTM. We used a dataset of 17,520 tweets that were pre-processed using cleaning, normalization, and tokenization. Then, the positive class is further detected to determine whether it is sarcasm. The model was trained using a transformer-based transfer learning method with a combination of hyperparameters: the number of epochs, batch size, dropout rate, and learning rate. The experimental results show that the RoBERTa-GRU model achieved the highest accuracy for sentiment classification at 97. 26%, whereas the RoBERTa-BiLSTM model excels in detecting sarcasm with an accuracy of 98. 74%. RoBERTa-BiLSTM excels in sarcasm detection because it provides a bidirectional sequential mechanism and better long-term memory, effectively leveraging RoBERTa's rich embedding to identify contextual contradictions that are characteristic of sarcasm. Meanwhile, RoBERTa-GRU succeeds in sentiment classification because its architecture is more concise yet effective enough to infer dominant sentiments that have been filtered from the robust representation provided by RoBERTa, making the model more efficient for less complex tasks.
Bayesian Hyperparameter Optimization of Lightweight CNNs for Facial Dermatological Classification Herimanto; Intan Rumondang Sianipar; Theo Samuel Dicunawi Aritonang; Ella Tasya Marito Silaban
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7020

Abstract

Convolutional Neural Networks (CNNs) have been widely applied for skin condition classification. However, fair comparisons across lightweight architectures are often hindered by inconsistent hyperparameter settings. This study investigates the performance of two efficient CNN architectures, EfficientNetB3 and MobileNetV3, for facial dermatological classification across seven skin condition categories. To ensure optimal and comparable performance, Bayesian hyperparameter optimization was employed, alongside data augmentation to improve generalization. Experimental results show that EfficientNetB3 achieved the highest accuracy of 91.91%, outperforming MobileNetV3 at 90.44%. Beyond model comparison, this work highlights the novelty of applying Bayesian optimization to achieve fair benchmarking of lightweight CNNs under limited dataset conditions. The best-performing model was further deployed as a mobile application using TensorFlow Lite and Flutter, demonstrating its potential for real-world dermatological support.
The Effect of Gamma Correction on the Accuracy of Vehicle Detection Using the YOLOv8 Algorithm Halawa, Fathree; Zamzami, Elviawaty Muisa; Tarigan, Jos Timanta
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7022

Abstract

Accurate vehicle detection under low-light conditions is a significant challenge in traffic surveillance systems and computer vision applications. Although YOLOv8 performs well under normal illumination, its accuracy decreases when processing low-light images due to reduced contrast and limited visual details. This study proposes the integration of gamma correction as a preprocessing method to enhance image brightness and improve YOLOv8 detection performance. The dataset consists of real ATCS traffic camera recordings from Medan City under varying lighting conditions. Gamma correction with three values (0.5, 1.5, and 2.0) was applied to evaluate its effect on detection accuracy. The results show that gamma 1.5 provides the best improvement, increasing mAP@0.5 by 0.14% and mAP@0.5:0.95 by 0.74%, and achieving the highest confidence score of 0.9678 while also producing more stable training convergence. The novelty of this study lies in applying gamma correction to YOLOv8 using real-world ATCS low-light data, demonstrating that simple preprocessing can enhance detection robustness without modifying the model architecture.
AI-Generated Narratives and Infographic Synthesis for Visualizing Climate Temperature Anomalies Yayah Durrotun Nihayah, Azed; Priyadi, Agus; Yunianto, Irdha; Fitro Nur Hakim; Wiwid Wahyudi; Nafeeza, Nafeeza
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7060

Abstract

Communicating long-term climate trends to non-specialist audiences remains a persistent challenge, despite the availability of well-validated global temperature datasets. While existing climate visualizations and reports provide accurate information, they often rely on expert-driven interpretations or static representations that limit accessibility and scalability. This study presents a proof-of-concept system that integrates analytical processing, rule-based narrative generation, and infographic synthesis to transform structured climate data into coherent public-facing communication artifacts. The proposed framework uses the NASA GISTEMP v4 dataset, covering annual global temperature anomalies from 1880 to 2024. It applies linear trend estimation and deterministic anomaly highlighting to extract salient temporal patterns. These analytical outputs are then translated into traceable natural language summaries and integrated with visual encodings within a single reproducible pipeline. The results confirm a persistent long-term warming trend, with several recent years exceeding high-anomaly thresholds, and demonstrate that analytical values, narrative descriptions, and visual emphasis can be generated consistently from a shared data source. Rather than introducing new climate indicators or predictive models, this study’s contribution lies in system-level integration: coupling data analysis, narrative synthesis, and visual composition into a unified, communication-oriented workflow. The framework is explicitly positioned as a proof of concept and does not claim causal attribution or empirical validation of user impact. Nonetheless, it demonstrates how transparent automation can reduce reliance on expert mediation while preserving scientific fidelity, supporting scalable climate communication systems.
Multi-Class Semantic Segmentation of Oil Palm Areas Using a VGG-19 U-Net Improvement Priyambodo, Tri Kuntoro; Widyaningsih, Maura; Wibowo, Moh Edi; Kamal, Muhammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7062

Abstract

UAV imagery-based semantic segmentation is crucial for mapping tropical agricultural areas such as oil palm plantations. The main challenges are overlapping vegetation objects, unclear boundaries, and spectral similarities between classes, which reduce the accuracy of conventional models. This study proposes a modified U-Net architecture with a VGG-19 backbone, achieved through hyperparameter tuning (M7) and the integration of residual blocks (M8), to enhance multi-class segmentation performance. Experiments were conducted on aerial imagery with two resolutions (512×512 and 256×256) using four-class and three-class scenarios. The results show that M7 and M8 consistently outperform the baseline model (M2) in terms of accuracy, precision, recall, and average Intersection over Union (IoU). In the 512x512 four-class scenario, M8 achieved the highest accuracy (87.40%), precision (88.32%), recall (86.32%), and MIoU (0.132). M7 reached similar accuracy (>86%) but trained significantly faster than the baseline. In the 256x256 scenario, M8 maintained strong performance with 86.44% accuracy and 0.302 MIoU. For the three-class experiment, M8 reached a top MIoU of 0.178. Accuracy, precision, and recall were all above 87%, showing improved recognition of minority classes such as waterways. Confusion matrix analysis confirmed that M8 provided more balanced class predictions. It also reduced false negatives for oil palm vegetation. M7 showed slight fluctuations, suggesting possible overfitting. These findings support M8 as a robust solution for UAV-based oil palm mapping and large-scale monitoring.
Forecasting IHSG Stock Prices Using an Attention-Based CNN-BiGRU Hybrid Deep Learning Munthe, Ibnu Rasyid; Rambe, Bhakti Helvi; Munthe, Shabrina Rasyid
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7064

Abstract

This study develops an IHSG stock price forecasting model using a hybrid CNN–BiGRU architecture enhanced by an attention mechanism. The key novelty lies in combining CNN-based local pattern extraction with BiGRU-based bidirectional temporal modeling, while attention selectively emphasizes the most informative time steps, improving representation quality for complex and noisy financial series. Historical IHSG data from public sources were preprocessed through feature engineering and normalization, followed by XGBoost-based feature selection to retain the most predictive variables. Model robustness was assessed in two settings: (i) the full dataset and (ii) a “cleaned” dataset excluding the extreme COVID-19 volatility period. The proposed model achieved strong accuracy, with MAE/RMSE of 0.0125/0.02 on the full dataset and 0.0167/0.03 on the cleaned dataset, while Pearson correlation remained close to 1 in both scenarios, indicating high alignment with actual IHSG movements. A 30-day ahead forecast produced a stable and realistic trend. Overall, the CNN–BiGRU with attention provides an effective and robust approach for capturing multi-scale temporal patterns in IHSG forecasting.

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