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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 24 Documents
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Search results for , issue "Vol 10 No 1 (2026): February 2026" : 24 Documents clear
Firefly Algorithm Under-sampling for Imbalance Data in Breast Cancer Survival Prediction Purba, Diya Namira; Namora Purba
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.v10i1.6439

Abstract

Breast cancer remains a major health challenge, affecting approximately 1.7 million individuals annually and often leading to severe complications. Predicting survival outcomes is difficult due to highly imbalanced data, with 3,408 death cases compared to only 616 survival cases. To address this issue, we applied the Firefly Algorithm–based under-sampling (FAUS) to balance the dataset and combined it with three machine learning classifiers: Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbor (KNN). Experimental results show that FAUS substantially improves predictive performance compared to conventional under-sampling. Among the tested models, RF achieved the highest F1-score of 0.79, while DT and KNN reached 0.72 and 0.68, respectively. The results indicate that FAUS is effective in preserving representative samples, thereby enhancing model performance in breast cancer survival prediction.
Real-Time Hand Gesture-Based Virtual Mouse System Using ESP32-CAM and OpenCV Farhan Alghifari Chaniago Saputro, Muhammad; Hadiyoso, Sugondo; Dyah Irawati, Indrarini
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.v10i1.6609

Abstract

This research develops a virtual mouse control system that uses real-time hand gesture recognition implemented on an ESP32-CAM–based Internet of Things (IoT) platform. By leveraging OpenCV for image processing, the system translates hand gestures into corresponding mouse actions, including cursor movement, clicking, and scrolling. The study evaluates system performance under different lighting conditions and Wi-Fi speeds. Results show that higher Wi-Fi speeds significantly reduce latency, enabling smoother real time gesture recognition and high definition video output, while lower speeds lead to noticeable delays and reduced accuracy. The system successfully enables remote cursor control through camera captured hand gestures, supporting functions such as left click, right click, scrolling, and dragging. In latency tests performed with an internet speed of approximately 60 Mbps, the system achieved an average delay of about 50 milliseconds. Under optimal lighting conditions with minimal background interference, it accurately tracked hand movements and recognized gestures such as pointing, clicking, dragging, and scrolling in real time, achieving an accuracy rate of 95%. Despite its lower resolution compared to conventional webcams, the ESP32-CAM proves to be an effective solution for virtual mouse control, particularly in scenarios where high-resolution imaging is unnecessary. Its IoT capabilities support remote operation, allowing users to control the virtual mouse from a distance as long as both the ESP32-CAM and the computer remain connected. Overall, the findings highlight the ESP32-CAM based IoT platform as a viable alternative for gesture based interaction in real applications, although further enhancements are needed to improve performance in challenging environments.
Optimization of a New Adaptive Stacking Ensemble Model Integrated with IoT for Stress Level Detection Based on Physiological Signals Muhardi; Mohd Rinaldi Amartha; Rika Melyanti; Yuda Irawan
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.v10i1.6770

Abstract

Mental health issues among college students are receiving increasing attention, particularly because of academic and social pressures and the impact of technology use. This study aims to develop a real-time stress level prediction model using a New Adaptive Stacking Ensemble approach based on physiological data and IoT devices. The data included heart rate, SpO₂, body temperature, and systolic and diastolic blood pressure. Five machine learning algorithms are used as base models: SVM, C4.5, Decision Tree, KNN, and Random Forest. The MLP serves as the meta-model, which is then optimized using Optuna. The model training process begins with pre-processing, feature standardization using StandardScaler, and data balancing using SMOTE. The results showed that the stacking model with the MLP meta-model achieved an accuracy of 90.00% under the individual Random Forest and KNN models, and increased to 97.00% after hyperparameter optimization. This model was then integrated with IoT devices using MAX30102, MLX90614, and digital tensiometer sensors, as well as a Streamlit interface to display real-time stress classification results. The system built not only excels in accuracy but can also be implemented to directly detect stress levels, thereby potentially supporting early intervention and mental health promotion in campus environments.
Comparative Analysis of Multispectral Image Classification Based on EfficientNetB0, ResNet152, DenseNet161, DenseNet121, and HSV Segmentation Melinda; Nurdin, Yudha; Mufti, Alfatirta; Anzella, Syifa; Rusdiana, Siti; D Acula, Donata
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.v10i1.6873

Abstract

This study established a classification system based on Convolutional Neural Networks (CNNs) to detect High-High Fluctuation (HHF) patterns in multispectral data derived from pure water (H2O) and a water-sodium hydroxide (NaOH) solution. This study combines HSV color-space-based segmentation to identify areas with the highest signal amplitude, thereby enhancing the feature extraction of the CNN model. Data augmentation techniques, including random flipping, rotation, and color jitter, along with training parameters such as a learning rate of 0.0001 and a batch size of 32, have been shown to effectively improve model generalization and reduce overfitting. Four different CNN architectures were evaluated: ResNet-152, DenseNet-161, DenseNet-121, and EfficientNet-B0. As a result, ResNet152 achieved the highest accuracy of 97.6%, attributed to its network depth and residual connections that effectively address the vanishing gradient problem. DenseNet161 and DenseNet121 also demonstrated competitive performance, achieving accuracies of 96.7% and 96.2%, respectively, which is supported by their dense connectivity that optimizes feature reuse. Conversely, EfficientNetB0, despite showing lower accuracy (90%), provides significant computational efficiency, making it suitable for real-time applications. These results underscore the importance of selecting a CNN architecture that balances accuracy and efficiency for multispectral data classification.
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.

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