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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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Robust Aggregation Strategies in Federated Learning for Credit Risk Assessment Mujahidin, Sulthonika Mahfudz Al; Riyadi, Michael Angello Qadosy; Dewi, Adinda Mariasti; Kamal, Mustafa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7133

Abstract

Financial institutions face challenges in credit risk assessment due to fragmented data and strict privacy regulations, which hinder predictive modeling and increase financial risks. Federated Learning (FL) enables privacy-preserving collaborative modeling without sharing raw data. This study evaluates five FL aggregation methods—Federated Averaging (FedAvg), Weighted Average, Median Aggregation, Federated Proximal (FedProx), and Stochastic Controlled Averaging (SCAFFOLD)—using logistic regression on the Credit Approval dataset (690 records, five clients) with non-IID label and feature distributions. Local models were trained and aggregated over 50 rounds. Median Aggregation outperformed the other methods, achieving an F1-score of 97.85% and a recall of 80.6% (vs. 72.3% for others), demonstrating robustness against data skewness. However, global model performance (85.22% for FedAvg, Weighted Average, FedProx, SCAFFOLD; 85.80% for Median) remained static across rounds, indicating limited convergence due to rapid local model convergence and non-IID challenges. The high communication cost of 50 rounds highlights a trade-off between accuracy and efficiency, necessitating optimized strategies like adaptive regularization or client sampling. This study advances theoretical understanding of FL under heterogeneity and provides practical guidance for secure, regulation-compliant credit risk modeling in financial institutions. Future work should explore larger datasets, multi-round convergence, and privacy mechanisms like differential privacy to mitigate risks such as model inversion attacks while ensuring compliance
Evaluating Steganography Detection in JPEG Images Using Gaussian Mixture Model and Cryptographic Keys Saputro, Indrawan Ady; Nugraha, Febrianta Surya; Sugiarto, Lilik; Prabowo, Iwan Ady
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6084

Abstract

This study introduces a novel approach that integrates Gaussian Mixture Models (GMM) with MD5 hash-based verification to detect hidden messages embedded via Least Significant Bit (LSB) steganography in JPEG images. Unlike previous methods, the proposed dual-layer technique combines probabilistic modeling with data integrity verification. The model was trained and evaluated using a dataset comprising both original and stego-JPEG images. The experimental results achieved an accuracy of 78.67% and a precision of 89.15%, indicating good class separation between stego and non-stego images (AUC-ROC = 0.8659). However, the recall rate of 69.70% suggests that there is room for improvement in detecting all stego instances. Although MD5 is a hash function rather than an encryption algorithm, it effectively aids in identifying data anomalies resulting from message embedding. Overall, this lightweight approach offers a practical solution for steganalysis and can be further enhanced through the integration of hybrid deep learning techniques in future research.
Speech-Based Virtual Assistant for Mental Health Support Through Natural Interaction Kurniawan, Dimas Rizqi; Avianto, Donny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6585

Abstract

Mental health is a significant global concern. Indonesia has reported high rates of depression and anxiety, compounded by limited emotional outlets. Although AI virtual assistants are prevalent in e-commerce and education, their application in mental health remains underexplored. Existing solutions are predominantly text-based and transactional, which restricts empathetic and natural interactions. This study develops a voice-based assistant by integrating Automatic Speech Recognition (ASR), a generative AI for empathetic responses, and a Text-to-Speech (TTS) module fine-tuned on an Indonesian dataset to adapt accent and prosody. The system underwent both technical evaluation and human testing to assess its feasibility and user experience. The results showed that the TTS model converged effectively with low loss. Human evaluation indicated 'good' interaction (MS = 3.91, SD = 0.02), 'good' AI responses (MS = 3.83, SD = 0.26), and 'fair' TTS naturalness (MOS = 3.27, SD = 0.05). Most participants found the assistant's responses meaningful, pleasant, and helpful in managing low to moderate anxiety. These results suggest that a voice-based assistant has the potential to support mental health in Indonesia. Future work should enhance speech naturalness and utilize a larger participant pool for evaluation.
A New Approach for Dynamic Analysis of Indonesian Food Prices using the PC Algorithm and Vector Autoregression Handhayani, Teny; Permana, Yudistira; Farouqi, Akmal; Firdausyan, Naufal; Sonata, Raffy; Yusuf Rumlawang Arpipi, Marcel; Lewenusa, Irvan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6601

Abstract

Food prices are important global issue and their relationship with fuel prices has become a main concern in society. An increase in the subsidized fuel price on 3 September 2022 has allegedly caused a rise in food (grocery) prices. This paper conducts an empirical study to analyze the relationships between food prices in Indonesia: rice, chicken, beef, egg, red chili, cayenne, shallot, garlic, cooking oil, and sugar. The study uses time series data of food prices from 1 January 2018 to 31 December 2023, which consists of food prices from 87 traditional markets in Indonesia. The commodity prices are obtained from online public data provided by Bank Indonesia. It divides the analysis (pre- and post-3 September 2022) to see how the relationship between food prices changes due to the increase in the subsidized fuel price. It performs the Peter Clark (PC) algorithm to generate causal graphs from real datasets where the true graphs are unknown, complements the analysis by performing Vector Autoregression (VAR) to investigate the dynamic relationship between food prices, especially how the subsidized fuel price increase changes its dynamic relationship. The causal graphs from pre- and post-increasing fuel prices show the changes in the role of variable relationships, e.g., sugar and beef. The VAR results also show an interesting change in the IRF pattern. The results from both the PC algorithm and VAR show that there is a structural change in the relationship between food prices and that there is a different effect of price shock due to the subsidized fuel price increase. It might have been an indication of a change in the consumption pattern in society as a response to a food price increase. This must be a huge task to do in maintaining food prices when there is an adjustment in the subsidized fuel prices.
CNN-Based Skin Cancer Classification with Combined Max and Global Average Pooling Fauzi, Chairani; Nagalay, Fitra Salam S.
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6617

Abstract

Skin cancer is one of the most threatening diseases to human health, with an increase in new cases each year. Early detection plays a crucial role in improving recovery rates, however, conventional diagnostic methods such as biopsy are often invasive, time-consuming, and costly. To address this issue, artificial intelligence-based diagnostic systems, particularly Convolutional Neural Networks (CNNs), offer a promising solution for enhancing diagnostic accuracy and efficiency. This study aims to evaluate the performance of a CNN model that combines Max Pooling and Global Average Pooling (GAP) in detecting skin cancer from digital dermoscopic images. The ISIC (International Skin Imaging Collaboration) dataset was used, focusing on two classes: malignant and benign. The combination of Max Pooling and GAP is intended to increase model precision while reducing the risk of overfitting. The experimental results show that the proposed model achieved a precision of 96.35%, indicating strong performance in minimizing false positives. However, the recall was relatively low at 85.99%, suggesting reduced sensitivity in detecting malignant cases. The overall accuracy of the combined model was 91.68%, slightly lower than the Max Pooling-only model (91.79%). Although the combination does not significantly improve accuracy, it effectively enhances precision to 96.35%. This is a critical advantage in a clinical setting, as it directly translates to minimizing false positive diagnoses and preventing patients from undergoing unnecessary invasive procedures like biopsies.
Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Sulistiyo, Mahmud Dwi; Putrada, Aji Gautama; Ihsan, Aditya Firman; Yunanto, Prasti Eko; Richasdy, Donny; Sailellah, Hassan Rizky Putra; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Comparative Analysis of CNN, MobileNetV2 and EffecientNetBO in Smart Farming System for Chili Leaf Disease Detection Arda, Abdul Latief; Syamsu Alam; Matalangi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6709

Abstract

Chili leaf diseases greatly affect agricultural productivity, making early and accurate detection essential to support smart farming systems. This study presents a comparative analysis of three deep learning architectures—Convolutional Neural Network (CNN), MobileNetV2, and EfficientNetB0—for detecting chili leaf diseases using RGB images. The dataset consists of three main disease classes: Bacterial Spot, Curl Virus, and White Spot. Each model was trained and evaluated using accuracy, precision, recall, F1-score, macro AUC, and training time as performance metrics. Experimental results show that MobileNetV2 achieved the highest performance with 99% accuracy, 0.99 F1-score, and 0.99 macro AUC, although it required the longest training time of 115.12 seconds. CNN demonstrated competitive results with 96% accuracy and the shortest training time of 60 seconds, while EfficientNetB0 performed poorly with only 38% accuracy and an F1-score of 0.18. These findings highlight that model architecture, dataset characteristics, and training configuration significantly influence performance outcomes. This study contributes to the development of intelligent agricultural monitoring systems by identifying the most suitable deep learning architecture for real-time chili leaf disease detection in smart farming applications.
Securing NFT Copyright with Robust DWT-Hessenberg-SVD Watermarking and RSA Signatures Kusuma, Muhammad Romadhona; Bahri, Efri Syamsul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6747

Abstract

In the digital era, protecting visual content from misuse and forgery is essential. This study proposes a robust image watermarking method by integrating Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HD), and Singular Value Decomposition (SVD), aiming to enhance watermark imperceptibility and resilience against common image attacks. Additionally, the system incorporates RSA digital signatures within the watermark metadata to ensure verifiable authenticity in NFT (Non-Fungible Token) applications. The method was implemented using Python and tested on multiple grayscale images across various attack scenarios, including noise addition and compression. Experimental results demonstrate high SSIM and PSNR values, confirming the method's effectiveness in maintaining both visual fidelity and embedded watermark integrity. These findings support the potential of this approach for secure and scalable NFT copyright protection.
A Hybrid Framework Combining U-Net, Ant Colony Optimization, and CNN for Rice Leaf Disease Classification under Class Imbalance Ongko, Erianto; Indrawati, Asmah; Sukiman, Sukiman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6910

Abstract

Accurate classification of rice plant diseases is essential for early intervention and precision agriculture. However, real-world datasets often suffer from complex backgrounds, high-dimensional features, and severe class imbalances, which compromise classification performance. This study proposes an integrated framework combining image segmentation using U-Net, feature selection via Ant Colony Optimization (ACO), hybrid sampling to handle class imbalance, and final classification using a Convolutional Neural Network (CNN). Segmentation isolates disease-affected areas, ACO optimizes feature subsets, and hybrid sampling balances class distribution using undersampling and SMOTE. The proposed method was tested on four rice leaf disease datasets—Brown Spot, Leaf Blast, Leaf Blight, and Leaf Scald—exhibiting significant class imbalance. Experimental results show that the proposed approach outperforms baseline models (SegNet, PspNet, and E-Net) across multiple metrics: Accuracy, IoU, Precision, and Recall. This indicates the framework’s robustness and potential for real-world deployment in precision agriculture. Future work will focus on model compression and real-time implementation in IoT systems.
A Validated Blockchain Model and Architecture for Public Health System for Data Security Fernando, Erick; Prabowo, Yulius Denny; Winanti, Winanti; Tjahjana, David; Johan, Monika Evelin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6934

Abstract

Blockchain has been identified as a technology that can be used in healthcare systems, especially in terms of data security, privacy, and interoperability. This article explores the application of blockchain in various medical processes ranging from patient registration, visit registration, initial examination, patient examination, diagnostic input, and drug preparation. This study was conducted at a healthcare facility that utilizes blockchain as a technology to manage patient data, medical records, and drug prescriptions. This study uses a combined approach between literature studies and qualitative methods using the Focus Group Discussion (FGD) technique for evaluates the impact of model blockchain. Model validation with domain experts in the health sector (doctors, nurses, administrators, IT experts, and pharmacists). The results research is a validated Model blockchain public health sector improves the security and privacy of patient data but also accelerates operational processes in the healthcare chain. This study concludes that blockchain can be a transformational solution for addressing major problems in the healthcare sector, although cross-sector collaboration is needed to ensure successful implementation.

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