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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 20 Documents
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Search results for , issue "Vol 9 No 1 (2025): February 2025" : 20 Documents clear
Comparison of Transfer Learning Architecture Performance for Indonesian Auction Object Classification Rofiq, Hanif Noer
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6082

Abstract

The Indonesian auction, one of the sources of Indonesia's income for Non-Tax State Revenue (PNBP), faces challenges in accurately classifying auction objects, limiting revenue optimisation. This research aims to compare the performance of several transfer learning architectures on the Indonesian Auction Object Dataset, which includes categories such as Buildings, Cars, Motorbikes, and Salvage Materials. Seven pre-trained transfer learning models—MobileNetV2, NASNetMobile, EfficientNetV2B0, DenseNet121, Xception, InceptionV3, and ResNet50V2—were evaluated against a baseline model, focusing on validation accuracy, model size, and computational efficiency. MobileNetV2, NASNetMobile, DenseNet121, Xception, InceptionV3, and ResNet50V2 all achieved 100% validation accuracy, outperforming the baseline model's 96.5% accuracy. MobileNetV2 stands out for its efficiency, reaching 100% accuracy in just eight epochs with a compact model size of 11.1 MB. In contrast, EfficientNetV2B0 performed poorly on this dataset, achieving only 25% validation accuracy. These findings confirm that transfer learning architectures can significantly improve auction object classification accuracy while reducing the model size and training time, highlighting the benefit of transfer learning for optimising Indonesian auction systems.
Identifying Rice Plant Damage Due to Pest Attacks Using Convolutional Neural Networks Tenriola, Andi; Azis, Putri Alysia; Kaswar, Andi Baso; Adiba, Fhatiah; Andayani, Dyah Darma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6125

Abstract

Rice (Oryza Sativa) is an important crop for meeting global food needs; however, one of the main challenges in its cultivation is the attack of stem borer pests, which can cause significant damage. This study aims to identify the damage caused by these pest attacks using Convolutional Neural Networks (CNN) methods. We developed and trained several CNN architectures, including the proposed architecture, MobileNet, and EfficientNetB0, to detect pest attacks on rice. The dataset used consists of 700 images per class taken directly from the field, where the images depict rice plants that have been peeled or opened to inspect for the presence of pests, specifically stem borer pests. To enhance the quality and diversity of the dataset, we applied a rigorous selection process, ensuring that only high-quality images were used. Additionally, augmentation techniques such as rotation were employed to expand the dataset to 2000 images per class. Labeling was carried out carefully to ensure that each image accurately reflected the condition of the pest attack. The results of the study indicate that the proposed CNN model can identify damage with high accuracy, thereby contributing to efforts to increase rice production through early detection of pest attacks using computer vision technology.
Agricultural Cultivation Cost Prediction Using Neural Networks and Feature Importance Analysis Salmania Putri; Fahrudin, Tora; Asti Widayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6003

Abstract

Agriculture is one of the most important sectors integral to human civilization, and technological adaptation is necessary to maintain its quality. This research aims to achieve high productivity in the agricultural sector by using neural networks or Deep Learning methods to predict the cost of agricultural cultivation, as well as identifying significant factors that affect the profitability of potato commodities with Feature Importance analysis. The research process includes the stages of Data Preparation, Data Understanding, Split Data Training, Classification Model Building, Training, and Evaluation. Evaluation techniques such as MAE, MSE, and R² were used to assess the effectiveness of the model. The results showed that the prediction model almost achieved optimal performance, with the Cost of Cultivation C2 factor having the greatest influence in understanding the data and guiding improvements to the significant factors affecting cultivation cost prediction. The main contribution of this research is the application of optimal Deep Learning methods to predict the cost of cultivation as well as identifying the main components that impact the profitability of potato farming in India.
Impact of Adaptive Synthetic on Naïve Bayes Accuracy in Imbalanced Anemia Detection Datasets Zuhanda, Muhammad Khahfi; Lisya Permata; Hartono; Erianto Ongko; Desniarti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6031

Abstract

This research aims to analyze the impact of the Adaptive Synthetic (ADASYN) oversampling technique on the performance of the Naïve Bayes classification algorithm on datasets with class imbalance. Class imbalance is a common problem in machine learning that can cause bias in prediction results, especially in minority classes. ADASYN is one of the oversampling methods that focuses on adaptively synthesizing new data for minority classes. In this study, the performance of the Naïve Bayes algorithm was tested on Anemia Diagnosis datasets before and after the application of ADASYN. This dataset contains 104 instances, 5 attributes, and 2 classes, and has an imbalance ratio of 3. The evaluation was carried out by comparing accuracy, confusion matrix, precision, recall, and F1-score to obtain a more comprehensive picture of the effectiveness of ADASYN in improving Naïve Bayes. The results of the study show that the performance of the oversampling method depends on the imbalance ratio so it is important to ensure that the oversampling method does not cause overfitting and this can be overcome by using ADASYN which only selects Selected Neighbors. The results showed that ADASYN significantly increased accuracy from 0.57 to 0.78, precision from 0.17 to 0.74, recall from 0.20 to 0.88, and F1-Score from 0.18 to 0.80. In this study, we also compared the application of ADASYN and SMOTE on the Naïve Bayes algorithm. The results show that ADASYN outperforms SMOTE across all key metrics—accuracy, precision, recall, and F1-Score—while the accuracy improvements were statistically significant (p-value = 0.00903).
The Internet-of-Things-based Fishpond Security System Using NodeMCU ESP32-CAM Microcontroller Sumari, Arwin Datumaya Wahyudi; Annurroni, Ilyas; Ayuningtyas, Astika
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6033

Abstract

Fish theft in ponds is a common problem, especially in freshwater fish farms. To solve this problem, a security system that can detect human movement and provide real-time notifications is needed. This research aims to design and implement an Internet of Things (IoT)-based fishpond security system using NodeMCU ESP32-CAM Microcontroller equipped with HB100 Radar Sensor to detect human entity movement with NodeMCU ESP32-CAM to take pictures of the approaching human entity, as well as Arduino Uno R3 to control system inputs and outputs. The system also sends real-time notifications and can be managed independently by a social media application. The results show that the system can detect human movement well, provide real-time notifications, and be handled easily. The test results show that the HB100 Radar Sensor can detect entities with a maximum distance of 9 meters with overall accuracy of 90%, the Buzzer performs well according to the human entity detected by the sensor, the Arduino Uno R3 successfully sends a trigger signal to NodeMCU ESP32-CAM to activate the OV2640 camera to capture the detected human entities with a maximum distance of up to 60 meters with an optimal distance between 1 to 9 meters. Integrated system test results show that all components of the fishpond security system
Enhancing Network Security: Evaluating SDN-Enabled Firewall Solutions and Clustering Analysis Using K-Means through Data-Driven Insights Ahmad Turmudi Zy; Isarianto; Rifa’i, Anggi Muhammad; Nugroho, Agung; Ghofir, Abdul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6056

Abstract

In the face of escalating and increasingly complex cyber threats, enhancing network security has become a critical challenge. This study addresses this issue by investigating the optimization of SDN-enabled firewall solutions using a data-driven approach. The research employs K-Means clustering to analyze attack patterns, aiming to identify and understand distinct patterns for improved firewall effectiveness. Through the clustering process, attack data was classified into three clusters: Cluster 0, indicating concentrated attack sources likely tied to high-activity regions or networks; Cluster 1, representing a dispersed distribution of attacks, pointing to diverse origins; and Cluster 2, linked to specific geographic regions or unique attack behaviors. The clustering efficacy was evaluated using the Silhouette Score (0.606) and the Davies-Bouldin Index (0.614), indicating meaningful and reliable clustering outcomes. These findings provide actionable insights into network threat patterns, enabling the refinement and enhancement of SDN-enabled firewalls. The study contributes to the field by demonstrating the potential of clustering techniques in uncovering patterns overlooked by traditional methods and paving the way for further research into alternative clustering algorithms and broader applications in network security.
CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach Yunidar, Yunidar; Yusni, Y; Nasaruddin, N; Arnia, Fitri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6068

Abstract

Stunting, a condition characterised by short stature, is a growth disorder caused by chronic malnutrition, which often begins in the womb. Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. Research shows that these physical differences can also be observed in facial features. Because faces provide important information and are commonly studied in digital image processing, in this study, we will compare the facial image classification performance of stunted children versus normal children using various Convolutional Neural Network (CNN) architectures. The evaluated architectures include MobileNetV2, InceptionV3, VGG19, ResNet18, EfficientNetB0, and AlexNet. To improve the learning process, augmentation techniques with Haar cascade and Gaussian filters were applied so that the data set increased from 1,000 to 6,000 images. After adding the dataset, training is carried out with an early stop approach to minimise overfitting. The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. The results show that the EfficientNetB0 architecture outperforms other models, achieving 100% accuracy. Early stopping has been shown to improve training efficiency and help prevent overfitting.
Real-Time Location Monitoring and Routine Reminders Based on Internet of Things Integrated with Mobile for Dementia Disorder Dwi Rangga Okta Zuhdiyanto; Yuli Asriningtias
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6105

Abstract

The increasing number of dementia sufferers worldwide demands a new approach to monitoring daily activities and locations to reduce the risk of getting lost. This study develops a real-time location monitoring and routine reminder system based on the Internet of Things (IoT), integrated with a mobile application. The system is designed to assist individuals with dementia, particularly elderly and younger adults with cognitive impairments, in performing daily routines independently, while providing a sense of security for families and caregivers through real-time location tracking features. This technology utilizes GPS for accurate location monitoring, daily activity reminders, and automatic notifications for caregivers in case of deviations from usual routes. The system development includes prototype creation that consisting of a mobile application and IoT tools such as the ESP32 WROOM microcontroller, Ublox Neo6M V2 GPS module, and SIM800L V2 GSM module. Functionality testing and impact evaluation were conducted to assess its effectiveness in improving the quality of life for dementia sufferers and facilitating monitoring for caregivers. With features such as daily reminders, emergency contacts, and real-time data integration, this system is intended not only for dementia patients but also for families and caregivers seeking tools to ensure the safety and comfort of the sufferers. It is expected that this research will enhance the independence of dementia patients in performing daily activities and provide innovative solutions through IoT technology to improve well-being across different age groups.
Prediction of Main Transportation Modes using Passive Mobile Positioning Data (Passive MPD) Farhan, Muhammad; Suadaa, Lya Hulliyyatus; Sugiri; Munaf, Alfatihah Reno Maulani Nuryaningsih Soekri Putri; Pramana, Setia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6128

Abstract

Indicators of the main mode of transportation used by domestic tourists during tourism trips cannot yet be estimated using Passive MPD which is recorded based on the location of the BTS that captures the cellular activity of domestic tourists. Previous research on identifying transportation modes from Passive MPD has its own shortcomings because it only relies on speed and travel time features. Meanwhile, there is Active MPD which is recorded using active geo-positioning and real-time, where the research involves many features and has a data structure similar to Passive MPD. Therefore, this research aims to conduct a study of the implementation of the method used to identify modes of transportation in Active MPDs to Passive MPDs as an approach to predicting the main modes of transportation. As a result, the transportation mode identification method in the Active MPD can be implemented in the Passive MPD. The best accuracy of 83.56% was obtained by the LightGBM model using all features. However, the Multinomial Logistic Regression model, which only uses 10 selected features, is the most effective and efficient model with an accuracy of 76.43% and a much shorter execution time
Evaluating Transformer Models for Social Media Text-Based Personality Profiling Hartanto, Anggit; Ema Utami; Arief Setyanto; Kusrini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6157

Abstract

This research aims to evaluate the performance of various Transformer models in social media-based classification tasks, specifically focusing on applications in personality profiling. With the growing interest in leveraging social media as a data source for understanding individual personality traits, selecting an appropriate model becomes crucial for enhancing accuracy and efficiency in large-scale data processing. Accurate personality profiling can provide valuable insights for applications in psychology, marketing, and personalized recommendations. In this context, models such as BERT, RoBERTa, DistilBERT, TinyBERT, MobileBERT, and ALBERT are utilized in this study to understand their performance differences under varying configurations and dataset conditions, assessing their suitability for nuanced personality profiling tasks. The research methodology involves four experimental scenarios with a structured process that includes data acquisition, preprocessing, tokenization, model fine-tuning, and evaluation. In Scenarios 1 and 2, a full dataset of 9,920 data points was used with standard fine-tuning parameters for all models. In contrast, ALBERT in Scenario 2 was optimized using customized batch size, learning rate, and weight decay. Scenarios 3 and 4 used 30% of the total dataset, with additional adjustments for ALBERT to examine its performance under specific conditions. Each scenario is designed to test model robustness against variations in parameters and dataset size. The experimental results underscore the importance of tailoring fine-tuning parameters to optimize model performance, particularly for parameter-efficient models like ALBERT. ALBERT and MobileBERT demonstrated strong performance across conditions, excelling in scenarios requiring accuracy and efficiency. BERT proved to be a robust and reliable choice, maintaining high performance even with reduced data, while RoBERTa and DistilBERT may require further adjustments to adapt to data-limited conditions. Although efficient, TinyBERT may fall short on tasks demanding high accuracy due to its limited representational capacity. Selecting the right model requires balancing computational efficiency, task-specific requirements, and data complexity.

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