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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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Quantum Perceptron: A Novel Approach to Predicting Unemployment Levels in North Sumatra Province Solikhun; Trianda, Dimas
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5815

Abstract

The application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect of this research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution in predicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions by applying the quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising results and opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis.
Cloud Node Auto-Scaling System Automation Based on Computing Workload Prediction Tri Fidrian Arya; Reza Fuad Rachmad; Achmad Affandi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5928

Abstract

Auto-scaling systems in cloud computing are important for handling application workload fluctuations. This research uses machine learning to predict resource requirements based on workload work patterns and design an automatic scaling system. The dataset used includes features of node name, time, CPU usage percentage, and RAM usage. The ML model is applied for prediction regression of CPU usage percentage, CPU load, and RAM usage, and then server workload is classified into four categories: Very High, High, Low, and Very Low. The autoscaling system used is horizontal scaling. From the results of this research, it was found that the stacking algorithm with the base learner Random Forest and XGBoost had better performance in producing predictive regression. Then, after performing stability testing using K-Fold cross-validation by classifying based on workload status, it was found that the Gradient Boosting algorithm had better results compared to other algorithms, namely for the percentage of CPU usage with an accuracy of 0.998, precision 0.9, recall 0.878, f1score 0.888; CPU load average 15 minutes with accuracy 0.997, precision 0.854, recall 0.863, f1score 0.863; Meanwhile, the percentage of RAM usage is accuracy 0.992, precision 0.986, recall 0.986, and f1score 0.986. However, the XGBoost algorithm also has test results that are almost the same as Gradient Boosting.
Improving Diabetes Prediction Accuracy in Indonesia: A Comparative Analysis of SVM, Logistic Regression, and Naive Bayes with SMOTE and ADASYN Rahmawati, Selly; Wibowo, Arief; Masruriyah, Anis Fitri Nur
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5980

Abstract

This study aims to enhance the accuracy of diabetes prediction models in Indonesia by comparing the performance of Support Vector Machines (SVM), Logistic Regression, and Naïve Bayes algorithms, both with and without synthetic oversampling techniques such as SMOTE and ADASYN. The research addresses the issue of imbalanced datasets in medical diagnostics, specifically in predicting diabetes among Indonesian patients, where such imbalance often leads to biased predictions. A comprehensive dataset comprising 657 patient records from a Regional General Hospital in Indonesia was used, with 70% of the data allocated for training and 30% for testing. The results indicate that the SVM model combined with SMOTE achieved the highest accuracy of 95.8% and an AUC of 99.1, underscoring the effectiveness of these techniques in improving prediction performance. The findings of this study highlight the importance of selecting appropriate oversampling methods and algorithms to optimize diabetes prediction accuracy in the Indonesian context, providing valuable insights for future healthcare strategies.
Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms Dwitiyanti, Nurfidah; Siti Ayu Kumala; Shinta Dwi Handayani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5514

Abstract

Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed based on Silhouette Score and Cluster Purity metrics. Results indicated that K-Means provided the best balance, forming six clusters with a Silhouette Score of 0.3245 and Cluster Purity of 0.7366, making it the most suitable for seismic zone analysis. K-Medoids closely followed with a Silhouette Score of 0.3158 and Cluster Purity of 0.7190. Although DBSCAN effectively handled noise, its negative Silhouette values indicated poor clustering quality. Fuzzy C-Means and K-AP underperformed, with K-AP generating an impractically high number of clusters (196) and the lowest Silhouette Score (0.2550). This study offers a novel, comprehensive comparison of clustering algorithms for Indonesian earthquake data, emphasizing a dual-metric evaluation approach. By identifying K-Means as the most effective algorithm, provides valuable insights for disaster mitigation and seismic risk analysis.
IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning Susanto; Stiawan, Deris; Santoso, Budi; Sidabutar, Alex Onesimus; Arifin, M. Agus Syamsul; Idris, Mohd Yazid; Budiarto, Rahmat
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5871

Abstract

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.
Manhattan Distance-based K-Medoids Clustering Improvement for Diagnosing Diabetic Disease Solikhun; Rahmansyah Siregar, Muhammad; Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5894

Abstract

Diabetes is a metabolic disorder characterized by blood glucose levels above normal limits. Diabetes occurs when the body is unable to produce sufficient insulin to regulate blood sugar levels. As a result, blood sugar management becomes impaired and there is no cure for diabetes. Early detection of diabetes provides an opportunity to delay or prevent its progression into acute stages. Clustering can help identify patterns and groups of diabetes symptoms by analyzing attributes that indicate these symptoms. In this study, researchers are using K-Medoid and Quantum K-Medoid methods for clustering diabetes data. Quantum computing utilizes quantum bits, or qubits, which can represent multiple states at the same time. Compared to classical computers, quantum computing has the potential for an exponential speedup in problem-solving. Researchers conducted a comparison between two methods: the classic K-Medoids method and the K-Medoids method utilizing quantum computing. The researchers found that both Quantum K-Medoid and Classic K-Medoid achieved the same clustering accuracy of 91%. In testing with the Quantum K-Medoids algorithm, it was found that the cost value in the 8th epoch showed a significant decrease compared to the Classical K-Medoids algorithm. This demonstrates that Quantum K-Medoid can be considered a viable alternative for clustering purposes.
Utilization of Household Organic Waste into Biogas and Integrated with IoT Indra Maulana, Bintang; Suryamiharja, Andhika; Wisesa, Pradipa Catya; Rendy Munadi; Sussi , Sussi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5906

Abstract

The increase in population impacts several environmental sectors, particularly the use of natural gas energy for household needs, such as LPG (Liquefied Petroleum Gas). This has resulted in the depletion of natural gas reserves and a rise in LPG imports. Additionally, the growing population contributes to the accumulation of household waste, which can lead to excessive leachate production and greenhouse gas emissions. This issue is particularly concerning in developing countries like Indonesia due to its negative environmental impact. This research aims to provide a solution and contribute to reducing household waste accumulation by utilizing organic waste to create renewable energy in the form of biogas as an alternative to LPG. Biogas is produced through the fermentation of organic waste. Nutrient-rich fluids containing sugar can enhance the performance of methanogenic bacteria in biogas formation. In this study, we conducted nutritional testing on molasses and coconut water to determine which nutrients optimize biogas production efficiency by monitoring the pressure of the generated biogas. Generally, biogas comprises methane and carbon dioxide. It is important to note that excessive methane can lead to explosions, while high carbon dioxide levels contribute to greenhouse gas emissions. The quantities of methane and carbon dioxide produced during biogas generation can be influenced by temperature and humidity. Therefore, monitoring pressure, temperature, humidity, methane, and carbon dioxide levels in the biogas production process using the Internet of Things (IoT) is a prudent approach. The results indicate that a substrate mixed with molasses produces biogas at twice the pressure compared to coconut water. Furthermore, optimal biogas production with ideal methane and carbon dioxide levels, occurs at temperatures between 25-35°C under high humidity conditions. This suggests that mesophilic methanogenic bacteria thrive in tropical climates.
An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISE Dataset for Software Defect Prediction Fatwanto, Agung; Nur Aslam, Muh; Ndugi, Rebbecah; Syafrudin, Muhammad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5910

Abstract

Software defect prediction is a practical approach to improving the quality and efficiency of software testing processes. However, establishing robust and trustworthy models for software defect prediction is quite challenging due to the limitation of historical datasets that most developers are capable of collecting. The inherently imbalanced nature of most software defect datasets also posed another problem. Therefore, an insight into how to properly construct software defect prediction models on a small, yet imbalanced, dataset is required. The objective of this study is therefore to provide the required insight by way of investigating and comparing a number of resampling techniques, classification algorithms, and evaluation measurements (metrics) for building software defect prediction models on CM1 NASA PROMISE data as the representation of a small yet unbalanced dataset. This study is comparative descriptive research. It follows a positivist (quantitative) approach. Data were collected through observation towards experiments on four categories of resampling techniques (oversampling, under sampling, ensemble, and combine) combined with three categories of machine learning classification algorithms (traditional, ensemble, and neural network) to predict defective software modules on CM1 NASA PROMISE dataset. Training processes were carried out twice, each of which used the 5-fold cross-validation and the 70% training and 30% testing data splitting (holdout) method. Our result shows that the combined and oversampling techniques provide a positive effect on the performance of the models. In the context of classification models, ensemble-based algorithms, which extend the decision tree classification mechanism such as Random Forest and eXtreme Gradient Boosting, achieved sufficiently good performance for predicting defective software modules. Regarding the evaluation measurements, the combined and rank-based performance metrics yielded modest variance values, which is deemed suitable for evaluating the performance of the models in this context.
Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning Parung, Ratu Anggriani Tangke; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5923

Abstract

Swiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network were employed to predict moisture content. Validation tests conducted on paper and swiftlet nests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications.
Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques Rahmatika, Nisa Adilla; Arnia, Fitri; Oktiana, Maulisa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5929

Abstract

Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among the tested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting and managing variations in both lighting and distance.

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