cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yuhefizar
Contact Email
jurnal.resti@gmail.com
Phone
+628126777956
Journal Mail Official
ephi.lintau@gmail.com
Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.
Location
,
INDONESIA
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 18 Documents
 1   2 
Search results for , issue "Vol 8 No 6 (2024): December 2024" : 18 Documents clear
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.
Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification Herimanto; Arie Satia Dharma; Junita Amalia; David Largo; Christin Adelia Pratiwi Sihite
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.v8i6.5982

Abstract

Accurately identifying facial skin types is essential for recommending the right skincare treatments and products. Misidentifying skin types can lead to negative consequences, such as irritation or worsening of skin conditions. This study investigated methods for classifying facial skin types into five categories: oily, acne-prone, dry, normal, and combination. A dataset of 1725 augmented facial images was used. Data augmentation techniques likely increased the dataset's diversity, which helps improve the model's generalization ability. The data underwent preprocessing, including rescaling, before being applied to two deep learning models, CNN and MobileNetV3. The models were evaluated based on accuracy and execution time to determine the most effective approach for classifying facial skin types. The CNN model achieved an accuracy of 64%, demonstrating its potential for image classification tasks. However, the MobileNetV3 model significantly outperformed CNN with an accuracy of 84%. This superior performance is attributed to MobileNetV3's advanced architecture, which is optimized for efficient feature extraction, and particularly relevant for capturing the subtle variations in facial skin types. Therefore, MobileNetV3 emerged as the more effective method for classifying facial skin types with higher accuracy.
Reducing Training Time in Skin Cancer Classification Using Convolutional Neural Network with Mixed Precision Implementation Ryandra Guntara, Raka; Hendriyana; Syawanodya, Indira
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.v8i6.5996

Abstract

In the field of skin cancer classification, machine learning and deep learning have been extensively utilized, particularly with convolutional neural network (CNN) architectures. However, there remains room for exploration to achieve optimal performance. This study investigates the use of the MobileNetV3Large architecture for transfer learning, chosen for its efficiency in low-power and memory-constrained applications. To further enhance performance, black-hat morphological transformation and oversampling techniques were applied to the ISIC 2020 dataset. Additionally, mixed precision training was implemented to reduce training time. The research aimed to compare the accuracy, precision, recall, F1-score, and training time of models trained with and without mixed precision. The findings revealed that while the model without mixed precision achieved superior performance with accuracy, precision, recall, and F1-score metrics reaching 98%, both models yielded an AUC-ROC of 1. Notably, mixed precision training significantly reduced training time by 1,646 seconds (27 minutes and 26 seconds), representing an 8.39% speed increase. These results suggest that mixed precision can meaningfully accelerate model training while maintaining competitive performance. The practical implications of this research include its potential to improve the efficiency of skin cancer classification models, making them more suitable for real-time clinical applications, particularly in resource-constrained environments.
Knowledge Management Foundation and Solutions Implementation in Indonesian Government Higher Educational Institution Sihombing, Boy Sandi Kristian; Fatoumatta Binta Jallow; Ghina Fitriya; Dana Indra Sensuse; Sofian Lusa; Damayanti Elisabeth; Nadya Safitri
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.v8i6.6005

Abstract

The performance of XYZ, a Government Higher Educational Institution (GHEI) in Indonesia is assessed through two unintegrated applications. The 2023 target performance was missed due to miscalculations outside applications while transforming large data amounts. Thus, business intelligence (BI) serves as a knowledge management (KM) tool to integrate those applications to achieve XYZ's target. Because BI is costly and has a 70% failure rate of development plans, a research model was evaluated to look at the current XYZ innovation capability for successful BI adoption from the KM foundation and KM solution implementation. This study used a quantitative method, employing a questionnaire for 94 civil servants and the partial least squares-structural equation model (PLS-SEM) for data analysis. Results indicate in the KM foundation, organizational (O) negatively influences KM process application (KMP) (β = -0.292, Pv = 0.010) while KM infrastructure (I) and process (P) positively influence KMP, but KM technology (T) does not. In KM solutions, KMP is proven to be linked to innovation capability when KM systems are lacking. Hence, several activities are suggested to activate T through T, O, P, and I. The model validated 80% of the hypotheses, laying the groundwork for future studies into which aspects of T strengthen innovation capabilities in GHEI.
The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection Ariatmanto, Dhani; Rifai, Anggi Muhammad
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.v8i6.6017

Abstract

The pervasive issue of fake news spreading rapidly on online platforms. causing a concerning dissemination of misinformation. The influence of fake news has become a pressing social problem, shaping public opinion in important events such as elections. This research focuses on detecting and classifying fake news using the Random Forest algorithm by investigating the impact of feature extraction techniques on classification accuracy, this study specifically employs the TF-IDF method. For this purpose, we used 44,898 English-language articles from the ISOT fake news dataset. The dataset is cleaned using tokenization and stemming then split into 75% training and 25% testing. The TF-IDF vectorizer technique was applied to convert text into numeric as feature extraction. This study has implemented a Random Forest classifier to predict real and fake news. The proposed model contributes to overall classification precision by comparing it to the existing models. This fake news detection highlights the efficacy of the TF-IDF vectorizer and Random Forest combination which achieved an impressive accuracy rate of 99.0%. This contribution highlights an effective strategy for combating misinformation through precise text classification.
The Memory Efficiency in a Receptionist Robot's Face Recognition System Using LBPH Algorithm Yudi, Endang Darmawan; Yesi Novaria Kunang; Zarkasi, Ahmad
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.v8i6.6048

Abstract

This research aims to develop a memory-efficient face recognition system for a receptionist robot using the Local Binary Patterns Histogram (LBPH) algorithm. Given the computational limitations of the Raspberry Pi, the system utilizes optimization techniques including grayscale conversion, noise reduction, and contrast adjustment to enhance processing efficiency. Testing demonstrates that the face recognition accuracy achieves 80.5% to 85.5% in offline mode, and 72% to 81% in real-time mode, with variations due to lighting conditions and facial expressions. The robot's servo motors exhibit a response time between 1.945 and 3.561 seconds, enabling responsive and interactive user engagement. The results suggest practical benefits for deploying face recognition in resource-constrained environments, enhancing the efficiency of robotic receptionist applications.
Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification Julianto, Veri; Ahmad Rusadi Arrahimi; Oky Rahmanto; Mohammad Sofwat Aldi
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.v8i6.6049

Abstract

Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. This research focuses on modifying Particle Swarm Optimization (PSO) for feature selection in identifying diseases in palm oil leaves. The PSO modification combined with logistic regression and Bayesian Information Criterion (BIC) significantly enhances KNN performance. Accuracy improved from 95.75% to 97.85%, while precision, recall, and F1-score reached approximately 98.80%. Additionally, the modified KNN+PSO achieved the shortest computation time of 0.0872 seconds, indicating high computational efficiency. These results demonstrate that the PSO modification not only improves accuracy but also computational efficiency, making it an effective method for enhancing KNN performance in detecting palm oil leaf diseases.

Page 1 of 2 | Total Record : 18

 1   2 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol 10 No 1 (2026): February 2026 Vol 9 No 6 (2025): December 2025 Vol 9 No 5 (2025): October 2025 Vol 9 No 4 (2025): August 2025 Vol 9 No 3 (2025): June 2025 Vol 9 No 2 (2025): April 2025 Vol 9 No 1 (2025): February 2025 Vol 8 No 6 (2024): December 2024 Vol 8 No 5 (2024): October 2024 Vol 8 No 4 (2024): August 2024 Vol 8 No 3 (2024): June 2024 Vol 8 No 2 (2024): April 2024 Vol 8 No 1 (2024): February 2024 Vol 7 No 6 (2023): December 2023 Vol 7 No 5 (2023): October 2023 Vol 7 No 4 (2023): August 2023 Vol 7 No 3 (2023): Juni 2023 Vol 7 No 2 (2023): April 2023 Vol 7 No 1 (2023): February 2023 Vol 6 No 6 (2022): Desember 2022 Vol 6 No 5 (2022): Oktober 2022 Vol 6 No 4 (2022): Agustus 2022 Vol 6 No 3 (2022): Juni 2022 Vol 6 No 2 (2022): April 2022 Vol 6 No 1 (2022): Februari 2022 Vol 5 No 6 (2021): Desember 2021 Vol 5 No 5 (2021): Oktober2021 Vol 5 No 4 (2021): Agustus 2021 Vol 5 No 3 (2021): Juni 2021 Vol 5 No 2 (2021): April 2021 Vol 5 No 1 (2021): Februari 2021 Vol 4 No 6 (2020): Desember 2020 Vol 4 No 5 (2020): Oktober 2020 Vol 4 No 4 (2020): Agustus 2020 Vol 4 No 3 (2020): Juni 2020 Vol 4 No 2 (2020): April 2020 Vol 4 No 1 (2020): Februari 2020 Vol 3 No 3 (2019): Desember 2019 Vol 3 No 2 (2019): Agustus 2019 Vol 3 No 1 (2019): April 2019 Vol 2 No 3 (2018): Desember 2018 Vol 2 No 2 (2018): Agustus 2018 Vol 2 No 1 (2018): April 2018 Vol 1 No 3 (2017): Desember 2017 Vol 1 No 2 (2017): Agustus 2017 Vol 1 No 1 (2017): April 2017 More Issue