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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
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
Articles 23 Documents
Search results for , issue "Vol 6 No 2 (2022): April 2022" : 23 Documents clear
Classification of Rupiah to Help Blind with The Convolutional Neural Network Method Octavian Ery Pamungkas; Puspa Rahmawati; Dhany Maulana Supriadi; Natasya Nur Khalika; Thofan Maliyano; Dicky Revan Pangestu; Eka Setia Nugraha; Mas Aly Afandi; Nurcahyani Wulandari; Petrus Kerowe Goran; Agung Wicaksono1
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3852

Abstract

Currency is an item humans require as a medium of exchange in transactions, including those with vision impairments. It can be challenging for certain blind people to identify currencies. This research aimed to help blind people identify nominal currency when in the transaction. Deep Learning with the CNN algorithm and preprocessing with a sequential model were used in this research. This algorithm is modeled as neurons in the human brain that communicate and learn patterns. Data collecting, preprocessing, testing, and evaluation are this research stage. Six hundred eighty-one datasets are used, consisting of IDR 50.000, IDR 75.000, and IDR 100.000. Model testing was carried out with different iterations of 5, 10, 15, and 20 epochs. Different epoch values will affect the time it takes the model to learn, but the length of the learning process will result in more accurate models. The highest result obtained from all epoch tests is 100%. The class prediction results for the 69 test data show that they can be predicted based on the actual class, indicating that the model is adequate. The results of this classification might be used to construct a smartphone app that would assist visually challenged people in recognizing the nominals.
Gaussian Distributed Noise Generator Design Using MCU-STM32 M. Nanak Zakaria; Achmad Setiawan; Ahmad Wilda Y; Lis Diana Mustafa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3684

Abstract

The random noise signal is widely used as a test signal to identify a physical or biological system. In particular, the Gaussian distributed white noise signal (Gaussian White Noise) is popularly used to simulate environmental noise in telecommunications system testing, input noise in testing ADC (Analog to Digital Converter) devices, and testing other digital systems. Random noise signal generation can be done using resistors or diodes. The weakness of the noise generator system using physical components is the statistical distribution. An alternative solution is to use a Pseudo-Random System that can be adjusted for distribution and other statistical parameters. In this study, the implementation of the Gaussian distributed pseudo noise generation algorithm based on the Enhanced Box-Muller method is described. Prototype of noise generation system using a minimum system board based on Cortex Microcontroller or MCU-STM32F4. The test results found that the Enhanced Box-Muller (E Box-Muller) method can be applied to the MCU-STM32F4 efficiently, producing signal noise with Gaussian distribution. The resulting noise signal has an amplitude of ±1Volt, is Gaussian distributed, and has a relatively broad frequency spectrum. The noise signal can be used as a jamming device in a particular frequency band using an Analog modulator.
Covid-19 Detection Using Convolutional Neural Networks (CNN) Classification Algorithm Melly Damara Chaniago; Amellia Amanullah Sugiharto; Qhistina Dyah Khatulistiwa; Zamah Sari; Agus Eko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3823

Abstract

Corona Virus, also known as COVID-19, is one of the new viruses in 2019. Viruses caused by an animal or human diseases are called coronaviruses. Coronavirus will direct respiration in humans. Humans who are exposed to the coronavirus will experience a respiratory infection. The research that will be made helps classify X-rays of the lungs of patients affected by the coronavirus. In this study, the classification of coronaviruses focuses on three classes, namely Covid, Normal, and Viral Pneumonia. This study uses a lung X-ray image dataset. This study has four folders, namely Scenario 1, Scenario 2, Scenario 3, and Scenario 4. This study will use the Convolutional Neural Network (CNN) method by using an architectural model including Convolutional 2D, activation layers, max-pooling layer, dropout layer, flatten, and dense layer. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. The result of the accuracy of Scenario 1 is 97.87%. In Scenario 2, the accuracy is 94.84%, Scenario 3 is 91.66%, and Scenario 4 is 91.41%.
Classification is one method in image processing. Image processing to search for similar images or with similarity ownership is called image matching or image matching. In the measurement of image matching, the original and fake logo objects are used. Ide Dewi Astria Faroek; Rusydi Umar; Imam Riadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3826

Abstract

The random noise signal is widely used as a test signal to identify a physical or biological system. In particular, the Gaussian distributed white noise signal (Gaussian White Noise) is popularly used to simulate environmental noise in telecommunications system testing, input noise in testing ADC (Analog to Digital Converter) devices, and testing other digital systems. Random noise signal generation can be done using resistors or diodes. The weakness of the noise generator system using physical components is the statistical distribution. An alternative solution is to use a Pseudo-Random System that can be adjusted for distribution and other statistical parameters. In this study, the implementation of the Gaussian distributed pseudo noise generation algorithm based on the Enhanced Box-Muller method is described. Prototype of noise generation system using a minimum system board based on Cortex Microcontroller or MCU-STM32F4. The test results found that the Enhanced Box-Muller (E Box-Muller) method can be applied to the MCU-STM32F4 efficiently, producing signal noise with Gaussian distribution. The resulting noise signal has an amplitude of ±1Volt, is Gaussian distributed, and has a relatively broad frequency spectrum. The noise signal can be used as a jamming device in a particular frequency band using an Analog modulator.
Critical Section Overhead Reduction for OpenMP Program by Nesting a Serial Loop to Increase Task Granularity of Parallel Loop Adnan Adnan; Intan Sari Areni; Zulkifli Tahir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3848

Abstract

This paper presents a simple method to reduce performance loss due to a parallel program's massive critical sections of parallel numerical integration. The method transforms a fine-grain parallel loop into a coarse grain parallel loop that nests a sequential loop. The coarse grain parallel loop is by nesting a loop block to make task granularities coarser than that naive one. In addition to the overhead reduction, the method makes the parallel work fraction significantly more significant than the serial fraction. As a result, nesting a serial loop within a parallel loop improves the parallel program's performance. Compared to the naïve method, which does not scale the performance of a parallel program of numerical integration, the nesting serial loop method scales a parallel program up to 3.26 times a fold relative to its sequential program on a quad-core processor. This result shows that the proposed method makes the parallel program much faster than the naïve method.
Live Forensic to Identify the Digital Evidence on the Desktop-based WhatsApp Triawan Adi Cahyanto; M Ainul Rizal; Ari Eko Wardoyo; Taufiq Timur Warisaji; Daryanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3849

Abstract

The live forensics method was used to acquire lawful digital evidence data from device memory in the WhatsApp application, particularly for desktop-based WhatsApp. There has been little research on live forensics on desktop-based WhatsApp applications. These studies involve mimicking crime cases in cyberspace using the Instant Messenger application. Much of the acquisition process is completed only once, even though many possible conditions may arise during the purchase process. Investigators or experts can employ digital evidence data discovery to identify crimes that have occurred. The stages of research in detecting digital evidence are data collecting, the examination process, and the acquisition of analysis and reporting outcomes. During the data-gathering phase, a case simulation dataset was obtained. The examination process stage results in the integrity of the duplicated data; data reduction is performed on data related to fundamental operating system components, influential application features, and incomplete data. According to the investigation findings, there are difficulties in looking for digital evidence, and the features of each digital evidence vary. The simulation file contained many reports on the finds of digital evidence. As a data acquisition method, the characteristics of live forensics are limited to the data retrieval process in RAM. Based on these findings, it is possible to conclude that the data collection and examination processing were completed effectively. The analysis results were acquired, and the report was presented with the indicated digital evidence. Further study can be paired with chip-off procedures on RAM devices for data recovery.
LRDDoS Attack Detection on SD-IoT Using Random Forest with Logistic Regression Coefficient Wahyuli Dwiki Nanda; Fauzi Dwi Setiawan Sumadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3878

Abstract

Software-Defined Internet of Things (SD-IoT) is currently developed extensively. The Software-Defined Network (SDN) architecture allows Internet of Things (IoT) networks to separate control and data delivery areas into different abstraction layers. However, Low-Rate Distributed Denial of Service (LRDDoS) attacks are a significant problem in SD-IoT networks because they can overwhelm centralized control systems or controllers. Therefore, a system is needed to identify and detect these attacks comprehensively. This paper built an LRDDoS detection system using the Random Forest (RF) algorithm as the classification method. The dataset used during the experiment was considered a new dataset schema with 21 features. The dataset was selected using feature importance - logistic regression to increase the classification accuracy results and reduce the computational burden of the controller during the attack prediction process. The results of the RF classification with the LRDDoS packet delivery speed of 200 packets per second (PPS) had the highest accuracy of 98.7%. The greater the delivery rates of the attack pattern, the increased accuracy results.
Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter Andi Nurkholis; Debby Alita; Aris Munandar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3906

Abstract

PPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel was obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values ​​of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the polynomial kernel model produces higher accuracy; both applied to the One Against One and One Against Rest methods. In contrast, the RBF kernel model produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus.
The Clustering Rice Plant Diseases Using Fuzzy C-Means and Genetic Algorithm Faza Adhzima; Yandra Arkeman; Irman Hermadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3912

Abstract

Rice is an agricultural sector that is very important for Indonesia's economy. The main problem with rice plants is pest and disease control which has a hazardous impact and economic losses for farmers. The apparent characteristics of rice leaves have a greater area than other plant structures; rice leaves can be applied for the early diagnosis of rice plant diseases. The approaches employed are fuzzy C-Means (FCM) and Genetic Algorithm-Fuzzy C-Means (GA-FCM). The center of the cluster is obtained while adopting genetic algorithms for optimization. The primary dataset used in this research is Teaching Sawah Farm IPB, and the second dataset is UCI Rice Leaf Diseases. According to the comparison results, the GA-FCM optimization results in a higher level of clustering precision with a 65% optimal cluster center point on the silhouette coefficient value compared to just 60% for FCM. This research shows that the proposed method can add 5% accuracy to the clustering results in correctly identifying rice plant diseases.
Designing an Ethereum-based Blockchain for Tuition Payment System using Smart Contract Service Moch Sholeh; Esther Yolanda Talahaturuson; Maulana Rizqi; Agustinus Bimo Gumelar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i2.3917

Abstract

The security and disclosure of information in online transaction data remain a sensitive subject until this day. Whenever data collection from a transaction process is accessible over the internet system, certain parties may misuse any one of these data. Blockchain technology, which includes the use of smart contracts, is thought to overcome this problem due to the blockchain's decentralized and distributed nature. Blockchain allows transaction data to be accessed openly and transparently while securely protected by hashing encryption owned by intelligent contracts. This enables users to have detailed access privileges to each transaction's data. The development of smart contracts will be carried out in the production of microservices payment gateways based on decentralized apps (DApps) on the Ethereum blockchain in this research, with the payment gateway generated being used in the tuition payment process. The Truffle framework and the Metamask wallet will be used to assist the Ethereum payment process during the DApps development process. Testing the functionality of each intelligent contract feature reveals that the payment system can be utilized effectively and that there are no issues that cause transaction failures.

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