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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

RESTI
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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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A Comparison of Deep Learning Approach for Underwater Object Detection Nurcahyani Wulandari; Igi Ardiyanto; Hanung Adi Nugroho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

In recent years, marine ecosystems and fisheries have become potential resources. Therefore, monitoring these objects will be essential to ensure their existence. One of the computer vision techniques is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning methods applied to underwater object detection, including Faster-RCNN, SSD, RetinaNet, YOLOv3, and YOLOv4. We trained five models on the RUIE dataset. The average detection time was used to compare how fast a model can detect an object within an image, and mAP was also applied to measure detection accuracy. All trained models have costs and benefits; SSD was fast but had poor performance; RetinaNet had consistent performance across different thresholds, but the detection speed was slow; YOLOv3 was the fastest and had acceptable performance comparable with RetinaNet; YOLOv4 was good at first, but performance dropped as threshold enlargement; also, YOLOv4 needed extra time to detect objects compared to YOLOv3. There are no models that are fully suited for underwater object detection; nonetheless, when the mAP and average detection time of the five models were compared, we determined that YOLOv3 is the best acceptable model among the evaluated underwater object detection models.

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)

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.

Sentiment Analysis of Beauty Product E-Commerce Using Support Vector Machine Method Muhammad Rio Pratama; Faza Abdillah Gunawan Soerawinata; Rafdi Reyhan Zhafari; Rendy; Helena Nurramdhani Irmanda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Customers who buy goods will provide an assessment in the form of a review. If negative reviews dominate an item, other customers will be reluctant to buy at that store, so customers look for other stores, affecting the store's revenue. Therefore, this study aims to classify e-commerce beauty product reviews using the Support Vector Machine to create a model to categorize beauty product reviews and analyze accuracy. The research phase begins by collecting 50,000 datasets consisting of 35,000 training data and 15,000 test data. After the data is collected, the data labeling stage is carried out, labeled positive and negative. Then the preprocessing step is carried out so that the data is ready to be processed in the feature extraction step. The feature extraction step aims to explore potential information that represents words. Furthermore, the resulting data is evaluated to obtain an accuracy value and determine whether the model made is feasible to use. The results showed that the Support Vector Machine could classify beauty product reviews well with an accuracy of 80.06%.

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)

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.

Platform Digital and Content Innovation to Increase Youth Interest in the Agricultural Sector Mambang M; Finki Dona Marleny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The use of digital technology is essential in increasing the younger generation's interest in the agricultural sector. Deficient awareness of youth in the agricultural sector, even though the agricultural sector has great potential and has a crucial role in handling anything. The methodology carried out in this study uses data collection, initial processing of data, analysis using python, evaluation, and validation of results. Content with agricultural topics, the use of the Internet of things on agriculture that contains the content of the role of the younger generation in the agricultural sector is then used as a dataset. Variables analyzed in these contents include the year of content creation, how many subscribers, number of viewers, and number of videos. In-person interviews with the younger generation were also conducted to explore information with variables in knowledge levels, family environment factors, land availability, social practice, risk factors, and income. The results and discussions of the analysis of content related to agriculture and the Internet of things showed the younger generation's interest in farming with the help of digital platforms. Of the 30 respondents used as a sample, prestige social has the highest value compared to other variables with 0,59. The results obtained from the analysis showed that the number of impressions on content related to the younger generation in the agricultural sector reached 248,882,953 impressions, and the number of impressions related to the Internet of Things content was as many as 23. 969 impressions. The use of technology with the digital Youtube platform is an excellent opportunity to give birth to various kinds of innovations by utilizing digital technology to support the sustainability of the agricultural sector in Indonesia.

The Generating Super Resolution of Thermal Image based on Deep Learning Ismail Ismail; Yefriadi Y; Yuhefizar Y; Fibriyanti; Zulka Hendri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The need for a high resolution to the thermal image is urgent and essential. The high resolution of the thermal image can give accurate information on the heat distribution map of the objects. The accurate heat distribution maps can give accurate temperature information. This accurate temperature measurement is used for measuring many objects such as electric motors, engines, the human body, and so on—this information is used to detect the anomalies of the object to find the damaged parts. The anomalies are considered damaged parts found in solar panels, agricultural fields, buildings, bridges, etc. As the super-resolution of thermal images is very important, generating them is compulsory. The camera for obtaining super-resolution thermal images is rare, not available in the common market. Furthermore, this kind of device is costly too. Therefore not all the users, such as farmers or technicians, can have them. In order to handle the problem, the proposed method has the purpose of generating super-resolution thermal images economically and is more accessible through the deep learning method. The dataset is taken from the solar panel. The results show that the proposed method can handle the low-resolution problem of thermal images.

Food Fraud Prevention using a Blockchain-Based System: Case Study Slaughterhouse in Sidoarjo Daniel Soesanto; Liliana L; Bambang Prijambodo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Data consistency and security are the most important thing to pay attention to in the supply chain of foodstuffs derived from livestock. However, the length of the existing supply chain and the lack of facilities to store and maintain the correctness of livestock data are problems in ensuring the security of the data. The use of blockchain in livestock has existed in Indonesia, but its scope is only to agriculture or only a tiny part of the livestock supply chain. Therefore, this research was conducted to develop the application of a blockchain-based livestock supply chain system in Indonesia. This research begins with conducting a literature study, analyzing the slaughterhouses. Then the development of a blockchain system for the food supply chain was carried out. System validation was carried out through interviews with the head of the slaughterhouses, breeders, and wholesalers. The results state that the system helps ensure the security of food supply chain data from livestock. However, the problem that has not been resolved is the validation process of data on the weight loss of foodstuffs in the supply chain process. This technique is expected to be used for halal certification in the future.

Towards Generating Unit Test Codes Using Generative Adversarial Networks Muhammad Johan Alibasa; Rizka Widyarini Purwanto; Yudi Priyadi; Rosa Reska Riskiana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Unit testing is one of the critical software development steps to ensure the software’s quality. Unit testing is often neglected despite its importance since it requires a significant amount of time and effort from the software developers to write them. Existing automated testing generating systems from past research still have shortcomings due to the limitations of the Genetic Algorithm (GA) in generating the appropriate unit test codes. This study explores the feasibility of using Generative Adversarial Networks (GAN) models to generate unit test code with the ability of GAN to cover GA’s drawbacks. We perform experimentations using four state-of-the-art GAN models to generate basic unit test codes and compare the results by analyzing the generated output codes using novel metrics proposed from past studies and performing a qualitative evaluation of the generated outputs. The results show that the generated codes have satisfactory quality scores (BLEU-2 of around 99%) from the models and adequate diversity scores (NLL-Div and NLL-Gen) in most models. Our study shows positive indications and potential in the use of GAN for automatic unit test code generation and suggests recommendations for future studies in GAN-based unit test code generation systems

Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features Rohmat Indra Borman; Yusra Fernando; Yohanes Egi Pratama Yudoutomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. For LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, primary axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall, and accuracy, it is obtained that the precision value is 85%, recall is 82%, and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by single-layer network architecture. This makes the computational process faster and does not burden the computational process.

A Model of Non-ASN Employee Performance Assessment Based on the ROC and MOORA Methods Haviluddin Haviluddin; Edy Budiman; Nurfaizi Amin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This study aims to assess the performance of non-ASN employees at the Human Resources Development Agency (BPSDM), East Kalimantan Province, Indonesia, to assist organizers in determining the feasibility of extending work contracts. The performance of 37 non-ASN employees has been assessed based on 12 criteria, including honesty, discipline, loyalty, responsibility, courtesy, commitment, ability and skills, neatness, communication, achievement, absence, and violations. In this study, the Rank Order Centroid (ROC) and Multi-Objective Optimization based on Ratio Analysis (MOORA) methods have been implemented to obtain rankings. Meanwhile, the confusion matrix (CM) method has also been used to measure the accuracy of both methods. Based on the experiment, the ROC method has been used to achieve the criteria weight, and the MOORA method has been utilized to rank all non-ASN employees based on the highest score. Where the CM suitability level of 81.1% has been gained so that the ranking of 37 non-ASN employees can be revealed. The study indicates that both methods can be implemented as alternative models for assessing the performance of non-ASN employees. Therefore, these methods are pretty effective, efficient, and relatively easy to use.

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Contact Name Yuhefizar

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Journal Mail Official ephi.lintau@gmail.com

Editorial Address Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

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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
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INDONESIA