Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)最新文献_第10页

Automatic Requirements Engineering: Activities, Methods, Tools, and Domains – A Systematic Literature Review 自动需求工程:活动、方法、工具和领域——系统的文献综述

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-01 DOI: 10.29207/resti.v7i3.4924

R. Delima, Khabib Mustofa, Anny Kartika Sari

{"title":"Automatic Requirements Engineering: Activities, Methods, Tools, and Domains – A Systematic Literature Review","authors":"R. Delima, Khabib Mustofa, Anny Kartika Sari","doi":"10.29207/resti.v7i3.4924","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4924","url":null,"abstract":"Requirements engineering (RE) is an initial activity in the software engineering process that involves many users. The involvement of various users in the RE process raises ambiguity and vagueness in requirements modeling. In addition, traditional RE is a time-consuming activity. Therefore various studies have been conducted to support process automation on RE. This paper conducts a systematic literature review (SLR) to obtain information about RE automation related to RE activities, methods/models, tools, and domains. SLR is done through 5 main stages: definition of research questions, conducting the search, screening for relevant papers, data extraction, mapping, and analysis. The data extraction and mapping are carried out on 155 relevant publications from 2016 to 2022. Based on the results from SLR, around 53% of the research focuses on RE automation in analysis and specifications, 40% focuses on elicitation, validation, and requirements management, and 7% focuses on requirements quality. NLP is the most used method in elicitation and specification, while for analysis, machine learning, NLP, and goal-oriented models are mostly used in automatic RE. Furthermore, many papers use specific models and methods for validation and requirements management. From the domain analysis results, it is obtained that more than half of the papers contribute directly to the RE domain, and some contribute to the development of RE automation in the software application domain. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds 自组织地图(SOM)算法在药用杂草图像分类中的实现

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-01 DOI: 10.29207/resti.v7i3.4755

Hendra Mayatopani, Nurdiana Handayani, Ri Sabti Septarini, Rini Nuraini, Nofitri Heriyani

{"title":"Implementation of Self-Organizing Map (SOM) Algorithm for Image Classification of Medicinal Weeds","authors":"Hendra Mayatopani, Nurdiana Handayani, Ri Sabti Septarini, Rini Nuraini, Nofitri Heriyani","doi":"10.29207/resti.v7i3.4755","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4755","url":null,"abstract":"Wild plants or weeds often become enemies or disturb the main cultivated plants. In its development, wild plants or weeds actually have ingredients that are beneficial to the body and can be used as medicine. However, many people still need knowledge about the types of weed plants that have medicinal properties, especially the leaves. The purpose of this research is to classify the image of weed leaves with medicinal properties based on color and texture characteristics with an artificial neural network using a Self-Organizing Map (SOM). To improve information in feature extraction, RGB and HSV color features are used as well as texture features with Gray Level Co-occurrence Matrix (GLCM). Furthermore, the results of feature extraction will be identified as groups or classes with the Self-Organizing Map (SOM) algorithm which divides the input pattern into several groups so that the network output is in the form of a group that is most similar to the input provided. The test produces a precision value of 91.11%, a recall value of 88.17% and an accuracy value of 89.44%. The results of the accuracy of the SOM model for image classification on medicinal weed leaves are in the good category. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130322462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Impact of IoT on The Storing Process of Leather Raw Material 物联网对皮革原料储存过程的影响

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-06-01 DOI: 10.29207/resti.v7i3.4427

Franciskus Antonius, A. Saepudin

{"title":"The Impact of IoT on The Storing Process of Leather Raw Material","authors":"Franciskus Antonius, A. Saepudin","doi":"10.29207/resti.v7i3.4427","DOIUrl":"https://doi.org/10.29207/resti.v7i3.4427","url":null,"abstract":"A critical step in processing leather raw material is the storing that keeps them in good condition and not easily damaged through the right temperature and humidity level, as otherwise the quality of leather raw material would not be consistent and its economic value would be low. This particular study, therefore, is to conduct an experiment that uses the Internet of Things (IoT) which allows proactive monitoring in keeping a specific temperature and humidity level in their storing process. Hence the subsequent leather processing can be done at the optimal level. The experimentation showed positive results as the use of IoT made storing process of leather raw materials became more proactive and run three times more effective, from just 8 hours to 24 hours, and also brought about a positive effect on the economic efficiency and effectiveness as it enable users to produce more consistent quality of leather raw material whilst the total operating costs remained and even lower. Besides its economic impact, IoT has increased the workers' and their relatives' welfare and social life driven by better income and less time consuming. It also brings about some positive environmental effects as it reduced carbon emissions by keeping energy waste at a lower level. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"21 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113979083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis of Electricity Company Service Quality Using Naïve Bayes 基于Naïve贝叶斯的电力公司服务质量情感分析

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-28 DOI: 10.29207/resti.v7i2.4627

Yuli Astuti, Yova Ruldeviyani, Faris Salbari, Aldiansah Prayogi

{"title":"Sentiment Analysis of Electricity Company Service Quality Using Naïve Bayes","authors":"Yuli Astuti, Yova Ruldeviyani, Faris Salbari, Aldiansah Prayogi","doi":"10.29207/resti.v7i2.4627","DOIUrl":"https://doi.org/10.29207/resti.v7i2.4627","url":null,"abstract":"In facing the era of technological disruption, a large company providing electricity in Indonesia, namely PT PLN is transforming to digitize all business processes and improve the quality of customer service. PLN Mobile application was developed in December 2020, and 18 million users have downloaded it. PLN Mobile application provides various electrical services for users. There are a lot of online opinions today. Organizations need to know the public perception of their product or service, sales projections, and customer happiness. Our research will identify public opinion (positive and negative) about PLN Mobile Application using sentiment analysis by taking review data from Google Play Store. Sentiment analysis is classified using Naïve Bayes and analyzed based on the dimensions of the quality of electricity services: empathy, responsiveness, and reliability. The results of this study indicate that Naïve Bayes is quite well used for binomial labels (positive and negative) with an accuracy of 73%. Still, for service quality dimensions, the accuracy is 45%. Indonesian language datasets are quite difficult to process due to non-standard language, foreign words, mixed language variations, and abbreviations. Determination of ground truth or manual labeling requires consistency and skilled personnel to determine the context of the text data to obtain a model with optimal performance. This study informs the classification of each dimension of the quality of electricity services in Indonesia based on positive and negative sentiment data for PLN Mobile Application users. Reliability received the most negative sentiments. This can be used for PT PLN to improve the quality-of-service reliability to customers. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127213832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices ARIMA与SARIMA预测原油价格的比较

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-28 DOI: 10.29207/resti.v7i2.4895

Vika Putri Ariyanti, Tristyanti Yusnitasari

{"title":"Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices","authors":"Vika Putri Ariyanti, Tristyanti Yusnitasari","doi":"10.29207/resti.v7i2.4895","DOIUrl":"https://doi.org/10.29207/resti.v7i2.4895","url":null,"abstract":"Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129645032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Decentralized Finance (DeFi), Strengths Become Weaknesses: a Literature Survey 去中心化金融(DeFi)，优势变成劣势:文献综述

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-28 DOI: 10.29207/resti.v7i2.4806

Aziz Perdana, HU ErikIman, Rianto

{"title":"Decentralized Finance (DeFi), Strengths Become Weaknesses: a Literature Survey","authors":"Aziz Perdana, HU ErikIman, Rianto","doi":"10.29207/resti.v7i2.4806","DOIUrl":"https://doi.org/10.29207/resti.v7i2.4806","url":null,"abstract":"The use of blockchain technology in Decentralized Finance (DeFi) has gained popularity, with 23 public companies and one country holding bitcoin. DeFi aims to create an open and decentralized financial ecosystem that is accessible to everyone, eliminates intermediaries like financial institutions, and is verifiable, immutable, globally accepted, fast, low-cost, anonymous, and non-custodial. Despite its benefits, the rapid growth of DeFi has led to increased security risks. This study assesses the validity of DeFi's superiority claims in light of security incidents and events in 2022 and Twitter trends. This study used a Systematic Literature Review from various research articles and news from 2022. This research found that DeFi's superiority claims seem to be inconsistent with what is being advertised. It also found that if DeFi is not properly prepared and audited, its strength (Anonymous, open-source, decentralized, non-custodial, eliminates third parties and regulation) may become its weakness. Despite this, users still exhibit high levels of trust and optimism, as seen in the most popular terms shared by user tweets during significant losses, with 301,654 unique tweets between April 30 and May 31, 2022 and 344,519 unique tweets between October 3 and December 3, 2022, that are crypto, nft, and blockchain.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127958171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis of Twitter Users to the PeduliLindungi Using Naïve Bayes Algorithm 推特用户对PeduliLindungi的情感分析Naïve贝叶斯算法

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-28 DOI: 10.29207/resti.v7i2.4684

Lia Ellyanti, Y. Ruldeviyani, Lelianto Eko Pradana, Andro Harjanto

{"title":"Sentiment Analysis of Twitter Users to the PeduliLindungi Using Naïve Bayes Algorithm","authors":"Lia Ellyanti, Y. Ruldeviyani, Lelianto Eko Pradana, Andro Harjanto","doi":"10.29207/resti.v7i2.4684","DOIUrl":"https://doi.org/10.29207/resti.v7i2.4684","url":null,"abstract":"Covid-19 was declared as a pandemic by World Health Organization (WHO) in March 2020, has a major impact on the lives. Indonesian’s government has made several efforts to suppress the spread of the virus by requiring the societies to use PeduliLindungi in every activity. There are many pros and cons from the societies in using PeduliLindungi, many reviews about the performance of this application found through playstore, app store or social media. Twitter is one of social media that allows the societies to express their feeling, idea, opinion, or critics about any topics. This study takes the review of PeduliLindungi from Twitter with period from June up to December 2021, which has the highest cases of covid-19 and tighter movement restriction from the government. The data collected were manually labeling into positive and negative class and processed using sentiment analysis with Naïve Bayes algorithm, give the result 64.69% positive sentiment and 35.5% negative sentiment regarding PeduliLindungi. The model tested using Naïve Bayes algorithm with 10-fold cross validation has the highest performance, the accuracy obtained is 95.86%, with precision 96.99% and recall 94.12%. The positive sentiment indicates the pro expression from society, like the data integration with vaccine certificate, PCR or antigen result, that makes the activities to entry public transport or public space easily. The negative sentiment indicates the cons expression from the societies, related with the performance of the application and the data security. The result of this study expected being reference, give insight, and information for developers and governments to build a better strategy in improving the performance of PeduliLindungi application.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125611173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Integrated Information System Implementation Strategy in Korlantas Polri Based on the Zachman Framework Approach 基于Zachman框架方法的Korlantas Polri集成信息系统实施策略

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-28 DOI: 10.29207/resti.v7i2.4842

Rachmi Azanisa Putri, Panca Hadi Putra, Ryan Randy Suryono

{"title":"The Integrated Information System Implementation Strategy in Korlantas Polri Based on the Zachman Framework Approach","authors":"Rachmi Azanisa Putri, Panca Hadi Putra, Ryan Randy Suryono","doi":"10.29207/resti.v7i2.4842","DOIUrl":"https://doi.org/10.29207/resti.v7i2.4842","url":null,"abstract":"Traffic Police Corps (Korlantas Polri) is the executor of the main duties of the Indonesian National Police in the areas of security, safety, order and smooth traffic. Korlantas has some information that can be accessed by the public, namely information on congestion, accidents, traffic flow status, vital objects, road conditions, data and visual images from CCTV, public service conditions, and traffic infrastructure. However, these data are stand alone and not integrated with their respective applications and systems. The purpose of this study is to analyze the strategy for implementing an integrated information system at Korlantas Polri and what steps can be taken to integrate the existing system. This study uses the Zachman Framework which is adapted to Enterprise Architecture Planning (EAP) and qualitative data collection methods by interviewing stakeholders who are involved in managing information systems at Korlantas Polri. The results obtained are the need for a data warehouse by implementing an AI based integrated database system, Geospatial Information System, Business Intelligence and DSS, as well as Smart Visualization to visualize existing data. Then standardize the need for equipment and support for improving the ability of personnel in the IT field. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image Transformation With Lung Image Thresholding and Segmentation Method 基于肺图像阈值分割方法的图像变换

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-26 DOI: 10.29207/resti.v7i2.4321

Sahat Sonang Sitanggang, Y. Yuhandri, Adil Setiawan

引用次数: 1

Leaf Image Identification: CNN with EfficientNet-B0 and ResNet-50 Used to Classified Corn Disease 叶片图像识别:CNN与EfficientNet-B0和ResNet-50用于玉米病害分类

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Pub Date : 2023-03-26 DOI: 10.29207/resti.v7i2.4736

Wisnu Gilang Pamungkas, Machammad Iqbal Putra Wardhana, Zamah Sari, Yufiz Azhar

{"title":"Leaf Image Identification: CNN with EfficientNet-B0 and ResNet-50 Used to Classified Corn Disease","authors":"Wisnu Gilang Pamungkas, Machammad Iqbal Putra Wardhana, Zamah Sari, Yufiz Azhar","doi":"10.29207/resti.v7i2.4736","DOIUrl":"https://doi.org/10.29207/resti.v7i2.4736","url":null,"abstract":"Corn is the second largest commodity in Indonesia after rice. In Indonesia, East Java is the largest corn producer. The first symptom of the disease in corn plants is marked by small brownish oval spots which are usually caused by the fungus Helminthoporium maydis, if left unchecked, farmers can suffer losses due to crop failure. Therefore it is important to provide treatment for diseases in corn plants as early as possible so that diseases in corn plants do not spread to other plants. In this study, the dataset used was taken from the kaggle website entitled Corn or Maize Leaf Disease Dataset. This dataset has 4 classifications: Blight, Common Rust, Grey leaf spot, and Healthy. This study uses the Convolutional Neural Network method with 2 different models, namely the EfficientNet-B0 and ResNet-50 models. The architectures used are the dense layer, the dropout layer, and the GlobalAveragePooling layer with a dataset sharing ratio of 70% which is training data and 30% is validation data. After testing the two proposed scenarios, the accuracy results obtained in the test model scenario 1, namely EfficientNet- B0 is 94% and for the second test model scenario, namely ResNet-50, the accuracy is 93%.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114329046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0