Scientific Journal of Informatics最新文献_第7页

Design and Evaluation of Smart Digital Signature Application User Interface for Document Legalization in COVID 19 Pandemic 面向新型冠状病毒大流行的智能数字签名应用用户界面设计与评估

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.34058

Gita Fadila Fitriana, Merlinda Wibowo, E. Aribowo

{"title":"Design and Evaluation of Smart Digital Signature Application User Interface for Document Legalization in COVID 19 Pandemic","authors":"Gita Fadila Fitriana, Merlinda Wibowo, E. Aribowo","doi":"10.15294/sji.v9i1.34058","DOIUrl":"https://doi.org/10.15294/sji.v9i1.34058","url":null,"abstract":"Abstract. Purpose: This study aims to build an effective user interface digital signature application for document legalization.Methods: The process of document publishing services and bureaucratic flows at universities affected by the pandemic can be simplified and accelerated with this application later. However, appropriate application design still needs to be done before application development. Therefore, application design is carried out on the User Interface (UI) to the prototype stage so that the application to be built can be more attractive, more effective, and efficient. In addition, to help the application succeed, it is also necessary to evaluate the design process. Result: This testing process is carried out to prevent failures from later application development by implementing an easy and frequently used test, namely usability testing, namely the Usability Scale (SUS) System. The tests carried out have shown that the design proposed in this study gets a good score of 80.5, so it is effective and efficient to be implemented and implemented in the development of smart digital signature applications.Novelty: This study is to design and evaluate the UI of the smart digital signature application to provide participation for the success of the application that will be developed later. Therefore, the smart digital signature application can be used effectively and efficiently as a document validation process during this pandemic, which is needed, especially in universities, to improve staff performance and support academic activities. The number of digital documents signed and sent will be the same as the documents received. Furthermore, the validity and integrity of the document can be monitored using the developed application.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41507493","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 Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter 推特上COVID-19疫苗主题多类分类的叠加集成分类器实现

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.31648

Rama Jayapermana, Aradea Aradea, Neng Ika Kurniati

引用次数: 6

Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks 反向传播神经网络与粒子群优化相结合的城市自来水厂产水量预测

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.29849

Arif Agustyawan, Tri Ginanjar Laksana, Ummi Athiyah

{"title":"Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks","authors":"Arif Agustyawan, Tri Ginanjar Laksana, Ummi Athiyah","doi":"10.15294/sji.v9i1.29849","DOIUrl":"https://doi.org/10.15294/sji.v9i1.29849","url":null,"abstract":"Abstract.Purpose: As the population grows, the need for clean water also increases. Municipal Waterworks (PDAM) is an institution that regulates and manages the procurement of clean water for the community. So, the amount of water produced and distributed should be adjusted to the demand for water. Predictions on PDAM water production need to be done as planning and better preparation and facilitating and assisting in decision-making.Methods: The study used the Neural Network backpropagation algorithm combined with Particle Swarm Optimization (PSO) to predict the amount of water PDAM should produce. Backpropagation has a good ability to make predictions. But backpropagation has a weakness that causes it to get stuck at a local minimum. This is influenced by the determination of weights that are not optimal. In this study, PSO had a role in optimizing error values on the network to gain optimal weight. Result: This study obtained MSE values in the training and testing process of 0.00179 and 0.00081 from the combination model of backpropagation ANN and PSO. It is smaller than the ANN model without using an optimization algorithm.Novelty: The combination of JST backpropagation and PSO can improve predictions' accuracy and produce optimum weights.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43978177","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

Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa 相似度度量算法在数据预处理阶段的性能比较:马来文文本归一化

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.30052

Achmad Yohni Wahyu Finansyah, Fnu Afiahayati, Vincent Michael Sutanto

{"title":"Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa","authors":"Achmad Yohni Wahyu Finansyah, Fnu Afiahayati, Vincent Michael Sutanto","doi":"10.15294/sji.v9i1.30052","DOIUrl":"https://doi.org/10.15294/sji.v9i1.30052","url":null,"abstract":"Purpose: More and more data are stored in text form due to technological developments, making text data processing more difficult. It also causes problems in the text preprocessing algorithm, one of which is when two texts are identical, but are considered distinct by the algorithm. Therefore, it is necessary to normalize the text to get the standard form of words in a particular language. Spelling correction is often used to normalize text, but for Bahasa Indonesia, there has not been much research on the spell correction algorithm. Thus, there needs to be a comparison of the most appropriate spelling correction algorithms for the normalization process to be effective.Methods: In this study, we compared three algorithms, namely Levenshtein Distance, Jaro-Winkler Distance, and Smith-Waterman. These algorithms were evaluated using questionnaire data and tweet data, which both are in Bahasa Indonesia.Result: The fastest normalization time is obtained by the Jaro-Winkler, taking an average of 31.01 seconds for questionnaire data and 59.27 seconds for tweet data. The best accuracy is obtained by the Levenshtein Distance with a value of 44.90% for the questionnaire data and 60.04% for the tweet data. Novelty: The novelty of this research is to compare the similarity measure algorithm in Bahasa Indonesia. Therefore, the most suitable similarity measure algorithm for Bahasa Indonesia will be obtained.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46118734","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

Aspect Based Sentiment Analysis of Product Review Using Memory Network 基于记忆网络的面向方面的产品评论情感分析

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.34094

Hilya Tsaniya Ismet, T. Mustaqim, D. Purwitasari

{"title":"Aspect Based Sentiment Analysis of Product Review Using Memory Network","authors":"Hilya Tsaniya Ismet, T. Mustaqim, D. Purwitasari","doi":"10.15294/sji.v9i1.34094","DOIUrl":"https://doi.org/10.15294/sji.v9i1.34094","url":null,"abstract":"Abstract. Purpose: Consumer opinion is one of the essential keys that affect the success of a product. Sentiment analysis of consumer opinion is needed to find out information about customer satisfaction for companies in the decision-making process. The traditional sentiment analysis process extracts a complete sentiment from a single sentence. However, it does not consist of only one sentiment in one sentence. The total number depends on the number of aspects that make up the sentence. Therefore, a sentiment analysis process is needed to pay attention to aspects.Methods: This research focuses on product reviews from Indonesian e-commerce on several aspects of sentiment. Uses fastText word embedding to avoid Out of Vocabulary in datasets and Gated Recurrent Units for aspect spread detection. Sentiment classification on aspects using the Memory Network method.Result: The experiment results showed that aspect-based sentiment classification predictions had an accuracy of 83% compared to 78% overall classification predictions for review texts, indicating that aspect-based sentiment analysis can improve model performance on product review classification predictions.Novelty: Most product reviews analysis use document-level classification to extract and predict sentiment reviews, aspect-based analysis can be applied to product reviews for better sentiment understanding, using Memory Network to store important information explicitly on aspects and polarity.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47112448","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: 7

Chili Classification Using Shape and Color Features Based on Image Processing 基于图像处理的辣椒形状和颜色特征分类

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.33658

Yobel Fernanda Sihombing, Anindita Septiarini, A. H. Kridalaksana, N. Puspitasari

{"title":"Chili Classification Using Shape and Color Features Based on Image Processing","authors":"Yobel Fernanda Sihombing, Anindita Septiarini, A. H. Kridalaksana, N. Puspitasari","doi":"10.15294/sji.v9i1.33658","DOIUrl":"https://doi.org/10.15294/sji.v9i1.33658","url":null,"abstract":"Abstract. Purpose: Chili is an agricultural product that has several varieties and is in great demand. It can be consumed directly or processed first. This study aims to classify the types of chili using color and shape features. The types of chili are divided into five classes: cayenne pepper, green chili, big green chili, big red chili, and curly chili. The chili classification method was evaluated using three parameters: precision, recall, and accuracy.Methods: This study applied the K-Nearest Neighbors (KNN) method with the Euclidean and Manhattan distance calculation algorithm and used two feature types: color and shape. The color features were extracted based on RGB color space by obtaining the mean and standard deviation values. Meanwhile, the shape features used aspect ratio, area, and boundary.Result: The evaluation results of the classification method were able to achieve the precision, recall, and accuracy values of 1.0, which means that all test data were classified correctly. The evaluation was applied with 210 training images and 90 test images based on the test results.Novelty: This study extracted two types of features: color and shape. Those features fed the KNN method by applying the Euclidean and Manhattan distance calculation algorithm; hence, the optimal results were achieved.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41719753","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: 2

Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization 基于C4.5和粒子群优化的幼儿营养状况分类

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.33158

Alwis Nazir, Amany Akhyar, Y. Yusra, Elvia Budianita

{"title":"Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization","authors":"Alwis Nazir, Amany Akhyar, Y. Yusra, Elvia Budianita","doi":"10.15294/sji.v9i1.33158","DOIUrl":"https://doi.org/10.15294/sji.v9i1.33158","url":null,"abstract":"Abstract. Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":"107 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41277289","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

A Combination of Forward Chaining and Certainty Factor Methods for Early Detection of Fever : Dengue Hemorrhagic Fever, Malaria and Typhoid 前向链和确定性因子相结合的方法早期检测登革热、疟疾和伤寒

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.33007

Z. E. Fitri, Elsa Manora Ramadania, N. Wibowo, I. P. Lesmana, A. M. N. Imron

{"title":"A Combination of Forward Chaining and Certainty Factor Methods for Early Detection of Fever : Dengue Hemorrhagic Fever, Malaria and Typhoid","authors":"Z. E. Fitri, Elsa Manora Ramadania, N. Wibowo, I. P. Lesmana, A. M. N. Imron","doi":"10.15294/sji.v9i1.33007","DOIUrl":"https://doi.org/10.15294/sji.v9i1.33007","url":null,"abstract":"Abstract. Purpose: Dengue Hemorrhagic and Malaria fevers are the most common arthropod-borne diseases caused by mosquito bites and they also have similar signs and symptoms. Based on the problems, the researcher makes an expert system that aims to help people early detect fever diseases. This system is expected to help and support the infectious disease prevention and control program by the Ministry of Health of the Republic of Indonesia.Methods: This study uses an expert system with a combination of Forward Chaining and Certainty Factor to detect the symptoms of fever. Forward Chaining is a technique that begins with gathering information related to known facts, then combining rules to produce conclusions. The certainty Factor method is used to define a measure of certainty against a fact or rule and to describe the level of expert confidence in dealing with problems. There are 32 symptoms of the disease consisting of dengue fever, malaria and typhoid, it was obtained based on the literature and interviews with internal medicine specialist with 20 case datasets.Result: Based on 20 test data, obtained one data that does not match the test results and the desired target so that the system accuracy obtained is 95%. In addition, the combination of Forward Chaining and Certainty factor has better accuracy when compared to expert systems in previous studies.Novelty: Forward Chaining to find three rules and assigning weights to the Certainty Factor that has been set by the expert makes the combination of the two methods produce better accuracy.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45230047","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

Backward Chaining Model for Identifying Learning Difficulties Experienced by Special Needs Children 识别特殊需求儿童学习困难的后向链式模型

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.33872

Rina Fiati, Diah Kurniati

引用次数: 0

Topic Modeling on WhatsApp User Reviews Using Latent Dirichlet Allocation 使用潜在Dirichlet分配的WhatsApp用户评论主题建模

Scientific Journal of Informatics Pub Date : 2022-05-31 DOI: 10.15294/sji.v9i1.34941

I. Kharisudin, Hera Masri'an

{"title":"Topic Modeling on WhatsApp User Reviews Using Latent Dirichlet Allocation","authors":"I. Kharisudin, Hera Masri'an","doi":"10.15294/sji.v9i1.34941","DOIUrl":"https://doi.org/10.15294/sji.v9i1.34941","url":null,"abstract":"Abstract.Purpose: Topic modeling is a practical algorithm for identifying topics in text data. This study aims to find issues of WhatsApp user reviews using Latent Dirichlet Allocation (LDA) and describe the characteristics of each case.Method: We used 1710 WhatsApp user reviews written 7-13 August 2020 on Google Play. This research was conducted with a qualitative method consisting of five stages: problem identification, data retrieval, preprocessing, modeling, and analysis. The modeling stage consists of making a Document-Term Matrix (DTM), determining the number of iterations and topics, and building a model. We use perplexity as to the indicator in determining the number of iterations and topics. A lower perplexity value indicates a better model performance. The analysis phase includes observations on the top terms and documents to label and describe the characteristics of each topic. Result: Topic modeling produces word-topic and document-topic assignments. The word-topic assignment contains words with high probability (top terms). Document-topic assignment reveals documents that have a high probability (top documents). The topics most frequently discussed were voice and video calls with 104 reviews, 86 reviews of call quality, photo and video quality with 100 reviews, and voice messages with 75 reviews. Novelty: In this research, a topic model has been generated for a user review of the WhatsApp application using Latent Dirichlet Allocation. The number of iterations in the modeling was determined based on the observation of the perplexity value, instead of randomly assigning iterations.","PeriodicalId":30781,"journal":{"name":"Scientific Journal of Informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48641720","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