Scientific Journal of Informatics
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
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<![CDATA[3D Animation Making Crafts Monel Jepara ]]>
<![CDATA[Listyorini, Tri]]>;
<![CDATA[Umam, Muhamad Khotibul]]>;
<![CDATA[Riadi, Aditya Akbar]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.31686
<![CDATA[Abstract. Monel Craft is a handicraft that is inherited from generation to generation by the people of Jepara. Monel accessories produced can be in the form of bracelets, necklaces, earrings, rings, and many other accessories. For its manufacturer using a drill, hacksaw, and smoothing machine. Monel marketing is still limited around the city of Jepara. Purpose: This research aims to introduce Monel more broadly with a 3D multimedia approach. So, there is an idea of what Monel is. In general, Monel has a shiny shape and is corrosion-resistant. The price is relative and can be ordered according to our wishes.Methods: The method used in this research is the method of multimedia development.Result: This research produces a 3D animation that is packaged attractively, so that it represents Monel craftsmen to introduce it to the whole community.Novelty: This research combines the multimedia method with the work of local wisdom from the city of Jepara, namely Monel. This research, entitled "Monel Craft 3D Animation", can increase knowledge about how to make it. And can be a medium of education and preservation of cultural arts in Jepara. ]]>
<![CDATA[Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization ]]>
<![CDATA[Nazir, Alwis]]>;
<![CDATA[Akhyar, Amany]]>;
<![CDATA[Yusra, Yusra]]>;
<![CDATA[Budianita, Elvia]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.33158
<![CDATA[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).]]>
<![CDATA[Design and Evaluation of Smart Digital Signature Application User Interface for Document Legalization in COVID 19 Pandemic ]]>
<![CDATA[Fitriana, Gita Fadila]]>;
<![CDATA[Wibowo, Merlinda]]>;
<![CDATA[Aribowo, Eko]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.34058
<![CDATA[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.]]>
<![CDATA[Chili Classification Using Shape and Color Features Based on Image Processing ]]>
<![CDATA[Sihombing, Yobel Fernanda]]>;
<![CDATA[Septiarini, Anindita]]>;
<![CDATA[Kridalaksana, Awang Harsa]]>;
<![CDATA[Puspitasari, Novianti]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.33658
<![CDATA[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.]]>
<![CDATA[Aspect Based Sentiment Analysis of Product Review Using Memory Network ]]>
<![CDATA[Ismet, Hilya Tsaniya]]>;
<![CDATA[Mustaqim, Tanzilal]]>;
<![CDATA[Purwitasari, Diana]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.34094
<![CDATA[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.]]>
<![CDATA[Performance Comparison of Similarity Measure Algorithm as Data Preprocessing Stage: Text Normalization in Bahasa ]]>
<![CDATA[Finansyah, Achmad Yohni Wahyu]]>;
<![CDATA[Afiahayati, FNU]]>;
<![CDATA[Sutanto, Vincent Michael]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.30052
<![CDATA[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.]]>
<![CDATA[Improvement Of Image Quality Using Convolutional Neural Networks Method ]]>
<![CDATA[Nugroho, Arief Kelik]]>;
<![CDATA[Permadi, Ipung]]>;
<![CDATA[Faturrahim, Muhammad]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.30892
<![CDATA[Abstract. Purpose: This desire for high resolution stems from two main application areas, namely improving pictorial information for human interpretation and assisting automatic machine perception in representing images or videos. Image resolution describes the detail contained in an image, the higher the resolution, the more detail there is. The resolution of a digital image can be classified into various types, namely pixel resolution, spatial resolution, temporal resolution, and radiometric resolution. In this context, we are interested in spatial resolution.Methods: Elements of a digital image consist of a collection of small images called pixels. Spatial resolution refers to the pixel density of an image and is measured in pixels per unit area. A quality digital image is determined by the size of the resolution it has. A low resolution or low-resolution is a drawback of a digital image because the information contained in the image means little compared to a high-resolution image.Result: Therefore, in this study, a digital image processing program was created in the form of Image Super-Resolution with the Convolutional Neural Network method to utilize low-resolution images to produce high-resolution images. With a fairly short training process, namely 6050 datasets with 100 CNN epochs, the average PSNR image is 5% higher.Novelty: Image quality can be improved by changing the parameters in the CNN method so that image quality can be improved.]]>
<![CDATA[Topic Modeling on WhatsApp User Reviews Using Latent Dirichlet Allocation ]]>
<![CDATA[Kharisudin, Iqbal]]>;
<![CDATA[Masri'an, Hera]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.34941
<![CDATA[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.]]>
<![CDATA[Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks ]]>
<![CDATA[Agustyawan, Arif]]>;
<![CDATA[Laksana, Tri Ginanjar]]>;
<![CDATA[Athiyah, Ummi]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.29849
<![CDATA[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.]]>
<![CDATA[Implementation of Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter ]]>
<![CDATA[Jayapermana, Rama]]>;
<![CDATA[Aradea, Aradea]]>;
<![CDATA[Kurniati, Neng Ika]]>
<![CDATA[ Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/sji.v9i1.31648
<![CDATA[Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research.Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner for the multi-class classification of COVID-19 vaccine topics on Twitter.Result: Based on the evaluation, the proposed Stacking Ensemble Classifier model shows 86% accuracy, 85% precision, 86% recall, and 85% f1-score.Novelty: The novelty is produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner.]]>
