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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : -
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : -
Articles 564 Documents
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Improvement Of Image Quality Using Convolutional Neural Networks Method Nugroho, Arief Kelik; Permadi, Ipung; Faturrahim, Muhammad
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.30892

Abstract

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.
Topic Modeling on WhatsApp User Reviews Using Latent Dirichlet Allocation Kharisudin, Iqbal; Masri'an, Hera
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.34941

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.
Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks Agustyawan, Arif; Laksana, Tri Ginanjar; Athiyah, Ummi
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.29849

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.
Implementation of Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter Jayapermana, Rama; Aradea, Aradea; Kurniati, Neng Ika
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.31648

Abstract

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.
Backward Chaining Model for Identifying Learning Difficulties Experienced by Special Needs Children Fiati, Rina; Kurniati, Diah
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.33872

Abstract

Identification of learning difficulties is the most important process because it determines the next step in conducting an assessment especially for students with special needs. The first step that needs to be taken in determining the learning difficulties of children with special needs is through identification, where each student has a different character. The purpose of the research is to identfy learning difficulties faced by children with special needs by using backward chaining model and give solutions to learning difficulties. The study's findings indicate that the main factor causing learning challenges for children with special needs is an internal component coming from the students themselves, namely their motivation (75%). The second component is an external factor that has to do with how parents educate their special needs children (25%). To solve the problems, students should have a high motivation in studying and parents should change the way they educate their children. It is suggested that parents should give more attention on students’ academic progress
A Combination of Forward Chaining and Certainty Factor Methods for Early Detection of Fever : Dengue Hemorrhagic Fever, Malaria and Typhoid Fitri, Zilvanhisna Emka; Ramadania, Elsa Manora; Wibowo, Nugroho Setyo; Lesmana, I Putu Dody; Imron, Arizal Mujibtamala Nanda
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.33007

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.
A Clustering Approach for Mapping Dengue Contingency Plan Husna, Farida Amila; Purwitasari, Diana; Sidharta, Bayu Adjie; Sihombing, Drigo Alexander; Fahmi, Amiq; Purnomo, Mauridhi Hery
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.36885

Abstract

Purpose: The dengue epidemic has an increasing number of sufferers and spreading areas along with increased mobility and population density. Therefore, it is necessary to control and prevent Dengue Hemorrhagic Fever (DHF) by mapping a DHF contingency plan. However, mapping a dengue contingency plan is not easy because clinical and managerial issues, vector control, preventive measures, and surveillance must be considered. This work introduces a cluster-based dengue contingency planning method by grouping patient cases according to their environment and demographics, then mapping out a plan and selecting the appropriate plan for each area.Methods: We used clustering with silhouette scoring to select features, the best cluster formation, the best clustering method, and cluster severity. Cluster severity is carried out by levelling the attributes of the average value to low, medium, high, and extreme, which are related to the plans each region sets for village type and season type.Result: In five years of data (2016-2020) ±15K cases from Semarang City, Indonesia, feature selection results show that environmental and demography group features have the biggest silhouette score. With these features, it is found that K-Means has a high silhouette score compared to DBSCAN and agglomerative with three optimum numbers of clusters. K-Means also successfully mapped the cluster severity and assigned the cluster to a suitable contingency policy.Novelty: Most of the research on DHF cases is about predicting DHF cases and measuring the risk of DHF occurrence. There are not many studies that discuss the policy recommendations for dengue control.
Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients Yustikasari, Yusi; Mubarok, Husni; Rianto, Rianto
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.32369

Abstract

Purpose: The implementation of this manual system is considered less accurate in obtaining the results of social assistance recipients. From these problems to overcome this problem, systematic calculations are needed. In processing data, a model is needed that can explain the data with its application, so a machine learning model is made that can help process the data.Methods: This study's classification of non-cash food social assistance receipts uses the K-Nearest Neighbor and Adaptive Boosting algorithms. This study will compare the performance of the two algorithms.Result: The results obtained for Adaptive Boosting are the best classification results with a maximum accuracy of 100% and produce a high AUC value of 1.0. In comparison, the ROC curve for the K-Nearest Neighbor algorithm produces an accuracy of 96% with an AUC value of 0.94.Novelty: ROC curves in the two algorithms are good classification results because the two graphs cross above the diagonal line and produce an AUC value included in the Excellent classification.
Effect of Traditional and Software-Defined Networking on Performance of Computer Network Sadiku, Isiaka Babatunde; Ajayi, Wumi; Sakpere, Wilson; John-Dewole, Temilola; Badru, R A
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.31315

Abstract

Purpose: Computer networks and the Internet are changing the way we communicate, learn, work, and even play. Conventional computer networks are not smart enough towards processes that contribute to improving online control transaction of services and demand for unlimited communication services. Hence, computer networking has to go smart.Methods: This paper explores the effect of different computer networking types - traditional computer networking (D0) and Software-Defined Networking (D1). The paper combined traditional computer networking (D0) with Software-Defined Network (D2) running applications (A1, A2, A3, A4 and A5) with the host sending 5 packets (P1, P2, P3, P4 and P5) across the networks emulated using Mininet network emulation to observe various performance parameters on the network.Result: It was observed that Application A1 recorded the highest bandwidth, throughput and latency. The least bandwidth, throughput and latency were observed in A4. The result showed that below 80% of the IPv4 packet size (65,507 bytes) of running application, the higher the bandwidth the higher the throughput. Also, the lower the latency the more statistically similar the jitter experienced. Packet P1 has the highest bandwidth and throughput usage with high latency. The results indicate that the higher the bandwidth and throughput, the higher the latency observed in the packet sent across the network. Traditional computer networking (D1) recorded the highest bandwidth and throughput with the highest jitter. The correlation result showed that the jitter decreases with increasing bandwidth and throughput.Novelty: This study provides information on traditional computer networking and Software-Defined Networking. The result validates studies that observed significant F-value and stability in the SDN application-awareness experiment.  
Travel Time Estimation Using Support Vector Regression on Model with 8 Features Kosasih, Rifki; Mardhiyah, Iffatul
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.37215

Abstract

Purpose: In travelling, we need to predict travel time so that itinerary is as expected. This paper proposes Support Vector Regression (SVR) to build a prediction model. In this case, we will estimate travel time in the Bali area. We propose to use a regression model with 8 features, i.e., time, weather, route, wind speed, day, precipitation, temperature and humidity information.Methods: In this study, we collect real-time data from Global Positioning System (GPS) and weather applications. We divide our data into two types: training dataset consisting of 177 data and testing dataset comprising 51 data. The Support Vector Regression (SVR) method is used in the training stage to build a model representing data. To validate the model, error measurements were carried out by calculating the values of R2, Accuracy, MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and Accuracy.Result: From the research results, the model obtained is the SVR model with parameters γ=0.125, ε=0.1 and C = 1000, which has a value of R2= 0.9860528612283006. Later, we predict travel times on testing data using the SVR model that has been obtained. Based on the result of the research, our model has a 0.8008 MAE (Mean Absolute Error), 1.2817 RMSE (Root Mean Square Error) and 95.3369% Accuracy.Novelty: In this study, we use 8 features to estimate travel time in the Bali area. Furthermore, we will compare the KNN regression method (previous studies) with Support Vector Regression (SVR) (proposed method) on a model with 8 features to estimate travel time.

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