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JUITA : Jurnal Informatika

JUITA
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Published by Universitas Muhammadiyah Purwokerto

ISSN : 20869398 EISSN : 25798901 DOI : 10.30595/JUITA

Core Subject : Science,

Computer Science & IT

UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 316 Documents

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Pattern Detection of Economic and Pandemic Vulnerability Index in Indonesia Using Bi-Cluster Analysis Wiwik Andriyani Lestari Ningsih; I Made Sumertajaya; Asep Saefuddin
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Bi-clustering is a clustering development that aims to group data simultaneously from two directions. The Iterative Signature Algorithm (ISA) is one of the bi-clustering algorithms that work iteratively to find the most correlated bi-cluster. Detecting economic and pandemic vulnerability using bi-cluster analysis is essential to get spatial patterns and an overview of Indonesia's economic and pandemic vulnerability characteristics. Bi-clustering using ISA requires setting the row and column threshold to form seventy combinations of thresholds. The best is chosen based on the average value of mean square residue to volume ratios. In addition, the similarity of the best bi-cluster with the other is also seen based on the Liu and Wang index values. The -1.0 row and -1.0 column threshold combinations were selected and produced the best bi-cluster with the smallest average value of mean square residue to volume ratios (0.00141). Based on Liu and Wang index values, it has more than 95% similarity with the combination of -1.0 row and -0.9 column thresholds and the -0.9 row and -1.0 column thresholds. These selected threshold combinations produce three bi-clusters with five types of spatial patterns and different characteristics because of the overlap between these three bi-clusters.

Requirements Conflict Detection and Resolution in AREM Using Intelligence System Approach Rosa Delima; Retantyo Wardoyo; Khabib Mustofa
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Requirements engineering (RE) is the process of defining user requirements that are used as the main reference in the system development process. The quality of the RE results is measured based on the consistency and completeness of the requirements. The collection of requirements from multiple stakeholders can cause requirements conflict and have an impact on the inconsistency and incompleteness of the resulting requirements model. In this study, a method for automatic conflict detection and resolution in the Automatic Requirements Engineering Model (AREM) was developed. AREM is a model that automates the process of elicitation, analysis, validation, and requirements specification. The requirement conflict detection method was developed using an intelligent agent approach combined with a Weighted Product approach. Meanwhile, Conflict resolution is made automatically using a rule-based model and clustering method. Testing the ability of the method to detect and resolve conflicting requirements was carried out through five data sets of requirements from five system development projects. Based on the test results, it is known that the system is able to produce a set of objects that have conflicts in the data requirements. For conflict resolution, experiments were conducted with five conflict resolution scenarios. The experimental results show that the method is able to resolve conflicts by producing the highest completeness value, but the results of conflict resolution also produce a number of soft goals. The success of the method in detecting and resolving conflicts in the model is able to overcome the problem of inconsistencies and incompleteness in the requirements model.

What do Indonesians talk when they talk about COVID-19 Vaccine: A Topic Modeling Approach with LDA Theresia Ratih Dewi Saputri; Caecilia Citra Lestari; Salmon Charles Siahaan
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

To end the COVID-19 pandemics, the government attempted to accelerate the vaccination through various programs and collaboration. Unfortunately, the number is still relatively small compared to the number of populations in Indonesia. There are some reasons attributed to this challenge, one of them being the reluctance of citizens to accept the COVID-19 vaccine due to various factors. Knowing this factor to increase public compliance, the vaccination program can be speed-up. Unfortunately, traditionally acquiring the knowledge related to COVID-19 vaccine rejection can be challenging. One of the ways to capture the knowledge is by conducting a survey or interview related to COVID-19 vaccine acceptance. This method can be inefficient in terms of cost and resources. To address those problem, we propose a novel method for analyzing the topics related to the COVID-19 Indonesians’ opinions on Twitter by implementing topic modeling algorithm called Latent Dirichlet Allocation. We gathered more than 22000 tweets related to the COVID-19 vaccine. By applying the algorithm to the collected dataset, we can capture the what is general opinion and topic when people discuss about COVID-19 vaccine. The result was validated using the labeled dataset that have been gathered in the previous research. Once we have the important term, the strategy based on can be determined by the medical professional who are responsible to administer the COVID-19 vaccine.

Model Structure of Fetal Health Status Prediction Emirul Bahar; Dewi Agushinta R.; Yuti Dewita Arimbi; Mariono Reksoprodjo
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

One of the issues of pregnant mothers in Indonesia is their access speed and accuracy services availability towards the prediction of fetus or baby conceived during pregnancy. Thus, the research aimed to obtain the ability to predict three ranges of a fetal target, namely normal, risk, and abnormal condition. This research emphasized the modeling aspect of supervised learning using seven different algorithms to obtain an optimal working score. Those are Decision Tree, Gradient Boosting, Random Forest, SVM, k-NN, AdaBoost, and Stochastic Gradient Descent (SGD). The structure process is mainly divided into two steps, pre-process model and the prediction model. An early data pre-process is needed before executing. Prediction output indicated that dataset test is valid, and can be proven by comparing between the testing data table and prediction and testing table diagram. The resulting model has described the sequence for simulating the training and testing data model to produce the highest working score from the seven selected algorithms. The simulated data based on the model created is proved its validity thru three main filter processes, which are missing data solution, outlier data control, and data normalization. The result obtained a working score that has data proximity with a low score range of 0.063 from model evaluation, confusion matrix, and prediction output.

Optimization of Simple Additive Weighting Method in Assessment of Research Reviewer Selection Fata Nidaul Khasanah; Sugeng Murdowo; Dhian Tyas Untari; David Nurmanto; Wafi Arifin
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Quality research will not be separated from controlling systems that require a review mechanism. This demand considers it necessary to form an assessment committee or reviewer that ensures that all processes proceed towards the target target. The internal reviewer selection process is carried out by looking at several requirements of each prospective reviewer. The selection process is carried out by looking at the requirements files one by one. For this reason, it is necessary to optimize the method that is able to manage the assessment data of prospective reviewers who have the highest rating value from the results of weight calculations. Decision making in determining internal reviewers requires a method that can provide optimal decision results in terms of relatively fast processing time. The decision support method applied in determining internal reviewers is Simple Additive Weighting (SAW). The reason for choosing the SAW method in this study, the method has a basic concept that is used to find weight values on the performance rating of each alternative on all attributes. The SAW method is commonly known as the weighted summation method. There are six criteria used and fifty-five records for alternatives used. The results of the SAW method ranking obtained by A20 have the highest preference value of 0.77. This study shows the optimality of the SAW method in providing decision results based on an accuracy test value of 80%.

New Selection Algorithm on Priority Service for Certification Queue Service Information System in BARISTRAND Rizky Dwi Nugroho; Anjik Sukmaaji; Endra Rahmawati; Arifin Pujiwidodo; Teguh Sutanto
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Queue management for product certification of perishable goods is a major problem faced by the Surabaya Industrial Standardization and Research Institute (BARISTAND). The purpose of this study is to combine the use of the First in first out (FIFO), First Expired First Out (FEFO) and Least Shelf Life First Out (LSFO) methods into an automatic queuing system that can ensure effective service performance in queue management. This research was conducted using qualitative methods with observations to collect data and processes about how the product certification queue process flow at the Surabaya Industrial Standardization and Research Institute (BARISTRAN). The results show that the service only requires an average service completion time of 0.14 products per minute, meaning that every hour it can serve approximately 8.4 products. The conclusion of the research system has succeeded in determining the queue based on the use of the First in first out (FIFO), First Expired First Out (FEFO) and Least Shelf Life First Out (LSFO) methods, and customers can perform the tracking process to find out the certification process for the registered products.

Gender Classification for Anime Character Face Image Using Random Forest Classifier Method and GLCM Feature Extraction Dadang Iskandar Mulyana; Vika Vitaloka Pramansah
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Japan has many entertaining and unique artworks, especially its signature animation, called anime. Anime is an animation art that is unique in that the characterizations, characters, and storylines are made to resemble human life. The characters have 2 genders called male and female with unique visuals and are the characteristics of each anime character to entertain the audience. Training large-scale data and complex textures because not all of the anime images owned are of high quality, making classification by Machine Learning Algorithms low in accuracy. This study will describe an experiment using an anime face image dataset to classify the gender, namely male or female. From this problem, this research implements feature extraction to produce unique features of anime images with Gray-Level Cooccurrence Matrix (GLCM) and uses the Random Forest Classifier which is a classification algorithm in Machine Learning to classify gender. The results of this study get a good accuracy value of 95%, using 3,612 images where the test data used is 723 images and Homogeneity5 feature being the most relevant feature in increasing the accuracy value with a value of 0.06378389.

DDoS Attacks Detection Method Using Feature Importance and Support Vector Machine Ahmad Sanmorino; Rendra Gustriansyah; Juhaini Alie
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

In this study, the author wants to prove the combination of feature importance and support vector machine relevant to detecting distributed denial-of-service attacks. A distributed denial-of-service attack is a very dangerous type of attack because it causes enormous losses to the victim server. The study begins with determining network traffic features, followed by collecting datasets. The author uses 1000 randomly selected network traffic datasets for the purposes of feature selection and modeling. In the next stage, feature importance is used to select relevant features as modeling inputs based on support vector machine algorithms. The modeling results were evaluated using a confusion matrix table. Based on the evaluation using the confusion matrix, the score for the recall is 93 percent, precision is 95 percent, and accuracy is 92 percent. The author also compares the proposed method to several other methods. The comparison results show the performance of the proposed method is at a fairly good level in detecting distributed denial-of-service attacks. We realized this result was influenced by many factors, so further studies are needed in the future.

Sentiment Analysis of the Convict Assimilation Program on Handling Covid-19 Aniq Noviciatie Ulfah; M Khairul Anam; Novi Yona Sidratul Munti; Saleh Yaakub; Muhammad Bambang Firdaus
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Coronavirus Disease-19 (Covid-19) is an infectious disease caused by the SARS-CoV-2 virus. The rapid spread of this disease has affected 216 other countries and regions, including Indonesia. In minimizing the spread and increasing losses, it is necessary to have several policies made by the Indonesian government in dealing with this. One of the policies taken by the government is the Convict Assimilation Program to prevent the spread of the virus in prisons. The Prisoner Assimilation Program fosters inmates by integrating prisoners into social life. Many media reported on the assimilation program in various media, including news portals, so that it became a forum for the public to express their opinions. News portals can be a source for getting public opinion. Therefore, sentiment analysis can be done to determine the sentiment of any existing public opinion. In this study, the analysis was carried out by applying one of the data mining methods, namely the Support Vector Machine, with positive, negative, and neutral sentiment labeling. The data used is audience comments in Indonesian with a dataset of 404 comments and then resampled so that the number of data becomes 669. The analysis uses the kernel Radial Basis Function (RBF), RBF with Grid Search, Polynomials, and Polynomials with grid search. Kernel RBF and Kernel Polynomial with Grid Search comparing test and training data 80%:20% with the highest accuracy of 95%.

Electroencephalogram as a Validation Method in Usability Testing Guntur Maulana Zamroni; Dinan Yulianto; Bella Saphira; Fadel N. Akhmad; Fakhira A. Zahrah
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.16000

Usability testing is a recommended main testing method to evaluate the usability or ease of use of a software. However, this approach can induce bias. Usability testing with survey method using questionnaire instruments and interview has a risk of generating lack of objectivity from the participants because the facilitator can influence the evaluation results. The evaluation process is not optimal and the biases that arise from usability testing can affect the success or failure of a software in the market. A method for validating usability testing results needs to be studied to ensure whether the test results obtained are valid and unbiased. Electroencephalogram (EEG) is one of the tool that can be used to validate usability testing. This study aims to conduct usability testing of the three most popular e-marketplace applications in Indonesia. From the results it can be seen that usability testing using user-based testing gives a fairly good result in term of accuracy. Usability testing using EEG gives lower results than user-based testing. Nevertheless, we cannot rule out EEG as a usability testing method in the future.

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Hindayati Mustafidah

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Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia

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Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Hindayati Mustafidah

Contact Email jurnal.juita@gmail.com

Phone +6285842817313

Journal Mail Official jurnal.juita@gmail.com

Editorial Address Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia

Location Kab. banyumas, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Hindayati Mustafidah

Contact Email
jurnal.juita@gmail.com

Phone
+6285842817313

Journal Mail Official
jurnal.juita@gmail.com

Editorial Address
Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia

Location
Kab. banyumas,

Jawa tengah

INDONESIA