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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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Editor Preface and Table of Content JUITA: Jurnal Informatika
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Editor Preface and Table of Content Vol 10 No. 1 Mei 2022

Seismic Data Quality Analysis Based on Image Recognition Using Convolutional Neural Network Hapsoro Agung Nugroho; Siti Hasanah; Mahmud Yusuf
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Seismometer monitoring and evaluation activities at the Indonesia Tsunami Early Warning System (InaTEWS) station can be carried out through a seismometer sensor calibration system with the use of the software of Seismic Data Quality Analysis. The software output is in the form of a spectrum image that represents the conditions of the seismometer following the spectrum results. The identification of the seismometer condition can be made by pattern recognition in the spectrum image. This study employed a neural network, specifically the Convolutional Neural Network (CNN), to analyse the pattern condition. The test results show that the performance of the system will be excellent if 1024 hidden layers are used. In addition, the epoch test shows that the system works well when given a maximum epoch value of 50. The test of image size gives the result that the system performance will result in good using input with a size of 30x20 pixels. The final results of the classification of spectrum images using CNN will exhibit the identification of seismometer. For the validation, the confusion matrix test shows that the corresponding findings are 80%, while the conflicting results are 20%.

Analysis of Factor in User Intention to Use the Covid-19 Tracking Application Retno Waluyo; Taqwa Hariguna; Agung Purwo Wicaksono
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Science and technology can be collaborated to create an application that can help to track the contacts of COVID-19 patients. Smartphone-based contact tracing applications have been adopted by more than 50 countries. One of which is in Indonesia. In March 2020, Indonesia launched a mobile application to track the contact of COVID-19 patients namely PeduliLindungi. During its usage, users find some issues about PeduliLindungi, such as potential data leaks, data misuse, and data inaccuracies. This research is aimed to develop a conceptual model to analyze factors that affect user intentions in using the PeduliLindungi application. The proposed conceptual model is the integration of EUCS, DeLone and McLean that is equipped by the system security variables. There were 288 respondents. The data is processed using SmartPLS 3.0. According to the results of the analysis, the proposed conceptual model has 83.1 percent for its accuracy. User satisfaction and system security give a positive and significant impact on user intentions. The variables of content, accuracy, format, ease of use, and timeliness give a positive and significant impact on user satisfaction. On the other hand, the system security has no positive and significant impact on user satisfaction. Meanwhile, user satisfaction and system security itself affects the user's intentions in using the PeduliLindungi application.

Analysis of Machine Learning Algorithm for Sleep Apnea Detection Based on Heart Rate Variability Muhammad Zakariyah; Umar Zaky
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Sleep apnea is a common problem with health implications ranging from excessive daytime sleepiness to serious cardiovascular disorders. The method for detecting and measuring sleep apnea is through breathing monitoring (polysomnography), which is time consuming and relatively expensive. Cardiovascular which is closely related to heart performance activities allows the use of electrocardiogram (heart rate variability) features to detect sleep apnea. This study aims to compare the results of sleep apnea detection using several machine learning algorithms. A total of 2,445 data were divided into 1,834 data as learning sets and 611 data as test sets. Evaluation of 10-fold cross-validation using all HRV features shows that neural network algorithm has the best performance compared to decision tree algorithm, k-nearest neighbor, and support vector machine with an accuracy rate (82.44% in the learning set, 79.21% in the test set consecutively), precision (85.54% and 82.70%), f-measure (87.70% and 85.67%), and AUC (0.867 and 0.832). Based on the results of performance testing using only selected HRV features (CVRR, HF, SD1/SD2 Ratio, and S-Region), the K-Nearest Neighbors, Support Vector Machine, and Neural Network algorithms experienced a decrease in performance. The use of all HRV features is recommended compared to only using selected HRV features, so it can help detect the presence/absence of sleep apnea much better.

Editor Preface and Table of Content JUITA: Jurnal Informatika
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Editor Preface and Table of Content Vol 10 No. 2 November 2022

Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method Aditya Salman; Mardhiya Hayaty; Ika Nur Fajri
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.

Cyberbullying Analysis on Instagram Using K-Means Clustering Ahmad Muhariya; Imam Riadi; Yudi Prayudi
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Social Media, in addition to having a positive impact on society, also has a negative effect. Based on statistics, 95 percent of internet users in Indonesia use the internet to access social networks. Especially for young people, Instagram is more widely used than other social media such as Twitter and Facebook. In terms of cyberbullying cases, cases often occur through social media, Twitter, and Instagram. Several methods are commonly used to analyze cyberbullying cases, such as SVM (Support Vector Machine), NBC (Naïve Bayes Classifier), C45, and K-Nearest Neighbors. Application of a number of these methods is generally implemented on Twitter social media. Meanwhile, young users currently use Instagram more social media than Twitter. For this reason, the research focuses on analyzing cyberbullying on Instagram by applying the K-Mean Clustering algorithm. This algorithm is used to classify cyberbullying actions contained in comments. The dataset used in this study was taken from 2019 to 2021 with 650 records; there were 1827 words and already had labels. This study has successfully classified the tested data with a threshold value of 0.5. The results for grouping words containing bullying on Instagram resulted in the highest accuracy, which is 67.38%, a precision value of 76.70%, and a recall value of 67.48%. These results indicate that the k-means algorithm can make a grouping of comments into two clusters: bullying and non-bullying.

Freshwater Fish Classification Based on Image Representation Using K-Nearest Neighbor Method Suwarsito Suwarsito; Hindayati Mustafidah; Tito Pinandita; Purnomo Purnomo
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Indonesia is a maritime and agricultural country with enormous world fishery potential. The large variety of fish is often confusing for ordinary people in recognizing types of fish, especially freshwater fish. It was stated that the types of freshwater fish often consumed by the Indonesian people are bawal (pomfret), betutu, gabus (cork), gurame (carp), mas (goldfish), lele (catfish), mujaer (tilapia), patin (asian catfish), tawes, and nila (tilapia nilotica). Some fish types have similar shapes, so it is tricky to tell them apart. Meanwhile, in the digitalization era today, Artificial Intelligence (AI)-based technology has become a demand in all areas of life. It is overgrowing, not apart from the fisheries sector. Therefore, in this study, the K-Nearest Neighbor (KNN) method was applied as one of the methods in AI to identify and classify freshwater fish species based on their images. The KNN method classifies new data into specific classes based on the distance between the new data and the closest k data through the learning process. This KNN model is built by preparing the dataset stages, separating the dataset into data-train and data-test with a ratio of 70%:30%, then building and testing the model. The dataset is freshwater fish images, totaling 100 images from 10 freshwater fish types. Model testing is done by measuring performance using a confusion matrix. Based on the test results, the model has an accuracy performance of 70%. Thus, KNN can be used as a model to identify freshwater fish species based on their image.

Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory) Muhammad Ibnu Alfarizi; Lailis Syafaah; Merinda Lestandy
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Humans in carrying out communication activities can express their feelings either verbally or non-verbally. Verbal communication can be in the form of oral or written communication. A person's feelings or emotions can usually be seen by their behavior, tone of voice, and expression. Not everyone can see emotion only through writing, whether in the form of words, sentences, or paragraphs. Therefore, a classification system is needed to help someone determine the emotions contained in a piece of writing. The novelty of this study is a development of previous research using a similar method, namely LSTM but improved on the word weighting process using the TF-IDF method as a further process of LSTM classification. The method proposed in this research is called Natural Language Processing (NLP). The purpose of this study was to compare the classification method with the LSTM (Long Short-Term Memory) model by adding the word weighting TF-IDF (Term Frequency–Inverse Document Frequency) and the LinearSVC model, as well to increase accuracy in determining an emotion (sadness, anger, fear, love, joy, and surprise) contained in the text. The dataset used is 18000, which is divided into 16000 training data and 2000 test data with 6 classifications of emotion classes, namely sadness, anger, fear, love, joy, and surprise. The results of the classification accuracy of emotions using the LSTM method yielded a 97.50% accuracy while using the LinearSVC method resulted in an accuracy value of 89%.

Prediction of O3 Concentration Level Using Fuzzy Non-Stationary Method Affi Nizar Suksmawati; Retantyo Wardoyo
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

The composition of air concentration is not constant. It constantly changes with minor changes at any time, so more than one measurement is needed to represent the air concentration level for a full day. The fuzzy non-stationary method can overcome uncertainty in an environment that is not constant or caused by minor temporal changes based on time variables. This study uses a non-stationary fuzzy method to determine the level of O3 concentration based on the input variables of temperature, humidity, and wind speed. The tests were conducted in September, October, and November using four types of implication process interpretation, namely interpretation 1 (classical logic), interpretation 2 (classical logic), interpretation 3 (algebraic), and interpretation 3 (standard). The test results in September showed a tendency for error percentage using the MAPE amount of 19, October's amount of 25, and November's amount of 18.

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Editorial Team

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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

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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