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JUITA : Jurnal Informatika

JUITA
Website

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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Classification of Customer Loans Using Hybrid Data Mining Eka Praja Wiyata Mandala; Eva Rianti; Sarjon Defit
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.

Improvement Quality of Software Requirements Using Requirement Negotiation System for Supporting Decision Egia Rosi Subhiyakto; Yani Parti Astuti
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

The Requirement Engineering phase, where all requests and software requirements of the user and the client are delivered, understood and agreed upon. However, often the developers are just too focused on implementing the software, even though the Requirements Engineering phase is a phase that can have a big impact. The impact is not only on the final product but also on the development process itself. In this study, the authors conducted software development negotiation of software requirements as a medium for stakeholders to negotiate the requirements of software products. In the negotiation system, the author will provide a means of decision support or group decision support system that a method of resolving conflicts. The main objectives of this work are twofold: 1) to assist the negotiation process between stakeholders and 2) to improvement quality software after negotiation. The workings of the E-Voting method are by giving choices to each sub-specification that has been chosen by stakeholders. We will select the choice that has the highest number of votes as a specification. We used prototyping as a method of developing a system life cycle because prototyping is very open to improvements that might occur after it releases the prototype version system. The results of evaluations show that the system has a high success rate based on 3 dimensions of testing, Performance (80.5%), Usability (78.5%), and User Satisfaction (78%).

Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification Afandi Nur Aziz Thohari; Liliek Triyono; Idhawati Hestiningsih; Budi Suyanto; Amran Yobioktobera
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Indonesia is a tropical country that has various skin diseases. Tinea versicolor, ringworm, and scabies are the most common types of skin diseases suffered by the people of Indonesia. The classification of the three skin diseases can be automatically completed by artificial intelligence and deep learning technology because the classification process using an expert will require a lot of money and time. The challenge in classifying skin diseases is in the process of collecting data. Because health data cannot be obtained freely, there must be approval from the patient or hospital. Therefore, to overcome the limited amount of data, Pre-Trained CNN is used. The Pre-Trained CNN model has many patterns from thousands of images, so we do not need many images to train the model. In this study, a comparison of five pre-trained CNN models was conducted, namely VGGNet16, MobileNetV2, InceptionResNetV2, ResNet152V2, and DenseNet201. The aim is to find out which CNN model can produce the best performance in classifying skin diseases with a limited amount of image data. The test results show that the ResNet152V2 model has the best classification ability with the highest accuracy, precision, recall, and F1 score values, namely 95.84%, 0.963, 0.96, and 0.956. As for the training execution time, the ResNet152V2 model has the fastest time to achieve 95% accuracy. That's happened because the addition of the dropout parameter is 20%.

Implementation of Least Mean Square Adaptive Algorithm on Covid-19 Prediction Sri Arttini Dwi Prasetyowati; Munaf Ismail; Badieah Badieah
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurred every month per case. The results showed that data pre-processing using min-max normalization was better than with Z-score normalization because the error prediction for pre-processing using min-max and z-score were 18% and 47%, respectively.

The Admission Decision Support System for Muhammadiyah Student Association Cadres Using the Profile Matching Method Rusydi Umar; Anton Yudhana; Jaka Dernata
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Technological advances are beneficial for humans in doing work, one of which is that technology can help humans make decisions. Muhammadiyah Student Association is a large organization. The establishment of this organization continues to have progress to make many students join the organization. With many students who want to join, the organization has difficulty determining the eligibility of each prospective member who wants to join because the organization must pay attention to factors that can support the assessment in the acceptance of its members. This research aims to conduct the selection process of organizational members using profile matching methods to help the organization solve the problems faced by making the right decision. Then the result obtained is a decision that follows the organization's expectations in the selection of new prospective members. The assessment used in this method consists of four aspects, two of which are core factors and two other aspects are secondary factors. The data processing results will become a ranking format so that this method can also determine the best candidate members based on ranking.

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network Murinto Murinto; Miftahurrahma Rosyda
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

The convolutional neural network (CNN) is a technique that is often used in deep learning. Various models have been proposed and improved for learning on CNN. When learning with CNN, it is important to determine the optimal parameters. This paper proposes an optimization of CNN parameters using logarithm decreasing inertia weight (LogDIW). This paper is used two datasets, i.e., MNIST and CIFAR-10 dataset. The MNIST learning experiment, the CIFAR-10 dataset, compared its accuracy with the CNN standard based on the LeNet-5 architectural model. When using the MNIST dataset, CNN's baseline was 94.02% at the 5th epoch, compared to CNN's LogDIWPSO, which improves accuracy. When using the CIFAR-10 dataset, the CNN baseline was 28.07% at the 10th epoch, compared to the LogDIWPSO CNN accuracy of 69.3%, which increased the accuracy.

Implementation of Principal Component Analysis and Learning Vector Quantization for Classification of Food Nutrition Status Jasman Pardede; Hilwa Athifah
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Balanced nutrition is very good in the process of child development. During the COVID-19 pandemic, consuming a balanced nutritious diet can keep a child's immune system from transmitting the virus. In determining the nutritional content of children's food during the pandemic, a classification of the nutritional content of children's food is carried out by applying the principal component analysis (PCA) dimension reduction method and the learning vector quantization (LVQ) classification method. The data used in this study amounted to 1168 data with 25 indicators of food nutrients. From the tests that have been carried out, the combination of the PCA-LVQ method produces an average accuracy of 58% with the highest accuracy of 60%. In addition, this study also compares the performance of the PCA dimension reduction method, independent component analysis (ICA) and factor analysis (FA) on the LVQ classification process. The final result of testing the three methods is that the FA method takes the fastest time, which is 4.10434 seconds and the PCA method produces the highest accuracy, which is 58.2%

Expert System of Dengue Disease Using Artificial Neural Network Classifier Hamdani Hamdani; Zainal Arifin; Anindita Septiarini
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Abstract – Expert systems can be applied to the classification of dengue fever. Dengue is a serious disease that can be fatal if not diagnosed and treated properly. Headache, muscle aches, fever, and rash are some of the most prevalent symptoms. Dengue fever is a disease that is endemic in various South Asian and Southeast Asian nations. Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome are the three types of dengue (DSS). Currently, these diseases may be classified using a machine learning approach with dengue symptoms as the input data. This study proposes implementing an Artificial Neural Network (ANN) with the Backpropagation (BPNN) algorithm as the classifier to categorize dengue types, divided into three categories: DF, DHF, and DSS. The dengue symptoms are represented by 21 attributes in the dataset. It was gathered from 110 patients. Cross-validation with k-fold 3, 5, and 10 were applied as the evaluation method. Three parameters were obtained to evaluate the ANN classification method: precision, recall, and accuracy. These were used to justify the most optimal performance. Cross-validation using k-fold 3 produced the best evaluation results, with precision, recall, and accuracy values of 97.3%, 97.3%, and 97.27%, respectively.

Android Game-Based Learning Media Recognizes the Structure and Functions of Plant and Animal Parts for Elementary School Endah Sudarmilah; Ikhwan Caesar Amri Pradana; Diah Priyawati
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Teaching and learning activities in Indonesia still employ the lecture method which is considered traditional, therefore it is necessary to refresh it by utilizing learning media in teaching and learning activities. One of the methods applied is using learning media based on android game in which it puts forward user experience to its user. With this interesting educational game, children will not realize that what they are doing includes learning and children will feel happy and want to learn. This application is intended for the android smartphone platform. This development method uses Research and Development (R&D) with an Analysis, Design, Development, Implementation, and Evaluation (ADDIE) development model. The results of the black box test from the research are that this android game-based learning media can run well.

Design of Intelligent Automated Quest Control System in the Covid-19 Era Yahfizham Yahfizham; Irwan Yusti
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

The rapid spread of corona virus disease 2019 (COVID-19), throughout direct of the human-to-human interaction makes the virus massively infect humans in all around the world. Until now, there has not been found the right way of healing it. This study aims to design of the intelligent automated quest control system capable for detecting COVID-19 by the body of temperature. The method approach was taken applied research, beginning with determining of the hardware using the ArduinoTM UNO microcontroller, the MLX90614 infrared thermometer, the TCRT5000 infrared reflective sensor, motor driver L293D, the output was displayed on a Liquid Crystal Display (LCD) screen, interaction control using Roller Limit Switch and instruction using the C programming language with Arduino IDE user interface. The system testing is done by comparing the temperature sensor readings infrared thermometer versus standard thermometer. Based on the results of a limited scale trial of 5 volunteers, an average error of 2.72% was obtained and the system worked well (opening or locking the door) in accordance with the temperature limits that had been set for detecting COVID-19. This research novelty that the simple and inexpensive design of the device system prevented and minimize the spread of COVID-19. The last, limitations of the system not being tested by the experts and large sample.

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Hindayati Mustafidah

Contact Email
jurnal.juita@gmail.com

Phone
+6285842817313

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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
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Abstract

Abstract

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Abstract

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