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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 17 Documents

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Search results for , issue "JUITA Vol. 10 No. 1, May 2022" : 17 Documents clear

Development of Fuzzy-Based Smart Drip Irrigation System for Chili Cultivation Sri Wahjuni; Wulandari Wulandari; M Kholili
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Chili plants often fail to harvest in the cultivation process due to improper irrigation. Soil temperature and humidity are essential parameters that affect the amount of water needed by plants in the watering process. This research aimed to apply fuzzy logic to the chili plants' irrigation system. The function of this system was to regulate watering due to the needs of the Chili plant automatically in a real-time fashion. The Sugeno fuzzy inference system (FIS) is embedded in a microcontroller to regulate the water based on the plant's needs appropriately. The system was tested on Chili plants located in the iSurf Computer Science Lab IPB University greenhouse. After four days of testing, the soil moisture sensor results were stable at optimal conditions, between 60%-80% after watering. It shows that the irrigation system has automatically regulated watering due to the Chili plant's needs.

Alphabet Recognition with Augmented Reality Technology Based on Android Using Extreme Programming Model Fitri Yanti; Jaka Sutresna
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Lack of literacy or interest in reading is the cause of children having difficulty recognizing the letters of the alphabet and assembling them into words or sentences because the letters are similar. Early childhood students have difficulty learning letters because there are too many letters of the alphabet that must be memorized, the number of letters of the alphabet there are 26 letters that must be memorized. In addition, early childhood students complain that reading is very difficult to pronounce because the way the teacher conveys reading techniques for students is difficult to understand so that it is boring for early childhood students. This makes it difficult for young children to pronounce letters. In software development using Extreme Programming (XP) where writing programs in pairs, two programming people work together to write programs. Currently, computer vision technology has been used in various industries, including trade, medicine, education, and so on. Augmented reality is one of the computer vision technologies. The technology of computer vision is to combine synthetic images into the real world or vice versa. By making an android application with an extreme programming method that utilizes Augmented Reality technology that can display 3D, animation, and sound so that it looks real, it makes early childhood interested and makes it easier for them to learn the letters of the alphabet.

Fraud Detection Using Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier Algorithms on Credit Cards Muhamad Sopiyan; Fauziah Fauziah; Yunan Fauzi Wijaya
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study are the Random Forest Classifier (RFC) algorithm is better than other algorithms because it has the highest accuracy the percentage of data-train is 100% and data-test is 99.99% and the evaluation of the AUC score as a result of algorithm testing is 0.9999.

Sentiment Analysis on Covid-19 Vaccination in Indonesia Using Support Vector Machine and Random Forest I Made Sumertajaya; Yenni Angraini; Jamaluddin Rabbani Harahap; Anwar Fitrianto
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

World Health Organization (WHO) stated Covid-19 as a global pandemic in March, 2020. This pandemic has influenced people’s life in many sectors such as the economy, health, tourism, and many more. One way to end this pandemic is to make herd immunity obtained through the vaccination program. This program still raises pros and cons at the beginning of its implementation in Indonesia. Many people doubt the safety and side effects of the vaccine. There are also pros and cons to vaccination programs in social media such as Twitter. This platform generates a huge amount of text data containing people's perceptions about vaccines. This research aims to predict sentiment using supervised learning such as support vector machine (SVM) and random forest and capture sentiment about vaccines in Indonesia in the first two weeks of the program. The result shows SVM was a better model than random forest based on the precision and F1-score metrics. The SVM approach produces a precision value of 0.50, a recall of 0.64, and an F1-score of 0.52. In the study, it was also found that tweets with neutral sentiment dominated the twitter user sentiment in the study period. Tweets with negative sentiment decreased after the first week of the COVID-19 vaccination program.

Design of Digital Evidence Collection Framework in Social Media Using SNI 27037: 2014 Adi Setya; Abba Suganda
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Social media is a place that people use to socialize. In addition to socializing, social media is also often used as a crime medium by certain people. In the evidentiary process, law enforcers have the duty to present the evidence used by the suspect in committing his crime. The method used in collecting digital evidence from social media must have a clear scientific basis and guidelines. If the method used is not known as a theory or method in digital forensics, this will undermine all expert testimony and evidence presented in the court. Making a framework that can be recognized by all judicial administrators (judges, public prosecutors, attorneys for defendants, witnesses and defendants) is a solution that can be used as a standard so that the evidence process runs well. The framework that has been created by the researcher is an update from the previous framework. The framework design is made using the Composite Logic method. The composite logic method will collaborate with the Digital Forensics Investigation Models framework to produce a new framework. Based on existing data and facts, this research has produced a framework with better performance than the previous framework.

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.

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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

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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