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JUITA : Jurnal Informatika
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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NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization Aji Gautama Putrada; Nur Alamsyah; Muhamad Nurkamal Fauzan; Syafrial Fachri Pane
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.v11i1.15140

Abstract

Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with ???? = ???????? to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely ???????????????????????????????? = ????. ????????????. At the same time, the model with the lowest performance is SVM with RBF, namely???????????????????????????????? = ????. ????????????. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is ???????????? = ????.???????????? to ????.????????????.
Smart Parking System Model Analysis with NodeMCU and IoT-Based RFID Sandhy Fernandez; Yetman Erwadi; Faisal Erlangga
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.v11i1.16908

Abstract

There is much conventional management parking nowadays. One of them is on campus 1 of Muhammadiyah University of Bengkulu. Many vehicles that do not belong to students or staff from the Muhammadiyah University of Bengkulu are parked in the inner area of campus. In addition, it causes vulnerable to direct physical contact with other people, while the government recommends not to have direct physical contact with other people in the era of the Covid-19 virus pandemic. So this study designs a smart parking system with Node MCU and IoT-based RFID. Parking systems that use RFID are able to filter incoming vehicles, increase security and efficiency in parking areas, and able to reduce the impact of the spread of the Covid-19 virus. The technology used is RFID as a user Id reader Sensor, Node MCU as a microcontroller, E-KTP as access to the parking area, servo as a parking portal driver, LCD as a sign whether access is accepted or not, and infrared as a distance sensor that will signal to a servo to close the portal. This system can be hoped to overcome the current problems.
Evaluation of Bicluster Analysis Results in Capture Fisheries Using the BCBimax Algorithm Cynthia Wulandari; I Made Sumertajaya; Muhammad Nur Aidi
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.v11i1.15457

Abstract

Biclustering is a simultaneous clustering technique by finding sub-matrixes that have the same similarity between rows and columns. One of the biclustering algorithms that is relatively fast and can be used as a reference for the comparison of several algorithms is the BCBimax algorithm. The BCBimax algorithm works by finding a sub-matrix containing element 1 of the formed binary data matrix. The selection of thresholds in the binarization process and the minimum combination of rows and columns are essential in finding the optimal bicluster. Capture fisheries have an important role in supporting sustainable growth in Indonesia, so information on the potential of fish species that have similarities in several provinces is needed in optimally mapping the potential. The BCBimax algorithm found 11 optimal biclusters in grouping capture fisheries data. The median of each variable is used as a threshold in the binarization process, and the minimum combination of row 2 and maximum column 2 is chosen to find the optimal bicluster result. The optimal average value of Mean Square Residual bicluster obtained is 0.405403 with the similarity of bicluster results (Liu and Wang index) which is different for each bicluster combination produced. All the bicluster results grouped the provinces and types of fish that had the same potential simultaneously.
Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation Leni Fitriani; Dewi Tresnawati; Muhammad Bagja Sukriyansah
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.v11i1.16166

Abstract

In Indonesia, Batik is one of the cultural assets in the field of textiles with various styles. There are many types of batik in Indonesia, one of which is Batik Garutan. Batik Garutan has different motifs that show the characteristics of Batik Garutan itself. Therefore, to distinguish the features of Batik Garutan from another batik, a system is needed to classify the types of batik patterns. Classification of batik patterns can be done using image classification. In image classification, there are methods to increase the size and quality of the limited training dataset by performing data augmentation. This study aims to obtain an image classification model by applying data augmentation. The image classification process is carried out using the Deep Learning method with the Convolutional Neural Network algorithm, which is expected to be helpful as a reference for research and can be applied to software development related to image classification. This study generated models from several experiments with different epoch parameters and dataset proportions. A system obtained the investigation with the best performance with a data proportion of 9:1, resulting in an accuracy value of 91 percent.
Designing a Chatbot Based on Full-Text Search and 3D Modelling as a Promotional Media Sevtian Ferdian; Aditiya Hermawan; Edy Edy
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.v11i1.15237

Abstract

A chatbot is an application that can respond and reply to questions from the questioner, without any human being involved. In general, chatbots are used to simplify and improve public services, so that users can get information quickly and directly, without time constraints. This research objective is to create promotional media in the form of chatbots in the 3D Virtual Tour to answer questions and provide general information about an object while providing more attractive visuals for users. The method used to run the chatbot is Full-Text Search. Full-Text Search is an efficient and powerful search method that is built into MySQL by default, which works by matching words between keywords and patterns in the database. This study succeeded in applying the Full-Text Search method as a search technique for chatbot patterns in the 3D Virtual Tour application at Buddhi Dharma University as a promotional medium. Evaluation using the User Acceptance Test (UAT) method yielded a value of 83.45% in a very satisfying category for users.
K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters M. Andang Novianta; Syafrudin Syafrudin; Budi Warsito
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.v11i1.16986

Abstract

The Special Region of Yogyakarta (DIY) has rivers that cross rural and urban areas that are still used by the community and industry. However, cases of river water pollution in DIY are a major issue in 2021. It is very important to classify rivers according to class so that further analysis and action can be carried out. This study conducted a grouping analysis of rivers in DIY based on water quality parameters such as Total Suspended Solid (TSS), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Phosphate, Fecal Coli, and Total Coliform. The grouping method uses the K-means algorithm. The data source is secondary data from the DIY Provincial Environment and Forestry Service. The data is in the form of 56 river samples observed in November 2020. The description of the data shows that the average of the 56 river water samples is 24.95 for TSS, 8.84 for DO, 4.33 for BOD5, 20.36 for COD, 0 .54 for Phosphate, 22.820 for Fecal Coli, and 59.210 for Total Coliform. The results of grouping with k=6 are the best compared to k = 2, 3, 4, 5, 7, and 8. The number of members in this grouping is n1 = 14, n2 = 1, n3 = 1, n4 = 5, n5 = 18, and n6 = 17. The cluster that has the highest average TSS, BOD, and COD values is the 3rd cluster (Rivers in Bantul and Sleman Regencies). The cluster that has the highest DO value is the 6th cluster (Rivers in Bantul Regency). The cluster that has the highest average Phosphate value is the 2nd cluster (Rivers in Bantul, Sleman, and Gunungkidul Regencies). The cluster that has the highest average Fecal Coli and Total Coliform values are the 4th cluster (Rivers in Bantul Regency, Yogyakarta City, and Sleman Regency).
Comparison of Classification Methods on Twitter Sentiment Analysis of PDAM Tugu Tirta Kota Malang Anisa Dewi Anggraeni; Muhammad Farhansyah; Muhammad Risky Pratama Hermawan; Galih Wasis Wicaksono; Christian Sri Kusuma Aditya
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.v11i1.15485

Abstract

The Regional Drinking Water Company (PDAM) Tugu Tirta is a public service company in Malang's drinking water distribution field. The company uses a customer complaint feature that is provided on the website. However, only a few people know about it and use it. From this problem, the researcher uses social media data, namely Twitter, to explore data sources and collect feedback tweets from the customer. However, analyzing the sentiment of the 1000 data used is elusive. The tweets contain unstructured text, so the researcher applies the labeling from the dataset, preprocesses the text, and then extracts the tweets by applying the classification methods by comparing Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), Short-Term Long-Term Memory (LSTM), and Indonesian BERT to achieve highly accurate results. The tests with six methods show that Logistic Regression and Indonesian BERT are the best methods, with an accuracy of 85%. In this study, we obtained an effective algorithm to classify a comment as positive, negative, or neutral related to the Tugu Tirta Regional Drinking Water Company (PDAM).
A Smart Greenhouse Production System Utilizes an IoT Technology Choirul Huda; Bety Etikasari; Pramuditha Shinta Dewi Puspitasari
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.v11i1.16191

Abstract

Food is an essential need for every living creature. Choosing the wrong food leads to serious problems e.g. indigestion, obesity, diabetes mellitus, stroke, including heart disease that causes death. To prevent those diseases from harming the body, people should be concerned about food consumption, for example by consuming organic food. Organic food is obtained by cultivating plants in a greenhouse to increase production, minimize risk, prevent disease, and be safer against environmental risk. However, some obstacles faced by farmers such as disease or pests, water supply, temperature, and so on.  Based on some previous research, the problem is dominated by soil moisture since the farmer has to water all plants manually. It has affected crop yields directly. If this phenomenon is not handled properly, farmers are threatened with losses so organic farming becomes a catastrophe. Therefore, in this research, an IoT technology is proposed to increase soil moisture in real time. The proposed system is also equipped with a Web-based information system to expose the cultivation phase, and market crops, as well as a tool for buyers as interaction media through the feedback provided. In the end, the proposed system is adequate to increase the productivity of vegetable cultivation grown in a greenhouse. Based on some experiments that have been done, the proposed method is capable to work optimally and effectively meet user needs by 95.55%.
Aspect-Based Sentiment Analysis for Indonesian Tourist Attraction Reviews Using Bidirectional Long Short-Term Memory Dwi Intan Af'idah; Puput Dewi Anggraeni; Muhammad Rizki; Aji Bagus Setiawan; Sharfina Febbi Handayani
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.v11i1.15341

Abstract

The tourism sector in Indonesia experienced growth and made a positive contribution to the national economy, but this growth has yet to reach its target. Therefore, the government of Indonesia has implemented a sustainable tourism development program by establishing ten priority tourism destinations. Aspect-based sentiment analysis (ABSA) towards tourist attraction reviews can assist the government in developing potential goals. The ABSA process compares with two deep learning models (LSTM and Bi-LSTM), which are considered to obtain good performance in text analysis. The shortcomings of previous ABSA research should have examined the performance of the aspect classification and sentiment classification models sequentially. This makes the performance obtained from the ABSA task invalid. Thus, this study is conducted to determine the version of the aspect classification model and the sentiment classification model individually and simultaneously. This study aims to develop an aspect-based tourist attraction sentiment analysis as an intelligent system solution for sustainable tourism development by applying the binary relevance mechanism and the best deep learning model from LSTM or Bi-LSTM. The test results showed that Bi-LSTM was superior in aspect and sentiment classification individually and simultaneously. Likewise, the aspect classification and sentiment classification test results sequentially Bi-LSTM outperformed that of LSTM. The average accuracy and f1 score of Bi-LSTM are 92.22% and 71,06%. Meanwhile, LSTM obtained 90,63% of average precision and 70,4% of f1 score.
Resampling Technique for Imbalanced Class Handling on Educational Dataset Anief Fauzan Rozi; Adi Wibowo; Budi Warsito
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.v11i1.15498

Abstract

Educational data mining is an emerging field in data mining. The need for accurate in identifying student accomplishment on a course or maybe an upcoming course can help the institution to build technology-aided education better. Educational data mining becoming a more important field to be studied because of its potential to produce a knowledge base model to help even the teacher or lecturer. Like another classification task, educational data mining has a common and frequently discovered problem. The problem that occurred in educational data mining specifically and classification tasks generally is an imbalanced class problem. An imbalanced class is a condition where the distribution of each class is not in the same proportion. In this research, it is found that the class distribution is severely imbalanced and it is a multiclass dataset that consists of more than two class labels. According to the problem stated beforehand, this paper will focus on the imbalanced class handling and classification with several methods on both of it such as Linear Regression, Random Forest and Stacking for classification and SMOTE, ADASYN, and SMOTE-ENN for the resampling algorithm. The methods are being evaluated using a 10-fold cross-validation and an 80-20 splitting ratio. The result shows that the best performance coming from the Stacking classification on ADASYN resampled dataset evaluated using an 80-20 splitting ratio with a 0.97 F1 score. The result of this study also shows that the resampling technique improves classification performance. Even though the no-resampling classification result produced a decent result too, it can be caused by several things such as the general pattern of the data for each class is already been good from the start. Thus, there is no real drawbacks if the original data is processed.

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