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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
Arjuna Subject : -
Articles 265 Documents
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Online Integrated Development Environment (IDE) in Supporting Computer Programming Learning Process during COVID-19 Pandemic: A Comparative Analysis Kusumaningtyas, Kartikadyota; Nugroho, Eko Dwi; Priadana, Adri
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09202

Abstract

COVID-19 has spread to various countries and affected many sectors, including education. New challenges arise in universities with study programs related to computer programming, which require a lot of practice. Difficulties encountered when students should setting up the environment needed to carry out programming practices. Furthermore, they should install a text editor called Integrated Development Environment (IDE) to support it. There is various online IDE that supports computer programming. However, students must have an internet connection to use it. After all, many students cannot afford to buy internet quotas to access online learning material during the COVID-19 pandemic. According to these problems, this study compares several online IDEs based on internet data usage and the necessary supporting libraries' availability. In this study, we only compared eleven online IDEs that support the Python programming language, free to access, and do not require logging in. Based on the comparative analysis, three online IDEs have most libraries supported. They are REPL.IT, CODECHEF, and IDEONE. Based on internet data usage, REPL.IT is an online IDE that requires the least transferred data. Moreover, this online IDE also has a user-friendly interface to place the left and right sides' code and output positions. It prevents the user from scrolling to see the results of the code that has been executed. The absence of advertisements also makes this online IDE a more focused appearance. Therefore, REPL.IT is highly recommended for users who have a limited internet quota, primarily to support the learning phase of computer programming during the COVID-19 pandemic.
Implementation Improvement Analysis of M-Library Application and Related Business Processes at XYZ University Khoiriyah, Rizqiyatul; Handayani, Devi; Bhimadi, Tungga
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09204

Abstract

XYZ University library provides offline and online book loan services. The library's online service uses the m-library application. This study aims to see the  implementation of the m-library application and see the extent of the evaluation of the application. The m-library application is used for the process of borrowing books, viewing catalogs, and reading book online. The method used in this research is qualitative descriptive through interviewing interviewees and observing applications. The results of this study are that there are several benefits and weaknesses to the m-library application. From these shortcomings, it can be recommended to increase the implementation of the m-library application, namely the need for application development towards menu flexibility and application services, ease of online membership registration process, compatibility of online and offline book catalogs and more effective use of application features according to the University library business process. This application also requires broader socialization to the university academic community so that its use is more optimum.
Online Public Access Catalogue: Factors Affecting Use E-Catalog Ardiani, Farida
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09206

Abstract

Online Public Access Catalog (OPAC) is one of the e-catalog information technologies applied in libraries. OPAC is a library information retrieval system that can be accessed online. State Islamic University of Sunan Kalijaga Yogyakarta has been using OPAC since 2012 and OPAC users are increasing from year to year. An information system will be used by users if it suits their needs. The successful implementation of OPAC raises questions about the factors that influence this success. For this reason, this study aims to determine the factors that influence users to use OPAC. Structural Equation Modeling (SEM) is a multivariate statistical technique which is a combination of factor analysis and regression analysis (correlation) which aims to examine the relationships between variables in a model. Processing using SEM will be carried out to find the relationship between the variables to be tested, which variables are interconnected, and are there any unrelated variables. The results of processing the variables using SEM can show what variables attract users to use the e-catalog. Acceptance of information systems can be measured by several evaluation models that have been developed at this time. There are many evaluation models used to measure. Technology Acceptance Model (TAM) is the appropriate model to use for this study, because this study is about the acceptance of a system. In addition, several previous studies used by researchers as references also used TAM as their study method to assess user acceptance of a system. This study modifies TAM, which is used to determine user acceptance of an information system, by adding three exogenous variables, information quality, perceived enjoyment, and user interface. Results of this study proved that information quality, user interface, perceived usefulness, perceived ease of use, and behavioral intention to use, are all factors that influence the actual use of OPAC. Perceived enjoyment is a variable that cannot be proved affects the actual use of OPAC.
Research Trend of Causal Machine Learning Method: A Literature Review Arti, Shindy; Hidayah, Indriana; Kusumawardani, Sri Suning
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09208

Abstract

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.
A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification Mustafid, Ahmad; Pamuji, Muhammad Murah; Helmiyah, Siti
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09207

Abstract

Deep Learning is an essential technique in the classification problem in machine learning based on artificial neural networks. The general issue in deep learning is data-hungry, which require a plethora of data to train some model. Wayang is a shadow puppet art theater from Indonesia, especially in the Javanese culture. It has several indistinguishable characters. In this paper, We tried proposing some steps and techniques on how to classify the characters and handle the issue on a small wayang dataset by using model selection, transfer learning, and fine-tuning to obtain efficient and precise accuracy on our classification problem. The research used 50 images for each class and a total of 24 wayang characters classes. We collected and implemented various architectures from the initial version of deep learning to the latest proposed model and their state-of-art. The transfer learning and fine-tuning method showed a significant increase in accuracy, validation accuracy. By using Transfer Learning, it was possible to design the deep learning model with good classifiers within a short number of times on a small dataset. It performed 100% on their training on both EfficientNetB0 and MobileNetV3-small. On validation accuracy, gave 98.33% and 98.75%, respectively.

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