IJID (International Journal on Informatics for Development) https://ejournal.uin-suka.ac.id/saintek/ijid <p>IJID is a biannual peer-reviewed journal published in June and December by the Faculty of Science and Technology, State Islamic University (UIN) Sunan Kalijaga Yogyakarta-Indonesia. The journal warmly welcomes contributions of innovative and not previously published works in subjects covered by the Journal from scholars of related disciplines. </p> Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta en-US IJID (International Journal on Informatics for Development) 2252-7834 <a href="http://creativecommons.org/licenses/by-nc-nd/4.0/" rel="license"><img style="border-width: 0;" src="https://i.creativecommons.org/l/by-nc-nd/4.0/80x15.png" alt="Creative Commons License" /></a><br /><span>IJID (International Journal on Informatics for Development)</span> is licensed under a <a href="http://creativecommons.org/licenses/by-nc-nd/4.0/" rel="license">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a> Implementation of Web Scraping to Build a Web-Based Instagram Account Data Downloader Application https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09201 Instagram has been used by many groups, such as business people, academics, to politicians, to take advantage of the insights gained by processing and analyzing Instagram data for various purposes. However, before processing and analyzing data, users must first pass data collection or downloading from Instagram. The problem faced is that most data collection methods are still done manually as for many parties that offer Instagram account data download services with various price options. This research applied a web scraping method to automatically build a web-based Instagram account data download application so that several parties can use it. The web scraping method was chosen because by using this method, researchers do not need to use Instagram's Application Programming Interface (API), which has access restrictions in retrieving data on Instagram. In this study, application testing was conducted on 15 Instagram accounts with various publications, namely between 100 and 11000. Based on the download data analysis results, the application of the web scraping method to download Instagram account data can successfully download a maximum of 2412 account data. In this application, users can download Instagram account data to Data Collection and then manage it like deleting and exporting data collection in the form of CSV, Excel, or JSON. Arif Himawan Adri Priadana Aris Murdiyanto Copyright (c) 2020 IJID (International Journal on Informatics for Development) http://creativecommons.org/licenses/by-nc-nd/4.0 2020-12-31 2020-12-31 9 2 59 65 10.14421/ijid.2020.09201 Online Integrated Development Environment (IDE) in Supporting Computer Programming Learning Process during COVID-19 Pandemic: A Comparative Analysis https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09202 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. Kartikadyota Kusumaningtyas Eko Dwi Nugroho Adri Priadana Copyright (c) 2020 IJID (International Journal on Informatics for Development) 2020-12-31 2020-12-31 9 2 66 71 10.14421/ijid.2020.09202 Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09203 The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%. Sari Ayu Wulandari Sutikno Madnasri Ratih Pramitasari Susilo Susilo Copyright (c) 2020 IJID (International Journal on Informatics for Development) 2020-12-31 2020-12-31 9 2 72 79 10.14421/ijid.2020.09203 Implementation Improvement Analysis of M-Library Application and Related Business Processes at XYZ University https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09204 <span>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.<br /></span> Rizqiyatul Khoiriyah Devi Handayani Tungga Bhimadi Copyright (c) 2020 IJID (International Journal on Informatics for Development) 2020-12-31 2020-12-31 9 2 80 86 10.14421/ijid.2020.09204 Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09205 Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency. Siti Rahmawati Muhammad Habibi Copyright (c) 2020 IJID (International Journal on Informatics for Development) 2020-12-31 2020-12-31 9 2 87 93 10.14421/ijid.2020.09205 Online Public Access Catalogue: Factors Affecting Use E-Catalog https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09206 <p>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.</p> Farida Ardiani Copyright (c) 2020 IJID (International Journal on Informatics for Development) 2020-12-31 2020-12-31 9 2 94 99 10.14421/ijid.2020.09206 A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09207 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. <em>Wayang</em> 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 <em>wayang</em> 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 <em>wayang</em> 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. Ahmad Mustafid Muhammad Murah Pamuji Siti Helmiyah Copyright (c) 2020 IJID (International Journal on Informatics for Development) 2020-12-31 2020-12-31 9 2 100 110 10.14421/ijid.2020.09207 Research Trend of Causal Machine Learning Method: A Literature Review https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/09208 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. Shindy Arti Indriana Hidayah Sri Suning Kusumawardani Copyright (c) 2020 IJID (International Journal on Informatics for Development) http://creativecommons.org/licenses/by-nc-nd/4.0 2020-12-31 2020-12-31 9 2 111 118 10.14421/ijid.2020.09208 Cover and Table of Content https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/2399 Ijid ijid Copyright (c) 2021 IJID (International Journal on Informatics for Development) 9 2 10.14421/ijid.2020.%x