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All Journal Indonesian Journal of Information System IJID (International Journal on Informatics for Development)
Prastyo, Pulung Hendro
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Computer Science & IT

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A Systematic Literature Review of Application Development to Realize Paperless Application in Indonesia: Sectors, Platforms, Impacts, and Challenges Prastyo, Pulung Hendro; Sumi, Amin Siddiq; Kusumawardani, Sri Suning
Indonesian Journal of Information Systems Vol 2, No 2 (2020): February 2020
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.876 KB) | DOI: 10.24002/ijis.v2i2.3168

Abstract

Going paperless is an ideal form of the information era with the advantages of being time-efficient, environmentally friendly, proper documentation management, and it is an important step to improve the perception of the organization in the environmental field. From the environmental perspective, paperless is a concrete step to reduce the use of trees for paper. The paperless concept has been proposed by the government and has been legally guaranteed, so various sectors have begun to implement the paperless concept such as in the government, education, and industry sectors. However, there has been limited research that studies how many sectors implement paperless applications, the platforms that are used to develop paperless applications, the impacts of using paperless applications and the challenges for Indonesia. Therefore, this study aims to find out more details in the use of paperless applications in terms of sectors, platforms, impacts, and challenges for Indonesia. The data used in this study are articles of journal accredited by Sinta discussing the development of paperless applications in the government, education, and industry sectors from 2010 to 2019. The data are analyzed using the Systematic Literature Review method (SLR). The results of this study indicate that the sector that constantly develops paperless applications is the education sector, while the dominant platform used to develop paperless applications is the website. The impact of using paperless applications has a positive impact both in terms of performance, budget savings, and solving environmental problems generated by paper waste. Paperless applications are the solution in the digital era in supporting environmental preservation. The challenge is how the government makes regulations to support paperless applications in all agencies and provides financial support to sectors in which the use of paper is classified as significant but lacks funds in implementing paperless applications. Paperless applications must also be easy to use, and users must be provided continuous training so that paperless applications can be implemented easier.
A Machine Learning Framework for Improving Classification Performance on Credit Approval Prastyo, Pulung Hendro; Prasetyo, Septian Eko; Arti, Shindy
IJID (International Journal on Informatics for Development) Vol. 10 No. 1 (2021): IJID June
Publisher : UIN Sunan Kalijaga Yogyakarta

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Abstract

Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.
Co-Authors Arti, Shindy Prasetyo, Septian Eko Sri Suning Kusumawardani Sumi, Amin Siddiq