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INDONESIA
Indonesian Journal of Information System
Published by Universitas Atma Jaya Yogyakarta
ISSN : 26230119     EISSN : 26232308     DOI : -
Core Subject : Science,
Computer Science & IT
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Articles 192 Documents
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Application of K-Nearest Neighbor Algorithm on Classification of Disk Hernia and Spondylolisthesis in Vertebral Column Irma Handayani
Indonesian Journal of Information Systems Vol. 2 No. 1 (2019): Agustus 2019
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v2i1.2352

Abstract

Vertebral column as a part of backbone has important role in human body. Trauma in vertebral column can affect spinal cord capability to send and receive messages from brain to the body system that controls sensory and motoric movement. Disk hernia and spondylolisthesis are examples of pathologies on the vertebral column. Research about pathology or damage bones and joints of skeletal system classification is rare whereas the classification system can be used by radiologists as a second opinion so that can improve productivity and diagnosis consistency of the radiologists. This research used dataset Vertebral Column that has three classes (Disk Hernia, Spondylolisthesis and Normal) and instances in UCI Machine Learning. This research applied the K-NN algorithm for classification of disk hernia and spondylolisthesis in vertebral column. The data were then classified into two different but related classification tasks: “normal” and “abnormal”. K-NN algorithm adopts the approach of data classification by optimizing sample data that can be used as a reference for training data to produce vertebral column data classification based on the learning process. The results showed that the accuracy of K-NN classifier was 83%. The average length of time needed to classify the K-NN classifier was 0.000212303 seconds.
Central Actor Identification of Crime Group using Semantic Social Network Analysis Sylvert Prian Tahalea; Azhari SN
Indonesian Journal of Information Systems Vol. 2 No. 1 (2019): Agustus 2019
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v2i1.2354

Abstract

The Police as law enforcers who authorize in terms of social protection are expected to do both the prevention and investigation efforts also the settlement of criminal cases that occurred in the society. This research can help police to identify the main actor faster and leads to solving crime-cases. The use of overall centrality is very helpful in determining the main actors from other centrality measures. The purpose of this research is to identify the central actor of crimes done by several people. Semantic Social Network Analysis is used to perform central actor identification using five centrality measurements, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and overall centrality. As for the relationship between actors, this research used social relation such as friendship, colleague, family, date or lover, and acquaintances. The relationship between actors is measured by first four centrality measures then accumulated by overall centrality to determine the main actor. The result showed 80.39% accuracy from 102 criminal cases collected with at least 3 actors involved in each case.
Web Scraping with HTML DOM Method for Data Collection of Scientific Articles from Google Scholar Alam Rahmatulloh; Rohmat Gunawan
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 | DOI: 10.24002/ijis.v2i2.3029

Abstract

Google Scholar is a web-based service for searching a broad academic literature. Various types of references can be accessed such as: peer-reviewed papers, theses, books, abstracts and articles from academic publishers, professional communities, pre-printed data centers, universities and other academic organizations. Google Scholar provides the profile creation feature of every researcher, expert and lecturer. Quantity of publication from an academic institution along with detailed data on the publication of scientific articles can be accessed through Google Scholar. A recap of the publication of scientific articles of each researcher in an institution or organization is needed to determine the research performance collectively. But the problems that occur, the unavailability of recap services for publishing scientific articles for each researcher in an institution or organization. So that the scientific article publication data can be utilized by academic institutions or organizations, this research will take data from Google Scholar to make a recap of scientific article publication data by applying web scraping technology. Implementation of web scraping can help to take the available resources on the web and the results can be utilized by other applications. By doing web scraping on Google Scholar, collective scientific article publication data can be obtained. So that the process of making scientific publications data recap can be done quickly. Experiments in this study have succeeded in taking 236 researchers data from Google Scholar, with 9 attributes, and 2,420 articles.
Comparative Analysis of Classification Methods of KNN and Naïve Bayes to Determine Stress Level of Junior High School Students Yohanes Christopher Tapidingan; Debby Paseru
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 | DOI: 10.24002/ijis.v2i2.3035

Abstract

Stress is generally defined as a state where someone is mentally disturbed as the response to the adversity that he/she experiences. Junior High School students usually are not aware of the stress that they encounter. This research aims to compare two classification methods of KNN and Naïve Bayes to determine stress level. The data of this research were gathered from 254 respondents from Catholic Junior High School of Don Bosco Bitung. The tests of k-cross validation and percentage split from the data showed that Naïve Bayes method excelled KNN method. With k=3, KNN accuracy reached 86.61% at the highest and Naïve Bayes reached 87.40%. Meanwhile, based on percentage split test, the average of Naïve Bayes accuracy was higher than KNN with percentage of 88.31%. Moreover, for the precision and recall, Naïve Bayes was higher than KNN with 88.30% and 87.40% seen from the k-cross validation.
Effects of a Logistic Reaction to Finite Difference Numerical Solutions of the Inviscid Burgers Equation Sudi Mungkasi
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 | DOI: 10.24002/ijis.v2i2.3054

Abstract

We solve the inviscid Burgers equation involving a logistic reaction. The goal is to investigate the effects of the logistic reaction to numerical solutions of the inviscid Burgers equation. We use standard and nonstandard finite difference methods. Based on our research results, the logistic reaction in the inviscid Burgers equation makes the shock wave propagates faster.
Publication Trends by Indonesia and Malaysia Affiliated Researchers About Public Policy, Technology, and Economics Robertus In Nugroho Budisantoso; Sudi Mungkasi
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 | DOI: 10.24002/ijis.v2i2.3061

Abstract

This paper aims to investigate the number of research articles authored by Indonesia and Malaysia affiliated researchers about three issues, namely, public policy, technology, and economics. We focus on articles indexed by Scopus in the ten year period 2010-2019. We obtain that Indonesia affiliated researchers are less productive from the beginning of the period. At the end of the period, Indonesia affiliated researchers produce more number of articles published internationally for public policy, technology, and economics issues. Furthermore, in total for any topics at the end of the period, Indonesia affiliated researchers publishes more articles. However, if we observe the number of published research articles with respect to the number of population, Indonesia’s is still left behind Malaysia’s.
The Analysis of Determining Credit Worthiness Using Weighted Product and SMART Methods in SPB Cooperatives Ai Ilah - Warnilah; Intan Nurul Hawa; Yani Sri Mulyani
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 | DOI: 10.24002/ijis.v2i2.3121

Abstract

The Savings and Loan Cooperative of Simpeunan Pameungkeut Banda is a cooperative that has been established in Tasikmalaya. The system of lending currently used at Simpeunan Pameungkeut Banda Cooperative is still manual, so the management requires quite a long time. Besides that, the cooperative has not had a system that supports in determining the credit worthiness decision. Thus, the credit is not on target, because it does not fit the specified criteria. For this reason, the writers were to make a research on the comparison of weighted product and smart methods in determining credit worthiness in Pameungkeut Banda Cooperative. The purpose of research was to determine who were eligible and who were not eligible to get the credit given the criteria that have been determined using the Weighted Product method and SMART methods. The results of the analysis using the Weighted Product method obtained the highest value of 0.067 and the lowest value of 0.032, while SMART method obtained the highest value of 79.2 and the lowest value of 25. The results show that SMART method is more effective in determining credit worthiness.
A Systematic Literature Review of Application Development to Realize Paperless Application in Indonesia: Sectors, Platforms, Impacts, and Challenges Pulung Hendro Prastyo; Amin Siddiq Sumi; Sri Suning Kusumawardani
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 | 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.
Solution of Dynamic Optimization Problems Constrained by the Fraction Penalty Method Hartono Hartono
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 | DOI: 10.24002/ijis.v2i2.3223

Abstract

This article discusses the application of fractional penalty method to solve dynamic optimization problem with state constraints. The main theories supporting the use of the method are described in some theorem and corollary. The theorems give sufficient conditons for the application of the method. Therefore, if all conditions mentioned in the theorems are met then the resulted solution will converge to the analytic solution. In addition, there are some examples to support the theory. The numerical simulation shows that the accuracy of the method is quite good. Hence, this method can play a role as an alternative method for solving dynamic optimization problem with state constrints.
Trojan Detection System Using Machine Learning Approach Mohd Faizal Ab Razak; M. Izham Jaya; Zahian Ismail; Ahmad Firdaus
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.5673

Abstract

Malware attack cases continue to rise in our current day. The Trojan attack, which may be extremely destructive by unlawfully controlling other users' computers in order to steal their data. As a result, Trojan horse detection is essential to identify the Trojan and limit Trojan attacks. In this study, we proposed a Trojan detection system that employed machine learning algorithms to detect Trojan horses within the system. A public dataset of Trojan horses that contain 2001 samples comprises of 1041 Trojan horses and 960 of benign is used to train the machine learning classification. In this paper, the Trojan detection system is trained using four types of classifiers which are Random Forest, J48, Decision Table and Naïve Bayes. WEKA is used for the execution of the classification process and performance analysis. The results indicated that the detection system trained with the Random Forest and Decision Table algorithms obtained the maximum level of accuracy.

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