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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 14, No 1 (2020): January 2020" : 5 Documents clear
Stemming javanese affix words using nazief and adriani modifications Aji Prasetya Wibawa; Felix Andika Dwiyanto; Ilham Ari Elbaith Zaeni; Rizal Kholif Nurrohman; AN Afandi
Jurnal Informatika Vol 14, No 1 (2020): January 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i1.a17106

Abstract

Stemming is the process of finding a basic word with several stages of affix removal. The main reason for stemming is to check spelling and machine translation and to support the effectiveness of the retrieval process. This study uses the Nazief and Adriani algorithm for stemming Javanese-influenced words. The first step taken is data collection and making a basic word dictionary. Then do the stemming process. Before stemming, modifications are made to the rules. The rules of the Nazief and Adriani algorithm, which are based on the morphology rules of the Indonesian language, are modified to suit the morphological rules of the Javanese language. Of the 366 words that were tested, it produced 351 correct basic words and 15 basic words that experienced errors. The results show that this algorithm can be used for stemming Javanese with an accuracy value of 95.9%.
Feature extraction in batik image geometric motif using canny edge detection Muhammad Fikri Hidayattullah; M. Nishom; Slamet Wiyono; Yustia Hapsari
Jurnal Informatika Vol 14, No 1 (2020): January 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i1.a15320

Abstract

One of Indonesia's priceless cultural heritages is batik. Even UNESCO was admitting that batik is an intellectual, cultural right of the Indonesian (October 2). Unfortunately, many Indonesian do not have sufficient knowledge about the various types of the existence of batik's motifs. In fact, in each of these motifs, many treasures must be maintained. Therefore, it is necessary to develop a model that can recognize batik motifs automatically. The model can be built using various kinds of pattern recognition algorithms. One of the most important stages in the introduction of batik motifs is the feature extraction. Feature extraction is needed to determine the parameters that able to define character a batik's motif. One feature extraction model that can be done is by using edge detection. This research focuses on feature extraction using Canny edge detection. The result of edge detection is forming the pattern of a batik motif. The pattern contains pixel values 0 and 1. These values can later be used as input at the classification stage.
Ubiquitous computing: a learning system solution in the era of industry 4.0 Wildan Toyib; Delvis Agusman; Hari Ramza
Jurnal Informatika Vol 14, No 1 (2020): January 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (70.672 KB) | DOI: 10.26555/jifo.v14i1.a15314

Abstract

Ubiquitous computing, which was initially advocated by Mark Weiser has become one of the keywords to express a vision of the near future of computing systems. The "ubiquitous world" is a ubiquitous computing environment with integrated networks; computer integrated manufacturing system (CIMS) and invisible computers which equipped sensor microchips and radio frequency identification systems. Anyone can access the ubiquitous computing systems anytime and anywhere broader, without individual awareness or skills. Ubiquitous computing is becoming crucial elements to organize the activities of groups of people by use of groupware under workforce mobility. The computer-supported cooperative work is transforming from telework to ubiquitous work with new information and communication technologies that support people working cooperatively. Ubiquitous learning is a demand for the knowledge workforce for more multi-skilled professionals. It is a new and emerging education and training system that integrating e-learning of cyberspace and mobile learning of physical space with a global repository that has the potential to be accessed by anyone at any place and anytime under ubiquitous integrated computing environment. In this paper, we discuss the study of emerging trends through the implementation of work and learning that influenced ubiquitous computing technology prospects. Furthermore, the perspective of ubiquitous work and learning system, gaining quality, and hence credibility with emerging information and communication technologies in education and training systems in the area of the education system are discussed. The experimental results showed that CIMS could improve the students learned more efficiently and achieved better learning performance.
Towards transparent machine learning models using feature sensitivity algorithm Ali A. Abaker; Fakhreldeen A. Saeed
Jurnal Informatika Vol 14, No 1 (2020): January 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i1.a16983

Abstract

Despite advances in health care, diabetic ketoacidosis (DKA) remains a potentially serious risk for diabetes. Directing diabetes patients to the appropriate unit of care is very critical for both lives and healthcare resources. Missing data occurs in almost all machine learning models, especially in production. Missing data can reduce the predictive power and produce biased estimates of models. Estimating a missing value around a 50 percent probability may lead to a completely different decision. The objective of this paper was to introduce a feature sensitivity score using the proposed feature sensitivity algorithm. The data were electronic health records contained 644 records and 28 attributes. We designed a model using a random forest classifier that predicts the likelihood of a developing patient DKA at the time of admission. The model achieved an accuracy of 80 percent using five attributes; this new model has fewer features than any model mentioned in the literature review. Also, Feature sensitivity score (FSS) was introduced, which identifies within feature sensitivity; the proposed algorithm enables physicians to make transparent, and accurate decisions at the time of admission. This method can be applied to different diseases and datasets.
Comparison of Knuth Morris Pratt and Boyer Moore algorithms for a web-based dictionary of computer terms Ali Khumaidi; Yusuf Aras Ronisah; Harjono Padmono Putro
Jurnal Informatika Vol 14, No 1 (2020): January 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i1.a17038

Abstract

Computer students need a dictionary of computer terms to deepen lectures. In developing dictionary applications, the term computer will choose the fastest and most efficient memory algorithm. The comparison algorithm is Knuth Morris Pratt (KMP) and Boyer Moore (BM) algorithm. Based on previous research, the KMP algorithm has a better performance compared to other string matching algorithms. However, other studies have concluded that the BM algorithm has better performance. Besides, the Zhu-Takaoka algorithm is more efficient than the KMP algorithm in dictionary development. The BM algorithm has the same search concept as the Zhu-Takaoka algorithm. The determination of the fastest and most efficient algorithm in this study uses the Exponential Comparison Method (ECM). ECM sets criteria for when searching and using the memory in the search process. The results of the comparison of the KMP and BM algorithm are the search time for the BM algorithm is 37.9%, and the KMP algorithm is 62.1%. The results of the use of search memory for the KMP algorithm are 50.6%, and the BM algorithm is 49.4%. The total ECM score shows that the BM algorithm is 0.55% better than the KMP algorithm.

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