cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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 15, No 3 (2021): September 2021" : 5 Documents clear
Classification of batik in southern coast area of java using convolutional neural network method Taufik Cahya Prayitna; Murinto Murinto
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20692

Abstract

Batik is a craft inherited from our ancestors from the archipelago which has a high aesthetic value. Batik has several kinds of motives. Perhaps only a few of the information related to batik can find out. Therefore, not everyone can know or recognize batik in the southern coastal areas of Java correctly. Convolutional Neural Network is a part of deep learning that can be used to recognize and detect objects in digital images. Convolutional Neural Network is a type of Artificial Neural Network that was created specifically so that it can work on data in the form of an array. Based on the results of the study, the results obtained were 100% accuracy for the training process and 99% for the testing process with 630 training data and 180 validation data. The accuracy results obtained by testing the model are 93,3 % with 90 test data. So it can be concluded that the CNN model that has been created can classify batik motifs well.
Classifying the characteristics of insurance shares: a k-means clustering approach Y Utami; I Zuhroh; V Prasetya; Mochamad Rofik
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a23372

Abstract

This study aims to apply the k-means clustering method in understanding the characteristics of insurance shares. The eight issuers are divided into three clusters based on price and rate of return. The k-means method's application shows that each cluster has different characteristics, especially for the price variable. Test with panel data regression also discovers different patterns between clusters 2 and 3 in responding to changes in interest rates. The findings of this study indicate that k-means clustering can be used as an initial analysis to understand the characteristics of issuers that investors can use to increase the optimal probability of return.
Information technology performance measurement and improvement recommendation in Indonesian retail company S W Perangin-angin; C H Primasari; Y P Wibisono
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a23495

Abstract

Information technology should work according to the needs and provide added value to the business. If the application of information technology does not provide added value to the business, information technology will only become a burden for the company. Therefore, it is necessary to measure performance to see to what extent the application of IT can support business processes and provide added value. This paper provides measurements and recommendations on IT governance in one of leading retail company in Indonesia. This research used descriptive quantitative research methods and IT Balanced Scorecard method that can provide an overview of IT performance in an organization based on four perspectives, such as Corporate Contribution, Customer Orientation, Operational Improvement, and Future Orientation. Based on the results of the analysis and measurement, the overall IT performance score was 62.64% where the score is in the “Moderate” category. The company contribution perspective got a score of 68.50%, the user orientation perspective was 63.00%, the operational improvement perspective was 62.06%, and the future orientation perspective was 57.44%. Several recommendations were constructed based on the consideration of the KPI value that must be improved. This can be a guide for other retail companies in formulating policies related to IT governance and enriching research in the field of IT performance measurement.
Random forest algorithm for algorithm for prediction of high school science students acceptance snmptn based on students assesment report U Pujianto; A R Taufani; J A Aziz
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a25413

Abstract

National Selection for State University (SNMPTN) is one of the selectionlines for admission of new students in Indonesia to enter State Universities byinvitation. Report card grades are one component of the assessment ofadmission of new students to enter state universities on this pathway. Thedifference in standards between universities in determining the admission ofSNMPTN applicants, causing the need to predict based on several relatedfactors. This research uses data mining techniques with Random forestalgorithm. From the results of research that has been done, it was found thatthe Random Forest algorithm can be used to predict students who are acceptedat SNMPTN based on report card grades, obtained from the results of theclassification process with the student report card report survey datasetreceived by SNMPTN, This is indicated by the accuracy, precision, and recallvalues of 93%. Optimization of the random forest algorithm using theoversampling technique with the SMOTE method can improve the classifier'sperformance due to the imbalanced class problem.
Rule based model for pneumonia (COVID-19) nursing care S Hendra; H R Ngemba; N W Sridani; G B R Lintin; K Tantakarnapa; R Nur; M A Indrajaya
Jurnal Informatika Vol 15, No 3 (2021): September 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i3.a25411

Abstract

Abstract. This research was conducted during the COVID-19 pandemic, when during the pandemic, many patients died. The mortality rate was caused by complications in the form of pneumonia in patients with deteriorating health conditions. This study aims to develop an inference model to become a decision support system in the enforcement of the clinical pathway of pneumonia COVID-19 nursing care. This research model is based on the application of NANDA International nursing diagnoses to determine the objectives of the Nursing Outcome Classification (NOC) and the interventions that must be carried out by the Nursing Intervention Classification (NIC). The data in this study were obtained from the results of expert interviews regarding the handling of pneumonia and medical literature on handling COVID-19 cases. The results of this study can guide the diagnosis and treatment of pneumonia caused by the COVID-19 virus, as well as a similar process that occurs with acute respiratory distress syndrome.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2021 2021


Reset
Filter By Issues
All Issue Vol. 19 No. 1 (2025): January 2025 Vol. 18 No. 3 (2024): September 2024 Vol 17, No 1 (2023): January 2023 Vol. 17 No. 1 (2023): January 2023 Vol 16, No 3 (2022): September 2022 Vol 16, No 2 (2022): May 2022 Vol 16, No 1 (2022): January 2022 Vol 15, No 3 (2021): September 2021 Vol 15, No 2 (2021): May 2021 Vol 15, No 1 (2021): January 2021 Vol 14, No 3 (2020): September 2020 Vol 14, No 2 (2020): May 2020 Vol 14, No 1 (2020): January 2020 Vol. 13 No. 2 (2019): July 2019 Vol 13, No 2 (2019): July 2019 Vol 13, No 1 (2019): January 2019 Vol 12, No 2: July 2018 Vol 12, No 1: January 2018 Vol 11, No 2 (2017): Juli Vol 11, No 2 (2017): Juli Vol 11, No 1 (2017): Januari Vol 11, No 1 (2017): Januari Vol 10, No 2 (2016): Juli Vol 10, No 2 (2016): Juli Vol 10, No 1 (2016): Januari Vol 10, No 1 (2016): Januari Vol 9, No 2 (2015): Juli Vol 9, No 2 (2015): Juli Vol 9, No 1 (2015): Januari Vol 9, No 1 (2015): Januari Vol 8, No 2 (2014): Juli Vol 8, No 2 (2014): Juli Vol 8, No 1 (2014): Januari Vol 8, No 1 (2014): Januari Vol 7, No 2: Juli 2013 Vol 7, No 2: Juli 2013 Vol 7, No 1: Januari 2013 Vol 7, No 1: Januari 2013 Vol 6, No 2: Juli 2012 Vol 6, No 2: Juli 2012 Vol 6, No 1: Januari 2012 Vol 6, No 1: Januari 2012 Vol 5, No 2: Juli 2011 Vol 5, No 2: Juli 2011 Vol 5, No 1: January 2011 Vol 5, No 1: January 2011 Vol 4, No 2: July 2010 Vol 4, No 2: July 2010 Vol 4, No 1: January 2010 Vol 4, No 1: January 2010 Vol 3, No 2: July 2009 Vol 3, No 2: July 2009 Vol 3, No 1: January 2009 Vol 3, No 1: January 2009 Vol 2, No 2: July 2008 Vol 2, No 2: July 2008 Vol 2, No 1: January 2008 Vol 2, No 1: January 2008 More Issue