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 9 Documents
 1 
Search results for , issue "Vol 14, No 2 (2020): May 2020" : 9 Documents clear
Information security analysis on physical security in university x using maturity model Khairunnisak Nur Isnaini; Siti Alvi Solikhatin
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a14434

Abstract

The threat of physical security can be from human factors, natural disasters, and information technology itself. Therefore, to prevent threats, we need the right tools to control current activities, evaluate potential impacts, and make appropriate plans so that business processes at X University will not be affected. This research starts by analyzing the problems that arise, followed by collecting the data needed, discussing the results, and making conclusions and recommendations that can be given. The method uses quantitative descriptive research. The research instrument uses interviews and questionnaire techniques. COBIT 5 is used as a framework for measuring the performance that is being implemented and will be achieved. Maturity models are used to measure current and future activities. The goal to be achieved is that the organization can create a physical security environment by the CIA principle (confidentiality, integrity, & availability). Positioning results are at level 3, meaning that the process is currently running in two main standard operating procedures. However, this evaluation specifically on the DSS5.5.5 subdomain (Providing Service Support-Managing physical security for IT Assets) in COBIT 5, and the results are still below the level 3 standard (Established Process), at 2.9 points. So, the right suggestion is to keep activities safe, one of which is to improve facilities and infrastructure, one of which is the use of biometric control in data center management rooms or other rooms with limited access.
Information security analysis on physical security in university x using maturity model Isnaini, Khairunnisak Nur; Solikhatin, Siti Alvi
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a14434

Abstract

The threat of physical security can be from human factors, natural disasters, and information technology itself. Therefore, to prevent threats, we need the right tools to control current activities, evaluate potential impacts, and make appropriate plans so that business processes at X University will not be affected. This research starts by analyzing the problems that arise, followed by collecting the data needed, discussing the results, and making conclusions and recommendations that can be given. The method uses quantitative descriptive research. The research instrument uses interviews and questionnaire techniques. COBIT 5 is used as a framework for measuring the performance that is being implemented and will be achieved. Maturity models are used to measure current and future activities. The goal to be achieved is that the organization can create a physical security environment by the CIA principle (confidentiality, integrity, & availability). Positioning results are at level 3, meaning that the process is currently running in two main standard operating procedures. However, this evaluation specifically on the DSS5.5.5 subdomain (Providing Service Support-Managing physical security for IT Assets) in COBIT 5, and the results are still below the level 3 standard (Established Process), at 2.9 points. So, the right suggestion is to keep activities safe, one of which is to improve facilities and infrastructure, one of which is the use of biometric control in data center management rooms or other rooms with limited access.
The comparison of machine learning methods for the detection of breast cancer Derisma, Derisma; Silvana, Meza
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17077

Abstract

Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.
The comparison of machine learning methods for the detection of breast cancer Derisma, Derisma; Silvana, Meza
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17077

Abstract

Breast cancer is a type of cancer that mostly found in many women. In Indonesia, breast cancer ranks first in hospitalized patients at every hospital. This study aimed to conduct a computation-based diagnose of breast cancer disease that could produce the state of cancer of an individual based on the accuracy of algorithm. This study used python programming and public dataset i.e. MIAS dataset. This dataset has been proven and widely used for a modeling and application of breast cancer classification. Feature extraction used Gray Level Co-occurrence Matrix (GLCM). The machine learning methods that were applied in this study were Decision Tree, SVM, Random Forest, Multilayer Perceptron, KNN, Logistic Regression and Naïve Bayes. In this study, Kernel SVM’s algorithm was the best classification algorithm of breast cancer disease with 100% accuracy rate and Naïve Beyes was the lowest with 63% of accuracy rate.
Developing support vector regression model to forcast stock prices of mining companies in Indonesia Dhanukhresna Hangga Yudhawan; Tuti Purwaningsih
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17283

Abstract

The modern era as it is now the world of stock investment is in great demand by investors, both long-term and short-term stock investments. Stock investment provides many benefits for investors. To get large profits, investors need to do an analysis in stock investments to predict the price of the shares to be purchased. Very volatile stock price movements make it difficult for investors to predict stock prices. The main hope of investors is to benefit from each price that changes from time to time or can be referred to as time series data. Data mining is a process of extracting large information from a data by collecting, using data, historical patterns of data relationships, and relationships in large data sets. Support vector regression has advantages in making accurate stock price predictions and can overcome the problem of overfitting by itself. PTBA, and ITMG are the leading coal mining companies in Indonesia, so many people want to invest in the company. ADRO, PTBA, and ITMG stock price prediction analysis using support vector regression algorithm has good predictive accuracy values, including. PTBA stock price have an R-square value of 97.9% in the RBF kernel and linear with MAPE respectively of 2,465 and 2,480. And for ITMG stock price it has an R-square accuracy of 94.3% in the RBF kernel and linear with MAPE respectively 5.874 and 5.875. These results indicate that the SVR method is best used for forecasting stock prices.
Developing support vector regression model to forcast stock prices of mining companies in Indonesia Yudhawan, Dhanukhresna Hangga; Purwaningsih, Tuti
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17283

Abstract

The modern era as it is now the world of stock investment is in great demand by investors, both long-term and short-term stock investments. Stock investment provides many benefits for investors. To get large profits, investors need to do an analysis in stock investments to predict the price of the shares to be purchased. Very volatile stock price movements make it difficult for investors to predict stock prices. The main hope of investors is to benefit from each price that changes from time to time or can be referred to as time series data. Data mining is a process of extracting large information from a data by collecting, using data, historical patterns of data relationships, and relationships in large data sets. Support vector regression has advantages in making accurate stock price predictions and can overcome the problem of overfitting by itself. PTBA, and ITMG are the leading coal mining companies in Indonesia, so many people want to invest in the company. ADRO, PTBA, and ITMG stock price prediction analysis using support vector regression algorithm has good predictive accuracy values, including. PTBA stock price have an R-square value of 97.9% in the RBF kernel and linear with MAPE respectively of 2,465 and 2,480. And for ITMG stock price it has an R-square accuracy of 94.3% in the RBF kernel and linear with MAPE respectively 5.874 and 5.875. These results indicate that the SVR method is best used for forecasting stock prices.
State of the art document clustering algorithms based on semantic similarity Karwan Jacksi; Niyaz Salih
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17513

Abstract

The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures.
Clustering based feature selection using Partitioning Around Medoids (PAM) Dewi Pramudi Ismi; Murinto Murinto
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17620

Abstract

High-dimensional data contains a large number of features. With many features, high dimensional data requires immense computational resources, including space and time. Several studies indicate that not all features of high dimensional data are relevant to classification result. Dimensionality reduction is inevitable and is required due to classifier performance improvement. Several dimensionality reduction techniques were carried out, including feature selection techniques and feature extraction techniques. Sequential forward feature selection and backward feature selection are feature selection using the greedy approach. The heuristics approach is also applied in feature selection, using the Genetic Algorithm, PSO, and Forest Optimization Algorithm. PCA is the most well-known feature extraction method. Besides, other methods such as multidimensional scaling and linear discriminant analysis. In this work, a different approach is applied to perform feature selection. Cluster analysis based feature selection using Partitioning Around Medoids (PAM) clustering is carried out. Our experiment results showed that classification accuracy gained when using feature vectors' medoids to represent the original dataset is high, above 80%.
Speech classification using combination virtual center of gravity and k-means clustering based on audio feature extraction Diah Kumalasari; Arief Bramanto Wicaksono Putra; Achmad Fanany Onnilita Gaffar
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17390

Abstract

Voice recognition can be done in a variety of ways. Sound patterns can be recognized by performing sound feature extraction. The trainer sound data is built from the best sound data selection using a correlation coefficient based on the level of similarity between sound data for optimal sound features. Extraction of voting features on this research using the Virtual Center of Gravity method. This method calculates the distance between the sound data against the center point of gravity with visualizations in the 3-dimensional form of white, black, and grey pattern spaces. The preprocessing process generates a complex number of data consisting of real numbers and imaginary numbers. The number will be calculated the distance to the Virtual Center of Gravity's pattern space using Euclidean Distance. The sound feature testing is done using K-Means Clustering by means of a speech classification data based sound. The results showed an accuracy of 92.5%.

Page 1 of 1 | Total Record : 9

 1 

Filter by Year

2020 2020


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