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 419 Documents
 37   38   39   40   41 
Association pattern of students thesis examination using fp-growth algorithms Ika Arfiani; Herman Yuliansyah; Tia Purwantias
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a17691

Abstract

The thesis examination is the final project for students to graduate from their majors. This thesis researches scientific work between a student and a supervisor in finding solutions to a problem. In the thesis examination, students must present their research results to be criticized by the examiner. This article aims to analyze the association pattern of student thesis examinations at a private university. Although the thesis's implementation has been carried out following procedures, to determine the composition of the board of examiners needs to be analyzed by examining the pattern of relationships between research topics, supervisors, and examiners. This study uses 448 data and uses FP-Growth Algorithms to find the rules. The research methodology starts from preparing the Dataset, cleansing data, selecting data, loading data into applications, transforming data, itemset frequencies, forming patterns, and analyzing rules. This study found 145 patterns of association rules with a minimum support value = 4 and a minimum trust value = 50%. The association rule pattern of 77.78% is under scientific group data. The benefits of the association pattern produced in this study can determine the composition of the examiners on the student thesis examination according to the research topic and scientific field of the examiners.
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.
Reality of the internet and social media addiction in Indonesian students Nasy`an Taufiq Al Ghifari; Akhmadi Surawijaya; Fitra Arifiansyah; Agus Komarudin; Denny Hidayat Tri Nugroho; Dimitri Mahayana
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a18951

Abstract

The use of the Internet and social media today is inseparable from the life of modern society. This can lead to an addiction to the Internet and social media. This research aims to answer whether the phenomenon of Internet and social media addiction is a scientific reality or not in Indonesia, especially in Indonesian Students who are undergoing adaptation of the learning process from offline to online due to the Covid-19 pandemic situation. Data collection was conducted with a survey of 2002 respondents. Before the questionnaire was distributed, a validity test and reliability test with Alpha Cronbach's were conducted, and the results showed that all questions on the questionnaire were valid and reliable. Based on the survey results, 20.18% of respondents experienced mild addiction, 4.85% of respondents experienced moderate addiction, and 0.45% of respondents experienced severe addiction to Internets. While the survey results for social media addiction were 14.99% of respondents experienced mild addiction, 4.7% of respondents experienced moderate addiction, and 0.45% of respondents experienced severe addiction. Judging by the philosophy of science, Internet and Social Media Addiction are said to be science and not pseudoscience because it has fulfilled the characteristics of science that is logical, empirical, and can be falsified. There needs to be special attention from the Indonesians about the addiction to the Internet and social media so that this addiction can be anticipated and the inflicted symptoms can be minimized.
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%.
Device-to-device (D2D) reliable transmission in the internet of things Tanweer Alam
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20275

Abstract

D2D stands for device-to-device communication, which is likely to perform a major impact in future mobile communications because it offers ultra-low latency for end user’s direct conversation. Throughout minimizing latency, increasing strength and improved transmission efficiency, and expanding telecommunication services, D2D services are seen as a successful innovation for emerging mobile communications. The D2D networking makes a unique contribution to the wireless world by simplifying data transfer among devices connected. D2D networking makes use of adjacent two nodes to maximize the use of existing infrastructure, low latency, boost throughput and expand service functionality. Within wireless networks, D2D communication is described as immediate interaction among two mobile devices without passing through the access point or network infrastructure. The fully integrated wireless communication would be built by integrating D2D and the Internet of Things. D2D enables the larger number of devices to be paired at a higher bandwidth frequency and with minimum latency. Building a new reliable framework for D2D communication of smart devices can be an important framework for improving the reliability of communication. Internet of Things is the process of communicating and sharing information between nearby devices. But there are many challenges to secure and reliable communication. Amongst the major concerns for wireless transmission has been identified as communication trust, and overcoming this issue could lead to sustained expansion in the usage and popularity of the Internet of Things. The proposed study develops a system for providing internet access to a network of smart devices connected to the internet of things. The significant contributions link the latest findings that incorporate the interaction framework's stability and provides secure internet networking for connected devices.
Mobile e-detection of Banyuwangi’s citrus fruit maturity using k-nearest neighbor Chairul Anam; Solehatin Solehatin
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a18183

Abstract

Banyuwangi is the largest oranges-producing city in East Java, and the orange produced is Siamese citrus fruit. Siamese is Banyuwangi local citrus fruit often found at the harvest time and has a sweet taste. To determine the citrus fruit level, people can detect it from the color and texture. In this modern era, people can use an application to determine the citrus fruits' maturity level. From the elements of color and texture, this research will add the citrus fruit's contours, namely the pore size of the citrus fruit and the distance between the curve of the tip of the orange. Taking pictures of citrus fruits will be following the application stages that will be used as the image of inputting the data. The detection is then conducted using the K-NN method based on several criteria based on the input image after the feature extraction process. The feature extraction stages are segmentation, normalization, thresholding, and thinning, which will be produced in several criteria: the maximum RGB value, the minimum RGB value, pore size, and the distance between the tip's curve of the orange. The research results that have been carried out are based on the research stages to get a similarity percentage following the inputted data. The E-Detection application can provide information to citrus farmers, especially beginner citrus farmers, to know the level of fruit maturity oranges to be harvested.
Hierarchical long short-term memory for action recognition based on 3D skeleton joints from Kinect sensor Nur Awal Hidayanto; Adhi Prahara; Riky Dwi Puriyanto
Jurnal Informatika Vol 15, No 1 (2021): January 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i1.a20106

Abstract

Action recognition has been used in a wide range of applications such as human-computer interaction, intelligent video surveillance systems, video summarization, and robotics. Recognizing action is important for intelligent agents to understand, learn and interact with the environment. The recent technology that allows the acquisition of RGB+D and 3D skeleton data and a deep learning model's development significantly increases the action recognition model's performance. In this research, hierarchical Long Sort-Term Memory is proposed to recognize action based on 3D skeleton joints from Kinect sensor. The model uses the 3D axis of skeleton joints and groups each joint in the axis into parts, namely, spine, left and right arm, left and right hand, and left and right leg. To fit the hierarchically structured layers of LSTM, the parts are concatenated into spine, arms, hands, and legs and then concatenated into the body. The model crosses the body in each axis into a single final body and fed to the final layer to classify the action. The performance is measured using cross-view and cross-subject evaluation and achieves accuracy 0.854 and 0.837, respectively, from the 10 action classes of the NTU RGB+D dataset.
Proposal of Image generation model using cGANs for sketching faces Nguyen Phat Huu; Nguyet Giap Thi
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20576

Abstract

The transition from sketches to realistic images of human faces has an important application in criminal investigation science to find criminals as depicted by witnesses. However, due to the difference between the sketch image and the real face image in terms of image detail and color, it is challenging and takes time to transform from hand-drawn sketches to actual faces. To solve this problem, we propose an image generation model using the conditional generative adversarial network with autoencoder (cGANs-AE) model to generate synthetic samples for variable length and multi-feature sequence datasets. The goal of the model is to learn how to encode a dataset that reduces its vector size. Using a vector with reducing the dimension, the autoencoder will have to recreate the image similar to the original image. The autoencoder aims to produce output as input and focus only on the essential features. Raw sketches over the cGANS create realistic images that quickly and easily make the sketch images raw images. The results show that the model achieves high accuracy of up to 75%, and PSNR is 25.5 dB that is potentially applicable for practice with only 606 face images. The performance of our proposed architecture is compared with other solutions, and the results show that our proposal obtains competitive performance in terms of output quality (25.5 dB) and efficiency (above 75%).
Well-Known brands recognition by automated classifiers using local and global features Hafsa Niaz; Usman Raza
Jurnal Informatika Vol 14, No 3 (2020): September 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i3.a18418

Abstract

From color and type to patterns and illustrations, brands sense to be recognizable and convey their values and personality. Here patterns and color are key elements, as they can play a vital role in brand recognition. The images used for brand classification were handpicked and collectively named as HKDataset. We have explored various feature extractors used for classification and used automated classifiers named Linear SVM to achieve higher accuracy while tuning the model parameters to achieve optimal performance. It has been observed that Support Vector Machines performs better when using GIST descriptors combined with Bag of SIFT features. We hope to apply deep learning and other sophisticated classifiers to much-expanded categories of brands in the future.
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.

Page 39 of 42 | Total Record : 419

 37   38   39   40   41 

Filter by Year

2008 2025


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