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
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Telematika : Jurnal Informatika dan Teknologi Informasi
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 1829667X     EISSN : 24609021     DOI : 10.31315
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 361 Documents
 24   25   26   27   28 
Analysis Of Factors Affecting Interest Kai Access Application Users Using Models Unified Theory Of Acceptance And Use Of Technology 2 (UTAUT 2) Rifki Firmansyah; Yuli Fauziah; Rifki Indra Perwira
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8482

Abstract

Purpose: This study aims to analyze the factors that influence user interest in the KAI Access application using the Unified Theory Of Acceptance And Use Of Technology 2 (UTAUT 2) model.Methodology: This study used the Structural Equation Modeling (SEM) method with two tests, namely the outer model and the inner model with the help of the SmartPLS Version 3 software. A total of 406 respondent data were used from the Special Region of Yogyakarta and also users of the KAI Access application.Results:  The results of the study show that of the fourteen hypotheses proposed in the study, only seven were accepted, namely social influence, facilitating conditions, hedonic motivation, price value, and habit. The strongest factors that have a significant effect are hedonic motivation and habit.State of the art: based on previous research, this study has quite similar characteristics but different cases, variables, and research samples.
The Implementation of Color Feature Extraction and Gray Level Co-occurrence Matrix Combination in K-Nearest Neighbor Classification Method for Tomato Leaf Disease Identification Sandy Wahyu Agusta; Wilis Kaswidjanti
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.10009

Abstract

Purpose: Tomato plants are quite important commodities in Indonesia. With a complete and good content of substances, tomatoes become a product that is widely consumed by the public. However, much of the decline in crop production is caused by plant disruptive organisms such as viruses and bacteria. Early identification of plant diseases is expected to prevent the spread of diseases caused by these organisms.Design/methodology/approach: In this study the data used in machine training are data from kaggle sites. This study uses the K-Nearest Neighbor classification method with a combination method of extracting feature on RGB, HSV and GLCM images to obtain the best accuracy value.Findings/Results: Based on the test results among the combination methods of feature extraction in the process of identifying tomato leaf diseases which are classified into 7, namely testing units of RGB, HSV, GLCM followed by a combination of RGB HSV, RGB GLCM, HSV GLCM, and RGB HSV GLCM methods obtained a comparison value of 71.5%, 72.9%, 79%, 82.5%, 90.6%, 87.4% and 87.7%. Based on these data, it was concluded that with the combination of the RGB GLCM method obtained the best accuracy value in the identification of tomato leaf disease with an accuracy rate of 90.6%.Originality/value/state of the art: The use of the K-Nearest Neighbor classification method in this study combines the collection of selected characteristics so as to get a comparison of 7 combination groups between RGB, HSV, and GLCM.
Sentiment Analysis Of Student Opinion Related To Online Learning Using Naïve Bayes Classifier Algorithm And SVM With Adaboost On Twitter Social Media Mohammad Rizal Ramli; Heni Sulastri; Rianto Rianto
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8827

Abstract

Twitter is one of the social media that functions to express opinions on issues or problems that are currently happening, such as problems in the social, economic, educational and other fields. One of the issues being discussed so far is online learning. The government has issued a policy, one of which is for all students to study at home online by using a network to be able to interact with each other like in the classroom. The government's reason for issuing this policy is to break the chain of the spread of the Covid-19 virus, which until now has not subsided. Regarding this online learning policy, there are pros and cons. This opinion is widely expressed on social media, one of which is Twitter. Sentiment analysis is a method for analyzing an opinion which aims to classify texts. The Naïve Bayes Classifier and Support Vector Machine methods are methods machine learning that can be used for sentiment analysis. The problem in classifying text is that the resulting accuracy is less than optimal, so feature selection or boosting is needed to improve its accuracy. In this study, optimization of boosting was carried out using Adaboost. The purpose of this study is to compare the performance of the algorithm before and after using Adaboost. The results of the sentiment analysis on online learning obtained the highest accuracy results by the Naïve Bayes Classifier algorithm coupled with Adaboost of 99.26%, with a precision of 99.39% and recall of 99.20%.
Quality Analysis of the Ahmad Dahlan University Digital Library Using the WebQual 4.0 and Importance Analysis Performance (IPA) Method. Ali Tarmuji; K Moch Reza Dwi Akbardillah
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.8846

Abstract

Purpose: This paper is the result of research which aims to obtain results of measuring the quality of web services from the Library Unit at Ahmad Dahlan University, especially from the perceptions of student users in order to prepare recommendations for improving services. This paper is the result of research which aims to obtain results of measuring the quality of web services, especially from perceptions student users in order to prepare recommendations for improving service mediaDesign/methodology/approach: Based on sampling data collected using a questionnaire and calculated using statistics. The next step is to measure the WebQuel 4.0 method, the results of which are combined with the Importance Performance Analysis (IPA) method to determine recommendations.Findings/result: The research results show that each independent variable, namely the usability variable and the information quality variable, partially has a relationship or is correlated with the dependent variable, namely user satisfaction, while the interaction service quality variable partially has no relationship or is uncorrelated with the dependent variable. The results of simultaneous hypothesis testing show that the independent variable has an effect on the dependent variable so that the hypothesis can be simultaneously accepted. Based on the analysis using the IPA method, there are three things in Quadrant 1 (Top Priority) which are not in accordance with user expectations and need to be improved, namely "the DIGILIB UAD web is easy to learn", "the DIGILIB UAD web has an attractive appearance", "the DIGILIB UAD web has the function of library web type”.Originality/value/state of the art: Based on previous research and the results of previous digilib web development, the research produced a new assessment of the quality measures of web services at UPT Libraries, and made it the main alternative for developing service media in a better direction.
Evaluation of IT Risk Management in DISKOMINFO of Magelang Regency using COBIT Framework 2019 Objectve EDM03 & APO12 Resti Ayunda Sari; Juwairiah Juwairiah
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.11867

Abstract

Purpose: This research aims to measure the current condition level (capability level) of DISKOMINFO and then conduct a Gap analysis so that it can provide recommendations for improving IT governance related to IT risk management.Design/methodology/approach: The framework used is COBIT 2019, which will focus on 2 objectives: EDM03 (Evaluate, Direct, and Monitor)  & APO12 (Align, Plan, and Organize). The data used in this study were obtained through interviews, observation, and distribution of questionnaires which had been mapped using the RACI Chart.Findings/result:  The results of the assessment show that the capability level/capability level according to DISKOMINFO is level 2 for each objective. Recommendations focus on making documentation of risk management activities in the form of risk guidelines, risk acceptance, activities for risk management methods, as well as the application of risk management evaluation of IT which is used by DISKOMINFO on a regular basis.Originality/value/state of the art:From various types of risk management research with different frameworks, this research will use the COBIT 2019 performance standards to carry out information technology risk management. Where COBIT 2019 is the latest version of COBIT which was prepared to help companies manage and manage resources to achieve existing goals. COBIT 2019 has a broader scope than ISO SO/IEC 17799:2005 which includes a combination of principles that have been embedded and known as reference models (such as COSO), and are aligned with IT standard infrastructure.
Quality Of Service (QoS) Analysis to Calculate Internet Network Performance Level DISKOMINFOTIK and OPD P3AP2KB Office Riau Province Mahdhan Ragil S; Iwan Iskandar; Reski Mai Candra
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10716

Abstract

Purpose: Among government agencies, the Office of Communication, Information and Statistics (DISKOMINFOTIK) which functions as an internet Support for Regional Apparatus Organizations (OPD), one of which is the OPD Office for Women's Empowerment, Child Protection, Population Control and Family Planning (P3AP2KB), this study was to determine the quality of the DISKOMINFOTIK network for OPD. P3AP2KB Office.Design/methodology/approach: Analyzing the quality of the internet network at DISKOMINFOTIK and in the P3AP2KB Office OPD room which has a lot of clients during busy hours, free hours, and quiet hours using the Quality Of Service (QoS) method with Throughput, packet loss, delay, TIPHON standard jitter and quantitative methodology.Findings/result: The results of the research prove that the parameter values of Throughput, packet loss, delay are at index 4 with the TIPHON measurement standard in the "Very Good" category, and jitter is index 3 with the TIPHON measurement standard in the "Good" QoS category.Originality/value/state of the art: There has been no previous research to calculate the quality of the internet network provided by DISKOMINFOTIK to Regional Apparatus Organizations (OPD), one of which is the OPD Dinas P3AP2KB, therefore this research was conducted. 
Design of a Generative AI Image Similarity Test Application and Handmade Images Using Deep Learning Methods Rifqi Alfaesta Prawiratama
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10096

Abstract

Purpose: The aim of this research is to develop a classification model using the Transformer approach, specifically the BEiT architecture, to differentiate between handmade images and AI Generative Art. The objective is to ensure the authenticity of art and address ethical and legal concerns related to AI Generative Art.Design/methodology/approach: The study utilizes the BEiT architecture within the Transformer approach to create a classification model. The training process uses Bidirectional Encoder representation from Image Transformers (BEiT) to improve image classification. The primary datasets are collected through a Python image scraper program. The BEiT workflow includes Pre-training, Masking, Inpainting, and Interface Design with Gradio.Findings/result: The Transformer model, using the BEiT architecture, achieves 96.34% accuracy and 0.0921 loss in differentiating handmade images and AI Generative Art. The model demonstrates a balanced precision and recall in each category, outperforming previous methods such as Convolutional Neural Network (CNN) and VGG16. The language used is clear, objective, and value-neutral, with a formal register and precise word choice. No changes in content were made. The Gradio interface was used to successfully test the model.Originality/value/state of the art: The research presents a state-of-the-art classification model that uses the Transformer approach, specifically the BEiT architecture, to differentiate between handmade and AI Generative Art images. The research presents a state-of-the-art classification model that uses the Transformer approach, specifically the BEiT architecture, to differentiate between handmade and AI Generative Art images. The text adheres to conventional structure and formatting features, including consistent citation and footnote style. The sentences and paragraphs create a logical flow of information with causal connections between statements. The text is free from grammatical errors, spelling mistakes, and punctuation errors. Additionally, the research is enhanced by the innovative approach to data collection using a Python image scraper program.
Social Media Analysis and Topic Modeling: Case Study of Stunting in Indonesia Muhaimin, Amri; Fahrudin, Tresna Maulana; Alamiyah, Syifa Syarifah; Arviani, Heidy; Kusuma, Ade; Sari, Allan Ruhui Fatmah; Lisanthoni, Angela
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10797

Abstract

Purpose: Stunting is a problem that currently requires special attention in Indonesia. The stunting rate in 2022 will drop to 21.6%, and for the future, the government has set a target of up to 14% in 2024. Rapid technological developments and freedom of expression on the internet produce review text data that can be analyzed for evaluation. This study analyzes the text data of Twitter users' reviews on stunting. The method used is a text-mining approach and topic modeling based on Latent Dirichlet Allocation.Design/methodology/approach: The methodology used in this study is Latent Dirichlet Allocation. The data was collected from twitter with the keyword 'stunting'. After, the data was cleaned and then modeled using the Latent Dirichlet Allocation.Findings/results: The results show that negative sentiment dominates by 60.6%, positive sentiment by 31.5%, and neutral by 7.9%. In addition, this research shows that 'children', 'decrease', 'number', 'prevention', and 'nutrition' are among the words that often appear on stunting.Originality/value/state of the art: This study uses the keyword stunting and analyzes it. Social media analytics show that the people of Indonesia are primarily aware of stunting. Also, the Latent Dirichlet Analysis can be used to create the model.
Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine Kartikadyota Kusumaningtyas; Muhammad Habibi; Irmma Dwijayanti; Retno Sumiyarini
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.9820

Abstract

Purpose: Social media, particularly Twitter, provides a venue for individuals to share their thoughts. The public's perception of mental illnesses is often debated on Twitter. So yet, no evaluation of community tweets connected to data on mental health conditions has been performed. The purpose of this study is to examine tweets linked to mental illnesses in Indonesia in order to identify the themes of conversation and the polarity trends of these tweets.Design/methodology/approach: To address this issue, the K-Means Clustering algorithm is utilized to aggregate tweet data that is used to find themes of conversation. The emotion polarity value of each cluster result was then determined using the Support Vector Machine (SVM) approach.Findings/results: This study generated five topic clusters based on tweets about mental illness. While sentiment analysis revealed that all clusters had more negative sentiment classes than positive. Cluster 4 and Cluster 5 had the highest number of negative sentiment values. These clusters emphasize the necessity of consulting with psychiatrists and psychologists if people have mental health disorders, as well as financing for mental health disorder treatment through BPJS Kesehatan services.Originality/value/state of the art: The analysis was done in two stages: data grouping to find themes of conversation using K-Means clustering and SVM to look for positive and negative polarity values associated to twitter data about mental illness.
Klasifikasi Penyakit Gangguan Jiwa menggunakan Metode Logika Fuzzy Mutmainnah Putri Kayla; Rizal Adi saputra
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.11789

Abstract

Purpose: This research aims to facilitate psychologists in handling individuals with mental disorders by categorizing them based on their symptoms and conditions using fuzzy logic, which mimics the functioning of the human brain.Design/methodology/approach: The categorization is performed by applying Mamdani fuzzy logic, designed in consultation with psychology experts. Ten initial symptoms each have parameters (Mild, Moderate, and Severe) as input variables, and the output variable involves mental health disorders such as Schizophrenia, Bipolar disorder, Eating disorders, and Anxiety. The fuzzy process employs the Mamdani method with IF-THEN rules and AND operators. The implementation of Mamdani fuzzy logic achieves adequate accuracy in classifying individuals with mental disorders, providing a strong foundation for a more targeted psychological approach. In the context of accuracy, fuzzification analysis for each health disorder can offer further insights.Findings/result: Results of the study for Schizophrenia, for instance, show a fuzzy diagram membership of approximately 0.4, indicating a potentially high level of thought impairment and interpersonal skills. Weighting for low, medium, and high is then assessed to categorize patients. A similar process is undertaken for Bipolar disorder, with special attention to the middle value and the strong relationship between two input values. Regarding mental illness, membership analysis indicates an increasing level of membership corresponding to condition groups, suggesting compatibility with existing rules.Originality/value/state of the art: These findings reinforce the Mamdani fuzzy logic implementation as a reliable approach in classifying individuals with mental disorders, with the potential to enhance psychological diagnosis and interventions more effectively

Page 26 of 37 | Total Record : 361

 24   25   26   27   28 

Filter by Year

2010 2025


Reset
Filter By Issues
All Issue Vol 22 No 3 (2025): Edisi Oktober 2025 Vol 22 No 2 (2025): Edisi Juni 2025 Vol 22 No 1 (2025): Edisi Februari 2025 Vol 21 No 3 (2024): Edisi Oktober 2024 Vol 21 No 2 (2024): Edisi Juni 2024 Vol 21 No 1 (2024): Edisi Pertama 2024 Vol 21, No 1 (2024): Edisi Februari 2024 Vol 20, No 3 (2023): Edisi Oktober 2023 Vol 20 No 3 (2023): Edisi Oktober 2023 Vol 20 No 2 (2023): Edisi Juni 2023 Vol 20, No 2 (2023): Edisi Juni 2023 Vol 20, No 1 (2023): Edisi Februari 2023 Vol 20 No 1 (2023): Edisi Februari 2023 Vol 19, No 3 (2022): Edisi Oktober 2022 Vol 19, No 2 (2022): Edisi Juni 2022 Vol 19, No 1 (2022): Edisi Februari 2022 Vol 18, No 3 (2021): Edisi Oktober 2021 Vol 18, No 2 (2021): Edisi Juni 2021 Vol 18, No 1 (2021): Edisi Februari 2021 Vol 17, No 2 (2020): Edisi Oktober 2020 Vol 17, No 1 (2020): Edisi April 2020 Vol 16, No 2 (2019): Edisi Oktober 2019 Vol 16, No 1 (2019): Edisi April 2019 Vol 15, No 2 (2018): Edisi Oktober 2018 Vol 15, No 1 (2018): Edisi April 2018 Vol 14, No 2 (2017): Edisi Oktober 2017 Vol 14, No 1 (2017): Edisi April 2017 Vol 13, No 2 (2016): Edisi Juli 2016 Vol 13, No 1 (2016): Edisi Januari 2016 Vol 12, No 2 (2015): Edisi Juli 2015 Vol 12, No 1 (2015): Edisi Januari 2015 Vol 11, No 1 (2014): Edisi Juli 2014 Vol 10, No 2 (2014): Edisi Januari 2014 Vol 10, No 1 (2013): Juli 2013 Vol 9, No 2 (2013): Edisi Januari 2013 Vol 9, No 1 (2012): Edisi Juli 2012 Vol 8, No 2 (2012): Edisi Januari 2012 Vol 8, No 1 (2011): Edisi Juli 2011 Vol 7, No 2 (2011): Edisi Januari 2011 Vol 7, No 1 (2010): Edisi Juli 2010 Vol 6, No 2 (2010): Edisi Januari 2010 More Issue