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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
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Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset Akbar, Ahmad Taufiq; Husaini, Rochmat; Prapcoyo, Hari
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.v20i2.9676

Abstract

Purpose: Our study aims to combine pre-processing methods to develop a training data model from the Indian diabetic Pima dataset so that it can improve the performance of machine learning in recognizing diabetesDesign/methodology/approach: This research was started through several stages such as collecting the Pima indian diabetes dataset, pre-processing including k-means clustering, oversampling using SMOTE, then undersampling the dataset whose cluster is a minority in each class. Furthermore, the dataset is classified using machine learning namely logistic regression through 10 cross validationFindings/result: The results of this classification performance show that the accuracy reaches 99.5% and is higher than the method in previous studies.Originality/value/state of the art:The method in this study uses SMOTE to handle data imbalances and k-means clustering to remove outliers by removing labels that do not match the majority cluster in each class so that clean data is produced and validation using logistic regression is more accurate than previous studies.Tujuan: Penelitian ini bertujuan untuk menerapkan metode pre-processing untuk membentuk model data latih dari dataset Pima Indian diabetes sehingga dapat meningkatkan performa mesin pembelajaran dalam mengenali diabetes.Perancangan/metode/pendekatan: Riset ini dimulai melalui beberapa tahap yakni pengumpulan dataset Pima Indian diabetes, pre-processing meliputi clustering, oversampling menggunakan SMOTE, kemudian undersampling pada dataset pada klaster  minoritas pada setiap kelas. Selanjutnya dataset diklasifikasikan menggunakan machine learning yakni metode regresi logistik melalui 10 cross validationHasil: Hasil dari performa klasifikasi ini menunjukkan akurasi mencapai 99,5% dan lebih tinggi daripada metode pada penelitian sebelumnya.Keaslian/ state of the art: Metode dalam penelitian ini menggunakan SMOTE untuk menangani ketidakseimbangan data dan k-means klastering untuk membuang outlier dengan cara menghapus label yang tidak sesuai dengan klaster mayoritas pada setiap kelas sehingga dihasilkan data yang bersih dan pada validasi menggunakan logistic regression lebih akurat daripada penelitian sebelumnya.
Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine Kusumaningtyas, Kartikadyota; Habibi, Muhammad; Dwijayanti, Irmma; Sumiyarini, Retno
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.
Performance Evaluation of Online Smart Parking System in Jakarta Suhada, Suhada; Hendriana, Yana
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.v20i3.9830

Abstract

Purpose: This research aims to evaluate the performance of the Online Smart Parking System application for 7 consecutive days from 06.00 to 18.00 to evaluate the features in the application, evaluate the level of compliance of the clerks with the use of the application and the level of constraints achievement or fulfillment of each of the rights and obligations of the parties, and identification of the level of achievement of revenue targets.Design/methodology/approach: This research was carried out through several stages which include Product Quality Evaluation, Usage Quality Evaluation, Collaboration Evaluation and Financial Evaluation or financial aspects.Findings/result: Design of Logical Framework Application System Online Smart Parking System.Originality/value/state of the art: This research focuses on evaluating the design results of the Online Smart Parking System application which is managed by UP Parking Department of Transportation DKI Jakarta with 3 partner applicators.
The Implementation of Color Feature Extraction and Gray Level Co-occurrence Matrix Combination in K-Nearest Neighbor Classification Method for Tomato Leaf Disease Identification Agusta, Sandy Wahyu; Kaswidjanti, Wilis
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.
Design of a Generative AI Image Similarity Test Application and Handmade Images Using Deep Learning Methods Prawiratama, Rifqi Alfaesta
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.
User Experience Analysis on Student Services Website using User Experience Questionnaire (UEQ) KPI and Importance Performance Analysis (IPA) (Case Study: UPN "Veteran" Yogyakarta) Wenerda, Vivo Putri; Fauziah, Yuli
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.v20i2.10216

Abstract

Purpose: This study aims to obtain an end-user assessment of User Experience on the Student Services website so that it can be used as a priority material for improvement for the Bureau of Academic, Student Affairs, Planning, and Cooperation (AKPK) of the National Development University (UPN) "Veteran" Yogyakarta, when developing a website further.Design/methodology/approach: The User Experience Assessment on the Student Services website refers to 6 aspects of the assessment of the User Experience Questionnaire (UEQ) KPI method. The existing results will be mapped into an IPA (Importance Performance Analysis) diagram.Findings/result: The results of user experience testing on the Student Services website using the UEQ method, get the Good category for the Efficiency (1.56) and Dependability (1.57) aspects, the Above average category for the Attractiveness aspect (1.28), Perspicuity (1.57), and Stimulation (1.15) and the Bad category on Novelty (-0.27). Mapping the results of the UEQ KPI to the IPA quadrant, getting the results of the Attractiveness, Perspicuity, Efficiency, and Dependability aspects are in Quadrant 1, the Stimulation aspect is in Quadrant 2, the Novelty aspect is in Quadrant 3, and no aspect is in Quadrant 4. Based on the results of the study, it can be concluded that the user experience value of the end user is good. Recommendations for improvement priorities for the Student Services website can further prioritize Novelty aspels that are in Quadrant 3 and in Bad condition.Originality/value/state of the art: The focus of this research is the same as previous research, namely analyzing User Experience with reference to the assessment aspects of the KPI User Experience Questionnaire (UEQ) and IPA (Importance Performance Analysis) methods. The difference that can be seen in this study is from the implementation of the method into different case studies with the objectives and urgency and problems described in accordance with the existing research object.
Analysis of Factors Affecting Intention to Use and User Satisfaction of Paylater Using DeLone & McLean Adoption Model Utari, Ulil Azmi; Fauziah, Yuli
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.v20i3.10643

Abstract

Purpose: This study aims to determine the factors that affect the intention to use and satisfaction of GoPayLater users in Yogyakarta, by assessing the relationship between variables so that recommendation for improvent can be given.Design/methodology/approach: This study uses the DeLone & McLean adoption model by Seddon which includes 5 constructs namely system quality, information quality, perceived usefulness, intention to use and user satisfaction. Primary data collection was conducted by distributing questionnaires using likert scale measurement to 128 GoPayLater users. The data analysis technique used is SEM-PLS to test the measurement model, structural model and test the hypothesis via SmartPLS software.Findings/results:Based on the results of hypothesis testing in this study, two hypotheses were rejected from eight hypothesises. These findings indicate that perceived usefulness has a positive and significant effect on intention to use, while the variables of system quality and information quality do not have a significant effect directly on intention to use GoPayLater. The R-Square test results show that system quality, information quality and perceived usefulness simultaneously have an effect of 34,4% on intention to use GoPayLater. This study also proves that variables of system quality, information quality and perceived usefulness have a positive and significant effect on GoPayLater user satisfacion, with the level of influence given simultaneously is 51,7% .Originality/value/state of the art: Several previous studies have tested GoPayLater from various aspects, but no research has been found that assesses the relationship and effect of system quality, information quality and perceived usefulness on intention to use and user satisfaction using the DeLone & McLean adoption model by Seddon. 
Quality Of Service (QoS) Analysis to Calculate Internet Network Performance Level DISKOMINFOTIK and OPD P3AP2KB Office Riau Province Ragil S, Mahdhan; Iskandar, Iwan; Candra, Reski Mai
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. 
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.
Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
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.v20i3.10804

Abstract

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year. 

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