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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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Classification of Indonesian Tale Categories using Support Vector Machine and FastText Feature Extraction Irmanda, Helena Nurramdhani; Astriratma, Ria; Zaidiah, Ati; Hadi, Muhammad Rahman; Putra, Nayandra Agastia
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.10867

Abstract

The purpose of this work is to develop a model to classify the various kinds of Indonesian folktales and to assess how well the support vector machine (SVM) approach and fastText feature extraction perform. The first phase of the study process is the gathering of data, namely the fairy tale dataset that has been annotated with categorizations for each genre of fairy tale. Following the collection of data, the pre-processing step is conducted. The purpose of the pre-processing step is to prepare the data for further processing in the subsequent stage. Following the completion of the preprocessing step, the training data and testing data are segregated. The subsequent step involves doing feature extraction using fastText. Moreover, the classification process is conducted using the Support Vector Machine (SVM) approach in order to get the ultimate outcome of the modeling process. The last phase involves assessing the performance of the constructed model. The categorization model for Indonesian fairy tales has a commendable accuracy rate of 85%, indicating its effectiveness. The aforementioned findings are substantiated by an accuracy metric of 85%, a recall metric of 85%, and an F1-score of 86%, indicating favorable outcomes.Previous researchs have not conducted any studies on the categorization of types of Indonesian fairy tales.
Visualization of Islamic Boarding School Location at Yogyakarta with Web-Based Geodesain Alfiani, Oktavia Dewi; Wahyuningrum, Dwi; Saifullah, Shoffan; Haekal, Haekal
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.v20i3.10885

Abstract

Purpose: This research produces a webGIS design that presents the geospatial location of buildings in the Krapyak Yogyakarta boarding school area to facilitate users outside the area when heading to the location of the boarding school whose buildings are scattered.Design/methodology/approach: By combining aerial photos from UAV mapping with Open Street Map. The combined results of both maps are presented in a webGIS built from HTML, CSS and OpenLayers scripting.Findings/result: Building a webGIS to present information on the location of Krapyak Islamic boarding schools that has been equipped with corrected coordinates and routes from the iconic city of Yogyakarta so that immigrants from outside the area can easily understand the use of the webgis. Originality/value/state of the art: From previous research, webGIS development only uses maps presented through openstreetmap where if users use existing online navigation applications have different coordinate system references (Soraya R, 2018). So by equalizing the map reference by combining the results of UAV mapping and correcting the shape of the building presented on openstreetmap, the spatial information from the webgis will have a position accuracy that is more in line with the truth.
Klasifikasi Penyakit Gangguan Jiwa menggunakan Metode Logika Fuzzy Kayla, Mutmainnah Putri; saputra, Rizal Adi
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
Evaluation of IT Risk Management in DISKOMINFO of Magelang Regency using COBIT Framework 2019 Objectve EDM03 & APO12 Sari, Resti Ayunda; 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.
RECOGNITION OF HIRAGANA JAPANESE HANDWRITING CHARACTERS USING SUPPORT VECTOR MACHINE AND SCALE INVARIANT FEATURE TRANSFORM Chintia Wardhani, Putu Raditha; Florestiyanto, Mangaras Yanu
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.12042

Abstract

The abundance of characters in Japanese Hiragana, the similarity in character shapes, and the lack of familiarity among the public with Hiragana in daily life make it difficult to learn. People tend to be more accustomed to romanized writing (alphabet) than specific characters, leading to difficulties in understanding Hiragana with its various sizes and shapes. This research aims to develop an effective and systematic Japanese Hiragana handwritten recognition system using Support Vector Machine (SVM) and Scale Invariant Feature Transform (SIFT) methods. The research methodology includes problem identification, literature review, data collection, data preprocessing, system design, implementation, and evaluation. The obtained data undergo augmentation and image preprocessing processes to create a larger variety and amount of data. Furthermore, feature extraction is performed on the data using the SIFT method before training the model using SVM. The research results show that the SVM-SIFT model achieves an accuracy of 0.928261, which is superior to the SVM model without SIFT with an accuracy of 0.389130. The best CV score for the SVM model without SIFT is 0.7746709410609622. Testing proves that the use of SVM SIFT is effective for classifying handwriting that varies in shape and size.
Prediction And Detection Of Type II Diabetes Mellitus Using The K-Nearest Neighbor Algorithm Lestari, Uning; hamzah, amir; Paays, Franco Albertino Karel
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.12384

Abstract

Purpose: High blood sugar causes Mellitus (DM), a metabolic disorder. DM affects human metabolism and causes many complications, such as heart disease, kidney problems, skin disorders, and slow healing. Therefore, using machine learning algorithms to implement an automatic diabetes diagnosis system is crucial for predicting DM.Design/methodology/approach: This research created a DM disease prediction system using machine learning with the K-Nearest Neighbor algorithm. The National Institute of Diabetes and Digestive and Kidney Diseases, Hospital Frankfurt, Germany, and the results of health surveys and medical research are the sources of two separate datasets used in the Kaggle platform data. The stages in Machine Learning include data merging, data cleaning, and data splittingFindings/result: This research produces the best prediction model at a ratio of 70:30, with the lowest MSE value on testing data, 0.217. With K Folding Cross-validation, it makes an average accuracy of 73.88%.Originality/value/state of the art: This research creates a prediction model for diabetes mellitus type 2 using two different datasets with 9 features. It makes a Machine Learning model using the KNN algorithm by importing the KneighborClassifier and evaluating it using the MSE (Mean Square Error) matrix and K Folding cross-validation to determine modelling accuracy
Evaluasi Kualitas Sistem Informasi Akademik dengan Standar ISO/IEC 25010 (Studi Kasus: Universitas ABC) Laudza, Nafal Adi Syamaidzar; Sofyan, Herry
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.12406

Abstract

Tujuan: Penelitian ini bertujuan untuk mengevaluasi kualitas sistem informasi akademik di Universitas ABC berdasarkan standar ISO/IEC 25010.Perancangan/metode/pendekatan: Penelitian ini menggunakan pendekatan pengujian yang terstruktur sesuai dengan kriteria-kriteria yang terdapat dalam ISO/IEC 25010.Hasil: Pengujian menggunakan standar ISO/IEC 25010 menunjukan bahwa tujuh dari delapan standar yaitu: (a) functional suitability berjalan 100% dengan nilai satu; (b) performance efficiency mendapatkan grade B; (c) compatibility mendapat skor 100%; (d) usability dengan skor rata-rata 78.6%; (e) reliability dengan skor 100%; (f) maintainability memenuhi aspek identifikasi Land; dan (g) portability memperoleh skor 100% telah memenuhi standar, sedangkan security pada level dua (medium) belum memenuhi standar ISO/IEC 25010.Keaslian/ state of the art: Penggunaan seluruh standar ISO/IEC 25010 memberikan analisis yang lebih luas dan komprehensif dalam melakukan evaluasi kualitas sistem informasi akademik di Universitas ABC. Menggunakan metode observasi dan pengujian langsung dengan berbagai alat pengujian memberikan informasi yang objektif dengan kondisi sistem yang sebenarnya.
System Usability Scale (SUS) As An Analysis Method For Official Website Rahmawati, Berty Dwi; Wibowo, Astrid Wahyu Adventri; Fitrianingrum, Sabrina Nugrahani
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.12918

Abstract

Purpose: A company expands its product marketing by utilizing information systems. The quality of its information system influences product marketing expansion. Only a few companies have maximized the use of company websites. Usability testing on the company's official website determines the page's usability level.Design/methodology/approach: To address this issue, usability testing is conducted in this research using the System Usability Scale, whose testing involves page users. This usability level measurement technique has its characteristics. This method can obtain the level of usability precisely with user respondents from the page.Findings/result: Website testing was carried out with a structured and accurate SUS questionnaire using ten questions. The SUS score calculation was 80, considered excellent and acceptable to users.Originality/value/state of the art: PT Inka's Information System has never been analyzed during design or development. With this usability test, it is hoped that website users can find out the perceptions and problems experienced by users when interacting with the official website
Ensembled Voting Techniques for Advanced Breast Cancer Prediction Septiani, Riska Kurnia; Rianto, Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13004

Abstract

Breast cancer is the most common type of cancer affecting women worldwide, with a significant increase in incidence rates each year. Information and Communication Technology (ICT) has made substantial contributions to the medical field, particularly through the use of Big Data and machine learning algorithms to enhance diagnostic accuracy and healthcare efficiency. This research aims to assess the performance of five breast cancer classification algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), k-Nearest Neighbors (k-NN), Logistic Regression, and Ensembled Voting, using the Breast Cancer Wisconsin (Diagnostic) dataset. The study findings indicate that all models achieved high levels of accuracy, precision, recall, and F1-Score, with Ensembled Voting reaching the highest accuracy of 98.57%. This study confirms that machine learning algorithms, particularly Ensembled Voting, can be relied upon to improve breast cancer diagnosis accuracy, thereby significantly contributing to better healthcare outcomes.
Integrating Multiple Machine Learning Models to Predict Heart Failure Risk Pinem, Tuahta Hasiholan; Rianto, M., Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13006

Abstract

The research aims to create and evaluate machine learning models for the prognosis of heart failure based on patient medical information. Various predictive models have been created employing algorithms like logistic regression, decision trees, random forests, K-nearest neighbors, naive Bayes, support vector machines (SVMs), neural networks, and ensemble voting classifiers. The dataset utilized comprises diverse clinical characteristics from patients diagnosed with heart failure. The data underwent division into training and testing sets in an 80:20 ratio. Metrics including accuracy, Cross Validation Score, and ROC_AUC Score score were used to assess the models' performance. The findings reveal that the Voting Classifier, amalgamating the Logistic Regression and Support Vector Classifier models, demonstrated superior performance with an accuracy of 88.04%, a cross-validation score of 91.01%, and a ROC_AUC score of 88.00%. Further scrutiny suggested that blood pressure and cholesterol levels serve as substantial indicators of heart failure. This study presents a notable advancement in the utilization of machine learning models for heart failure prediction by scrutinizing diverse algorithms and pinpointing the most pertinent clinical characteristics. These outcomes hint at the potential for the development of machine learning-driven clinical tools to facilitate early detection and enhance medical interventions.

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