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
 22   23   24   25   26 
Performance Analysis of XGBoost Algorithm to Determine the Most Optimal Parameters and Features in Predicting Stock Price Movement Affan Ardana
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9329

Abstract

Purpose: The research aims to find the best parameters and features for predicting stock price movement using the XGBoost algorithm. The parameters are searched using the RMSE value, and the features are searched using the importance value.Design/methodology/approach: The research data is the stock data of Amazon.com company (AMZN). The dataset contains the Date, Low, Open, Volume, High, Close, and Adjusted Close features. The dataset is ensured to have no missing data by handling missing values. The input feature is selected using the Pearson Correlation feature selection method. To prevent the difference between the highest and lowest stock price from being too far apart, the data is scaled using the scaling method. To avoid bias that may appear in the prediction result, cross-validation is used with the Min Max Scaling method, which will devide the dataset into training data and testing data within a range of 30 days after the training data. The parameters to be tested include n_estimator = 500, early stopping round = 3, learning rate = 0.01, 0.05, 0.1, and max_depth (tree depth) = 3, 4, 5.Findings/result: The result of the research that a learning rate of 0.05 and a tree depth of 5 obtained the lowest RMSE result compared to other models, with an RMSE of 0.009437. The Low feature obtained the highest importance value among all the models built.Originality/value/state of the art: This study used testing data within a range of 30 days after the training data and used a combination of parameters, including n_estimator = 500, early stopping round = 3, learning rate = 0.01, 0.05, 0.1, amd max_depth (tree depth) = 3, 4, 5. 
Implementation of Mel-Frequency Cepstral Coefficient as Feature Extraction using K-Nearest Neighbor for Emotion Detection Based on Voice Intonation Revanto Alif Nawasta; Nur Heri Cahyana; Heriyanto Heriyanto
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9518

Abstract

Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN as an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts on YouTube. Some of the methods used in this study are pitch shifting for data augmentation, MFCC for feature extraction on audio data, basic statistics for taking the mean, median, min, max, standard deviation for each coefficient, Min max scaler for the normalization process and KNN for the method classification.Findings/result: Because testing is carried out separately for each gender, there are two classification models. In the male model, the highest accuracy was obtained at 88.8% and is included in the good fit model. In the female model, the highest accuracy was obtained at 92.5%, but the model was unable to correctly classify emotions in the new data. This condition is called overfitting. After testing, the cause of this condition was because the pitch shifting augmentation process of one tone in women was unable to solve the problem of the training data size being too small and not containing enough data samples to accurately represent all possible input data values.Originality/value/state of the art: The research data used in this study has never been used in previous studies because the research data is obtained by downloading from Youtube and then processed until the data is ready to be used for research.
Monitoring Development Board based on InfluxDB and Grafana Noprianto Noprianto; Vivi Nur Wijayaningrum; Rokhimatul Wakhidah
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.7643

Abstract

Purpose: Designing a sensor data monitoring system using a time series database and monitoring platform on a Development Board device.Design/methodology/approach: It begins with a requirement analysis, such as the preparation of the required software and hardware, followed by the creation of the system architecture that will be adopted. Then the development process from a predetermined design to the testing process to ensure the dashboard page can display data according to a predetermined scenario.Findings/result: From the research that has been done, produces a design of sensor data that is sent using the MQTT protocol via Node-RED, then stored in a time series database (InfluxDB) and displayed on the Grafana dashboard display.Originality/value/state of the art: Sensor data monitoring dashboard on Development Board devices
Application of Expert System Identification of Horticultural Plant Diseases with Certainty Factor and Forward Chaining for Smart Village Concept Development Damar Wicaksono; Imam Adi Nata
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9358

Abstract

Purpose: This research was conducted to help identify diseases early and provide suggestions for recommendation systems for these plants in general that are beneficial for farmers.Design/methodology/approach: This research goes through several stages, namely planning , analysis, design, and implementation.Findings/result: CLIPS-based Horticultural Plant Disease Identification Expert SystemOriginality/value/state of the art: In the process of diagnosing plant diseases, it requires the accuracy and thoroughness of an expert or experts on symptoms that indicate a disease because of the similarity of these symptoms. Misdiagnosis of existing symptoms causes differences in the results of the diagnosis with the actual disease suffered by the plant. Along with the development of technology, a system was devised that would help report early identification of diseases and provide suggestions for recommendation systems for these plants in general that are beneficial to farmers.
Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University) Waode Siti Nurul Hassanah; Yunda Puji Lestari; Rizal Adi Saputra
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.8925

Abstract

Purpose: To detect cracks in the walls of buildings using digital image processing and the Naïve Bayes Algorithm.Design/methodology/approach: Using the YCbCr color model for the segmentation process and the HSV color model for the feature extraction process. This study also uses the Naïve Bayes Algorithm to calculate the probability of feature similarity between testing data and training data.Findings/result: Detecting cracks is an important task to check the condition of the structure. Manual testing is a recognized method of crack detection. In manual testing, crack sketches are prepared by hand and deviation states are recorded. Because the manual approach relies heavily on the knowledge and experience of experts, it lacks objectivity in quantitative analysis. In addition, the manual method takes quite a lot of time. Instead of the manual method, this research proposes digital-based crack detection by utilizing image processing. This study uses an intelligent model based on image processing techniques that have been processed in the HSV color space. In addition, this study also uses the YcbCr color space for feature extraction and classification using the Naïve Bayes Algorithm for crack detection analysis on building walls. The accuracy of the research test data reached 88.888888888888890%, while the training data achieved an accuracy of 93.333333333333330%.Originality/value/state of the art: This study has the same focus as previous research, namely detecting cracks in building walls, but has different methods and is implemented in case studies.
Design Automatic Parking Application of Amikom Purwokerto University Atmaja Jalu Narendra Kisma; Hendra Marcos
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.8933

Abstract

 Purpose: This study aims to deal with parking problems in the area of Amikom University, Purwokerto. In addition, this research is designed to implement theoretical and practical knowledge that has been obtained in lectures.Design/methodology/approach: In research on parking design applications in the Amikom University area, Purwokerto, library study methods and literature study methods are used. The amount of data can add insight and can make it easier to process data in research.Findings/result: This application will be able to help more Amikom Purwokerto University residents, especially in the Faculty of Computer Science. The use of this application will help find parking areas in FIK areas such as Basement Parking, Front Parking and Field Parking. In addition, security will be helped by this application because if it is implemented, vehicles parked in the reserved area will be tidier and safer. In addition, security does not need to find an empty parking area for users.Originality/value/state of the art: This research focuses on parking system design like previous studies. However, this research focuses more on designing parking applications at Amikom Purwokerto University. 
Sentiment Analysis of Cryptocurrency Exchange Application on Twitter Using Naïve Bayes Classifier Method Andhika Octa Indarso; Helena Nurramdhani Irmanda; Ria Astriatma
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9044

Abstract

Purpose: The growth and development of the digital currency industry also presents a variety of applications for conducting transactions using these currencies, including utilizing cryptocurrency exchanges to make investments. InI ndonesia, there are two applications that fall into the category of the largest cryptocurrency exchange and are recognized by Bappebti (Commodity Futures Trading Regulatory Agency), namely TokoCrypto and Indodax. Both applications are analyzed based on the sentiments of their users on Twitter.Design/methodology/approach: In this study the data collected is data originating from social media Twitter and has the keywords "indodax" or "#indodax" and "tokocrypto" or "#tokocrypto". The data used is between January 2021 – January 2022. The data collected from Twitter is processed using the Naïve Bayes Classifier algorithm.Findings/result: From the results of the analysis, it was found that the Indodax application has a higher positive sentiment percentage value of 9% compared to TokoCrypto.Originality/value/state of the art: The use of the Naïve Bayes algorithm in this study supports sentiment analysis of cryptocurrency exchange application users to consider which application has better positive sentiment for investing in digital currency or cryptocurrency.
Application Random Forest Method for Sentiment Analysis in Jamsostek Mobile Review Tasya Auliya Ulul Azmi; Luthfi Hakim; Dian Candra Rini Novitasari; Wika Dianita Utami Dianita Utami
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.8868

Abstract

Purpose: This study aims to monitor the service quality of JMO applications from time to time by classifying JMO user reviews into the class of positive, neutral, and negative sentiments.Design/methodology/approach : The method used in this study is the random forest classification method. Data processing in this study uses feature extraction, TF-IDF and labeling with the lexicon-based method.Findings/result: Based on the research results, it was found that the highest frequency of classification was the positive class with 17571 reviews compared to the neutral class with 8701 reviews and the negative class with 3876 reviews with an accuracy evaluation value of 93%, precision 88%, recall 93%, and f1-score 90%.Originality/value/state of the art:This study uses 150737 reviews that have been pre-processed using the random forest method and TF-IDF and lexicon-based feature extraction.
Autoregressive Integrated Moving Average (ARIMA) Models For Forecasting Sales Of Jeans Products Jenny Meilila Azani Cahya Permata; Muhammad Habibi
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.7868

Abstract

Purpose: To be able to compete with other companies, it is necessary to estimate and forecast jeans products that will be ordered according to consumer demand every month, so that there is no excess inventory and product shortage. If there is a shortage of goods, the consumer will be disappointed with the seller, and vice versa if the goods are overstocked, the quality will continue to decline to the detriment of the seller and the buyer, resulting in a shortage of materials.Methodology: To overcome the problem of selling jeans products, the ARIMA method is suitable to overcome the problem of forecasting the stock of jeans sales. ARIMA model is a model that completely ignores the independent variables in making forecasts. ARIMA uses past and present values of the dependent variable to produce accurate short-term forecasting.Results: The built forecasting has a MAPE accuracy rate of 17.05% so it can be said that predicting has good results according to the criteria. Forecasting results in the following year show that sales tend to increase from the previous year.Originality: This research was conducted using sales data of jeans products at company XYZ and using the ARIMA method which previous researchers have never done.
Input Variable Selection for Oil Palm Plantation Productivity Prediction Model Andiko Putro Suryotomo; Agus Harjoko
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9674

Abstract

Purpose: This study aims to implement and improve a wrapper-type Input Variable Selection (IVS) to the prediction model of oil palm production utilizing oil palm expert knowledge criteria and distance-based data sensitivity criteria in order to measure cost-saving in laboratory leaf and soil sample testing.Methodology: The proposed approach consists of IVS process, searching the best prediction model based on the selected variables, and analyzing the cost-saving in laboratory leaf and soil sample testing.Findings/result: The proposed method managed to effectively choose 7 from 19 variables and achieve 81.47% saving from total laboratory sample testing cost.Value: This result has the potential to help small stakeholder oil palm planter to reduce the cost of laboratory testing without losing important information from their plantation.

Page 24 of 37 | Total Record : 361

 22   23   24   25   26 

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