Garuda - Garba Rujukan Digital

Two-stage Gene Selection and Classification for a High-Dimensional Microarray Data Masithoh Yessi Rochayani; Umu Sa'adah; Ani Budi Astuti
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.569

Microarray technology has provided benefits for cancer diagnosis and classification. However, classifying cancer using microarray data is confronted with difficulty since the dataset has high dimensions. One strategy for dealing with the dimensionality problem is to make a feature selection before modeling. Lasso is a common regularization method to reduce the number of features or predictors. However, Lasso remains too many features at the optimum regularization parameter. Therefore, feature selection can be continued to the second stage. We proposed Classification and Regression Tree (CART) for feature selection on the second stage which can also produce a classification model. We used a dataset which comparing gene expression in breast tumor tissues and other tumor tissues. This dataset has 10,936 predictor variables and 1,545 observations. The results of this study were the proposed method able to produce a few numbers of selected genes but gave high accuracy. The model also acquired in line with the Oncogenomics Theory by the obtained of GATA3 to split the root node of the decision tree model. GATA3 has become an important marker for breast tumors.

Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression Mufni Alida; Metty Mustikasari
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.537

As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error values are compared. The Linear Kernel has C = 7, max_iter = 100. The Polynomial Kernel has gamma = 1, degree = 1, max_iter = 4000 and C = 700. The RBF kernel has gamma = 0.03, epsilon = 0.007, max_iter = 2000 and C = 100. Linear kernels have advantages in terms of processing time compared to Polynomial and Radial Basis Function (RBF) kernels with an average processing time of 0.18 seconds. Besides that, in terms of accuracy and error, the RBF kernel has advantages over the Linear and Polynomial kernels with the value R2 = 95.94% and RMSE = 1.25%.

Rupiah Exchange Prediction of US Dollar Using Linear, Polynomial, and Radial Basis Function Kernel in Support Vector Regression Alida, Mufni; Mustikasari, Metty
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.537

As a developing country, Indonesia is affected by fluctuations in foreign exchange rates, especially the US Dollar. Determination of foreign exchange rates must be profitable so a country can run its economy well. The prediction of the exchange rate is done to find out the large exchange rates that occur in the future and the government can take the right policy. Prediction is done by one of the Machine Learning methods, namely the Support Vector Regression (SVR) algorithm. The prediction model is made using three kernels in SVR. Each kernel has the best model and, the accuracy and error values are compared. The Linear Kernel has C = 7, max_iter = 100. The Polynomial Kernel has gamma = 1, degree = 1, max_iter = 4000 and C = 700. The RBF kernel has gamma = 0.03, epsilon = 0.007, max_iter = 2000 and C = 100. Linear kernels have advantages in terms of processing time compared to Polynomial and Radial Basis Function (RBF) kernels with an average processing time of 0.18 seconds. Besides that, in terms of accuracy and error, the RBF kernel has advantages over the Linear and Polynomial kernels with the value R2 = 95.94% and RMSE = 1.25%.

Virtual Reality Headset Implementation on Parsec Cloud Gaming Platform Muhammad Fadhil Rahadiansyah; Ridha Muldina; Sussi Sussi
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.578

Virtual reality (VR) based games are a type of game that provides immersive gaming experience, allowing players to dive into the virtual world of the game being played. VR-based games require a high minimum computer specification, so thin clients cannot play VR-based games properly. This research aims to see how to enable thin computers to play VR-based games by utilizing cloud gaming technology. Using a high specification computer as a server, an android device as a VR headset, this Final Project implements a VR headset device so that it can be used in conjunction with cloud gaming services to be able to play VR-based games on thin computers and see how well the implementation by seeing the result from computer resources used and the Quality of Services. With Parsec cloud gaming services, the application carried out in this Final Project can run well on computers with low specifications. CPU usage on the client computer when the service is running is high at 91% usage, with 2818 MB RAM usage. Quality of Service is obtained when setting the highest quality preset, with a throughput of 16MB with a delay of about 2 ms. VR games that are played can run well with a minimum bandwidth of 15 Mbps selected from the Frame per Second (FPS) results obtained to reach 56 FPS with medium quality settings.

Virtual Reality Headset Implementation on Parsec Cloud Gaming Platform Rahadiansyah, Muhammad Fadhil; Muldina, Ridha; Sussi, Sussi
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.578

Virtual reality (VR) based games are a type of game that provides immersive gaming experience, allowing players to dive into the virtual world of the game being played. VR-based games require a high minimum computer specification, so thin clients cannot play VR-based games properly. This research aims to see how to enable thin computers to play VR-based games by utilizing cloud gaming technology. Using a high specification computer as a server, an android device as a VR headset, this Final Project implements a VR headset device so that it can be used in conjunction with cloud gaming services to be able to play VR-based games on thin computers and see how well the implementation by seeing the result from computer resources used and the Quality of Services. With Parsec cloud gaming services, the application carried out in this Final Project can run well on computers with low specifications. CPU usage on the client computer when the service is running is high at 91% usage, with 2818 MB RAM usage. Quality of Service is obtained when setting the highest quality preset, with a throughput of 16MB with a delay of about 2 ms. VR games that are played can run well with a minimum bandwidth of 15 Mbps selected from the Frame per Second (FPS) results obtained to reach 56 FPS with medium quality settings.

Exploiting Web Scraping for Education News Analysis Using Depth-First Search Algorithm Endah Ratna Arumi; Pristi Sukmasetya
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.548

Online news is one source of data that is always up to date and provides information or factual data. The search engine is one of the features for users to be able to enter keywords based on the expected category quickly. The development of education in Indonesia makes it essential to discuss, in this study using unstructured data in online news with the keyword Education included as a parameter, and adding search methods in the field of Artificial Intelligence so that the data becomes more accurate. Data that used here was from online news, namely CNN Indonesia, Detikcom, and Liputan6. Using Python Programming with depth-first search method (DFS), when compared with the results data for relevant news. Web erosion using DFS will be very helpful in searching because this method can check the date data was sent and then track the destination URL. Of the three online media sites, Detikcom produces the highest monthly data yielding an average of 885 news about education. At the same time, Liputan6 has the least amount of data on average, 28 news per month, but the data obtained are very relevant compared to Detikcom and CNN Indonesia.

Exploiting Web Scraping for Education News Analysis Using Depth-First Search Algorithm Arumi, Endah Ratna; Sukmasetya, Pristi
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.548

Online news is one source of data that is always up to date and provides information or factual data. The search engine is one of the features for users to be able to enter keywords based on the expected category quickly. The development of education in Indonesia makes it essential to discuss, in this study using unstructured data in online news with the keyword Education included as a parameter, and adding search methods in the field of Artificial Intelligence so that the data becomes more accurate. Data that used here was from online news, namely CNN Indonesia, Detikcom, and Liputan6. Using Python Programming with depth-first search method (DFS), when compared with the results data for relevant news. Web erosion using DFS will be very helpful in searching because this method can check the date data was sent and then track the destination URL. Of the three online media sites, Detikcom produces the highest monthly data yielding an average of 885 news about education. At the same time, Liputan6 has the least amount of data on average, 28 news per month, but the data obtained are very relevant compared to Detikcom and CNN Indonesia.

Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms Riyan Amanda; Edi Surya Negara
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.505

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is NaÃ¯ve Bayes, with an accuracy rate of 88% and an average precision of 90%.

Analysis and Implementation Machine Learning for YouTube Data Classification by Comparing the Performance of Classification Algorithms Amanda, Riyan; Negara, Edi Surya
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.505

Every day, people around the world upload 1.2 million videos to YouTube or more than 100 hours per minute, and this number is increasing. The condition of this continuous data will be useless if not utilized again. To dig up information on large-scale data, a technique called data mining can be a solution. One of the techniques in data mining is classification. For most YouTube users, when searching for video titles do not match the desired video category. Therefore, this research was conducted to classify YouTube data based on its search text. This article focuses on comparing three algorithms for the classification of YouTube data into the Kesenian and Sains category. Data collection in this study uses scraping techniques taken from the YouTube website in the form of links, titles, descriptions, and searches. The method used in this research is an experimental method by conducting data collection, data processing, proposed models, testing, and evaluating models. The models applied are Random Forest, SVM, Naive Bayes. The results showed that the accuracy rate of the random forest model was better by 0.004%, with the label encoder not being applied to the target class, and the label encoder had no effect on the accuracy of the classification models. The most appropriate model for YouTube data classification from data taken in this study is NaÃ¯ve Bayes, with an accuracy rate of 88% and an average precision of 90%.

Website Based Greenhouse Microclimate Control Automation System Design Muhammad Hafiz; Irfan Ardiansah; Nurpilihan Bafdal
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.575

Microclimate control is very important for plants cultivation in a greenhouse, two of microclimate variables are temperature and humidity, this variable can be controlled using several methods, one option is to use the misting cooling system, but this process is still done manually. This study aims to create a greenhouse microclimate control system that can be automatically displayed and controlled via a website. This research uses engineering design methods. The results show that the system can automatically turn on the misting cooling system when temperatures are above 30 â„ƒ and RH below 80%. Greenhouse microclimate data can be displayed and controlled via a website. The UV index greatly influences the performance of the misting cooling system on temperature and RH conditions in the greenhouse, while the UV index rises to 12 the temperature cannot be lowered and RH cannot be increased, but when the UV index falls from 12 the temperature can be reduced by Â± 3 â„ƒ and RH can be increased by Â± 12%.

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Journal Mail Official
jurnal@if.uinsgd.ac.id

Editorial Address
Gedung Fakultas Sains dan Teknologi Lt. 4 Jurusan Teknik Informatika Jl. A.H. Nasution No. 105 Cibiru Bandung 40614 Telp. (022) 7800525 / Fax (022) 7803936 Email : jurnal@if.uinsgd.ac.id

Location
Kota bandung,

Jawa barat

INDONESIA

Abstract

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Abstract

Abstract

Abstract

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Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official jurnal@if.uinsgd.ac.id

Editorial Address Gedung Fakultas Sains dan Teknologi Lt. 4 Jurusan Teknik Informatika Jl. A.H. Nasution No. 105 Cibiru Bandung 40614 Telp. (022) 7800525 / Fax (022) 7803936 Email : jurnal@if.uinsgd.ac.id

Location Kota bandung, Jawa barat INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
jurnal@if.uinsgd.ac.id

Editorial Address
Gedung Fakultas Sains dan Teknologi Lt. 4 Jurusan Teknik Informatika Jl. A.H. Nasution No. 105 Cibiru Bandung 40614 Telp. (022) 7800525 / Fax (022) 7803936 Email : jurnal@if.uinsgd.ac.id

Location
Kota bandung,

Jawa barat

INDONESIA