{"id":23221,"date":"2024-12-15T13:32:30","date_gmt":"2024-12-15T06:32:30","guid":{"rendered":"https:\/\/stei.itb.ac.id\/?page_id=23221"},"modified":"2024-12-16T08:25:26","modified_gmt":"2024-12-16T01:25:26","slug":"model-prediksi-gas-rumah-kaca","status":"publish","type":"page","link":"https:\/\/stei.itb.ac.id\/en\/prima\/model-prediksi-gas-rumah-kaca\/","title":{"rendered":"Model Prediksi Gas Rumah Kaca"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid kepala vc_custom_1734236300248 vc_row-has-fill\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"container\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"vc_btn3-container vc_btn3-inline vc_do_btn\" ><button class=\"vc_general vc_btn3 vc_btn3-size-lg vc_btn3-shape-rounded vc_btn3-style-modern vc_btn3-icon-left vc_btn3-color-white\" onclick=\"history.back()\"><i class=\"vc_btn3-icon fas fa-home\"><\/i> Back to Home<\/button><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1734170418007\"><div class=\"wpb_column vc_column_container vc_col-sm-3\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Kridanto Surendro<\/strong><br \/>\nSTE-ITB<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-3\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Neng Ayu Herawati<\/strong><br \/>\nSTE-ITB<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-3\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-3\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1734168902943\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Abstrak<\/strong><br \/>\nThe Indonesian government is committed to reducing greenhouse gas emissions by 29%, equivalent to 834 million tons of CO2e, by 2030. The energy sector is the main priority for emission reduction, with a target reduction of 314 million tons of CO2e. One method of reducing emissions is through carbon trading and the implementation of a carbon tax scheme in the carbon trading sector at coal-fired power plants starting in 2022. Several studies on GHG emissions only focus on calculating carbon emissions and have not applied prediction models. Meanwhile, the carbon emission prediction conducted by Zhu et al. only uses a regression model, so the prediction results will depend on independent variables that cannot yet make long-term predictions. This study will involve a carbon emission prediction model to provide information for decision makers in optimizing carbon emission predictions.<\/p>\n<p><strong>Keyword:<\/strong> Model Prediksi, Emisi Karbon, Machine Learning<\/p>\n<p><strong>Pendahuluan<\/strong><br \/>\nIn this study, a predictive model was developed using machine learning, incorporating time series and regression models for a Steam Power Plant (PLTU). The time series model was used to predict each feature or variable as input to the regression model to predict carbon emissions.<\/p>\n<p>Then, carbon trading decision information is generated using rule-oriented decision support systems. The carbon emission prediction results are used to inform carbon trading decisions in the next period. This research will involve specific data based on activity at the coal-fired power plant (PLTU), using daily granularity for greater accuracy and greater data collection to improve model performance.<\/p>\n<p>Carbon emission prediction modeling will compare model results using linear regression and Method 4, or CEMS. The data used in the CEMS method is real-time data on each unit's operational time and gross electricity. Furthermore, the data used in Method 1 consists of historical activity converted to CO2e emissions, namely CO2, CH4, and N2O emissions calculated using Global Warming Potential (GWP) values, along with gross electricity data.<\/p>\n<p><strong>Research Methodology<\/strong><br \/>\nThe methodology used in this research is the Design Science Research Methodology (DSRM). The research stages in DSRM can be seen in Figure 1. DSRM has six research stages starting from problem identification, defining research objectives, design, creation, demonstration, evaluation, and communication.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1734169162466 vc_row-o-content-bottom vc_row-flex\"><div class=\"wpb_column vc_column_container vc_col-sm-4\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div  class=\"wpb_single_image wpb_content_element vc_align_center wpb_content_element\">\n\t\t\n\t\t<figure class=\"wpb_wrapper vc_figure\">\n\t\t\t<div class=\"vc_single_image-wrapper   vc_box_border_grey\"><img loading=\"lazy\" decoding=\"async\" width=\"853\" height=\"148\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Design-Science-Research-Methodology.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Design Science Research Methodology\" \/><\/div><figcaption class=\"vc_figure-caption\">Gambar 1. Design Science Research Methodology<\/figcaption>\n\t\t<\/figure>\n\t<\/div>\n<\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-4\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div  class=\"wpb_single_image wpb_content_element vc_align_center wpb_content_element\">\n\t\t\n\t\t<figure class=\"wpb_wrapper vc_figure\">\n\t\t\t<div class=\"vc_single_image-wrapper   vc_box_border_grey\"><img loading=\"lazy\" decoding=\"async\" width=\"814\" height=\"963\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Rancangan-Solusi.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Rancangan Solusi\" \/><\/div><figcaption class=\"vc_figure-caption\">Gambar 2. Rancangan Solusi<\/figcaption>\n\t\t<\/figure>\n\t<\/div>\n<\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-4\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div  class=\"wpb_single_image wpb_content_element vc_align_center wpb_content_element\">\n\t\t\n\t\t<figure class=\"wpb_wrapper vc_figure\">\n\t\t\t<div class=\"vc_single_image-wrapper   vc_box_border_grey\"><img loading=\"lazy\" decoding=\"async\" width=\"649\" height=\"535\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Prediksi-Emisi-Karbon.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Prediksi Emisi Karbon\" \/><\/div><figcaption class=\"vc_figure-caption\">Gambar 3. Prediksi Emisi Karbon<\/figcaption>\n\t\t<\/figure>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1734168902943\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Discussion and Results<\/strong><br \/>\nBased on the results of the best model test scores are:<\/p>\n<p>a. Linear Regression:<br \/>\nLinear Regression is the best model because the data in the unit has a linear relationship between features and linear predictions are better.<br \/>\nThe effectiveness of Linear Regression as the best model shows that the data has a strong linear relationship, making it easier for the model to make more accurate predictions.<\/p>\n<p>b. Random Forest Regression:<br \/>\nRandom Forest Regression is the best model in Unit 8, this is because the data characteristics in Unit 8 have more complex relationships.<br \/>\nThe success of Random Forest Regression as a model in Unit 8 shows that the data in this unit is more complex or non-linear so that Random Forest Regression can capture this relationship better than other models.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1734169162466 vc_row-o-content-bottom vc_row-flex\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div  class=\"wpb_single_image wpb_content_element vc_align_center wpb_content_element\">\n\t\t\n\t\t<figure class=\"wpb_wrapper vc_figure\">\n\t\t\t<div class=\"vc_single_image-wrapper   vc_box_border_grey\"><img loading=\"lazy\" decoding=\"async\" width=\"807\" height=\"757\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Perbandingan-nilai-prediksi-nilai-aktual-setiap-feature.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Comparison of predicted values &amp; actual values of each feature\" \/><\/div><figcaption class=\"vc_figure-caption\">Figure 4. Comparison of predicted values &amp; actual values of each feature<\/figcaption>\n\t\t<\/figure>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_row-fluid vc_custom_1734168902943\"><div class=\"wpb_column vc_column_container vc_col-sm-12\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Kesimpulan<\/strong><br \/>\nThis research produces a predictive model that can provide information on carbon emissions, coal fuel consumption, and gross electricity to optimize carbon emissions, as well as information that can support decision-making in carbon trading activities. The research results can be used to inform coal-fired power plants (PLTU) in planning more effective carbon trading strategies by comprehensively considering environmental and financial factors.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"Kembali ke Beranda Kridanto Surendro STE-ITB Neng Ayu Herawati STE-ITB Abstrak Pemerintah Indonesia berkomitmen melakukan penurunan emisi Gas Rumah Kaca sebesar 29% atau setara dengan 834 juta ton CO2e pada [...]","protected":false},"author":1,"featured_media":0,"parent":22933,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-23221","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/23221","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/comments?post=23221"}],"version-history":[{"count":2,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/23221\/revisions"}],"predecessor-version":[{"id":23580,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/23221\/revisions\/23580"}],"up":[{"embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/22933"}],"wp:attachment":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/media?parent=23221"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}