{"id":23357,"date":"2024-12-15T17:28:28","date_gmt":"2024-12-15T10:28:28","guid":{"rendered":"https:\/\/stei.itb.ac.id\/?page_id=23357"},"modified":"2024-12-16T08:40:36","modified_gmt":"2024-12-16T01:40:36","slug":"enhancing-telco-infrastructure-slas-in-underdeveloped-regions-of-indonesia-a-neural-network-and-genetic-algorithm-approach-for-analysis-and-optimization","status":"publish","type":"page","link":"https:\/\/stei.itb.ac.id\/en\/prima\/enhancing-telco-infrastructure-slas-in-underdeveloped-regions-of-indonesia-a-neural-network-and-genetic-algorithm-approach-for-analysis-and-optimization\/","title":{"rendered":"Enhancing Telco Infrastructure SLAs in Underdeveloped Regions of Indonesia: A Neural Network and Genetic Algorithm Approach for Analysis and Optimization"},"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-4\"><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>Daffa Farros AlfarobbySTEI<\/strong><br \/>\nSTEI ITB<\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Aulia RoyyanSTEI<\/strong><br \/>\nSTEI ITB<\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><strong>Jabar Nur Muhammad<\/strong><br \/>\nSTEI ITB<\/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_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>Abstract<\/strong><br \/>\nThis study explores the integration of Neural Networks (NN) and Genetic Algorithms (GA) to enhance Service Level Agreements (SLAs) in telecommunication infrastructure, particularly in Indonesia\u2019s 3T regions (Tertinggal, Terdepan, dan Terluar). The research focuses on leveraging NNs for identifying data patterns and GA for optimizing SLA performance. Through a combination of Long Short-Term Memory (LSTM) models and cloud-based platforms, this framework offers a solution to inefficiencies in data processing and manual analysis. Results demonstrate significant potential for improving SLA monitoring, reducing troubleshooting efforts, and fostering equitable digital access in underserved areas.<\/p>\n<p><strong>Keyword:<\/strong> Neural Networks; Genetic Algorithms; SLA Optimization; LSTM; Telecommunications; Big Data; AI in 3T Regions.<\/p>\n<p><strong>Introduction<\/strong><br \/>\nService Level Agreements (SLAs) in telecommunications are essential for establishing clear expectations regarding service quality, responsibilities, and performance metrics between providers and customers. These agreements face challenges such as infrastructure limitations in remote areas and the necessity for advanced monitoring systems. The cases of NMS A and NMS B exemplify the complexity of SLAs, which include guarantees on network uptime, internet speed, response times, and compensation structures. The landscape of SLA management has evolved significantly due to advanced computational techniques like Neural Networks (NN) for data processing and pattern recognition, Genetic Algorithms (GA) for optimization, and Long Short-Term Memory (LSTM) models for time-series forecasting. These technologies enable telecommunications providers to enhance predictive accuracy, real-time monitoring, and proactive service optimization, marking a substantial advancement in the management of SLAs.<\/p>\n<p><strong>Research Method<\/strong><br \/>\nTo develop a reliable SLA optimization framework, data was collected from Network Monitoring Systems (NMS) proceed to Data Cleaning to preprocess raw data by removing inconsistencies and null values for compatibility with machine learning models; Feature Engineering to select and normalize relevant features, including time-series metrics like uptime and service availability trends. A Recurrent Neural Network (RNN) architecture with Long Short-Term Memory (LSTM) layers was implemented, testing six configurations.<br \/>\n\u2022 Model A1: 64-32-16 layers<br \/>\n\u2022 Model A2: 128-128-64 layers<br \/>\n\u2022 Model A3: 256-256-128 layers<br \/>\n\u2022 Model B1: 64-32 layers<br \/>\n\u2022 Model B2: 128-128 layers<br \/>\n\u2022 Model B3: 256-256 layers<\/p>\n<p>The 3-layer models (A1, A2, A3) are designed to handle complex patterns and higher capacity tasks, making them suitable for learning long-range dependencies, though they may incur increased computational costs and a higher risk of overfitting. In contrast, the 2-layer models (B1, B2, B3) are optimized for simpler temporal dependencies, providing lower computational costs and faster training times while effectively capturing essential temporal dynamics with a reduced risk of overfitting.<br \/>\nThe system design comprised three subsystems: a Python-based backend deployed on Google Cloud Platform (GCP), a React-based frontend for real-time SLA monitoring, and a TensorFlow-developed prediction model for SLA forecasting.<\/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-6\"><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=\"481\" height=\"87\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/System-Architecture-1.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"System Architecture\" \/><\/div><figcaption class=\"vc_figure-caption\">Picture 1. System Architecture<\/figcaption>\n\t\t<\/figure>\n\t<\/div>\n<\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-6\"><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=\"485\" height=\"360\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Detailed-System-Architecture.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Detailed System Architecture\" \/><\/div><figcaption class=\"vc_figure-caption\">Picture 2. Detailed System Architecture<\/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>Implementation<\/strong><br \/>\nThe model training process involved using TensorFlow to train the models on a clean dataset, incorporating validation to monitor for overfitting and underfitting. Performance was assessed using metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R\u00b2). Concurrently, a responsive web dashboard was developed to display SLA predictions, uptime\/downtime metrics, and restitution trends. For deployment, the trained model was implemented on Google Cloud Platform (GCP) using TensorFlow Serving to ensure scalability, while the web application was hosted on a cloud platform for easy stakeholder access. The system&#8217;s performance was evaluated based on the aforementioned metrics, which served as fitness functions for assessing model accuracy<\/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=\"1066\" height=\"445\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Fittness-Function-Equation.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Fittness Function Equation\" \/><\/div><figcaption class=\"vc_figure-caption\">Picture 3. Fittness Function Equation<\/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 &amp; Result<\/strong><br \/>\nThe development of three LSTM-based predictive models (A1, A2, A3, B1, B2, &amp; B3) yielded varied outcomes in terms of Mean Absolute Error, Mean Squared Error, and Coefficient of Determination.<\/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-6\"><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=\"513\" height=\"249\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Epoch-Loss-for-Six-Models.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Epoch Loss for Six Models\" \/><\/div><figcaption class=\"vc_figure-caption\">Picture 4. Epoch Loss for Six Models<\/figcaption>\n\t\t<\/figure>\n\t<\/div>\n<\/div><\/div><\/div><div class=\"wpb_column vc_column_container vc_col-sm-6\"><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=\"836\" height=\"588\" src=\"https:\/\/stei.itb.ac.id\/wp-content\/uploads\/Performance-Metrics-Summary.jpg\" class=\"vc_single_image-img attachment-full\" alt=\"\" title=\"Performance Metrics Summary\" \/><\/div><figcaption class=\"vc_figure-caption\">Table 1. Performance Metrics Summary<\/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>The results of the LSTM model architectures reveal important performance trade-offs for predictive tasks based on evaluation metrics. Three-Layer Models (A-Series): The A-series models (A1, A2, A3) show improved performance with increased capacity, indicated by lower MAE and MSE values and higher R\u00b2 scores. Model A3, with the largest configuration achieves the best performance demonstrating its ability to capture complex temporal dependencies. However, this complexity raises the risk of overfitting, necessitating careful regularization. Two-Layer Models (B-Series): The B-series models (B1, B2, B3) are less complex but exhibit a significant performance gap compared to the A-series, with higher MAE and MSE values and lower R\u00b2 scores. While increasing capacity within the B-series improves performance, these models struggle to capture intricate patterns.<\/p>\n<p><strong>Conclusion<\/strong><br \/>\nAmong the tested models, Model A2 stands out as the most reliable configuration, achieving a balance between accuracy and computational efficiency. Future improvements will involve integrating more diverse datasets and advanced machine learning techniques to further enhance the system&#8217;s reliability and adaptability. The successful implementation of this solution signifies a notable advancement in optimizing telecommunications infrastructure, ultimately leading to improved service delivery and increased customer satisfaction.<\/p>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"Kembali ke Beranda Daffa Farros AlfarobbySTEI STEI-ITB Aulia RoyyanSTEI STEI-ITB Jabar Nur Muhammad STEI-ITB Abstract This study explores the integration of Neural Networks (NN) and Genetic Algorithms (GA) to enhance [...]","protected":false},"author":1,"featured_media":0,"parent":22933,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-23357","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/23357","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=23357"}],"version-history":[{"count":2,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/23357\/revisions"}],"predecessor-version":[{"id":23598,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/pages\/23357\/revisions\/23598"}],"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=23357"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}