{"id":28729,"date":"2026-02-14T01:03:36","date_gmt":"2026-02-13T18:03:36","guid":{"rendered":"https:\/\/stei.itb.ac.id\/?p=28729"},"modified":"2026-10-09T09:19:43","modified_gmt":"2026-10-09T02:19:43","slug":"galeri-proyek-if3211-komputasi-spesifik-domain-2","status":"publish","type":"post","link":"https:\/\/stei.itb.ac.id\/en\/galeri-proyek-if3211-komputasi-spesifik-domain-2\/","title":{"rendered":"IF3211 Project Gallery &#8211; Domain-Specific Computing"},"content":{"rendered":"<div class=\"wpb-content-wrapper\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p style=\"text-align: left;font-size: 16px;color: #555\">The following is a collection of innovations by ITB Informatics Engineering students in the subject <strong>IF3211 Komputasi Spesifik Domain<\/strong> Semester II of the 2024\/2025 Academic Year. This gallery showcases various technological approaches such as <em>Machine Learning<\/em>, <em>Deep Learning<\/em>, and simulations to solve challenges in bioinformatics, ecology, and health.<\/p>\n<p><\/br><\/p>\n\n\t\t<\/div>\n\t<\/div>\n\n<div class=\"fullwidth\" ><div class=\"vc_row wpb_row 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_tta-container\" data-vc-action=\"collapse\"><div class=\"vc_general vc_tta vc_tta-accordion vc_tta-color-grey vc_tta-style-accordion_style1 vc_tta-shape-rounded vc_tta-o-shape-group vc_tta-controls-align-default\"><div class=\"vc_tta-panels-container\"><div class=\"vc_tta-panels\"><div class=\"vc_tta-panel vc_active\" id=\"1770917220075-11cf590d-d699\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770917220075-11cf590d-d699\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Analisis Sekuens DNA\/RNA<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/i85Z0V1mFk8?si=67WGRqRPuf3aRZ__\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Survival Analysis of Allogeneic Hematopoietic Cell Transplant Patients Against Race Using a Machine Learning Approach<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522015 Yusuf Ardian Sandi<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522027 Muhammad Al Thariq Fairuz<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522067 Randy Verdian<\/li>\n<\/ul>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\"><\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><strong>\ud83e\uddea Abstrak:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Allogeneic Hematopoietic Stem Cell Transplantation (Allogeneic HCT) shows variable outcomes in patients. This study developed and evaluated machine learning models (XGBoost, CatBoost, and LightGBM) to predict event-free survival (EFS) in Allogeneic HCT patients, with a focus on predictive performance and racial equity. Using a dataset from the Center for International Blood and Marrow Transplant Research (CIBMTR), data were processed through stages of missing value imputation, target variable transformation using Kaplan-Meier estimation, and categorical feature encoding. Hyperparameter optimization was performed with Optuna and 10-fold cross-validation was used for model evaluation, with the primary metric being the C-index adjusted to account for variance between racial groups. Results showed LightGBM achieved the highest C-index (0.6691), outperforming CatBoost (0.6673) and XGBoost (0.6665). Further analysis identified disparities in EFS across racial groups, with patients from the \u201cMultiple Race\u201d group showing the best outcomes, while the \u201cWhite\u201d group showing the lowest outcomes, and identified important features such as Disease Risk Index (DRI) score and donor age. This study highlights the significant potential of machine learning in predicting the prognosis of Allogeneic HCT and emphasizes the crucial consideration of equity in the development of predictive medical models.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770917220087-c018ee8a-ac58\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770917220087-c018ee8a-ac58\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Prediksi Struktur Protein<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/SE8MJKeLgZo?si=N_iDy7VICq6BzLZi\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Protein Secondary Structure Prediction by TCN-BiLSTM-MHA model with VAE-BiLSTM Embedding<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522083 Evelyn Yosiana<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522103 Steven Tjhia<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Accurate prediction of protein secondary structures is essential for elucidating biological functions and accelerating drug discovery. This study proposes a novel hybrid deep learning architecture combining VAE-BiLSTM embedding with a TCN-BiLSTM-MHA predictor to forecast secondary structures from amino acid sequences. Protein sequences are first encoded into a latent space using a Variational Autoencoder with Bidirectional LSTM (VAE-BiLSTM). These embeddings are then processed by a Temporal Convolutional Network integrated with BiLSTM and Multi-Head Attention (TCN-BiLSTM-MHA) for structure classification. Evaluated on TS115 and CB513 datasets for Q3 (3-class) and Q8 (8-class) prediction, our model achieves peak test accuracies of 63.44% (Q3) and 47.20% (Q8). Key findings demonstrate that increasing the latent dimension from 32 to 64 significantly enhances performance across all metrics, while incorporating physicochemical properties (pK, pI, \u0394\u0394G\u00b0) yields marginal improvements. Non-converging loss curves at 20 epochs indicate substantial unrealized accuracy gains with extended training. Performance gaps relative to state-of-the-art models (e.g., Zhao et al. &#8216;s 90.8% Q3 accuracy) are attributed to the unidirectional TCN implementation and hardware constraints limiting dataset scope. This work validates the efficacy of VAE-derived embeddings for protein representation and establishes latent space optimization as critical for hybrid architectures.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><div class=\"vc_tta-panel\" id=\"1770951149274-f777e53c-87e7\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770951149274-f777e53c-87e7\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Application of Machine Learning in Biology<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/ZgddfSsX7o4?si=PhRaQ2_waVmAtBIu\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Development of a Machine Learning Model for Detecting Genetic Mutations Contributing to Breast Cancer<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>18222120 Aqila Ataa<\/li>\n<li>18222124 Fadian Alif Mahardika<\/li>\n<li>18222135 Nicolas Jeremy M S<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Breast cancer is one of the most common types of cancer and is the leading cause of cancer death in women worldwide. One of the main factors contributing to breast cancer is genetic mutations in tumor suppressor genes. Early detection of these genetic mutations is crucial for the prevention and management of breast cancer. This study proposes the development of a Deep Learning-based machine learning model to detect genetic mutations in DNA sequences, specifically in the BRCA1 and BRCA2 genes, with the aim of providing fast and accurate detection results. The applied models include Temporal Convolutional Network (TCN), One-dimensional Convolutional Neural Network (1D-CNN), and Recurrent Neural Network (RNN) BiLSTM, using sequential labeling techniques to identify the type and index of mutations in each nucleotide. The DNA sequence data used comes from breast cancer patient samples from the Catalogue of Somatic Mutation in Cancer (COSMIC). The test results show that TCN has the best performance with an F1-score of 0.8602, outperforming ID-CNN (0.8325) and BiLSTM (0.8325), and showing a shorter detection time. Analysis of the detected mutations shows a predominance of C\u2192A and C\u2192T mutations, consistent with the literature on human genomic mutations as driver mutations in many tumor suppressor genes. This research contributes to the use of machine learning for bioinformatics analysis, particularly in the automated and rapid detection of genetic mutations, which is expected to support early diagnosis and more effective management of breast cancer.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770951732942-aa429e2b-e984\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770951732942-aa429e2b-e984\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Analisis Metegenomik<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/8Qn0FI8kLyk?si=EQrHPPg8cPwlpAfV\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Characteristics of Rhizosphere Microbial Communities Due to Urban Waste Through a Metagenomic Approach<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522014 Raden Rafly Hanggaraksa B<\/li>\n<li>13522084 Dhafin Fawwaz Ikramullah<\/li>\n<li>13522114 Muhammad Dava Fathurrahman<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>The rhizosphere is a biologically active soil zone strongly influenced by root activity, where microbial communities play a crucial role in nutrient cycling and ecosystem health. This study aimed to examine how urban wastewater exposure affects the structure and diversity of bacterial communities in the rhizosphere, identify dominant or significantly altered taxa, and identify potential microbial biomarkers that play a role in the response to environmental stress. Analysis was conducted on 27 metagenome samples (BioProject PRJNA1229183) using 16S rRNA gene sequencing (V3\u2013V4) and analyzed with QIIME2, DADA2, and PICRUSt2. The results showed differences in microbial community structure between samples based on \u03b2-diversity analysis, with the dominance of the phyla Proteobacteria, Actinobacteriota, and Firmicutes. Several genera such as Pseudomonas, Geobacter, Hydrogenophaga, and Acidovorax were identified as having important roles in nitrogen fixation and bioremediation. Genetic function prediction revealed the dominance of rpoE and ABC-type transporter genes associated with membrane stress responses, which have potential as molecular biomarkers. This study provides initial insights into rhizosphere microbial adaptation to urban wastewater stress and identifies taxa and functional genes relevant for soil ecosystem monitoring and restoration strategies in urban environments.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770952076606-c2b425b6-6bcf\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770952076606-c2b425b6-6bcf\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Evolution<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/HPbeTEsn1-8?si=O5Qm0vKmwcQ6w6ZI\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Simulation of the Evolution of Antibiotic Resistance in Escherichia coli through Mutation, Horizontal Gene Transfer, and Natural Selection<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>18222071 Richie Leonardo<\/li>\n<li>18222033 Anthony Bryant Gouw<\/li>\n<li>18222013 Aththariq Lisan Q.D.S<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Evolution is a concept in biology that explains the changes in the characteristics of an organism and is passed down to the next generation. Living organisms use evolution to adapt to their environment, thereby increasing their lifespan. Mutations represent changes in genetic material. To study this, a simulation will be designed to visualize evolution. Bacteria will be used as the primary research subject due to their unicellular nature and relatively simple genetic structure. The simulation specifically focuses on the evolution of antibiotic resistance in bacterial populations, where selection pressure from antibiotic exposure triggers mutations that enable bacterial survival. The simulation model uses a genetic algorithm to represent the processes of mutation, natural selection, and bacterial reproduction in environments containing varying concentrations of antibiotics. This evolution is then visualized to observe patterns of resistance development and the dynamics of bacterial evolution over time. The simulation results are expected to provide a better understanding of the mechanisms of antibiotic resistance evolution and the factors influencing its rate of development at the population level.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770952082081-66da6f92-528f\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770952082081-66da6f92-528f\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Biological Diversity<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/xX7SHeW8f3o?si=u8OVrB8jqAuWQyL6\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>PhyloGeoVis: Computational Phylogenomics and Interactive Geospatial Visualization for Orangutan Conservation Prioritization<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522070 Marzuli Suhada M.<\/li>\n<li>13522072 Ahmad Mudabbir Arif<\/li>\n<li>13522116 Naufal Adnan<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Orangutan conservation faces critical challenges due to habitat fragmentation and genetic bottlenecks across three species: Sumatran (Pongo abelii), Bornean (Pongo pygmaeus), and Tapanuli (Pongo tapanuliensis). Traditional conservation approaches lack integration of genomic diversity with spatial distribution data. This paper presents PhyloGeoVis, a computational framework that combines phylogenomic analysis with interactive geospatial visualization to prioritize orangutan conservation efforts. Our approach employs multiple sequence alignment, maximum likelihood phylogenetic reconstruction, population viability analysis, and GIS integration to analyze genomic sequences from NCBI GenBank databases. The system provides conservation decision support through interactive visualization of genetic diversity patterns, identification of genomic regions under selection pressure, and extinction risk assessment. Performance evaluation demonstrates computational efficiency and biological relevance for conservation practitioners.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770952288574-6ff452a4-3641\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770952288574-6ff452a4-3641\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Plant Form and Function<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/rthegAGtPAs?si=rDifK7mJlemKtxo3\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Modeling Genetic Regulatory Networks in Arabidopsis thaliana with Graph Neural Networks<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522022 Renaldy Arief Susanto<\/li>\n<li>13522066 Nyoman Ganadipa Narayana<\/li>\n<li>13522092 Sa&#8217;ad Abdul Hakim<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Gene Regulatory Networks (GRNs) play a crucial role in understanding complex molecular mechanisms underlying plant growth and development. This study presents a novel computational approach for modeling GRNs in Arabidopsis thaliana using Graph Neural Networks (GNNs). We utilized gene co-expression data from the ATTED-II database to construct correlation graphs where nodes represent genes and edges represent significant correlations between gene pairs. The GNN architecture was implemented using PyTorch to learn complex regulatory patterns and generate low-dimensional vector embeddings for each gene. These embeddings were then used to identify hub genes and construct the regulatory network structure. To evaluate the constructed GRN, we employed GO enrichment analysis by comparing positive and negative gene sets based on their regulatory distance from identified master regulator genes. Our results demonstrate that PSBO1 emerges as a key master regulator gene, with GO enrichment analysis showing significantly lower p-values and FDR rates for genes directly regulated by PSBO1 compared to distantly regulated genes (difference factor of less than 10^-6). This validation confirms PSBO1&#8217;s central role in photosynthesis regulation, consistent with biological literature. Our approach successfully demonstrates the effectiveness of GNNs in inferring regulatory relationships from co-expression data and identifying biologically meaningful hub genes in plant regulatory networks.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770952295060-f12c9ba6-7a0a\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770952295060-f12c9ba6-7a0a\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Animal Form and Function<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/4oh-gESAU6k?si=I7QCAc0_w8w4_dUY\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Identifying Patterns of Mammalian Adaptation to Various Habitats with Machine Learning<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522008 Ahmad Farid Mudrika<\/li>\n<li>13522016 Zachary Samuel Tobing<\/li>\n<li>13522120 M. Rifki Virziadeili Harisman<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>Mammalian species have evolved diverse physiological and morphological adaptations to survive in extreme environments, mainly classified to be aquatic and marine, terrestrial, and non-aquatic caves and subterranean. Understanding these adaptation patterns is crucial for evolutionary biology research and conservation efforts. This study presents a machine learning approach to classify mammalian species into their primary habitats based on biological traits. We developed supervised classification models using a comprehensive dataset of 1456 mammalian species with 31 biological features including life history traits, morphological traits, reproductive traits, ecological traits, and behavioral traits. Random Forest, combined with Multi Output Classifier was evaluated using holdout testing. The model achieved high classification accuracy and identified key biological features most indicative of habitat adaptation. Results demonstrate the effectiveness of machine learning in revealing complex species-environment relationships and provide insights for predicting mammalian responses to environmental changes.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"vc_tta-panel\" id=\"1770952299735-20d45ded-de2f\" data-vc-content=\".vc_tta-panel-body\"><div class=\"vc_tta-panel-heading\"><h4 class=\"vc_tta-panel-title vc_tta-controls-icon-position-left\"><a href=\"#1770952299735-20d45ded-de2f\" data-vc-accordion data-vc-container=\".vc_tta-container\"><span class=\"vc_tta-title-text\">\ud83c\udf3f Ecology<\/span><i class=\"vc_tta-controls-icon vc_tta-controls-icon-plus\"><\/i><\/a><\/h4><\/div><div class=\"vc_tta-panel-body\"><div class=\"fullwidth\" ><div class=\"vc_row wpb_row vc_inner vc_row-fluid\"><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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<p><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/I1eiSbADOEg?si=BlgWAaKyfocyGhzR\" width=\"560\" height=\"315\" frameborder=\"0\"><\/iframe><\/p>\n\n\t\t<\/div>\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_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<h3>Agent-Based Simulation to Understand Species Richness Dynamics on Islands<\/h3>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<h5><\/h5>\n<h5><\/h5>\n<h5><strong>\ud83d\udc65 Group Members:<\/strong><\/h5>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<ul>\n<li>13522073 Juan Alfred Widjaya<\/li>\n<li>13522081 Albert<\/li>\n<li>13522111 Ivan Hendrawan Tan<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fullwidth\" ><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\">\n\t<div class=\"wpb_text_column wpb_content_element\" >\n\t\t<div class=\"wpb_wrapper\">\n\t\t\t<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stMarkdown\" data-testid=\"stMarkdown\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>&nbsp;<\/p>\n<h5><strong style=\"letter-spacing: 0.05em\">\ud83e\uddea Abstrak:<\/strong><\/h5>\n<div class=\"element-container st-emotion-cache-w59gc2 e1f1d6gn4\" data-stale=\"false\" data-testid=\"element-container\">\n<div class=\"stAlert\" data-testid=\"stAlert\">\n<div class=\"st-ae st-af st-ag st-ah st-ai st-aj st-ak st-al st-am st-bc st-ao st-ap st-aq st-ar st-as st-at st-au st-av st-aw st-ax st-ay st-az st-bb st-b1 st-b2 st-b3 st-b4 st-b5 st-b6 st-b7 st-b8\" role=\"alert\" data-baseweb=\"notification\" data-testid=\"stNotification\">\n<div class=\"st-b9 st-ba\">\n<div class=\"st-emotion-cache-1tbdc6l e1e4pi9i0\" data-testid=\"stNotificationContentInfo\">\n<div class=\"st-emotion-cache-1nmtqlb e1eexb540\">\n<div class=\"st-emotion-cache-d4qd9r e1nzilvr4\" data-testid=\"stMarkdownContainer\">\n<p>This study developed an agent-based simulation model (ABM) to study the dynamics of bird species richness and population in the Galapagos Islands. The simulation environment was constructed from GIS data of the Galapagos Islands, which was processed using GeoPandas and Shapely into a spatial grid containing habitat types. Each agent represented a single bird with biological attributes such as reproductive rate, mortality, energy range, habitat preference, and dispersal ability, normalized per weekly step. The core processes of random immigration from the mainland, dispersal, energy acquisition, mortality, and reproduction were run at discrete timescales over several simulation years. Output data included total population trends per species, species richness per island (Species Area Relationship and Species Isolation Relationship), and final distribution maps of individuals over the habitat map. Simulation results showed that island size, habitat type, and isolation distance synergistically influenced variations in species richness, consistent with island biogeography theory. Validation using the Jaccard Index and Root Mean Square Error (RMSE) against field population estimation data confirmed the model's ability to replicate population composition and numbers with varying degrees of accuracy across islands. This model provides a quantitative tool for understanding ecological patterns in island ecosystems and supports data-driven conservation planning.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"Berikut adalah kumpulan inovasi mahasiswa Teknik Informatika ITB dalam mata kuliah IF3211 Komputasi Spesifik Domain Semester II Tahun Ajaran 2024\/2025. Galeri ini menampilkan berbagai pendekatan teknologi seperti Machine Learning, Deep [...]","protected":false},"author":748,"featured_media":28736,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1595],"tags":[2769,2772,2767,2765,2763],"class_list":["post-28729","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fariska-zakhralativa-ruskanda","tag-sdg-10","tag-sdg-15","tag-sdg-3","tag-sdg-4","tag-sdg-9"],"_links":{"self":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/posts\/28729","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/users\/748"}],"replies":[{"embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/comments?post=28729"}],"version-history":[{"count":9,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/posts\/28729\/revisions"}],"predecessor-version":[{"id":28745,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/posts\/28729\/revisions\/28745"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/media\/28736"}],"wp:attachment":[{"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/media?parent=28729"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/categories?post=28729"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stei.itb.ac.id\/en\/wp-json\/wp\/v2\/tags?post=28729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}