{"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":"2026-02-14T01:14:45","modified_gmt":"2026-02-13T18:14:45","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":"Galeri Proyek IF3211 &#8211; Komputasi Spesifik Domain"},"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\">Berikut adalah kumpulan inovasi mahasiswa Teknik Informatika ITB dalam mata kuliah <strong>IF3211 Komputasi Spesifik Domain<\/strong> Semester II Tahun Ajaran 2024\/2025. Galeri ini menampilkan berbagai pendekatan teknologi seperti <em>Machine Learning<\/em>, <em>Deep Learning<\/em>, dan simulasi untuk menyelesaikan tantangan di bidang bioinformatika, ekologi, dan kesehatan.<\/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>Analisis Kelangsungan Hidup Pasien Transplantasi Sel Hematopoietik Alogenik terhadap Ras dengan Pendekatan 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><\/h5>\n<h5><strong>\ud83d\udc65 Anggota Kelompok:<\/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>Transplantasi Sel Punca Hematopoietik Alogenik (HCT Alogenik) menunjukkan hasil yang bervariasi pada pasien. Penelitian ini mengembangkan dan mengevaluasi model machine learning (XGBoost, CatBoost, dan LightGBM) untuk memprediksi event-free survival (EFS) pada pasien HCT Alogenik, dengan fokus pada kinerja prediktif dan aspek kesetaraan ras. Menggunakan dataset dari Center for International Blood and Marrow Transplant Research (CIBMTR), data diproses melalui tahap imputasi nilai yang hilang, transformasi variabel target menggunakan estimasi Kaplan-Meier, dan encoding fitur kategorikal. Optimasi hyperparameter dilakukan dengan Optuna dan validasi silang 10-kali (10-fold cross-validation) digunakan untuk evaluasi model, dengan metrik utama berupa C-index yang disesuaikan untuk memperhitungkan varians antar kelompok ras. Hasil menunjukkan LightGBM mencapai C-index tertinggi (0.6691), mengungguli CatBoost (0.6673) dan XGBoost (0.6665). Analisis lebih lanjut mengidentifikasi adanya disparitas EFS antar kelompok ras, pasien dari kelompok &#8220;Lebih dari satu ras&#8221; menunjukkan hasil terbaik, sementara kelompok &#8220;Putih&#8221; menunjukkan hasil terendah, serta mengidentifikasi fitur-fitur penting seperti skor Disease Risk Index (DRI) dan usia donor. Penelitian ini menyoroti potensi signifikan machine learning dalam prediksi prognosis HCT Alogenik dan menekankan krusialnya pertimbangan aspek kesetaraan dalam pengembangan model medis prediktif.<\/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 Anggota Kelompok:<\/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 Penerapan Machine Learning dalam Biologi<\/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>Pengembangan Model Machine Learning untuk Deteksi Mutasi Genetik yang Berkontribusi terhadap Kanker Payudara<\/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 Anggota Kelompok:<\/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>Kanker payudara merupakan salah satu jenis kanker yang paling umum dan menjadi penyebab utama kematian akibat kanker pada wanita di seluruh dunia. Salah satu faktor utama yang berkontribusi terhadap kanker payudara adalah mutasi genetik pada gen penekan tumor. Deteksi dini mutasi genetik ini sangat penting untuk pencegahan dan pengelolaan kanker payudara. Penelitian ini mengusulkan pengembangan model machine learning berbasis Deep Learning untuk mendeteksi mutasi genetik pada sekuens DNA, khususnya pada gen BRCA1 dan BRCA2, dengan tujuan memberikan hasil deteksi yang cepat dan tepat. Model yang diterapkan meliputi Temporal Convolutional Network (TCN), Convolutional Neural Network satu dimensi (1D-CNN), dan Recurrent Neural Network (RNN) BiLSTM, menggunakan teknik sequential labeling untuk mengidentifikasi jenis dan indeks mutasi pada setiap nukleotida. Data sekuens DNA yang digunakan berasal dari sampel pasien penderita kanker payudara dari Catalogue of Somatic Mutation in Cancer (COSMIC). Hasil pengujian menunjukkan bahwa TCN memiliki kinerja terbaik dengan F1-score 0.8602, mengungguli ID-CNN (0.8325) dan BiLSTM (0.8325), serta menunjukkan waktu deteksi yang lebih rendah. Analisis mutasi yang terdeteksi menunjukkan dominasi mutasi C\u2192A dan C\u2192T, yang konsisten dengan literatur mutasi genom manusia sebagai mutasi driver pada banyak gen penekan tumor. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis bioinformatika, khususnya dalam mendeteksi mutasi genetik secara otomatis dan cepat, yang diharapkan dapat mendukung diagnosis dini dan pengelolaan kanker payudara yang lebih efektif.<\/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>Karakteristik Komunitas Mikroba Rizosfer Akibat Limbah Perkotaan Melalui Pendekatan Metagenomik<\/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 Anggota Kelompok:<\/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>Rizosfer merupakan zona tanah yang aktif secara biologis dan sangat dipengaruhi oleh aktivitas akar, di mana komunitas mikroba memainkan peran penting dalam siklus nutrisi dan kesehatan ekosistem. Penelitian ini bertujuan untuk mengkaji bagaimana paparan limbah perkotaan memengaruhi struktur dan keanekaragaman komunitas bakteri di rizosfer, mengidentifikasi taksa dominan atau yang mengalami perubahan signifikan, serta menemukan biomarker mikroba potensial yang berperan dalam respons terhadap stres lingkungan. Analisis dilakukan terhadap 27 sampel metagenom (BioProject PRJNA1229183) menggunakan sekuensing gen 16S rRNA (V3\u2013V4) dan dianalisis dengan QIIME2, DADA2, dan PICRUSt2. Hasil menunjukkan adanya perbedaan struktur komunitas mikroba antar sampel berdasarkan analisis \u03b2diversitas, dengan dominasi filum Proteobacteria, Actinobacteriota, dan Firmicutes. Beberapa genus seperti Pseudomonas, Geobacter, Hydrogenophaga, dan Acidovorax teridentifikasi memiliki peran penting dalam fiksasi nitrogen dan bioremediasi. Prediksi fungsi genetik mengungkap dominasi gen rpoE dan ABC-type transporters yang terkait dengan respons stres membran, berpotensi sebagai biomarker molekuler. Studi ini memberikan pemahaman awal mengenai adaptasi mikroba rizosfer terhadap tekanan limbah perkotaan serta mengidentifikasi taksa dan gen fungsional yang relevan untuk strategi pemantauan dan pemulihan ekosistem tanah di lingkungan perkotaan.<\/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>Simulasi Evolusi Resistensi Antibiotik pada Escherichia coli melalui Mutasi, Transfer Gen Horizontal, dan Seleksi Alam<\/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 Anggota Kelompok:<\/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>Evolusi merupakan salah satu konsep dalam ilmu biologi yang menjelaskan tentang perubahan sifat dari suatu makhluk dan diwariskan ke generasi selanjutnya. Evolusi digunakan makhluk hidup untuk beradaptasi dengan lingkungan sehingga dapat meningkatkan rentang hidupnya. Mutasi berperan sebagai bentuk perubahan yang dilakukan pada materi genetik. Untuk dapat mempelajari hal tersebut, akan dirancang simulasi yang dapat memvisualisasikan evolusi. Akan digunakan bakteri sebagai subjek penelitian utama karena sifatnya yang uniseluler dan memiliki struktur genetik yang bisa tidak terlalu komplek. Simulasi secara khusus berfokus pada evolusi resistensi antibiotik pada populasi bakteri, dimana tekanan seleksi dari paparan antibiotik akan memicu mutasi yang memungkinkan bakteri bertahan hidup. Model simulasi menggunakan genetic algorithm untuk merepresentasikan proses mutasi, seleksi alam, dan reproduksi bakter dalam lingkungan yang mengandung antibiotik dengan konsentransi bervariasi. Evolusi ini kemudian divisualisasikan agar dapat mengamati pola perkembangan resistensi, dan dinamikan evolusi bakteri dari waktu ke waktu. Hasil simulasi diharapkan dapat memberikan pemahaman yang baik tentang mekanisme evolusi resistensi antibiotik dan faktor yang mempengaruhi kecepatan perkembangannya di tingkat populasi.<\/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 Anggota Kelompok:<\/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 Anggota Kelompok:<\/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 Anggota Kelompok:<\/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>Simulasi Agent-Based untuk Memahami Dinamika Kekayaan Spesies di Pulau<\/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 Anggota Kelompok:<\/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>Penelitian ini mengembangkan model simulasi berbasis agen (Agent-Based Modeling, ABM) untuk mempelajari dinamika kekayaan dan populasi spesies burung di Kepulauan Galapagos. Lingkungan simulasi dibangun dari data GIS Pulau Galapagos, yang diolah menggunakan GeoPandas dan Shapely menjadi grid spasial berisi tipe habitat. Setiap agen merepresentasikan satu individu burung dengan atribut biologis seperti laju reproduksi, mortalitas, rentang energi, preferensi habitat, dan kemampuan dispersi, yang dinormalisasi per langkah mingguan. Proses inti adalah imigrasi acak dari daratan utama, dispersal, perolehan energi, kematian, dan reproduksi dijalankan pada rentang waktu diskrit selama beberapa tahun simulasi. Data keluaran mencakup tren populasi total per spesies, kekayaan spesies per pulau (Species Area Relationship dan Species Isolation Relationship), serta peta distribusi akhir individu di atas peta habitat. Hasil simulasi menunjukkan bahwa ukuran pulau, tipe habitat, dan jarak isolasi secara sinergis mempengaruhi variasi kekayaan spesies, sesuai teori biogeografi pulau. Validasi menggunakan Indeks Jaccard dan Root Mean Square Error (RMSE) terhadap data estimasi populasi lapangan mengkonfirmasi kemampuan model mereplikasi komposisi dan jumlah populasi dengan tingkat akurasi yang bervariasi di setiap pulau. Model ini menyediakan alat kuantitatif untuk memahami pola ekologis di ekosistem pulau dan mendukung perencanaan konservasi berbasis data.<\/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":[],"class_list":["post-28729","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fariska-zakhralativa-ruskanda"],"_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}]}}