Analisis Metode Pattern Based Approach Question Answering System pada Dataset Hukum Islam berbasis Bahasa Indonesia

Hukum islam merupakan ketentuan perintah dari Allah SWT yang memiliki hukum yang berbeda-beda. Dibutuhkanwaktuyanglamadalamprosespencarianinformasisecaramanualmengingatbanyaknyajenis darihukumislam. DaripermasalahandiatasdenganbantuanQuestionAnsweringSystemdapatmengatasi permasalahan tersebut. Tujuan dari penelitian ini untuk membantu pengguna dalam mencari informasi yang dibutuhkan dengan masukan berupa pertanyaan dengan kategori properti yaitu (OBJECT) Apa, (PERSON) Siapa, (LOCATION) Dimana, (TIME) Kapan dan (COUNT) Berapa. Pada penelitian ini Question Answering System diimplementasikan dengan metode Pattern Based Approach berdasarkan penggolonganpola. Padapenelitianinididapatkanhasilakurasijawabansebesar64,5%,padasetiaptipekategori pertanyaan ”Apa”, ”Kapan”, ”Berapa”, ”Siapa”, dan ”Dimana” dengan akurasi jawaban sebesar 63,3%, 65%, 73,3%, 65% dan 40%. Dari hasil akurasi yang didapatkan bahwa metode Pattern Based Approach mampu diimplementasikan pada Question Answering System untuk mengatasi permasalahan yang terjadi diatas. Katakunci: HukumIslam,QuestionAnsweringSystem,PatternBasedApproac

Zoological Park : Kebun Binatang Virtual Interaktif menggunakan perangkat Nirsentuh Leapmotion bagi Penyandang Tunadaksa

Anak berkebutuhan khusus (ABK) adalah anak dengan karakteristik khusus yang berbeda dengan anak pada umumnya tanpa selalu menunjukan pada ketidakmampuan mental, emosi atau fisik. Salah satunya adalah tunadaksa, secara definitive tunadaksa adalah ketidakmampuan anggota tubuh untuk melaksanakan fungsinya disebabkan oleh berkurangnya kemampuan anggota tubuh untuk melaksanakan fungsi secara normal, akibat luka, penyakit, atau pertumbuhan tidak sempurna. Sehingga untuk kepentingan pembelajarannya perlu layanan khusus. Keterbatasan tunadaksa mempengaruhi kemampuan eksplorasi mereka mengenal lingkungan sekitar. Kurangnya eksplorasi mereka mengenai dunia luar dan lingkungan sekitar membuat mereka cenderung merasa apatis, malu, rendah diri, sensitif dan kadang-kadang pula muncul sikap egois. Untuk mengembangkan pola pikir mereka dalam hal mengenal lingkungan sekitar salah satunya adalah mengenal lingkungan kebun binatang. Proses tersebut dibantu dengan bantuan device oculus rift dan leapmotion, karena kedua device tersebut dapat menunjang proses digitalisasi kebun binatang. Untuk membantu permasalahan diatas munculah ide untuk membuat aplikasi dengan judul “Zoological Park : kebun binatang virtual interaktif menggunakan perangkat nirsentuh leapmotion bagi penyandang tunadaksa”. Adapun hasil akhir dari aplikasi ini berupa simulasi kebun binatang dalam media virtual yang disajikan menggunakan 3D Objek dalam visualisasinya. Model simulasi yang dibuat didukung dengan kemampuan interaksi antara pengguna dan aplikasi melalui sensor gerak tangan untuk menambah kesan nyata dalam penggunaannya. Kata Kunci : Tunadaksa, Kebun Binatang, Oculus rift, Leapmotion, Virtual Reality

Segmentasi Atrial Septal Defect menggunakan Convolutional Neural Networks berbasis V-NET

Analisis citra medis merupakan sebuah topik yang sangat diminati, karena sangat diperlukan dalam proses mendiagnosis penyakit. Salah satu analisis citra yang telah diteliti saat ini adalah penyakit jantung bawaan pada janin. Ada banyak jenis penyakit jantung bawaan pada janin salah satunya yaitu ASD. Penyakit jantung bawaan memiliki peran penting untuk melakukan diagnosis kelainan pada jantung khususnya janin. Salah satu penyakit jantung bawaan pada janin adalah atrial septal defect. Ada banyak cara yang dilakukan untuk proses melakukan diagnosis pada analisis citra medis yaitu segmentasi. Segmentasi khususnya pada gambar merupakan salah satu kunci dalam melakukan proses diagnosis pada analisis citra medis. Convolutional neural networks (CNNs) merupakan teknik pembelajaran dalam yang sering digunakan khususnya pada segmentasi gambar. Penelitian ini menerapkan sebuah teknik pembelajaran dalam untuk melakukan proses segmentasi pada penyakit jantung bawaan berdasarkan citra gambar. Pendekatan yang diusulkan menggunakan CNNs dengan arsitektur V-NET yang digunakan pada gambar atrial septal defect. Sebagai hasil penelitian hasil kinerja yang didapatkan dengan menggunakan evaluasi matriks piksel akurasi sebesar 96 % mean akurasi 91% dan mean iu sebesar 86%

Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network

Skin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298

Dermatitis Atopic and Psoriasis Skin Disease Classification by using Convolutional Neural Network

Skin is the one of the body parts that play a large role in human physical body. There are so many functions of the skin such as offering protection against fungal infection, bacteria, allergy, viruses and controls the temperature of the body. But, the reported shown that the skin disease is the most common disease in humans among all age groups and a significant root of infection. The diagnosis of skin diseases involves several tests. Due to this, the diagnosis process is seen to be intensely laborious, time-consuming and requires an extensive understanding aspecially for the skin disease that have similar symptoms. Two skin diseases that have similar symptoms and most misdiagnosed are atopic dermatitis and psoriasis. Convolutional Neural Network for image processing and classifying have been developed for more accurate classification of skin diseases with different architectures. However, the accuracy in determining skin lesions using CNNs is on the average level. The factors that affect the accuracy result of a CNN is the depth where gradients vanished as the network goes deeper. Another factor is the variance in the training set which means the need of the large size of training set. Hence, in this study we tried 10 CNN architecture to get the best result for classifying dermatitis atopic and psoriasis. These are VGG 16, VGG 19, ResNet 50, ResNet 101, MobileNet, MobileNet V2, DenseNet 121, DenseNet 201, Inception and Xception. Experimental result shown that the inception V3 architecture give the best result with accuracy for data testing 84%, accuracy for unseen data 82% and confusion matrix with True positive obtained is 248, True Negative is 61, False positive is 54 and False Negative 298

Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory

Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients

Identification of Indonesian Authors Using Deep Neural Networks

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision

Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification

Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs

Aorta Detection with Fetal Echocardiography Images Using Faster Regional Convolutional Neural Network (R-CNNs)

The fetal heart structure has an important role in analyzing the location of abnormalities in the heart. The aorta is one of the fetal heart structures, which has an essential part in exploring how the fetal heart is structured. To see the fetal heart structure can be seen with the help of an echocardiography tool in the form of ultrasound to see ultrasound images of the fetal heart. In ultrasound image data, detection is challenging because of its low image features, shadows, and contrast levels. So that is the first to do it yourself in one of the points of the culture in the culture in the aorta. The approach in this study uses deep learning in cases using Faster Regional Convolutional Neural Network (R-CNNs) with the R-CNNs mask method. The proposed approach has been applied to 151 ultrasound images of the fetal heart for the aortic region. The evaluation results were tested by evaluating metrics on the detection object with an mAP value of 83.71%

Cloud-based ECG Interpretation of Atrial Fibrillation Condition with Deep Learning Technique

The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded cloud-computing algorithm in nonmetropolitan areas using a telehealth surveillance system. By using a single-lead electrocardiogram (ECG) recorder, we performed a prospective AF screening study. Both ECG measurements were evaluated and interpreted by the cloud-computing algorithm and a cardiologist on the telehealth monitoring system. The proposed cloud-computing based on Convolutional Neural Network (CNN) algorithm for AF detection had an accuracy of 99% sensitivity of 98%, and specificity of 99%. The overall satisfaction performance for the process of AF screening, and it is feasible to conduct AF screening by using a telehealth monitoring system containing an embedded cloud-computing algorithm