Pengaruh Program Studi Dan Pengetahuan Keuangan Terhadap Perilaku Pengelolaan Keuangan Mahasiswa Dengan Locus Of Control Sebagai Variabel Mediasi

This study investigates factors affecting personal financial management behaviors by examining the relationships among four factors including academic diciplines, financial knowledge, locus of control and financial management behaviors. Source of data obtained from the distribution of questionnaires to 396 students. The sample is taken from 6 city, such as: Gresik, Bangkalan, Mojokerto, Surabaya, Sidoarjo, Lamongan (Gerbang Kertasusila) which were randomly selected for the study. The findings indicate that (1) academic diciplines and external locus of control significantly negative relate to financial management behaviors (2) Financial knowledge significantly positive relate to financial management behaviors. In addition, the results of sobel test, support for the indirect effect of financial knowledge on financial management behavior through external locus of control. Keywords: academic diciplines, financial knowledge, locus of control, financial management behavior

Pengaruh Program Studi Dan Pengetahuan Keuangan Terhadap Perilaku Pengelolaan Keuangan Mahasiswa Dengan Locus Of Control Sebagai Variabel Mediasi

This study investigates factors affecting personal financial management behaviors by examining the relationships among four factors including academic diciplines, financial knowledge, locus of control and financial management behaviors. Source of data obtained from the distribution of questionnaires to 396 students. The sample is taken from 6 city, such as: Gresik, Bangkalan, Mojokerto, Surabaya, Sidoarjo, Lamongan (Gerbang Kertasusila) which were randomly selected for the study. The findings indicate that (1) academic diciplines and external locus of control significantly negative relate to financial management behaviors (2) Financial knowledge significantly positive relate to financial management behaviors. In addition, the results of sobel test, support for the indirect effect of financial knowledge on financial management behavior through external locus of control. Keywords: academic diciplines, financial knowledge, locus of control, financial management behavior

ECG Signal Denoising Using 1D Convolutional Neural Network

Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input signals into compact feature representations and a decoder that reconstructs the cleaned signals. The proposed architecture was tested using the MIT-BIH Noise Stress Test Database, which includes ECG recordings with simulated noise conditions. Performance evaluation metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Mean Absolute Deviation (MAD) were used to assess the model's effectiveness. Results showed a low MSE of 0.034, a high SNR of 15.8 dB, and a MAD of 0.754, indicating significant noise reduction and high-quality signal reconstruction. These findings demonstrate that the 1D CNN architecture effectively reduces various types of noise in ECG signals, thereby enhancing signal quality and facilitating more accurate analysis and diagnosis. The model's ability to maintain the integrity of crucial ECG features while removing noise suggests its potential utility in clinical applications for improving cardiovascular disease diagnosi

Text Classification Using Long Short-Term Memory With GloVe Features

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations with regard to large-scale dataset training. Deep Learning is a proposed method for solving problems in text classification techniques. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 9

Multilabel Classification for News Article Using Long Short-Term Memory

oai:ojs.sjia.ilkom.unsri.ac.id:article/14Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited when there is small labeled data and leads to the difficulty of capturing semantic relationships. In this case, it requires a multi-label text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multi-label text classification techniques. By comparing the seven proposed Long Short-Term Memory (LSTM) models with large-scale datasets by dividing 4 LSTM models with 1 layer, 2 layer and 3-layer LSTM and Bidirectional LSTM to show that LSTM can achieve good performance in multi-label text classification. The results show that the evaluation of the performance of the 2-layer LSTM model in the training process obtained an accuracy of 96 with the highest testing accuracy of all models at 94.3. The performance results for model 3 with 1-layer LSTM obtained the average value of precision, recall, and f1-score equal to the 94 training process accuracy. This states that model 3 with 1-layer LSTM both training and testing process is better.  The comparison among seven proposed LSTM models shows that model 3 with 1 layer LSTM is the best model

Perbandingan Kinerja Neural Network dengan Metode Klasifikasi Tradisional dalam Mendiagnosis Penyakit Jantung: Sebuah Studi Komparatif

Dalam dunia medis, penyakit jantung menjadi salah satu penyebab kematian terbanyak. Oleh karena itu, perlu dikembangkan sistem yang dapat membantu dalam deteksi dan diagnosis penyakit jantung. Dalam penelitian ini, kami menggunakan proses neural network untuk membantu dalam deteksi penyakit jantung dengan menggunakan data training dan testing yang telah dikumpulkan. Data yang digunakan terdiri dari berbagai fitur klinis dan faktor risiko yang dikumpulkan dari pasien yang terkena penyakit jantung. Hasil dari penelitian lain untuk mendiagnosa penyakit jantung dengan metode klasifikasi tradisional menunjukkan akurasi: Logistic Regression 88.52%, K-Nearest Neighbors 78.69%, Random Forest Classifier 86.89%, dan Tuned K-Nearest Neighbors 85.25%. Sedangkan, model neural network yang dikembangkan dapat mengklasifikasikan pasien berdasarkan kondisi jantung mereka dengan akurasi mencapai 91%. Proses pelatihan model melibatkan penggunaan algoritma optimasi RMSprop, dengan cross-validation dan parameter tuning yang dilakukan untuk mencapai hasil terbaik. Model ini mampu memproses input dengan kecepatan tinggi dan menghasilkan hasil klasifikasi yang akurat. Neural network dapat membantu diagnosis awal penyakit jantung bagi tenaga medis. Namun, peningkatan akurasi dan keandalan membutuhkan penelitian lebih lanjut dengan data yang lebih besar dan fitur klinis yang beragam. Dengan optimalisasi model ini, diharapkan penanganan penyakit jantung menjadi lebih efektif dan efisien

Text Classification Using Long Short-Term Memory With GloVe Features

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations concerning large-scale dataset training. In this case, a multi-label text classification technique is needed to be able to group four labels from the news article dataset. Deep Learning is a proposed method for solving problems in text classification techniques. This experiment was conducted using one of the methods of Deep Learning Recurrent Neural Network with the application of the architecture of Long Short-Term Memory (LSTM). In this study, the model is based on trial and error experiments using LSTM and 300-dimensional word embedding features with Global Vector (GloVe). By tuning the parameters and comparing the eight proposed LSTM models with a large-scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95. Besides, LSTM with the GloVe feature gets graphic results that are close to good-fit on average

Klasifikasi Teks Multilabel pada Artikel Berita Menggunakan Long Short-Term Memory dengan Word2Vec

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.Klasifikasi teks multilabel adalah tugas mengategorikan teks ke dalam satu atau lebih kategori. Seperti pembelajaran mesin lainnya, kinerja klasifikasi multilabel terbatas ketika ada data kecil berlabel dan mengarah pada kesulitan menangkap hubungan semantik. Dibutuhkan teknik klasifikasi teks multilabel yang dapat mengelompokkan empat label dari artikel berita untuk penelitian ini. Deep Learning adalah metode yang diusulkan untuk memecahkan masalah dalam klasifikasi teks multilabel. Beberapa contoh metode deep learning yang digunakan untuk pengklasifikasian teks antara lain Convolutional Neural Networks, Autoencoder, Deep Belief Networks, dan Recurrent Neural Networks (RNN). RNN merupakan salah satu arsitektur yang paling popular yang digunakan dalam Pemrosesan Bahasa Alami (PBA) karena struktur recurrent cocok untuk proses teks bervariabel panjang. Salah satu metode deep learning yang diusulkan pada penelitian ini adalah RNN dengan penerapan arsitektur Long Short-Term Memory (LSTM). Dalam penelitian ini untuk mendapatkan model yang optimal pada klasifikasi teks dilakukan percobaan trial dan error menggunakan LSTM dengan fitur word embedding Word2Vec 300 dimensi. Dengan tuning hyperparameter dan membuat perbandingan delapan model LSTM yang diusulkan dengan dataset skala besar, dan untuk menunjukkan bahwa LSTM dengan fitur Word2Vec dapat mencapai kinerja yang baik dalam klasifikasi teks. Hasil penelitian menunjukkan bahwa klasifikasi teks menggunakan LSTM dengan fitur Word2Vec memperoleh akurasi tertinggi pada model kelima dengan 95,38%, sedangkan rata-rata nilai presisi, recall, dan F1-score adalah 95%. Selain itu, LSTM dengan fitur Word2Vec mendapatkan hasil grafik yang dekat dengan good-fit untuk model ketujuh dan kedelapan.  &nbsp

Peran dan Fungsi Yayasan At-Thoharoh dalam Mengembangkan Keagamaan Masyarakat di Nagori Manik Maraja

This study aims to find out how the role of the At-Thoharoh Manik Maraja Foundation in managing madrasas, especially in achieving. In addition, this study also wants to know how the function of the foundation, especially in the religious field of madrasas managed by the foundation. Foundations have an important role in people's lives, namely helping people to improve their welfare through education. In addition, the existence of a foundation can help achieve community goals in the social field, both humanitarian and religious. A foundation may earn profits by conducting various businesses, but the profits obtained may only be used for social purposes, not for personal interests. This study uses a qualitative approach. The results of the study indicate that the role of the Darul Irfan foundation in providing education in At-Thoharoh is quite good, although not optimal. The function of fostering the foundation has been carried out, especially in religious guidance, managerial guidance to the head and also financial management. In addition to coaching, the foundation also helps in resolving conflicts that occur between individuals and between units/foundations. The Foundation already has a clear mechanism for conflict resolution