Penerapan Smart Monitoring Tarpaulin Fish bagi Pembudidaya Ikan Aliran Sungai Jembatan Kembar di Kelurahan Loktabat Utara Banjarbaru berbasis MQTT

Smart Monitoring Tarpaulin Fish merupakan pengelolaan kualitas air tentang upaya memantau kualitas air sehingga dapat tercapai kualitas air kondisi yang diinginkan sesuai dengan kondisi alamiahnya. Pada kegiatan budidaya perikanan, untuk keseimbangan ekosistem perairan dalam suatu wadah yang terbatas bahwa pH akan rendah dan kandungan oksigen terlarut akan berkurang, sebagai akibatnya konsumsi oksigen akan menurun, aktivitas pernafasan ikan naik dan selera makan ikan akan berkurang. Menurut Rochyani (2018) bahwa faktor penentu kualitas air untuk kolam budidaya ikan antara lain keasaman atau kebasaan air, kekeruhan air, suhu air, kandungan oksigen, dan kandungan garam. Warga di pesisir sungai jembatan kembar Loktabat Utara Kota Banjarbaru saat ini telah berbudidaya perikanan. Pengelolaan budidaya perikanan memerlukan pemantauan secara berkala dikarenakan perlunya pengamatan kualitas air budidaya perairan. Pembudidaya ikan sungai jembatan kembar Loktabat Utara rata-rata bekerja juga sebagai buruh harian, sehingga ada kalanya tidak dapat memantau kondisi kolam. Maka dibutuhkan teknologi yang dapat memudahkan dalam memantau pengelolaan kondisi kolam budidaya perikanan. Penggunaan smart monitoring tarpaulin fish ini menjadi salah satu solusi untuk mengatasi masalah tersebut, yaitu kolam terpal berbasis IoT (Internet of Things). Kondisi ini memantau kondisi suhu, dan kondisi tds air dengan menggunakan koneksi internet broadband berbasis MQTT (Message Queuing Telemetry Transport) serta bertenaga surya. Hasil implementasi ini terpenuhinya pemantauan secara real time kondisi kolam budidaya ikan hingga 80%. Penurunan kematian ikan hingga 30% karena percepatan penanganan kualitas air

A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

The research aims to compare the classification performance of natural disaster messages classification from Twitter. The research experiment covers the analysis of three-word embedding-based extraction feature techniques and five different models of deep learning. The word embedding techniques that are used in this experiment are Word2Vec, fastText, and Glove. The experiment uses five deep learning models, namely three models of different dimensions of Convolutional Neural Network (1D CNN, 2D CNN, 3D CNN), Long Short-Term Memory Network (LSTM), and Bidirectional Encoder Representations for Transformer (BERT). The models are tested on four natural disaster messages datasets: earthquakes, floods, forest fires, and hurricanes. Those models are tested for classification performanc

Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection

The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine,  Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. The models were tested on a dataset with 140 samples that were grouped into four class labels, and each sample has 2160 features. Those models were tested for classification performance. This research shows Random Forest and 1D CNN have the best performance

Gender Classification of Twitter Users Using Convolutional Neural Network

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets an

Perancangan dan pembuatan sistem informasi perhitungan HPP pada perusahaan Logam Asir

Perusahaan Logam Asir adalah perusahaan rumah tangga (home industry) yang bergerak dibidang pemproduksian alat - alat dapur stailess steel dengan bahan baku lembaran stainless stell. Kegiatan administrasi yang ada pada perusahaan ini diantaranya pembelian, penjualan dan proses produksi. Tetapi kendala utama yang dihadapi oleh perusahaan tersebut adalah mengenai sistem administrasi didalamnya. Dengan adanya program aplikasi ini diharapkan dapat membantu kinerja perusahaan ini dalam meningkatkan sistem administrasi yang ada menjadi lebih baik dan berjalan sesuai kebutuhan. Tahapan yang dilakukan dalam pembuatan project ini yaitu analisis, desain data flow diagram, desain entity relationship diagram, desain database dan implementasi pemprogramannya. Pembuatan project ini menggunakan bahasa pemprograman Borland Delphi 7.0, Database yang digunakan Microsoft Access dan koneksi database menggunakan Data Source (ODBC). Hasil akhir dari pembuatan project ini adalah input pembelian, input penjualan, input produksi daily, input biaya daily, perhitungan harga pokok produk, perhitungan harga pokok penjualan, semua laporan yaitu laporan stock, laporan customer, laporan supplier, laporan pembelian, laporan penjualan, laporan produksi daily, dan perhitungan HPP

Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification

Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification

Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.  Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy