Implementasi Otomasi Untuk Manajemen Perangkat Jaringan Dan Server Secara Terpusat Pada Sebuah Aplikasi Dengan Metode NDLC (Network Development Life Cycle)

NetwManage adalah aplikasi jaringan untuk otomatisasi jaringan, mengelola jaringan secara otomatis tanpa harus mengakses langsung setiap perangkat satu per satu, karena hal tersebut akan mengabiskan waktu dan tenaga yang banyak. Mengembangkan fungsi seperti Backup Configuration untuk menjaga keutuhan sistem jaringan yang sedang berjalan, sehingga jika terjadi error atau perangkat mati, system backup telah menyimpan secara otomatis di server pada interval waktu yang ditentukan, dan pengguna dapat menyimpan data yang diterima di server sebelum pengaturan ulang di jaringan atau melakukan pemulihan konfigurasi pada perangkat di jaringan tersebut. Dengan pengaturan tertentu pada infrastruktur jaringan sehingga hanya pengguna tertentu yang mengetahui untuk mengakses aplikasi web tersebut. Aplikasi ini dirancang untuk mendukung administrator jaringan untuk menangani infrastruktur jaringan yang cukup kompleks, terutama di perusahaan. Proses penelitian ini menggunakan metode Network Development Life Cycle (NDLC), metode ini berorientasi pada enam tahapan dengan siklus yang tidak memiliki awal atau akhir yaitu analisa, desain, simulasi dengan prototipe, implementasi, pemantauan, manajemen

Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

Sistem Akuisisi Dan Monitoring Performa Mesin Produksi Berbasis Internet of Things (IoT) Dengan Metode Logging Data

Perkembangan teknologi Internet of Things (IoT) telah membawa dampak positif pada berbagai sektor industri, termasuk industri manufaktur. Dalam industri manufaktur, pemantauan dan akuisisi data performa mesin produksi menjadi hal yang krusial untuk meningkatkan efisiensi dan kualitas produksi. Penelitian ini bertujuan untuk mengimplementasikan sistem akuisisi dan monitoring performa mesin produksi berbasis IoT pada industri manufaktur dengan menggunakan metode logging data. Tahap analisis dilakukan dengan melakukan observasi terhadap mesin produksi untuk memahami karakteristik dan spesifikasi mesin yang akan menjadi fokus dalam pengembangan sistem. Pengumpulan data performa mesin dilakukan dengan menggunakan sensor yang terhubung ke mesin produksi. Data performa yang terkumpul diolah dan dianalisis secara real-time menggunakan metode logging data untuk mendapatkan informasi yang relevan tentang kondisi operasional mesin. Hasil implementasi sistem akuisisi dan monitoring menunjukkan bahwa penggunaan teknologi IoT dengan metode logging data memberikan manfaat besar dalam memantau dan mengoptimalkan performa mesin produksi. Data yang terkumpul secara real-time memungkinkan identifikasi permasalahan potensial, pencegahan kerusakan mesin yang tidak terduga, serta meningkatkan efisiensi operasional dan kualitas produk

Water Quality Monitoring System with Parameter of pH, Temperature, Turbidity, and Salinity Based on Internet of Things

This research aims to monitor the quality of water used for aquariums. The physical parameters used are water pH, water temperature, water turbidity, and water salinity. Using a pH sensor, temperature sensor, turbidity sensor, and salinity conductivity sensor with Arduino as the controller. The prototype method used in this research, starting from the formulation, research, building stages to testing and evaluating the results of the research. The working process of the system is when the system is activated, the sensors will detect and capture the amount of value contained in the water, then the data from the sensor is sent to a database in the cloud using an ethernet shield that is connected to the media router as a liaison for the internet network then displayed on the website dashboard in the form of graphs and monitoring record tables in real time. The sensors function to detect water quality, where quality standards have been set in this system, namely temperature standards of 27-30°C, pH standards of 7.0-8.0, turbidity standards of 2.5-5 ntu, and salinity of 20-28 ppt. If the sensor detects non-compliance with water quality standards, the buzzer in this system will sound. From the results of system testing, sensors can detect water quality in real time within 5-10 seconds. Based on the research results, this water quality monitoring system is effective to help ensure the quality of the water in the aquarium so that it always meets the standards

Stock Price Prediction using Prophet Facebook Algorithm for BBCA and TLKM

Stocks are an investment instrument that is starting to be in great demand by the public today. However, stock prices are fluctuating, making people feel doubts about when they are going to invest. To overcome these doubts, we need a way to predict stock prices. This study aims to predict stock price fluctuations using Facebook's Prophet Algorithm to help people decide their investment in stock. The research object used is BBCA and TLKM stock price data in the form of a time series from 03 May 2021 to 28 April 2022 with stock price testing data for the next week, namely 01 May 2022 to 07 May 2022. From the training and testing process done, a prediction is produced that is very close to the original value. Using the RMSE, MSE and MAE measurements, we get RMSE 49.6, MSE 2462.1 and MAE 37.5 for BBCA and RMSE stocks, namely 21.3, MSE 456.5 and MAE 19.2 for TLKM shares. The conclusion is that Facebook's Prophet Algorithm is suitable for predicting stock prices

Implementation of Random Search Algorithm with FSSRS (Fixed Step Size Random Search) for Applicating the Patrol System Based on Mobile Computing

Environmental security is very influential for the sustainability of human life. In order for environmental security to remain in a safe condition, a system is needed that can control the environment, such as patrolling at every point to ensure that the environmental conditions are safe. However, it is felt that this is not enough if the patrol system is not assisted by tools or systems that are digitalized and integrated with community service officers, such as firefighters, ambulances, and police, and are easy for officers to use when conducting patrols. So, it is necessary to schedule patrols to several points with different routes for each activity so that it is not easily read by unwanted parties in terms of crime. In order for the system to obtain patrol scheduling in a timely and efficient manner, an appropriate and efficient algorithm is needed, the algorithm is random search with FSSRS (Fixed Step Size Random Search) which can suggest random and precise patrol scheduling. From the results of training using four iterations, namely 50, 100, 150, and 200, the best value was produced in the 200th iteration. Data was taken from the results of a case study survey with eight patrol points using coordinates at each point. So, it can be concluded that the FSSRS algorithm is effectively used to randomize patrol points and can be implemented in the application patrol system

PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES

Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024