Smart Farming Untuk Pengaturan Suhu Ruangan Pada Budidaya Jamur Tiram Berbasis Backpropagation

The problem with mushroom cultivation is the difficulty of regulating the room temperature of mushrooms, especially oyster mushrooms. The optimal production of oyster mushrooms is at temperatures between 25 C - 27 C. To regulate or manipulate humidity and room temperature to water the kumbung or mushroom room. The watering process is carried out several times to stabilize the room temperature during the day.To overcome the watering that is done manually, Automatic Temperature Control and Monitoring of Oyster Mushrooms Based on GSM Sim800l Arduino Uno is made. This tool uses a DHT11 sensor, relay, 16x2 LCD, GSM Sim 800L, and Stepdown. The test was carried out in a mushroom kumbung measuring 10.7m long, 5.9m wide, and 3.5m high. Watering time is done by observing the data at room temperature. The data is then studied using a backpropagation. This method aims to identify the pattern of watering time so that the optimal watering time is produced. The test results show that the tool can monitor the temperature and humidity of the kumbung mushroom with the following values: temperature 27°C - 33°C and humidity 70% - 90%. The introduction of mushroom watering patterns with BPNN showed an error rate of 40%

Controlling and Monitoring of Temperature and Humidity of Oyster Mushrooms in Tropical Climates

Controlling the temperature and humidity of oyster mushroom cultivation is done manually by spraying air on the mushroom container so it takes a lot of time and effort. This is done to meet the requirements for growing oyster mushrooms which are strongly influenced by temperature and humidity conditions so that they can grow well. In this study, a device for controlling and monitoring the temperature and humidity of oyster mushroom cultivation was made automatically based on Arduino UNO. This tool can regulate and monitor the temperature and humidity in oyster mushroom cultivation automatically so that the temperature and humidity can be maintained without having to spend a lot of time and effort. The components used in building the automatic temperature and humidity controller for mushroom cultivation based on the Arduino UNO are the dht11 sensors, Arduino UNO, L298N driver, relay, and 16x2 I2C LCD. From the results of the tests that have been carried out, it can be concluded that the temperature and humidity control and monitoring device for automatic oyster mushroom cultivation based on Arduino UNO has been able to work well in regulating and monitoring temperature and humidity as expected

Optimasi Radial Basis Function Neural Network dengan Growing Hierarchial Self Organizing Map pada Data Time Series

Salah satu model JST yang sesuai dengan peramalan data time series, adalah model Radial Basis Function Network (RBFN). Jaringan syaraf tiruan Radial Basis Function merupakan jaringan feed-forward yang memiliki tiga lapisan, yaitu lapisan masukan (input layer), lapisan tersembunyi (hidden layer) dan lapisan keluaran (output layer). Besarnya dimensi input pada jaringan syaraf menyebabkan menurunnya kemampuan komputasi suatu model jaringan. Salah satu cara untuk mengatasi hal tersebut adalah dengan mereduksi dimensi input. Dalam penelitian ini jaringan syaraf tiruan Radial Basis Function dipadukan dengan metode Growing Hierarchical Self Organizing Map (GH-SOM). Penggunaan teknik clustering data pada proses awal, memungkinkan mengurangi dimensi input dengan kehilangan informasi yang minimum. Sehigga dapat mengoptimalkan proses prediksi dengan menggunakan pendekatan RBFN. Prediksi harga saham dengan Optimasi metode Radial Basis Function neural network dengan Menggunakan Growing Hierarchical Self Organizing Map, dengan jumlah vektor data sebanyak 364 dengan SSE sebesar 0,074713 diperoleh akurasi sebesar 94,03

Convolutional Long Short-Term Memory (C-LSTM) For Multi Product Prediction

The retail company PT Terang Abadi Raya has a solid commitment to supporting distributors of LED lights and electrical equipment who have joined them, helping to spread their products widely in various regions. To face increasingly intense market competition, it is essential to produce high-quality products to win the competition and meet consumer demands. To achieve this, efficient production planning is necessary. The Convolutional Long Short-Term Memory (C-LSTM) method is used in this study to forecast product sales at PT Terang Abadi Raya. The research results show that C-LSTM has the potential to predict sales effectively. Evaluation is conducted using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The calculations reveal that the smallest values are obtained at epoch 10, with an MAE of 0.1051 and a MAPE of 22% in the testing data. For the cable data, the smallest values are found at epoch 100, with an MAE of 0.0602 and a MAPE of 44% in the testing data. The Long Short-Term Memory (LSTM) method with ten neurons produces the most minor errors during training

Rancang Bangun Sistem Informasi Pengajuan Tugas Akhir dan Kerja Praktek di STIMIK STIKOM INDONESIA

Pengajuan tugas akhir (TA) dan pengajuan kerja praktek (KP) di STMIK STIKOM Indonesia untuk saat ini masih manual, dimana mahasiswa yang akan mengajukan Tugas Akhir (TA) atau Kerja Praktek (KP) hanya dicatat pada sebuah kertas dan pelaporan hasil pengajuan tersebut dilakukan secara sederhana dengan menghitung jumlah mahasiswa yang mencatat secara manual. Sistem pengajuan TA & KP dibangun dengan tujuan dapat mempercepat penentuan dosen pembimbing, memudahkan integrasi dengan sistem SINTESYS dan mengatur data mahasiswa yang akan melakukan pengajuan TA & KP, dapat di kelola dengan baik di STMIK STIKOM Indonesia

Peramalan Tingkat Kunjungan Wisatawan dengan Metode Average Based Fuzzy Time Series dan Markov Chain Model di Sriphala Resort & Hotel

Bali merupakan salah satu daerah tujuan pariwisata dunia, ini dapat dilihat dari data kunjungan wisatawan ke Bali periode Januari 2014 sampai dengan Desember tahun 2014 yang mencapai 3.766.638 kedatangan dari berbagai Negara. Naik turunnya kunjungan wisatawan berimbas pada hunian hotel maupun villa, yang mempengaruhi keuntungan dari hotel maupun vill. Untuk mengetahui tingkat hunian wisatawan, digunakan teknik permalan atau prediksi. Salah satu metode yang khusus digunakan dalam proses peramalan adalah metode Average Based Fuzzy Time Series dan Markov Chain Model. Metode Fuzzy Time Series menangkap pola data pada data histori sebelumnya kemudian digunakan untuk memproyeksikan data yang akan datang. Akan tetapi dalam metode ini terdapat kelemahan, yaitu jika terdapat nilai data yang ekstrim akan mengakibatkan model interval peramalan sangat luas. Untuk memperbaiki kelemahan tersebut, maka metode Average Based Fuzzy Time Series diinduksikan dengan model Markov Chain untuk memperoleh hasil peramalan yang lebih baik. Hasil yang diperoleh dari proses peramalan menggunakan data histori occupancy pada periode Januari 2010 sampai periode Desember 2013, menghasilkan peramalan dengan tingkat akurasi sebesar 80,2628

Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

The distribution of pests in rice plants results in significant losses in production and damage to rice plants for farmers, seen from data on the area of rice borer attacks in the province of Bali in Tabanan district. Therefore, by predicting the distribution of rice pests, we can know the pattern of pest attacks so that we can anticipate them because predicting can provide accuracy and error values through the test results. One of the prediction models is BPNN, where BPNN's advantages for solving complex problems are very suitable for use where large amounts of data are involved and many input/output variables, BPNN is also capable of modeling nonlinear relationships between input and output variables, which may be difficult to capture by this type of predictive model. other. Backpropagation includes supervised learning, which means it can learn from labeled examples and can make accurate predictions on new, unlabeled data. Split data using K-fold cross-validation serves to assess the process performance of an algorithmic method by dividing random data samples and grouping the data as many as K k-fold values

Sentiment Analysis Using Backpropagation Method to Recognize the Public Opinion

 Improve the service quality of tourism actors by conducting sentiment analysis on digital platforms owned by tourism businesses and collecting negative sentiments to improve the quality of services from companies owned by tourism businesses. The growth of the hospitality industry in Indonesia is experiencing rapid growth every year. The tourism industry, part of the hospitality industry, also does not escape the influence of positive and negative sentiments. One method to perform accurate sentiment analysis is Backpropagation Neural Network. Based on the results of tests on the neural network, the best accuracy is obtained when using one hidden layer with the first layer of 10 neurons. The learning rate is 0.000002, where the accuracy is 71.630%. More epochs do not guarantee better accuracy. Based on the results of the research that has been done, suggestions for further researchers are to analyze the review dataset processing method so that it gets a cleaner dataset and is expected to improve better accuracy

Analisis Portofolio Saham Perusahaan Agribisnis di Bursa Efek Indonesia

Stock portfolio analysis is the analysis conducted by diversifying or selective combining shares in investment, with a portfolio of risks to be minimized. every sector of Indonesia Stock Exchange which includes shares of agribusiness companies, have an equal risk investments. The purpose of this study is to find out the formation of optimal portfolios using the Capital Asset Pricing Model (CAPM) model approach and Arbitrage Pricing Model (APT). With both models, an investor will be able to find out the composition of stocks, especially shares of companies engaged in agribusiness, as well as directly to find out the proportion of each stock and its risks and benefits arising from the formation of the portfolio. The results showed that by using the CAPM and APT models in the calculation of the formation of optimal portfolios in stocks excange Agribusiness, produces a portfolio with stocks composision 3 froms, namely AALI, TBLA and UNSP. APT model approach shows a greater value than the CAPM models, the highest return value can be generated is 14.088% while that may happen is losses -0268% while the CAPM models yield the highest return rate of 5.74% and the losses will be experienced by -14.54 %. significant difference in outcome is caused from the different assumptions of the models above. From these results should be expected investors to take investment decisions of agribusiness firms stocks in the Indonesia Stock Exchange with respect to the results obtained from the approach to the CAPM and APT models, in addition to the results obtained from both models are also investors give the attention of fundamental and technical factors companies that investors are not wrong and more confident in make investment decisions

GDSS Development of Bali Tourism Destinations With AHP and Borda Algorithms Based on Tri Hita Karana

Development of Bali tourist destinations using the concept of local wisdom Tri Hita Karana (THK). THK is a concept that contains the philosophy of community life in Bali which means three causes of welfare. This concept is needed to realize tourism, culture and nature. In determining a decision to develop an object in a tourist destination using the THK concept, knowledge from several stakeholders is needed. To combine decisions from several stakeholders is needed. GDSS is a computer-based system that can support the Bali Provincial Government Tourism Office and several components involved in THK to take a decision in developing an object in a tourist destination. To determine the decision of each individual used the AHP model. The AHP model is a model that can solve complex multi-criteria problems into a hierarchy. This AHP model will produce alternative individual decisions from the results of parameter weight processing for each individual. Based on the final result of the GDSS, the development of Bali tourism destinations based on THK is in the form of ranking of the six parameters used (Promotion of tourist destinations, Improvement of facilities, Human Resources, Synergy, Environmental preservation, Setting of holy places). The alternative that has the highest value is used as a reference in developing a THK-based tourist destination