Judul: Pemodelan Pendinginan Evaporatif pada Rumah Tanaman Tipe Arch Menggunakan Jaringan Saraf Tiruan
Abstrak: Budidaya tanaman di dalam rumah tanaman di daerah yang beriklim tropika basah menghadapi kendala tingginya suhu udara pada siang hari ketika cuaca cerah. Penggunaan pendingin evaporatif dapat menjaga suhu udara di dalam rumah tanaman agar lebih sesuai bagi pertumbuhan tanaman. Namun di daerah beriklim tropika basah penggunaan pendingin evaporatif menyebabkan kelembaban udara di dalam rumah tanaman sangat tinggi sehingga tanaman mudah terserang jamur. Hubungan suhu dan kelembaban udara di dalam rumah tanaman tipe arch dengan kondisi iklim dan kondisi operasi pendingin evaporatif perlu diketahui untuk menjadi pertimbangan perbaikan rancangan maupun pengelolaan rumah tanaman tersebut. Penelitian ini bertujuan membangun model prediksi suhu dan kelembaban udara di dalam rumah tanaman berdasarkan kondisi iklim dan kondisi operasi pendingin evaporatif. Model dibangun menggunakan algoritma Jaringan Saraf Tiruan (JST) propagasi mundur. Model JST yang dibangun ternyata mampu memprediksi suhu dan kelembaban udara dengan baik yaitu dengan nilai Root Mean Square Error (RMSE) 0,297°C untuk prediksi suhu udara dan 2,862% untuk prediksi kelembaban udara., Crops cultivation inside the greenhouse in humid tropical climatic region faces problem of high air temperature during sunny days. The use of evaporative cooling could keep air temperature inside the greenhouse more growth. However, in humid tropical climates, the use of evaporative cooling causes the humidity of the air in the greenhouse to be very high so that the crops are susceptible to fungi. The relationship between air temperature and humidity inside the arch-type greenhouse with the climatic condition and operating condition of evaporative cooling have to be known for greenhouse design evaluation as well as its management considerations. This research aims to develop a predictive model for air temperature and humidity inside the greenhouse based on climatic condition and operating condition of evaporative cooling. The model was developed by using Artificial Neural Network (ANN) backpropagation algorithm. The developed ANN model evidently could well predicted air temperature and humidity with RMSE values of 0,297°C for temperature model and 2,862% for humidity model.
Keyword: artificial neural networks, evaporative cooling, greenhouse, modeling

Judul: Artificial Neural Networks Modeling of Air Temperature inside the Greenhouse during Evaporative Cooling with Cooled Water
Abstrak: Suhu udara merupakan salah satu parameter yang berperan penting dalam pertumbuhan tanaman. Kondisi suhu udara di dalam greenhouse pada umumnya lebih tinggi daripada di luar greenhouse ketika saat cuara terik pada siang hari. Tingginya suhu udara tersebut dapat memicu terjadinya stress pada tanaman. Salah satu upaya untuk menurunkan suhu udara di dalam greenhouse adalah dengan menggunakan sistem evaporative cooling. Namun, sistem tersebut belum mampu menurunkan suhu mencapai syarat lingkungan tumbuh untuk tanaman dataran tinggi. Penyempurnaan sistem evaporative cooling dengan penambahan suatu water chiller dapat menurunkan suhu sehingga mendekati suhu optimum untuk tanaman dataran tinggi. Suatu model untuk menduga suhu udara di dalam greenhouse akan sangat penting. Penelitian ini bertujuan mengembangkan model Jaringan Saraf Tiruan (JST) atau Artificial Neural Network (ANN) untuk menduga suhu udara di dalam greenhouse di titik-titik representasi. Pengembangan model ANN dilakukan dengan menentukan nilai terbaik untuk beberapa parameter model. Parameter model tersebut antara lain random state, learning rate initial, momentum, dan hidden layer sizes. Model ANN yang dikembangkan memiliki kinerja yang sangat baik dalam menduga suhu udara di dalam greenhouse di titik-titik representasi dengan nilai R2 diatas 0,99, RMSE dibawah 0,12 oC, dan MAE dibawah 0,076 oC., Air temperature is one of the parameters that play a role in plant growth. The air temperature inside the greenhouse is usually higher than outside the greenhouse during the day when the weather is fine. The high temperature of the air can trigger stress in plants. One of the efforts to reduce the air temperature inside the greenhouse is to use an evaporative cooling system. However, the system has not been able to reduce the temperature to the requirements of the growing environment for highland plants. Improvement of the evaporative cooling system by adding a water chiller could reduce the temperature to close to the optimum temperature of highland plants. A model to predict air temperature inside the greenhouse would be very important. This study aims to develop an Artificial Neural Network (ANN) model to estimate air temperature inside the greenhouse at the points of representation. ANN model was developed by determining the best values for some model parameters. The model parameters include random state, initial learning rate, momentum, and hidden layer sizes. The developed ANN model performs very well in estimating air temperature inside the greenhouse at representation points with values of R2 above 0.99, RMSE below 0.12 oC, and MAE below 0.076 oC.
Keyword: Artificial Neural Network (ANN), evaporative cooling, greenhouse, water chiller

Judul: Penentuan Nilai Eigen dan Vektor Eigen dari Matriks Tridiagonal 2-Toeplitz dengan Pendekatan Polinomial Chebyshev
Abstrak: The eigenvalues and eigenvectors of a matrix can be determined by finding its characteristic polynomials. The characteristic polynomials of a tridiagonal 2-Toeplitz matrix is shown to be closely connected to polynomials which satisfy the Chebyshev recurrence relationship. If the order of the matrix is odd, then the eigenvalues are found explicitly in terms of the Chebyshev zeros and the eigenvectors are found in terms of the polynomials satisfying the recurrence relationship. For even ordered matrices, the situation is more complicated. The problem in these cases is that although the Chebyshev recurrence formula is still applied, its initial values are not generating Chebyshev polynomials
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