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: Prediction of Air Temperature from Cooling Pad during Evaporative Cooling in Greenhouse Using Artificial Neural Network
Abstrak: Kondisi lingkungan rumah tanaman yang mendekati ideal dapat direkayasa dengan menerapkan pengaturan pada berbagai parameter lingkungan yang mempengaruhi pertumbuhan tanaman. Satu diantara parameter penting adalah suhu udara di dalam rumah tanaman. Udara dalam rumah tanaman berasal dari udara yang keluar dari cooling pad, sehingga diperlukan prediksi suhu udara dari cooling pad. Penelitian ini bertujuan mengembangkan model prediksi suhu udara yang keluar dari cooling pad pada evaporative cooling menggunakan Jaringan Saraf Tiruan (JST) dengan backpropagation algorithm. Penelitian ini dilakukan pada rumah tanaman yang dilengkapi sistem evaporative cooling dan sistem kontrol adaptif. Air pada sistem evaporative cooling didinginkan oleh chiller, kemudian dialirkan menuju cooling pad. Namun evaporative cooling memerlukan rekayasa sistem pendinginan air sehingga dapat menurunkan suhu udara yang keluar dari cooling pad. Penelitian ini dilakukan dalam dua experimental run pada sistem pendinginan air untuk membandingkan dan menemukan model prediksi terbaik pada setiap percobaan. Model prediksi JST experimental run 1 mampu memprediksi suhu udara dari cooling pad lebih baik daripada model prediksi JST experimental run 2. Nilai MAE, RMSE, dan R2 pada experimental run 1 masing masing adalah 0,47°C; 0,65°C; dan 0,7379. Sedangkan nilai MAE, RMSE, dan R2 pada experimental run 2 sebesar 0,50°C; 0,68°C; dan 0,7011., Greenhouse environmental conditions close to ideal can be engineered by adjusting various environmental parameters influencing plant growth. One of the critical parameters is the air temperature in the greenhouse. The air in the greenhouse comes from the air coming out of the cooling pad, so it is necessary to predict the temperature of the air. This research aims to develop a prediction model for the air temperature leaving the cooling pad in evaporative cooling using Artificial Neural Networks (ANN) with a backpropagation algorithm. This research was conducted in a greenhouse with evaporative cooling and adaptive control systems. The chiller cools the water in the evaporative cooling system and then flows to the cooling pad. However, evaporative cooling requires engineering a water-cooling system to reduce the temperature of the air coming out of the cooling pad. This research was conducted in two experimental runs on the water-cooling system to compare and find the best prediction model in each experiment. The ANN prediction model for experiment run 1 could predict the air temperature of the cooling pad better than the ANN prediction model for experiment run 2. MAE, RMSE, and R2 values in experiment run 1 were 0.47°C, 0.65°C, and 0.7379, respectively, while MAE, RMSE, and R2 values in experiment 2 were 0.50°C, 0.68°C, and 0.7011.
Keyword: chiller/chiller, cooling pad/cooling pad, evaporative cooling/evaporative cooling, jaringan saraf tiruan/artificial neural network, pemodelan suhu udara/air temperature modelling

Judul: Grup dan Homomorfisma Grup pada Rubik Revenge
Abstrak: Rubik cube is a kind of puzzle game. A player must arrange rubik sides, which have been disordered, back to the position where every side has the same colour. This paper does not focus on the settlement solution, but instead on proving the existence of a group homomorphism between rubik movement group and S96 symmetric permutation group in rubik revenge. Firstly, it has been proved that rubik movement set is a group. Then, it also has been proved that the M set, which contains numeric labels of 1 until 96 in cubinos sides, is S96 symmetric permutation group.
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