Judul: Artificial Neural Network Modelling of Tatsoi Growth on Hydroponic System
Abstrak: Pemodelan Jaringan Saraf Tiruan Pertumbuhan Sawi Pagoda pada Sistem Hidroponik. Dibimbing oleh HERRY SUHARDIYANTO. Sawi pagoda merupakan sayuran yang kaya akan nutrisi. Sawi pagoda umumnya dibudidayakan secara konvensional. Namun berkurangnya lahan pertanian menjadi sebuah kendala. Budidaya di dalam greenhouse dengan sistem hidroponik dan root zone cooling (RZC) membantu mengoptimalkan produktivitas tanaman. Oleh karena itu, dilakukan penelitian ini untuk mempelajari hubungan antara pertumbuhan sawi pagoda terhadap faktor-faktor lingkungan. Selama percobaan, suhu harian rata-rata di dalam greenhouse berkisar antara 33.1C hingga 39.1C. RZC telah dapat menjaga suhu perakaran dalam rentang 24.4C hingga 26.7C. Parameter lingkungan yang diamati meliputi suhu udara di dalam greenhouse, radiasi matahari, kelembapan relatif udara di dalam greenhouse, dan suhu perakaran. Parameter tanaman meliputi bobot segar, bobot kering, jumlah daun, dan luas permukaan daun. Hubungan parameter-parameter tersebut telah dapat diterangkan sesuai model ANN yang telah dikembangkan yaitu menggunakan algoritma backpropagation. Model ANN terbaik memiliki momentum 0.9, learning rate 0.1, dan arsitektur 11-2-2. Evaluasi model ANN menghasilkan nilai R2 sebesar 0.98800 dan nilai RMSE bobot segar dan bobot kering secara berturut-turut sebesar 3.54870 dan 0.24627. Nilai-nilai tersebut menunjukkan bahwa model ANN dapat memprediksi bobot segar H+2 dan bobot kering H+2 dengan sangat baik., Artificial Neural Network Modelling of Tatsoi Growth on Hydroponic System. Supervised by HERRY SUHARDIYANTO. Tatsoi is a vegetable that is rich in nutrition. Tatsoi is usually cultivated conventionally. However, the decreasing agricultural land has become a problem. Cultivation in a greenhouse with a hydroponic and root zone cooling (RZC) system helps optimize the plant’s productivity. Therefore, this research was performed to study the correlation between tatsoi’s growth and environmental factors. During the experiment, the daily average air temperatures inside the greenhouse were between 33.1C and 39.1C. RZC kept the root zone temperature between 24.4C and 26.7C. The observed environmental factors were outside air temperature, solar radiation, relative humidity of inside air, and root zone temperature. The plant’s growth parameters were fresh weight, dry weight, number of leaves, and leaf area. The correlation between these parameters has been explained through the ANN model developed using the backpropagation algorithm. The best ANN model figures were 0.9 momentum, 0,1 learning rate, and 11-2-2 as the architecture. The model evaluation produced an R2 value of 0.98800 and fresh weight and dry weight RMSEs of 3.54870 and 0.24627, respectively. These values show that the model could predict fresh weight D+2 and dry weight D+2 very well.
Keyword: Artificial Neural Network, Greenhouse, Hidroponik, Suhu perakaran

Judul: Artificial Neural Network Modeling  and Optimization with Genetic Algorithm on Photosynthetic Rate of Lettuce in a  Floating Raft Hydroponic System
Abstrak: Pada beberapa tahun terakhir, komoditas selada mengalami peningkatan permintaan sehingga perlu diikuti dengan peningkatan produktivitas. Produktivitas budidaya selada dapat dilakukan melalui kontrol dan optimasi terhadap parameter-parameter lingkungannya. Budidaya tanaman secara hidroponik rakit apung dalam rumah tanaman dapat memudahkan kontrol terhadap faktor-faktor lingkungan. Hubungan antara parameter-parameter lingkungan seperti kelembaban relatif udara, suhu udara, densitas fluks foton fotosintetik (PPFD) sinar matahari, dan suhu daerah perakaran terhadap laju fotosintesis sebagai indikator produktivitas tanaman telah dimodelkan menggunakan Jaringan Saraf Tiruan (JST) pada budidaya tanaman selada “Grand Rapids” secara hidroponik rakit apung di dalam rumah tanaman. Model JST yang dikembangkan kemudian digunakan untuk mencari nilai optimal parameter-parameter lingkungan menggunakan algoritma genetika nilai asli. Hasil pemodelan JST dengan komite model menunjukkan RMSE dan R2 masing-masing 0,7475 µmol m-2 s-1 dan 0,8664. Nilai optimum kelembaban relatif udara, suhu udara, PPFD, dan suhu daerah perakaran optimal berdasarkan komite model yang dikembangkan berturut-turut adalah 50,32%, 32,34 oC, 504,77 µmol m-2 s-1, dan 23,12 oC untuk menghasilkan estimasi laju fotosintesis sebesar 9,91 µmol m-2 s-1. Optimasi suhu daerah perakaran terhadap rataan per sampel data budidaya tanpa perlakuan pendinginan perakaran menunjukkan rata-rata peningkatan estimasi laju fotosintesis sebesar 1,52 µmol m-2 s-1., In recent years, the demand for lettuce has increased, therefore it should be followed by an increase in productivity. Lettuce productivity can be promoted through control and optimization of its environmental factors. Lettuce cultivation using floating-raft hydroponic system within a greenhouse could facilitate control over these environmental factors. The relation of several environment parameters, such as air relative humidity, air temperature, photosynthetic photon flux density (PPFD), and root-zone temperature to photosynthetic rate as an indicator of productivity of lettuce cultivated using a floating-raft hydroponic system within a greenhouse was modeled using Artificial Neural Network (ANN). The ANN model then was evaluated and used for optimization using real-valued genetic algorithm method. The model evaluations on RMSE and R2 were 0,7475 µmol m-2 s-1 and 0,8664, respectively. Optimum values of air relative humidity, air temperature, PPFD, and root-zone temperature based on the developed ANN model were 50,32%, 32,34 oC, 504,77 µmol m-2 s-1, and 23,12 oC, respectively, to produce an estimated photosynthetic rate of 9,91 µmol m-2 s-1. Root-zone temperature optimization on measured data of samples without root-zone cooling was estimated to increase photosynthetic rate by 1,52 µmol m-2 s-1.
Keyword: artificial neural network, floating-raft hydroponic, real-value genetic algorithm, root-zone cooling

Judul: Konstruksi Hukum Grup Kurva Eliptik Atas F3m
Abstrak: Elliptic curves upon F3m can be divided in two cases, i.e. supersingular and nonsupersingular case. Based on group laws on elliptic curves, a new arithmetic upon F3m can be formulated as an addition process of two random points on the curve. This addition process can then be applied to the ElGamal algorithm.
Keyword: