Judul: Pemodelan Jaringan Syaraf Tiruan untuk Memprediksi Awal Musim Hujan Berdasarkan Suhu Muka Laut
Abstrak: Anjatan-Indramayu is the one of agriculture regions in Indonesia. The success or failure of the harvest each year depends on water availability in the region. Therefore we need an accurate method to predict the beginning of rainy season. The method used to predict in this research is artificial neural network (ANN) backpropagation. Result of ANN prediction accuracy is measured by R2 and RMSE. This research uses ECHAM4p5_CA sea surface temperature (SST) is one of the sea surface temperature models with the months of June, July and August. The domain of SST is selected based on the correlation of 5% and 10% for each of the month June, July, and August. This research uses ANN architecture with two parameters: neuron hidden (NH) and learning rate (LR). The number of hidden neurons used in this research are 5, 10, 20, and 40, and the learning rates are 0.3, 0.1, and 0.01. The best prediction result correlation of 5% using the ANN is for June with R2 is 51% and RMSE 3.03 at NH 10 and LR 0.01, July with R2 is 48% and RMSE 3.39 at NH 20 and LR 0.1, and August with R2 is 75% and RMSE 2.51 at NH 40 and LR 0.01. The best prediction result correlation of 10% using the ANN is for June with R2 is 44% and RMSE 3.32 at NH 5 and LR 0.3, July with R2 is 42% and RMSE 3.42 at NH 10 and LR 0.1, and August with R2 is 71% and RMSE 3.37 at NH 20 and LR 0.01. The conclusion from this research is the neuron hidden and learning rate with different values affect R2 and RMSE.
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Judul: Prediksi Awal Musim Hujan di Wilayah Waingapu Menggunakan Jaringan Syaraf Tiruan Backpropagation
Abstrak: Curah hujan merupakan faktor utama dari iklim. Kondisi iklim yang tidak menentu dapat menjadi penyebab kegagalan dalam bertani. Hal ini, membuat para petani sulit dalam memprediksi waktu yang pas untuk bercocok tanam. Oleh karena itu, diperlukan berbagai informasi hasil prediksi iklim sehingga lebih bermanfaat dalam bidang pertanian. Salah satunya adalah dengan memprediksi awal musim hujan (AMH) untuk menekan risiko gagal panen. Penelitian ini bertujuan menentukan awal musim hujan berdasarkan data curah hujan harian di wilayah Waingapu berdasarkan teori Liebmann, menentukan korelasi dalam penentuan prediktor JST antara awal musim hujan dengan peubah indeks iklim global, dan menerapkan metode jaringan syaraf tiruan (JST) untuk memprediksi awal musim hujan. Data indeks iklim global yang digunakan adalah data Southern Oscillation Index (SOI) dan SST Nino 3. Hasil perhitungan menggunakan teori Liebmann AMH terjadi antara bulan September sampai Desember. Prediktor yang digunakan adalah bulan Juli, Agustus dari data SOI dan bulan Juli, Agustus dari data SST Nino 3.4. Hasil penelitian prediksi ini memperoleh model terbaik dengan nilai 𝑅������2 sebesar 0.69 dan nilai RMSE sebesar 25.39.
Keyword: awal musim hujan, jaringan syaraf tiruan, SOI, SST Nino 3.4

Judul: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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