Peramalan data time-series kini menjadi suatu kebutuhan dalam dunia bisnis maupun lingkungan hidup. Software Windows POM – QM memiliki tools dan beberapa metode yang dapat digunakan untuk peramalan data time-series. Diperlukan sebuah analisa hasil peramalan untuk mengetahui efektivitas dan akurasi metode-metode tersebut dalam meramalkan data time series agar dapat digunakan sebagai referensi metode dalam mendapatkan hasil yang optimal. Nilai MAPE digunakan sebagai ukuran perbandingan dalam analisa hasil perbandingan peramalan. Data time series yang digunakan adalah data curah hujan di Kabupaten Jombang. Hasil peramalan dengan metode Exponential Smoothing memiliki rata – rata nilai MAPE terkecil dibandingkan dengan metode lainnya yaitu sebesar 2,4 dengan pola data hasil peramalan berfluktuasi sebagaimana data sebenarnya. Sehingga penggunaan metode Exponential Smoothing dapat digunakan sebegai referensi dalam peramalan data time series dengan pola data musiman dengan menggunakan software Windows POM-QM.

<p>Let <span class="math"><em>H</em></span> and <span class="math"><em>G</em></span> be two simple graphs. The concept of an <span class="math"><em>H</em></span>-magic decomposition of <span class="math"><em>G</em></span> arises from the combination between graph decomposition and graph labeling. A decomposition of a graph <span class="math"><em>G</em></span> into isomorphic copies of a graph <span class="math"><em>H</em></span> is <span class="math"><em>H</em></span>-magic if there is a bijection <span class="math"><em>f</em> : <em>V</em>(<em>G</em>) ∪ <em>E</em>(<em>G</em>) → {1, 2, ..., ∣<em>V</em>(<em>G</em>) ∪ <em>E</em>(<em>G</em>)∣}</span> such that the sum of labels of edges and vertices of each copy of <span class="math"><em>H</em></span> in the decomposition is constant. A lexicographic product of two graphs <span class="math"><em>G</em>₁</span> and <span class="math"><em>G</em>₂, </span> denoted by <span class="math"><em>G</em>₁[<em>G</em>₂], </span> is a graph which arises from <span class="math"><em>G</em>₁</span> by replacing each vertex of <span class="math"><em>G</em>₁</span> by a copy of the <span class="math"><em>G</em>₂</span> and each edge of <span class="math"><em>G</em>₁</span> by all edges of the complete bipartite graph <span class="math"><em>K</em>_{<em>n</em>, <em>n</em>}</span> where <span class="math"><em>n</em></span> is the order of <span class="math"><em>G</em>₂.</span> In this paper we provide a sufficient condition for <span class="math">$\overline{C_{n}}[\overline{K_{m}}]$</span> in order to have a <span class="math">$P_{t}[\overline{K_{m}}]$</span>-magic decompositions, where <span class="math"><em>n</em> &gt; 3, <em>m</em> &gt; 1, </span> and <span class="math"><em>t</em> = 3, 4, <em>n</em> − 2</span>.</p>

AbstrakPendekatan peramalan data time series dengan model jaringan syaraf Radial Basis Function (RBF) memiliki sifat supervised learning karena data yang dilatihkan berupa pasangan data input dan target yang diidentifikasi dan diekstrapolasi pola dan hubungannya. 73,45 and 8,1903E-05 with 532,36 and 9,8648E-05. In other hand, out-sample forecasting result using RBF models divergen with the record data.

Some institution arrangements are developed to manage the fishery resources, one of which is a communitybased management. Over the past few years, this community has been believed to be one of the effective management. According to Schlager and Ostrom (1992); Imperial and Yandle (2005), this management was usually the adoption of local knowledge of communities around their resources. Awig Awig growing in East Lombok is one of a growing community based management in Indonesia in managing fishery resources. Based on this information, this research aimed to (1) diagnose the awig awig institution growing in East Lombok, (2) identify and analyze the external factors influencing the institution, and (3) evaluate awig awig institution linkages to sustainable coastal fisheries. This study used the approach of the Institutional Analysis and Development (IAD) framework and GS production surplus bioeconomy. The results showed that so far awig awig institution has not been able to overcome the pressure from the inside and from the outside in the form of appropriation externality, technology externality, and assignment problem, therefore, this caused the decline of fishery resources.