Judul: Kajian Model Hidden Markov Diskret dan Aplikasinya pada DNA.
Abstrak: Setiap kejadian berkaitan erat dengan penyebab kejadian. Jika penyebab kejadian tersebut tidak diamati secara langsung dan membentuk rantai Markov, maka pasangan kejadian dan penyebabnya dapat dimodelkan dengan model Hidden Markov (Hidden Markov Model, HMM).
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Judul: Kajian Model Hidden Markov Diskret dengan Algoritme Rabiner dan Aplikasinya pada DNA
Abstrak: Setiap kejadian berkaitan erat dengan penyebab kejadian. Jika penyebab kejadian tersebut tidak diamati secara langsung dan membentuk rantai Markov, maka pasangan kejadian dan penyebabnya dapat dimodelkan dengan model Hidden Markov (Hidden Markov Model, HMM). Model Hidden Markov (X, Y) adalah sebuah model yang dibangun oleh rantai Markov X = {Xk;k E N} yang tidak diamati, serta proses observasi Y = {Ykik E N} yang dipengaruhi oleh X. Ciri model Hidden Markov adalah: {X} yaitu sebuah rantai Markov dengan ruang state berhingga Sx = {1,2,...,N} yang homogen dan ergodic. aij = P(Xk+1 = j |Xk = i), untuk i,j = 1,2, ... N. ANXN = (aij) adalah matriks transisi dari X dengan a¿; ≥ 0 N dan Σ=1αij = 1. π; = P(X₁ = j) dengan π = (π1,...,πN). π = E[X] dan memenuhi Aл = π dan Σ=1; = 1. {Ykik E N} yaitu sebuah proses observasi dengan ruang state Sy = {1, 2,..., M}. Proses {Xk, Yk; k E N}, dihubungkan oleh distribusi peluang bersyarat b; (j) = P(Yk = j |Xk = i), j = 1,2, ... M; i = 1,2, ..., N. BNXM = (b)), dengan b; (j) ≥ 0, 1 b; (i) = 1, untuk i = 1,2,..., N. Diasumsikan {YKIXk} saling bebas. Dari ketiga ciri tersebut, diperoleh karakteristik model Hidden Markov yaitu: 2 λ = (A, B, π). Diberikan barisan observasi y₁, Y2, ... Yk; Yk € Sy untuk k = 1,2,..., K dan λ = (A, B, π). Masalah dasar dari model Hidden Markov, yaitu: 1. Menghitung peluang munculnya barisan observasi y1, V2, ... YK- 2. Memilih barisan state yang memaksimumkan peluang observasi. 2. Memilih barisan state yang memaksimumkan peluang observasi. 3. Melakukan pendugaan terhadap parameter-parameter model Hidden Markov λ = (A, B, π), sehingga P(Y₁ = 1, ..., YK = уK2) maksimum. Solusi dari 3 masalah di atas berturut-turut dapat diperoleh dengan algoritme forward, algoritme Viterbi dan algoritme Baum-Welch re-estimation (Rabiner 1989). Didefinisikan peubah forward, a (j) = P(Y₁ = Y1, ..., Yk = Yk, Xk = j|λ). Dengan induksi, ak (j) dapat dihitung sebagai berikut: 1. Inisialisasi: a₁(j) = P(Y₁ = Y₁, X1 = jλ) = πjbj(y1), j = 1,2,... N. 2. Induksi: ak+1) = P(Y₁ = Y1,..., Yk+1 = Yk+1, Xk+1 = jλ) = [=1 αk (i) αij]bj (k+1), j = 1,2, ... N; 1 ≤ k ≤ K − 1. Li=1 3. Terminasi: P(Y₁ = Y1,..., YK = Yk|2) = Σ=1 P(Y₁ = Y1,..., YK = YK, XK = j]) = Σ=1αk(j) K Pada masalah kedua, akan dipilih barisan state {x}=1 sehingga P(Y₁ = y1,..., YK = YK, X1 X1, ..., XK = xx 2) adalah maksimum. Didefinisikan, Sk (j) = maxx1,x2, xk-1 P(Y1 = Y1, ..., Yk = Yk, X1 X1, ..., Xk = j|λ). = Barisan yang menghasilkan (i) yaitu, j) = arg maxi=1,...,N Sk-1 (i)αij......dst
Keyword: Markov chain, discrete hidden Markov model, forward algorithm, Viterbi algorithm, Baum-Welch algorithm

Judul: Pembukaan Lahan Kelapa Sawit (Elaeis guineensis Jacq.) di Kebun Batang Toru, PTPN III (Persero) Tapanuli Selatan, Sumatera Utara
Abstrak: The internship was conducted at Batang Toru Plantation PTPN III (Persero), South Tapanuli, North Sumatera from November 2011 until February 2012. The purposes of the internship is improve knowledge, gain work experience and studying the management aspects land clearing of oil palm, both technical and managerial aspects. The primary data were obtained directly from the field by doing discussions or interviews with the foreman and assistant division as well as through direct observation in the field. The secondary data were obtained from the company data or company records. Based on observation during the internship can be conclude that the main issues in land clearing oil palm is conducting enterpasing basically been done at wetlands, this is due to stage process and work capacity of land clearing. Field observations focused on the land clearing activities which includes several parameters : the stages of land clearing process and land clearing techniques are used, it relates to land condition, the condition of vegetation, land clearing techniques, equipment requirements and time sheet working that is obtained by comparing a standard operation of PTPN III (Persero) Plantation. PT Nusantara Plantation III (Persero) as plantation companies state-owned enterprises (BUMN), which seeks to expand oil palm plantations owned. Total land area of the concession rights of exploitation (HGU) plantation development area Batang Toru PT PTPN III (Persero) Tapanuli District is an area of 1 500 hectares by Regents consent decree South Tapanuli. Status of the land is the location permits a secondary forest and other land uses (APL). Entirely divided into 2 locations and 5 work packages groups of land with an area of each location I is 548.43 ha and 775.98 ha II locations. Land clearing palm oil (Elaeis guineensis Jacq.) at Batang Toru Plantation, PTPN III (Persero), South Tapanuli, North Sumatra with the general condition of the area is the 67% of them in the form of coastal peatland with a depth of 1 – 2 m, while 33% of them are in dry area the form of land with the structure of clay soil, sand and dust. Land clearing technique used is fully mechanical engineering, non-burning and land clearing rules apply wet application of water management and road construction with the installation technique and overall gambangan in the management of most of peaty land in Batang Toru, South Tapanuli District, using standard procedures operation for peatland that has been poured in the No. Permentan. 14 of 2009. PTPN III (Persero) to cooperate with 3rd party (three) conduct land clearing by mechanical means to obtain efficient results. Occupation is conducted using a mechanical excavator with an average achievement 1 HK = 0.83 ha/HKT (8 BU/day), resulting in the complete work area 1 block area (30 ha) on average takes 37 HKT normal conditions without a hitch (weather, appliance repair).
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