Judul: Penyelesaian Cutting Stock Problem Satu Dimensi Menggunakan Algoritme Particle Swarm Optimization
Abstrak: Cutting stock problem (CSP) merupakan permasalahan pemotongan persediaan yang sering dijumpai di banyak industri. Tujuannya untuk mendapatkan sisa pemotongan seminimum mungkin, namun tetap memenuhi permintaan pelanggan. Ada beberapa metode yang dapat digunakan untuk menyelesaikan permasalahan ini. Pada karya ilmiah ini, digunakan metode heuristik particle swarm optimization (PSO) sebagai penyelesaian masalah CSP. Pada tahap pertama penulis menentukan kombinasi pola pemotongan yang mungkin dapat digunakan dengan bantuan bahasa pemrograman Python. Selanjutnya, dibuat model matematis dan kendala sesuai pola yang tersedia. Kemudian, algoritme PSO digunakan untuk menentukan kombinasi pola potongan supaya menghasilkan sisa pemotongan yang paling sedikit. Diawali dengan inisialisasi parameter yang digunakan. Kemudian setelah diimplementasikan, menghasilkan kombinasi pola pemotongan rol kertas yang optimum yaitu sebanyak tiga rol untuk pola satu, 29 rol untuk pola 23, enam rol untuk pola 27 dan 29 rol untuk pola 32., Cutting stock problem (CSP) is an inventory cutting problem that is often encountered in many industries. The goal is to get the remaining cutting to a minimum, while still meeting customer demands. There are several methods that can be used to solve this problem. In this scientific paper, the heuristic particle swarm optimization (PSO) method is used to solve the CSP problem. In the first stage the author determines the possible combination of cutting patterns that can be used with the help of the Python programming language. Next, a mathematical model and constraints are made according to the available patterns. Then, the PSO algorithm is used to determine the combination of cut patterns in order to produce the least amount of cutting residue. It begins with the initialization of the parameters used. Then after it was implemented, it resulted in the optimum combination of aluminum roller cutting patterns as many as three rolls for pattern one, 29 rolls for pattern 23, six rolls for pattern 27 and 29 rolls for pattern 32.
Keyword: cutting stock problem, particle swarm optimization, trim loss

Judul: One Dimensional Cutting Stock Problem Optimization with Ant Colony Optimization
Abstrak: Cutting Stock Problem (CSP) satu dimensi merupakan permasalahan optimisasi pemotongan bahan yang berfokus pada satu pola pemotongan yaitu panjang atau lebar. Tujuannya adalah untuk menentukan pola pemotongan yang meminimumkan stok yang terpakai. Salah satu metode untuk menyelesaikan CSP yaitu metode metaheuristik. Pada penelitian ini, metode yang digunakan untuk menyelesaikan CSP satu dimensi yaitu metode metaheuristik Ant Colony Optimization (ACO). Metode ACO diimplementasikan menggunakan bantuan bahasa pemrograman Python pada kasus pemesanan kusen dengan item sebanyak 213 dan panjang stok 4000 mm. Hasil yang diperoleh yaitu pola pemotongan terbaik dengan nilai fitness tertinggi 99,848% pada parameter tingkat kepentingan relatif dari informasi heuristik yaitu 2. Stok yang terpakai sebanyak 64 kayu dan sisa pemotongan sepanjang 200 mm., One dimensional Cutting Stock Problem (CSP) is a material cutting optimization problem focusing on one cutting pattern, namely length or width. The goal is to determine a cutting pattern that minimizes used stock. One of the methods to solve CSP is the metaheuristic method. In this study, the metaheuristic method used to solve one dimensional CSP is Ant Colony Optimization (ACO). The ACO method is implemented using Python programming language in the case of ordering frames with 213 items and 4000 mm stock length. The results obtained are the best cutting pattern with the highest fitness is 99,848% at the relative importance parameter of the heuristic information is 2. The stock used is 64 wood and the trim loss is 200 mm.
Keyword: metaheuristic method, ant colony optimization, one dimensional cutting stock problem

Judul: Antibody Responses against Newcastle Disease and Evaluation of Infectious Bronchitis Maternal Derived Antibody on Vaccinated Layers.
Abstrak: Information of maternal derived antibody (MDA) and onset of immunity after vaccination is important to determine appropriate vaccination programme of chickens. The aim of this study was to evaluate duration of Infectious Bronchitis (IB) MDA and evaluate onset of immunity Newcastle Disease (ND) vaccination among layers. All layers were vaccinated using live ND and Infectious Bursal Disease (IBD) at one day old age, and received Avian Rhinotracheitis (AR) vaccine at 4 day old age. Serum samples were collected at various ages: 7, 10, 14, 20, 22, 24, 29 and 35 day old. The presence of ND antibodies was measured by the hemagglutination inhibition (HI) test, whilst the presence of IB antibodies was measured by enzyme-linked immunosorbent assay (ELISA). Our study showed that ND antibodies were at positive level until 24 day old age, and reached protective level ≥ 25 only at 7 day old age, whilst IB MDA were positive up to 17 day old age. This result indicating the needs of ND booster vaccination at least at 24 day old and IB vaccination at 17 day old age.
Keyword: antibody, Infectious Bronchitis, layers, Newcastle Disease, vaccination