Judul: Solving 0-1 Knapsack Problem Using Migrating Birds Optimization Algorithm and Particle Swarm Optimization Algorithm
Abstrak: Masalah knapsack merupakan permasalahan optimisasi kombinatorial yang diklasifikasikan sebagai NP-complete problem. Tujuannya adalah untuk memperoleh keuntungan maksimum tanpa melebihi kapasitas knapsack dengan objek yang dipilih. Ada beberapa metode yang dikembangkan untuk permasalahan ini, salah satunya adalah metode meta-heuristic. Pada karya ilmiah ini, digunakan metode meta-heuristic migrating birds optimization (MBO) dan particle swarm optimization (PSO) untuk menyelesaikan masalah knapsack. Hasil yang diperoleh menunjukkan bahwa algoritme MBO menghasilkan keuntungan yang relatif lebih besar dibandingkan algoritme PSO., Knapsack problem is a combinatorial optimization problem which is classified as an NP-complete problem. Its aim is to achieve maximum benefit without exceeding the capacity of the knapsack with the selected objects. There are several methods developed for this problem, one of them is a meta-heuristic method. In this manuscript, migrating birds optimization (MBO) and particle swarm optimization (PSO) as meta-heuristic methods will be used to solve the knapsack problem. The result obtained shows that the MBO algorithm produces a relatively larger benefit than the PSO algorithm.
Keyword: knapsack problem, meta-heuristic method, migrating birds optimization, particle swarm optimization

Judul: Solving Travelling Salesman Problem Using  Ant Colony Optimization and Particle Swarm Optimization Algorithm
Abstrak: Travelling Salesman Problem (TSP) adalah salah satu permasalahan distribusi yang sering kali ditemui. Penyelesaian dari permasalahan dalam Travelling Salesman Problem ini adalah mencari rute terpendek atau jarak minimum. Terdapat beberapa metode yang dikembangkan untuk menyelesaikan permasalahan ini. Mulai dari metode eksak hingga metode modern atau meta heuristic. Dalam penelitian ini, dua metode heuristik digunakan untuk menyelesaikan Travelling Salesman Problem (TSP), yaitu Particle Swarm Optimization (PSO) dan Ant Colony Optimization (ACO). Solusi yang diperoleh kemudian dibandingkan dengan menggunakan metode eksak dalam bentuk Mixed-Integer Linear Programming (MILP). Hasil yang didapat menunjukkan waktu eksekusi metode Particle Swarm Optimization lebih baik daripada metode Mixed-Integer Linear Programming dan algoritme Ant Colony Optimization (ACO). Namun, ada perbedaan yang mencolok dari ketiga metode dalam total jarak perjalanan., The Travelling Salesman Problem (TSP) is one of the distribution problems that is often encountered. The solution of TSP is to find the shortest route or minimum distance. Several methods have been developed to solve this problem. Starting from exact methods to modern methods or meta heuristics. In this study, two heuristic methods will be used to solve TSP, Particle Swarm Optimization and Ant Colony Optimization. The solution obtained will be compared with the solution obtained using the exact method in the form of Mixed-Integer Linear Programming (MILP). The results obtained show that the execution time of the Particle Swarm Optimization method is better than the Mixed-Integer Linear Programming method and Ant Colony Optimization algorithm. However, there is a significant difference among the three methods in the total travel distance.
Keyword: ant colony optimization, integer linear programming, particle swarm  optimization, particle swarm  optimization

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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