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Abstrak :
Product mix merupakan informasi yang penting dalam menyusun rencana produksi suatu perusahaan manufaktur. Product mix yang tidak optimal dapat menyebabkan suatu rencana produksi tidak berjalan dengan baik. Perencanaan produksi yang kurang baik dapat mengurangi kemampuan bersaing perusahaan. Penentuan product mix hendaknya memperhitungkan faktor kendala yang dimilild perusahaan dan menggunakan informasi biaya yang akurat agar kegiatan produksi yang dilaksanakan berjalan dengan baik sesuai dengan jadwal produksi dan anggaran biaya yang telah ditetapkan. Oleh karena itu, digunakanlah konsep TOC (Theory of Constraints) untuk memperhitungkan kendala perusahaan dan konsep ABC (Activity-Based Costing) yang dapat memberikan informasi biaya yang akurat untuk memeroleh product mix yang optimal. Analisa penentuan product mix dengan metode ABC-TOC dan metode saat ini dimulai dari menentukan biaya produksi dengan sistem biaya saat ini dan sistem ABC. Kemudian kendala diidentifikasi dan dieksploitasi dengan menentukan product mix yang optimal untuk tiga model yaitu model ABC-TOC, model Saat Ini dan model Saat Ini-TOC. Product mix yang optimal diperoleh dengan menggunakan programa liner. Input data yang berbeda memberikan product mix yang berbeda. Product mix ABC-TOC menyarankan perusahaan untuk memproduksi 76,344 produk, walaupun lebih kecil dibandingkan dengan yang disarankan product mix model Saat Ini dan product mix model Saat Ini-TOC, namun informasi product mix yang dihasilkan lebih realisitis. Informasi biaya sistem ABC yang lebih akurat dan kendala mesin yang telah diidentifikasi dengan langkah-langkah TOC dapat memberikan product mix yang lebih optimal.

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Product mix is an important information in production planning of a manufacturing company. Product mix that isn't optimal will cause the production plan doesn't work well, thus will reduce company's competitiveness. In determining the product mix must consider the company's constraints and accurate cost information, so that the production activity that is implemented according to the plan may work well. So that, the concept of TOC (Theory of Constraints) is used to identify the constrains and the concept of ABC (Activity Based Costing) is used to give more accuracy cost information, to obtain the optimal product mix. Analysis of determining product mix using ABC-TOC method and current method starts with calculating the production cost using the current cost system and ABC. Then, the constraints are identified and exploited by determining the optimal product mix for the three model which are current model, current-TOC model, ABCTOC model. The optimal product mix can be determined with linear programming. A different input data will result ini a different product mix. Product mix using ABC-TOC model suggests the company to produce 76,344 products, even this number is smaller than other product mix, its information more realistic. Cost information from ABC and the constraints that has been identified are the input for ABC-TOC model formulating. Cost information form current cost system is used to formulating the current model.

Abstrak :
Penelitian ini bertujuan untuk menganalisis pendekatan aproksimasi minimax, LS-SVM, dan GPR untuk proses pemodelan semivariogram pada metode kriging. Proses ini adalah bagian tahap dalam operasi kriging yang biasanya dilakukan untuk proses interpolasi dan fusi. Kriging sendiri telah banyak digunakan untuk memprediksi nilai spasial yang terbukti lebih baik dalam memprediksi proses dibandingkan dengan metode deterministik, di mana kriging dikategorikan sebagai pada metode interpolasi stokastik. Pendekatan konvensional untuk proses pemodelan semivariogram menggunakan metode weighted least square dengan menggunakan fungsi tertentu. Fungsi yang tersedia untuk metode ini antar lain stable, exponential, spherical, dan lain-lain. Beberapa pembaharuan untuk kasus pemodelan semivariogram saat ini telah dibuat dengan menggunakan teknik regresi seperti LS-SVM. Selain itu sebagai bagian dari kebaruan, pendekatan aproksimasi minimax, LS-SVM, dan GPR yang diusulkan untuk kasus ini dapat meningkatkan akurasi pada hasil interpolasi, dalam hal ini diimplementasikan pada metode ordinary kriging. Pendekatan baru, yang dapat disebut sebagai minimax kriging ini dapat mengurangi eror. Minimax berkontribusi pada prediksi bobot nilai semivariogram lebih baik daripada weighted least square dan proses komputasi yang lebih cepat daripada metode berbasis SVM dan GPR. ......This study aims to analyze the approach of Minimax, LS-SVM, and GPR approximation for the semivariogram modeling process in the kriging method. This process is part of the stage in kriging operations that are usually carried out for interpolation and fusion processes. Kriging itself has been widely used to predict spatial values which are proven to be better in predicting processes compared to deterministic methods, where kriging is categorized as a stochastic interpolation method. The conventional approach to the semivariogram modeling process uses the weighted least square method using certain functions. Functions available for this method include stable, exponential, spherical, and others. Several updates to the case of semivariogram modeling have now been made using regression techniques such as LS-SVM. Apart from that as part of the novelty, the proposed Minimax, LS-SVM, and GPR approximation approaches for this case can improve the accuracy of the interpolation results, in this case implemented in the ordinary kriging method. This new approach, which can be called minimax kriging, can reduce errors. Minimax contributes to the predicted weighting of semivariogram values better than weighted least square and faster computing processes than SVM and GPR-based methods.

Abstrak :
Preliminary cost estimates play an important role in project decisions at the beginning of design phase of construction project under a limited definition of scope and constraints in available information and time. This study proposes a new approach of preliminary cost estimation model using regression analysis incorporated with adaptive neuro fuzzy inference system (ANFIS). Regression analysis (RA) is used for determination of the significant parameters as input variables in ANFIS model. Datasets of 55 low-cost apartment projects in Indonesia were compiled to demonstrate the advantage of the proposed method. The mean absolute percent error (MAPE) of testing data of the proposed model is 3.98% whereas the MAPE of RA and neural network (NN) models are, respectively, 6.92% and 10.12%, thus indicating better accuracy performance of the proposed model over the latter ones.

Abstrak :
[Penelitian ini bertujuan untuk membantu PT Metal Diameter menyusun rencana produksi yang tepat dengan menerapkan product mix algorithm dan drum buffer rope berdasarkan prinsip five focusing steps dari theory of constraints dalam rangka memaksimalkan throughput. Metode penelitian yang digunakan dalam penelitian ini adalah metode deskriptif dengan data primer. Perencanaan produksi berdasarkan metode tersebut dimulai dengan mengidentifikasi sumber daya kendala. Sumber daya kendala yang telah teridentifikasi akan dieksploitasi melalui aplikasi product mix algorithm dan buffer, setelah itu sumber daya nonkendala akan disubordinasi ke sumber daya kendala dengan membuat jadwal produksi optimal melalui aplikasi rope. Selanjutnya, penelitian ini menyarankan PT Metal Diameter untuk menerapkan metode tersebut dalam penyusunan rencana produksi karena lebih sesuai dengan keadaan lini produksi yang sebenarnya, yang mana terdapat sumber daya yang mengalami keterbatasan kapasitas dan terdapat kemungkinan gangguan internal (breakdown). ......This research aims to help PT Metal Diameter in preparing proper production plan by applying product mix algorithm and drum buffer rope based on five focusing steps principle from theory of constraints in order to maximize throughput. The method used in this research is descriptive method with the primary data. Production planning based on that method starts with identifying resource constraints. Resource constraints that have been identified will be exploited through product mix algorithm and buffer application, while non-resource constraints will be subordinated to the resource constraints by creating optimal production schedule through the rope application. Furthermore, this study suggests PT Metal Diameter to apply this method in preparing proper production plans because it will be more in line with the actual state of the production line, which have resources with limited capacity and the possibility of internal disturbances (breakdown). This research aims to help PT Metal Diameter in preparing proper production plan by applying product mix algorithm and drum buffer rope based on five focusing steps principle from theory of constraints in order to maximize throughput. The method used in this research is descriptive method with the primary data. Production planning based on that method starts with identifying resource constraints. Resource constraints that have been identified will be exploited through product mix algorithm and buffer application, while non-resource constraints will be subordinated to the resource constraints by creating optimal production schedule through the rope application. Furthermore, this study suggests PT Metal Diameter to apply this method in preparing proper production plans because it will be more in line with the actual state of the production line, which have resources with limited capacity and the possibility of internal disturbances (breakdown). This research aims to help PT Metal Diameter in preparing proper production plan by applying product mix algorithm and drum buffer rope based on five focusing steps principle from theory of constraints in order to maximize throughput. The method used in this research is descriptive method with the primary data. Production planning based on that method starts with identifying resource constraints. Resource constraints that have been identified will be exploited through product mix algorithm and buffer application, while non-resource constraints will be subordinated to the resource constraints by creating optimal production schedule through the rope application. Furthermore, this study suggests PT Metal Diameter to apply this method in preparing proper production plans because it will be more in line with the actual state of the production line, which have resources with limited capacity and the possibility of internal disturbances (breakdown). This research aims to help PT Metal Diameter in preparing proper production plan by applying product mix algorithm and drum buffer rope based on five focusing steps principle from theory of constraints in order to maximize throughput. The method used in this research is descriptive method with the primary data. Production planning based on that method starts with identifying resource constraints. Resource constraints that have been identified will be exploited through product mix algorithm and buffer application, while non-resource constraints will be subordinated to the resource constraints by creating optimal production schedule through the rope application. Furthermore, this study suggests PT Metal Diameter to apply this method in preparing proper production plans because it will be more in line with the actual state of the production line, which have resources with limited capacity and the possibility of internal disturbances (breakdown).;This research aims to help PT Metal Diameter in preparing proper production plan by applying product mix algorithm and drum buffer rope based on five focusing steps principle from theory of constraints in order to maximize throughput. The method used in this research is descriptive method with the primary data. Production planning based on that method starts with identifying resource constraints. Resource constraints that have been identified will be exploited through product mix algorithm and buffer application, while non-resource constraints will be subordinated to the resource constraints by creating optimal production schedule through the rope application. Furthermore, this study suggests PT Metal Diameter to apply this method in preparing proper production plans because it will be more in line with the actual state of the production line, which have resources with limited capacity and the possibility of internal disturbances (breakdown)., This research aims to help PT Metal Diameter in preparing proper production plan by applying product mix algorithm and drum buffer rope based on five focusing steps principle from theory of constraints in order to maximize throughput. The method used in this research is descriptive method with the primary data. Production planning based on that method starts with identifying resource constraints. Resource constraints that have been identified will be exploited through product mix algorithm and buffer application, while non-resource constraints will be subordinated to the resource constraints by creating optimal production schedule through the rope application. Furthermore, this study suggests PT Metal Diameter to apply this method in preparing proper production plans because it will be more in line with the actual state of the production line, which have resources with limited capacity and the possibility of internal disturbances (breakdown).]

Abstrak :
Perencanaan produksi merupakan aspek penting yang hams diperhatikan oleh sebuah perusahaan rnanufaktun Walaupun demikian, rencana produksi yang disusun oleh perusahaan seringkali tidak beljalan densn nama theory of constraints (TOC) dapat Cligunakan untuk menyusun rencana produksi berdasarkan kendala. Perencanaan produksi dimulai dengan mengidentifikasi kendaia yang dimiliki perusahaan Setelah itu, kendala dieksploitasi dengan cara menentukan produc! mb: yang optimal yang akan memaksimaikan keunlungan. Product mix yang optimal dapat ditentukan dengan menggunakan prograrna linier. Ketepatan hasil yang diperolch dari programa linier tergantung pada keakuratan input data yang meliputi data biaya produk. Inforrnasi biaya produk yang lebih akurat dapat diperoleh dengan nlenggunakan metode activity-based cosring (ABC). Product mix yang optimal ini merupakan input bagi penyusunan jadwal produksi. Jadwal produksi disusun berdasarkan metode drum-buffer-rope (DBR)- Perencanaan produksi dilanjutkan dengan membuat suatu rencana koordinasi unluk mendulcung implementasi jadwai produksi yang telah djbuat. Dengan demikian perencanaan produksi yang dibuat dapat memberikan arahan bagi pelaksanaan kegiatan produksi dengan baik.

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Production planning is important aspect that must be considered by a manufacturing company. However, it's often that the company's production plan doesn't work well. Thus, reducing the company's competitiveness in the globalization era. Production planning should consider restrictive bottlenecks of a company so that production activity that is implemented according to the plan may work well. A concept that is known as theory of constraints (TOC) can be used to make production plan based on bottlenecks. Production planning starts with identifying bottlenecks of company. Then, bottlenecks are exploited by determining the optimal product mix that maximize company's profit. The optimal product mix can be determined with linear programming. The accuracy of linear programming's result depends on the accuracy of input, including product cost. More accuracy in product cost information can be obtained by applying activity-based costing (ABC) method. This optimal product mix is input for production scheduling. Production scheduling is made according to drum-buffer-rope (DBR) method. Production planning is continued with a coordination plan to support the implementation of production schedule. Thus, production planning that has been made could give the right direction for production activity implementation.

Abstrak :
Optimisasi portofolio adalah masalah fundamental pada lingkungan keuangan, dimana investor membentuk portofolio yang sesuai dengan yang diharapkan dengan mendapatkan return optimal dan risiko minimal. Pada skripsi ini, membahas masalah optimisasi portofolio dengan kendala di bidang keuangan seperti biaya transaksi, kardinalitas, dan kuantitas dibawah asumsi bahwa return dari aset berisiko adalah bilangan fuzzy. Karena hal tersebut, digunakan model optimisasi portofolio yaitu, mixed integer model nonlinear programming problem. Pertama, data saham di diversifikasi berdasarkan 7 skor rasio finansial EPS, PER, PEG, ROE, DER, Current Ratio dan Profit Margin dengan Agglomerative Clustering untuk menghasikan klaster yang homogen berdasarkan risiko. Selanjutnya, setiap klaster dicari proporsi dalam portofolio dengan menggunakan algoritme heuristik yaitu modified artificial bee colony MABC algorithm, dimana pada algoritme tersebut terdapat proses inisialisasi populasi yang dibangun berdasarkan pendekatan chaotic initialization. Pada akhirnya, return yang dihasilkan dibandingkan dengan S P 500 index return 12,34 dan Sharpe ratio 2,7 . Hasil dari performa Agglomerative Clustering Modified Artificial Bee Colony Algoritm yang dievaluasi menggunakan data aktual, menghasilkan nilai tertinggi dari rata-rata return sebesar 29,96 dan Sharpe ratio sebesar 17,562.

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Portfolio optimization problem is a fundamental matter in the financial environment, where the investors form a satisfactory portfolio by obtaining optimal return and minimal risk. In this undergraduate thesis, we discuss the portfolio optimization problem with real world constraints such as transaction costs, cardinality, and quantity under the assumption that the returns of risky assets are fuzzy numbers. Thus, a mixed integer model nonlinear programming problem is discussed. At first, stock data is diversified based on their financial ratio scores the scores of EPS, PER, PEG, ROE, DER, Current Ratio and Profit Margin by using agglomerative clustering to produce a homogeneous cluster. Next, weight of each stock in the stock portfolio are determined using a modified artificial bee colony MABC algorithm, where in the algorithm there is a process of chaotic initialization approach. Finally, the obtained return will be compared to both the S P 500 index return 12,34 and Sharpe ratio 2,7. The results form the performance of Modified Artificial Bee Colony Algoritm with Agglomerative Clustering in portfolio optimization, evaluated based on some actual dataset show that the higher level of return is 29,96 and Sharpe ratio is 17,562.