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"Mempertimbangkan kompleksitas permasalahan proyek, metode simulasi merupakan suatu pendekatan analisa risiko yang memadahi. Metode simulasi adalah teknik analisa yang berupaya rnenirukan perilaku sistem yang dikaji. Metode simulasi memungkinan menganalisa proyek dari dua sudut padang utama proyek yaitu cost dan durasi secara rinci dan mendekali nyata. Oleh karena itu penulis mencoba menganalisa risiko-risiko yang akan muncul dalam Proyek VAC RS. Brawijaya sehingga dapat dicarikan jalan keluarnya. Dalam skripsi ini dibahas bagaimana mengidentifikasi risiko dengan Failure Mode Effect Anaysis dan melakukan pembobotan dengan RPN yang berisi Severity, Occurance dan Detection. Setelah melakukan identifikasi dan pembobotan proses analisa risiko selanjutnya mengunakan bantuan software @_Risk for Project untuk melakukan simulasi. Setelah model diverifikasi dan divalidasi, maka model dirurming untuk rnendapatkan data sebagai bahan untuk melakukan analisa risiko cost dan durasi, sehingga dapat diketahui baseline cos: dan base durasi untuk proyek tersebut. Dari hasil analisa tersebut diketahui bahwa dengan cost proyek RS. Brawijaya 1.8 milyar maka memiliki tingkat risiko sebesar 51.79% dan risiko proyek mengalami kemunduran sekitar 27%.

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Considering complexity problems of project, simulation method is an approach of risk analysis. Simulation method is analysis technique coping to imitate behavior of studied system. Simulation method possible to analyze the project from two point of view ; namely cost and duration of the project. Therefore this research try to analyze risks that emerge in VAC Project at RS. Brawijaya so that can be looked for way out. In this research is studied how to identify risk with Failure Mode of Effect Analysis and conduct weighting used RPN which is containing Severity, occurrence and Detection. After steps identify and process weight analyze risk than use soiiware @Risk for Project to do simulation. After verification model and validation, hence model is rurming to get data upon which to analyze risk of cost and duration, so that can be known cost and duration baseline project. From the analysis result known that cost of project RS. Brawijaya 1.8 billion hence owning risk storey ;level equal to 51.79% and risk of project duration lagging down 27%."

"Indonesia Fuels demand increase every year; so that fuels subsidy increases progressively and APBN (National Budget) will become worst. With Parliament (DPR) approvement, government shall cut down the fuels subsidy step by step, and to determine type and total volume of fuels subsidized on year 2006, the type of fuels subsidized are Premium (regular Gasoline), Diesel Oil and Kerosene with total consumption equal to 41,58 million kL that was mean around 30% decreasing of subsidized fuel alocation. Analysis level of confidence in amount of Diesel Oil demand for transportation and Kerosene demand for household and small industries base on quota compared with the result of formulation from historical consumption conducted by Monte-Carlo simulation.

The result of simulation indicate that in 20012 Diesel oil demand for transportation base on quota having confidence level of 78,8%, and base on formulation having confidence level of 33,l%. While the result of simulation for Kerosene demand for household and small industries base on quota having confidence level of 62.6%, and base on formulation having confidence level of 80. 4%."

"ABSTRAKUntuk memenuhi kebutuhan energi seiring dengan menurunnya produksi minyak, pemanfaatan gas menjadi solusi untuk dapat memenuhi kebutuhan energi. Produksi gas Indonesia pada tahun 2015 adalah 8,102 MMSCFD berdasarkan data dari SKK Migas.Saat ini pemerintah sedang menggalakkan pembangunan jaringan gas kota di Indonesia, salah satunya di Kecamatan Muara Satu Kota Lhokseumawe. Salah satu perusahaan pemerintah berencana akan membangun 89.383 sambungan rumah tangga sampai tahun 2017 di seluruh Indonesia.Mengingat akan ada banyak proyek jaringan gas kota, penting bagi kita menganalisis risiko yang mungkin terjadi selama konstruksi maupun selama operasi dari jaringan gas tersebut. Pada penelitian ini dilakukan identifikasi risiko, kemudian mengelompokkan risiko tersebut sesuai kategorinya, kemudian dilakukan analisis kualitatif dan analisis kuantitatif dengan simulasi Monte Carlo, lalu hasilnya dievaluasi. Dengan menghitung risiko secara keseluruhan, risiko proyek jaringan gas di Kota Lhokseumawe ini tergolong rendah dengan nilai risk 3,72.Hasil penelitian ini adalah berupa strategi untuk menurunkan risiko-risiko dan kerugian yang mungkin timbul. Dengan adanya strategi penanganan terhadap risiko-risiko yang mungkin timbul, kerugian biaya dapat diturunkan menjadi 10% atau sekitar 4,5 milyar rupiah.

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ABSTRACTTo fulfill energy demand because of decreasing oil production, converting oil to gas is a good choice. Based on data from SKK Migas in 2015, Indonesia produced 8.102 MMSCFD gas.Nowdays, Indonesia?s government encourages gas pipeline project for citygas in several cities in Indonesia. One of company owned by government plans to build 89.383 connection until 2017 in Indonesia.In the future, there are many citygas projects in Indonesia, risk analysis during construction and operation of pipeline project will be an important thing. This research included risk indentification, grouping the risk into their categories, then we analyze the risk both qualitative and quantitative using Monte Carlo simulation. The summary of all risk calculation shows that this project has low risk with value 3.72.The results of this research are strategies to reduce and manage the risks during pipeline construction and operation of citygas project. By applying the strategies, risks can be reduced from 29% to 10%. It is equivalent with 4,5 billion rupiahs."

"ABSTRAKTingkat ketidakpastian pada tahap FEED yang cukup tinggi mengharuskan perencana untuk mengalokasikan biaya kontingensi guna mengantisipasi tindakan korektif yang harus dilakukan sebagai respon terhadap resiko yang terjadi. Metode yang digunakan untuk menghitung biaya kontingensi pada penelitian ini adalah Monte Carlo Simulation yang permodelannya didapatkan dari analisa regresi dimana contruct dari penelitian ini berbasis PMBOK 2013. Faktor resiko dominan yang dinilai berpengaruh signifikan terhadap kenaikan biaya proyek FEED adalah Faktor Resiko Planning Controlling yang dipengaruhi oleh Faktor Eksternal, Faktor Lemahnya Kompetensi Kontraktor Engineering dalam Mengelola Proyek, Faktor Resiko Kurangnya Pengetahuan Tim Teknis mengenai Change Management. Dengan melakukan simulasi monte carlo menggunakan data responden pada tingkat kepercayaan 95 , maka didapatkan bahwa alokasi biaya kontingensi sebesar 11,16 pada anggaran proyek FEED dapat mengantisipasi kenaikan biaya yang terjadi. Dari penelitian ini didapatkan bahwa dalam proses penyusunan anggaran, saat melakukan estimasi biaya kontinensi sebaiknya tidak menggunakan persentase kontingensi proyek terdahulu karena masing - masing proyek memiliki tingkat kesulitan yang berbeda dan risiko yang berbeda juga. Untuk itu, proses penyusunan risk register pada proses penyusunan anggaran menjadi komponen yang penting.

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ABSTRACTThe high level of uncertainty of the FEED stage requires a planner to allocate cost contingency to anticipate the corrective actions that may occur as risk responses. The cost contingency calculation method used in this research is the Monte Carlo Simulation. The dominant risk factor that significantly affect cost increase are planning controlling risk due to external factors, shortcomings of engineering contractor competence in managing project, technical team lack of knowledge regarding change management. By using respondents data as monte carlo simulation inputs with 95 of confidence level, it is shown that the cost contingency allocation of 11,16 in FEED budget estimation is sufficient to anticipate any potential cost increase. This research shows that in the process of budget settings, planners are suggested not to use the contingency percentage from previous projects because every project has their own difficulty level and different kind of risk. Therefore, the process of constructing risk register as a part of budget estimation process becomes crucial."

"Dengan semakin banyaknya kebutuhan masyarakat akan layanan perbankan yang terus menerus selama 24 jam maka peranan electronic banking sebagai hentuk layanan bank yang dapat memenuhi tuntutan tersebut juga semakin meningkat. Untuk menerapkan manajemen risiko atas kegiatan operasional maka harus dilakukan proses identifikasi, proses pengukuran dan proses monitor.Hasil dari proses pengukuran risiko akan menjadi dasar dari metode mitigasi risiko yang akan digunakan. Sebagai salah satu metode yang umum dipakai untuk mengukur risiko VaR akan nnenyatakan suatu jumlah kerugian maksimum dari nilai uang yang mungkin hilang pada suatu periode tertentu dan dalam confidence level atau probabilita tertentu. Operational VaR merupakan suatu nilai kerugian maksimum pada suatu periode tertentu akibat dari adanya resiko operasional.Dalam karya akhir ini, dikemukakan langkah-langkah perhitungan Operational VaR dengan menggunakan simulasi Monte Carlo dengan data yang berasal dari kerugian operasional pada Group EIectronic Banking Bank ABC. Simuiasi Monte Carlo sesungguhnya adalah proses pembuatan distribusi kerugian untuk mendapatkan nilai Operational VaR dengan menggabungkan antara distribusi frequency of loss dengan distribusi severity of loss.Model simulasi Monte Carlo tersebut harus melalui pengujian backtestingg terlebih dahulu untuk memastikan model tersebut dapat diterima atau tidak secara statistik dalam menghasilkan nilai Operational VaR. Jika model tersebut dapat diterima maka Operational VaR yang dihasilkan dapat digunakan untuk menentukan kerugian maksimum pada periode waktu tertentu. Berdasarkan perhitungan yang dilakukan dengan menggunakan data yang berasal dari kerugian operasional pada Group Electronic Banking selama periode 2004-2005 dapat dikemukakan bahwa Operational VaR sejumlah Rp. 8.444.738.,- merupakan kemungkinan kerugian operasional maksimum yang dapat terjadi pada satu hari dengan probabiliti kejadian sebesar 95%.Berdasarkan hasil pengujian dengan backtesting ternyata metode pengukuran risiko operasional dengan menggunakan simulasi Monte Carlo dapat diterima secara statistik. Dengan dernikian dapat disarankan penerapan metode simulasi Monte Carlo untuk mengukur besarnya nilai kerugian yang akan terjadi pada satu periode tertentu dimasa yang akan datang.

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The requirement of banking services that can operates continuously during 24 hour and 7 days a week can be fulfill by electronic banking. Implementing the risk management process of operational activity can be done by identify, measure and monitor the activity.Result from measurement process will become the base to choose the method of risk mitigation will be used. VaR is one of popular method to measure risk, that will result art amount of a maximum loss of money that is possible to lose at one particular specified period and in a certain level of confidence. Operational Var represent an amount of maximum of loss at one particular specified period effect of operational risk.This thesis, explain the calculation process of Operational Var steps by step using Monte Carlo simulation. The data used in this simulation is coming from operational loss activity held by Electronic Banking of Bank ABC. The Monte Carlo Simulation actually is the process to get a model of distribution of loss as an Operational Var value by joining the distribution of loss and the distribution of severity.The result generated from Monte Carlo simulation need to pass the back testing examination to ascertain that the model can be accepted statistically in yielding value of Operational Var. If the model accepted then the Operational Var value generated can be used to determine the maximum of loss in certain probability and period of time. The result from Monte Carlo simulation which is using operational loss data coming from Electronic Banking activity during period 2004-2005, in generated the Operational Var value of Rp. 8.404.738.,- which is representing the possibility to happened of maximum of loss from operational activity equal to 95%.The back testing examination of Operational VaR value that is generated by Monte Carlo simulation, resulted that the model can be accepted statistically. Thereby this model of distribution of loss can be suggested to be implemented in measuring value of loss that can happened in certain level of probability and at certain period of time."

"ABSTRAKProses produksi logam timah merupakan rangkaian dari beberapa tahap proses seperti penambangan, penyimpanan material, peleburan dan pemurnian yang menghasilkan balok timah. Setiap proses mempunyai variabel-variabel yang sangat mempengaruhi produksi logam timah. Data historis menunjukkan penggunaan bahan-bahan pada setiap tahap proses produksi logam timah bervariasi dan acak. Multiple regression digunakan untuk menghubungkan keterkaitan antara variabel-variabel independen dan variabel-variabel dependen yang merupakan variabel-variabel acak yang resiko. Formula yang dihasilkan oleh multiple regression menjadi dasar untuk membangun model regresi. Teknik Monte Carlo digunakan untuk melakukan simulasi dari model regresi dan menghasilkan nilai-nilai ramalan. Prediksi atau peramalan ini dijadikan dasar dalam analisa proses produksi yang rentan terhadap resiko kegagalan. Analisa resiko terhadap prediksi di masa yang akan datang tersebut diharapkan mampu mengurangi dampak dari resiko.

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ABSTRACT

Production process of tin metal is series of some states of processes like mining, storage, melting, and refining to produce tin ingot. Every state of process has variables which are very influencing the production of tin ingot. Historical data shows the use of materials in each production process of tin ingot is varies and random. Multiple regression is used to the correlationship of the between independent variables and dependent variables which are randomly risk variables. The formulas that produced by the multiple regression become basis for developing regression model. Monte Carlo technique is used for simulating the regression model to produce forecast values. The prediction or forecasting values risk used to analyse the production process failure to risk. The risk analyses future prediction is to lower the risk."

"Using the application of risk measuring, Operational Value at Risk (VaR) , is measuring risk that could arise and thus leading to a certain nominal that is used for reserve, including for managing a company. The method that is used for measuring operational risk is the LDA Aggregation Method. Historical data of the operational risk on the internal process from receiving shrimp process at PT. X is based on the internal audit result of PT X. Furthermore, with the Aggregation method it will form the Aggregation Loss Distribution with Aggregate Best Frequency Distribution, Poisson, distribution and Best Severity Distribution , Exponential Distribution .Computation is conducted with the spreadsheet Excel with 10.000 times of Monte Carlo simulation to calculate the maximum potential loss of Operational VaR based on the back testing result of the Kupiec Test, the model could be implemented for measuring the operational internal process risk from receiving shrimp process at PT. X.. The the next step for obtaining an accurate model . PT. X. should then regularly up - date the model with the newest data and validate the test through back testing process."

"Dalam industri minyak dan gas, pipa dipaksa bekerja 24 jam sehari selama satu tahun, atau bahkan selama beberapa dekade. Karena digunakan untuk mendukung distribusi sejumlah besar minyak, gas, dan air dan bahkan dengan jarak yang sangat jauh, medan yang dilewati oleh jaringan pipa sangat beragam, mulai dari laut, dataran rendah, lembah, dan di tanah, kemudian di operasi akan ditemukan dalam berbagai macam masalah. Sangat penting untuk mendeteksi kegagalan di masa depan dari awal tahun atau di mana pipa baru saja dipasang. Penelitian ini memfokuskan pada analisis risiko, pipa yang akan dianalisis adalah pipa yang berusia di bawah 2 tahun, dan untuk melihat potensi risiko kegagalan pipa, akan menggunakan metode Failure Mode and Effect Analysis (FMEA) dengan penambahan Simulasi Monte Carlo di bagian FMEA (Severity, Occurrence, Detection). Untuk melihat potensi kegagalan dari struktur pipa, itu akan menganalisis kerugian dinding berdasarkan pada ketebalan dinding awal, di mana pipa dinding tidak boleh terdegradasi sebanyak 80% dari total dinding awal. Dengan semua metode ini, memungkinkan semua perusahaan pengguna pipa untuk memantau kegagalan pipa di masa depan, terutama untuk pipa yang baru dipasang.

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In the oil and gas industry, pipeline is forced to work 24 hours a day for one year, or even for decades. Because it is used to support the distribution of large amounts of oil, gas, and water and even with very long distances, the terrain passed by pipelines is very diverse, starting from the sea, lowlands, valleys, and in the ground, then in the operation will be found in many kinds of problems. It is really important to detect future failures from early year or where the pipe has just been installed.

This research is focusing on the risk analysis, the pipes to be analyzed are pipes that are under 2 years old, and to see the potential risk of pipe failure, will use the Failure Mode and Effect Analysis (FMEA) method with the addition of a Monte Carlo simulation in the Occurence (Severity, Occurrence, Detection) section. To see the potential failure of the pipe structure, it will analyze wall loss based on the initial wall thickness, where the wall pipe should not be degraded as much as 80% of the total initial wall. With all of these methods, it allows all pipe user companies to monitor pipeline failures in the future, especially for newly installed pipes."

"Risiko operasional adalah salah satu risiko yang cenderung sulit untuk diantisipasi dan dampaknya seringkali di luar perkiraan bank. Pengukuran Value at Risk (VaR) menjadi penting agar bank dapat menghitung beban modal untuk risiko operasional sesuai dengan profil risikonya. Tesis ini membandingkan perhitungan VaR risiko operasional pada PT Bank ABC dengan dua metode yaitu Monte Carlo Simulation dan Extreme Value Theory. Berdasarkan backtesting terhadap kedua metode tersebut, pengukuran risiko operasional pada Bank ABC lebih realistis jika menggunakan Monte Carlo Simulation.

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Operational risk in banking is one of the most difficult risk to anticipate and its impact to bank?s losses sometimes unpredictable. Measuring Value at Risk (VaR) then become important to enable bank to calculate capital charges for operational risk in accordance with its risk profile. This research attempts to compare between Extreme Value Theory method and Monte Carlo Simulation to calculate operational risk capital charge in PT Bank ABC. Based on backtesting procedures, it reveals that Monte Carlo Simulation is more suitable for Bank ABC's risk profile."

"The objective of this research is to examine maximum losses when investor invests on syariah based stock. Markowitz model is used for constructing the optimal portfolio. Value at Risk Model is also used for calculating the expected losses. The research indicates that volatility seems to cluster in a predictable fashion. Therefore the research forecasts variances used exponentially weighted moving average (EWMA) model. This research also aims to evaluate whether the EWMA model can predict variances reasonably well. The data used in this research are syariah based stock which had been included in Jakarta Islamic Index (JII) during the year 2005-2006. This research provides that VAR models using an EWMAforecast are good enough for predicting risk. The number of exception of 508 daily datas are only less than 5% or valid at confident level 95%. As benchmark we also use historical method and monte carlo simulation to compare performance of EWMA forecast."