Judul: Parallel Computing for Medicinal Plant Identification System Using Fuzzy Local Binary Pattern
Abstrak: As biological image databases are increasing rapidly, automated species identification based on digital data is of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) on medicinal plant identification system. We propose parallel computing on medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP). The main goal of the research was to measure the efficiency of parallel computing on medicinal plant image processing and evaluation whether this approach is reasonable for handling large data sets. This research proposes two models of parallel design to identify medicinal plant. The first model used data parallel design and the second model used task parallel design on FLBP process. Both of model are applied on the computer cluster, which consists of eight computers with the same spesicification. The development of the parallel design used the message-passing model with MPI library and the C/C++ language programming. The parallel computation performance was evaluated by speed up, efficiency and iso-efficiency. The experimental result shows that both of the parallel design models can reduce the computing time of the image feature extraction on a medicinal plant identification system. The values of the speedup on the first model by the time of the extraction was 1 440 image data by using 8 processors, is 7.64 with efficiency value of 0.95. The second model uses 8 processors generated the value of the speedup is 6.9 with efficiency value 0.87. The result of the comparation values between the speedup and the efficiency of both design model shows that first model has better performance by the time of the extraction 1 440 leaf images. This is affected by the process of image area dividing on the second model that requires more complex of communication cost. We analyzed that in the second model if we add processor would effect on communication cost. This condition did not occurred on the first model, so that the speedup and efficiency are better than the second model. The experimental result of query image extraction shows that the speedup on the first model is 6.73, with efficiency value of 0.84. The second model produces speedup value is 7.96, with efficiency value of 0.99. The different between speedup value and efficiency produced is affected by the dividing of data from those two parallel models. The first model divides 20 combination of the operator and FLBP threshold. The process of data dividing on first model enables unideal data partitioning. The ideal data dividing is at the time of each processors compute the data with the same volume. The unideal data dividing may cause an idle condition. The idle condition can affect the performance and the parallel time generated by the first model. The dividing of the data on second model by dividing the image area equal to the processors used. The data dividing on the second model is ideal condition, so that the speedup and efficiency are better than first model.
Keyword: Fuzzy Local Binary Pattern, High Performance Computing, Parallel Computing.

Judul: Clustering Optimization using Particle Swarm Optimization for Medicinal Plant Identification System based on Digital Image
Abstrak: Indonesia is a country with a high level of biodiversity and there are 22,500 species of medicinal plants. The percentage of medicinal plant used by Indonesian people is only 4.4% or as much as 1,000 species. The people’s lack of knowledge becomes one of the cause of this low use of medicinal plant in Indonesia. People’s insight about medicinal plant can be increased by developing an identification system of medicinal plant. In this research, we used fuzzy local binary pattern (FLBP) to extract the feature. The number of digital image that used in this research are 1,440 leaf image. Unsupervised learning technique known as clustering is proposed to compare the results of the identification of medicinal plants with classification techniques. Clustering methods used is the k-means clustering and fuzzy c-means clustering. The disadvantages of both method is that the results are sensitive to the selection of initial cluster centers and the calculation of local solutions to achieve optimal conditions. This research applied a particle swarm optimization (PSO) method to overcome the weaknesses of k-means clustering and fuzzy c-means clustering. The application of PSO method in the identification system as a purpose to improve the identification of medicinal plants. This research builds four models of clustering is the k-means clustering, fuzzy c-means clustering, PSO based k-means clustering and PSO-based fuzzy c-means clustering. The development of clustering model using C++ programming language. Evaluation model of clustering using an accuracy value indicates that the PSO method can improve the identification of the k-means clustering and fuzzy c-means clustering. Accuracy of k-means clustering resulted 41.67% and PSO-based k-means clustering is 48%. Accuracy of fuzzy c-means clustering resulted in 50% and PSO based fuzzy c-means clustering is 52.33%. Evaluation of clustering models using computational time indicates the addition of time on the PSO method. The addition of algorithms led to growing number of data processing. Computation time on k-means clustering 291.3 seconds and PSO based k-means clustering 403.3 seconds. Computation time on fuzzy c-means clustering 59.65 seconds and PSO based fuzzy c-means clustering 60.1 seconds. PSO based fuzzy c-means clustering is a better method for generating high accuracy and faster computing time.
Keyword: Fuzzy C-Means Clustering, Fuzzy Local Binary Pattern, K-Means Clustering, Particle Swarm Optimization.

Judul: Peluang Bertahan Suatu Perusahaan Asuransi Motor
Abstrak: Asuransi sudah tak asing lagi di telinga kita_ Banyak orang yang tertarik akan asuransi, sebab adanya asuransi dapat mengurangi kerngian secara finansia! yang diakibatkan oleh kejadiankejadian yang secara tiba-tiba yang tidak diharapkan. Yang akan dibahas di sini adaJah perusahaan asuransi yang menangani kendaraan motor lika nanti ditemukan kata perusahaan asuransi (insurer), maka yang dimaksud di sini adalah perusahaan asuransi motor. Salah satu contoh kejadian yang sering terjadi adalah tabrakan yang sangat merugikan bagi pemilik kendaraan tersebut. Tetapi jika pemilik kendaraan motor tersebul telah menjadi peserta asuransi, maka kerugian yang diderita akan berkurang dengan adanya jaminan dan perusahaan asuransi tersebut Resiko seseorang mengalami peristiwaperistiwa seperti di atas memang Iidak bisa diperhitungkan secaca pasti, namun dalam perasuransian resiko tersebut dapat dialihkan ke dalam sekelompok besar orang di mana resiko perorangan dibagi rata pada banyak orang (peser1a asuransi)_ Asuransi merupakan cara pengelolaan resiko yang ekonomis dan tepa! guna, karena asuransi adalah pemindahan resiko mumi dari peserta asuransi kepada perusahaan asuransi. Perusahaan asuransi merupakan salah satu kegiatan bisnis yang dapat mendatangkan kamtungan bagi pemiliknya_ Namun seperti halnya bisnis yang lain, perusahaan asuransi dapat juga mengalami kerugian yang akhimya dapat menyebabkan kebangkrutan. Perusahaan asuransi akan tetap bertahan jika total klaim yang hams dibayar oleh perusahaan--asuransi tersebut lebih keeil atau setidaknya sarna dengan total dana bersih yang tersedia. Total dana bersih adalah dana awal yang dimiliki ditambah total premi yang diterima ditambah dana yang berasal dan sumber lainnya dikurangi pengeluaran lainnya. Tetapi jika total klaim lebih besar dari total dana bersih yang tersedia., maka perusahaan 3.QJransi tersebut akan mengalami kerugian yang pada akhimya akan mengakibatkan kebangkrutan. Komponen yang mempengaruhi total dana bersih yang tersedia adalah total premi yang harus dibayar oleh para pemegang polis (nasabah), yaitu orang-orang yang lelah menjadi peserta asuransi. Meskipun besamya premi yang dibebankan kepada setiap individu pemegang polis asuransi telah ditetapkan berdasarlcan perhitungan, namun kerugian bisa saja terjadi. Hal ini diakibatkan oleh banyaknya klaim yang datans, yang mengakibatkan nilai total klaim sangat besar, sehingga melebihi total dana yang dimiliki perusahaan asuransi tersebut Semua itu tidak dapal diprediksi karena kedatangan klaim tersebut merupakan kejadian aeak dan besarnya klaim merupakan peubah acak. Perusahaan asuransi berusaha melindungi perusahaannya dari kebangkrutan_ Dengan kata lain agar perusahaannya tetap benahan, maka perusahaan asuransi akan mengasuransikan kembali (reinwral1ce) perusahaannya kepada perusahaan asuransi lain yang lebih hesar atau lebih kuat. Sehingga perusahaan asul1lnsi tersebut membayar premi dan dapat mengajukan klaim kepada perusahaan asurans.i yang lebih besar.
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