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Abstrak :
Telah dilakukan penelitian tentang kepadatan dan indeks makanan terbesar kerang kima Tridacna maxima di perairan Pulau Pasumpahan dari bulai Juni-Oktober 2003. Lokasi pencuplikan kerang T. maxima dibagi atas tiga strata yaitu strata I (sebelah barat Pulau Pasumpahan yang terdiri dari rataan karang dengan padang lamun yang banyak), strata II ( sebelah utara Pulau Pasumpahan yang terdiri dari rataan karang yang didominasi oleh makroaiga) dan strata III (sebelah timur Pulau Pasumpahan yang terdiri dari ekosistem mangrove). Pada masing-masing strata diambil tiga individu kerang T. maxima. Kerang yang didapatkan diambil lambungnya kemudian isi lambung kerang T. maxima dianalisis. Hasil penelitian didapatkan bahwa komposisi makanan alami kerang T. maxima yaitu fitoplankton dan zooplankton. Berdasarkan strata pencuplikan sampel kepadatan makanan alami kerang T. maxima terbesar didapatkan pada strata III yaitu 1813,54 individu plankton/individu kerang dan terendah strata II yaitu 1256,52 individu plankton/individu kerang. Kepadatan makanan alami kerang T. maxima tertinggi berdasarkan kelas yaitu Bacillariophyceae, kemudian diikuti kelas Crustaceae dan terendah kelas Rotifera, Nilai indeks makanan terbesar yang tertinggi didapatkan dari kelas BaciHariophyceae yaitu 30,47dan terendah kelas Rotifera yaitu 0,93. Dari nilai indeks makanan terbesar didapatkan bahwa makanan utama kerang T. maxima adalah plankton dari kelas Bacillariophyeceae dan Crustacea.

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The Density And Index Of Preponderance Of The Giant Clam (Tridacna Maxima) In The Water Pasumpahan Island, Padang City, West Sumetera. The research has been done about the density and index of preponderance of the giant clam Tridacna maxima in the water Pasumpahan Island from June to October in 2003. The giant clam T. maxima colletion sites in the water Pasumpahan Island were divided three strata i, e. strata I ( at west Pasumapahan Island still have coral reef with seagrass), strata II (at north Pasumapahan Island which the coral reef was dominated by macro algae), strata III (at east Pasumpahan Island with mangrove ecosystem). At each strata the giant clam T. maxima were collected hree T. maxima. The giant clam was taken its stomach and then the stomach of giant clam were analysed. Results of the research showed that the composition of the natural food giant clam were phytoplankton and zooplankton. Based on the samples colletion mat the highest density from natural food of giant clam T. maxima were found at strata III 1813.54 plankton/ shell individu and the lowest density of natural food of giant clam T, maxima were 1256.52 plankton/shell individu at strata II. The highest density of natural food of giant clam T. maxima based on class were Bacillariophyceae and then Crustacea and the lowest density were Rotifera. The highest index of preponderance was found from Bacillariophyceae (30.47) and the lowest were Rotifera (0.93). Food preponderance index was calculed to indicate that the phytoplankton of Bacillariophyceae (30.47) and Crustacea (27.41) were the most preference food by T. maxima.

Abstrak :
Tujuan dari penelitian ini adalah untuk memformulasikan dan menganalisa aktivitas antimikroba pada ekstrak etanol dari biji dan buah Jeruk Bali (Citrus maxima) dibandingkan dengan ekstrak etanol dari biji dan buah grapefruit (Citrus paradisi). Ekstrak etanol dari Jeruk Bali dan grapefruit diselidiki untuk aktivitas antimikroba terhadap Bacillus subtilis, Staphylococcus aureus, Escherichia coli dan Candida albicans. Efek antimikroba dilakukan dengan metode difusi agar. Aktivitas antimikroba dibandingkan dengan aktivitas antibiotik spektrum luas sebagai kontrol positif. Meskipun kedua ekstrak etanol tidak menunjukkan aktivitas antimikroba terhadap Candida albicans, ekstrak etanol dari pomelo yang digunakan dalam penelitian ini memberikan hasil positif dengan efek mematikan pada Bacillus subtilis, Staphylococcus aureus dan Escherichia coli dengan zona inhibisi dari 22 - 30 mm, dan lebih unggul dari ekstrak etanol grapefruit (17-25 mm). Ekstrak etanol dari Jeruk Bali memiliki efek antimikroba yang baik, dan dapat dipakai sebagai alternatif dari pengawet alami untuk kosmetik. ......The purpose of this study is to formulate and analyze the antimicrobial activity of ethanolic extract of Indonesian pomelo (Citrus maxima) seeds and pulp compared to the grapefruit (Citrus paradisi) seeds and pulp ethanolic extract. Ethanolic extracts of pomelo and grapefruit seeds and pulp were investigated for activity against Bacillus subtilis, Staphylococcus aureus, Escherichia coli and Candida albicans. The level of antimicrobial effects was established using agar diffusion method. Their antibacterial and antifungal activity was compared to the activity of broad spectrum antibiotic as a control. Although both of the ethanolic did not show any antimicrobial activity against Candida albicans, the ethanolic extract of pomelo seeds and pulp used in this research gave positive results with lethal effect on Bacillus subtilis, Staphylococcus aureus and Escherichia coli with zones of inhibition ranging from 22 – 30 mm in diameter, which is more superior to grapefruit seeds and pulp ethanolic extract (17 – 25 mm). Ethanolic extract of pomelo seeds and pulp has a good antimicrobial effect, which makes it a good natural preparation for use as an alternative preservative for cosmetic.

Abstrak :
ABSTRAK
Penentuan struktur patahan dalam eksplorasi sangat penting karena struktur patahan berasosiasi dengan permeabilitas tinggi yang umumnya adalah reservoar. Dalam penelitian ini dilakukan studi komparasi penetuan struktur patahan menggunakan metode FHD dan HVDM yang diaplikasikan pada model sintetik gravitasi dan data riil gravitasi. Identifikasi struktur patahan pada FHD dan HVDM ditunjukkan dengan nilai maksimum, dimana FHD didapatkan dari akar penjumlahan kuadrat turunan pertama arah horizontal X dan turunan pertama arah horizontal Y sedangkan HVDM didapatkan dari akar penjumlahan kuadrat horizontal, vertical dan diagonal coefficient dari proses DWT 2 dimensi. Berdasarkan hasil model sintetik gravitasi, FHD lebih baik menentukan struktur patahan dibandingkan dengan HVDM sedangkan untuk data riil gravitasi respon FHD dan HVDM memiliki respon yang hampir sama. Pada data riil gravitasi, tidak dapat ditentukan metode mana yang lebih baik dalam menetukan struktur patahan karena tidak adanya data geofisik lain sebagai data pendukung dalam menggambarkan struktur bawah permukaan di daerah penelitian ?Y?.

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ABSTRACT
Fault structure determination during exploration is crucial since it associates with high permeability that, in general, called reservoir. Through this research, comparative study of fault structure determination using FHD and HVDM methods which are applied toward gravity synthetic model and gravity real data has been conducted. Identification of fault structure on FHD and HVDM are shown by maximum value, which FHD resulted from root sum square of horizontal X first derivative and horizontal Y first derivative meanwhile HVDM is obtained by horizontal, vertical and diagonal coefficient root sum square of 2-Dimensional DWT. Based on the result of gravity synthetic model, FHD could determine fault structure in a better way than HVDM whereas on gravity real data FHD and HVDM have a slightly similar response. On gravity real data, the better way to determine fault structure could not be concluded because there is no another geophysics data provided as supporting data to portray subsurface structure at research area "Y".

Abstrak :
Backpropagation neural network backpropagation adalah salah satu algoritma machine learning yang dapat digunakan untuk melakukan klasifikasi data. Klasifikasi data dilakukan dalan serangkaian proses training dan testing. Pada akhir proses testing yang juga merupakan akhir dari proses backpropagation, akan didapatkan nilai recognition rate. Nilai recognition rate merupakan nilai yang menandakan banyaknya data yang berhasil diklasifikasi dengan benar pada proses testing terhadap seluruh testing dataset. Recognition rate erat kaitannya dengan masalah underfitting, overfitting, local minima, dan local maxima. Keempat masalah ini menyebabkan nilai recognition rate yang didapatkan kurang optimal. Namun biasanya untuk menangani keempat masalah ini dilakukan pengaturan pada beberapa paramter, misalnya learning rate, momentum, jumlah layer, jumlah nodes, weights, dan lain-lain. Pada tulisan ini akan dijelaskan program optimasi yang melakukan pengaturan pada nilai inisialisasi weights untuk menangani keempat tersebut. Program ini melakukan inisialisasi weights menggunakan genetic algorithm pada backpropagation yang mengimplementasikan k-fold crossvalidation. Untuk menguji dan membandingkan program optimasi terhadap program implementasi backpropagation yang tidak dioptimasi program non-optimasi, digunakan empat dataset, yaitu iris flower dataset, seeds dataset, wine dataset, dan EEG dataset buatan. Pada akhir pengujian didapatkan hasil bahwa program optimasi berhasil mendapatkan nilai recognition rate lebih tinggi pada iris flower dataset, yaitu 97.33 pada program optimasi dan 96.67 pada program non-optimasi. Kemudian didapatkan pula nilai recognition rate yang lebih tinggi pada seeds dataset, yaitu 93.33 pada program optimasi dan 92.86 pada program non-optimasi. Lalu didapatkan pula nilai recognition rate yang lebih tinggi pada EEG dataset buatan, yaitu 37.5 pada program optimasi dan 35.94 pada program non-optimasi. Sedangkan pada wine dataset didapatkan nilai recognition rate yang sama antara program optimasi dan program non-optimasi, yaitu 99.44.

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Backpropagation neural network backpropagation is one of machine learning algorithms that can be used to classify data. The data classification is done in a series of trainig and testing processes. At the end of testing process that is also the end of backpropagation process, the algorithm will produce recognition rate value. Recognition rate value indicates the total of correctly classified data in testing process againts all data in testing dataset. Recognition rate value related to underfitting, overfitting, local minima, and local maxima problems. However, to handle these problems adjusting some parameters are necessary to be done. These parameters are learning rate, momentum, number of layers, number of nodes, weights, etc. In this writting will be explained an optimization program that adjusts the initialization values of weights to handle those four problems. This program initializes weights using genetic algorithm on backpropagation implementing k fold crossvalidation. To test and compare the optimization program with a program that implements backpropagation without optimization non optimzation program four datasets will be used, those are iris flower dataset, seeds dataset, wine dataset, and artificial EEG dataset. At the end of the test, the results show that optimization program obtained higher recognition rate value on iris flower dataset, that is 97.33 on optimization program againts 96.67 on non optimization program. Other than that, optimization program obtained higher recognition rate value on seeds dataset, that is 93.33 on optimization program againts 92.86 on non optimization program. Also, optimization program obtained higher recognition rate value on artificial EEG dataset, that is 37.5 on optimization program againts 35.94 on non optimization program. However, the optimization program obtained an equal recognition rate value on wine dataset, that is 99.44.

Abstrak :
A discussion of developments in the field of bifurcation theory, with emphasis on symmetry breaking and its interrelationship with singularity theory. The notions of universal solutions, symmetry breaking, and unfolding of singularities are discussed in detail. The book not only reviews recent mathematical developments but also provides a stimulus for further research in the field.

Abstrak :
This book provides a study aid on the finite element method. Based on the free computer algebra system Maxima, it presents routines to symbolically or numerically solve problems in the context of plane truss and frame structures. This allows readers to not only check classical hand calculations but also understand the computer implementation of the method. The mechanical theories focus on the classical one-dimensional structural elements, i.e. bars, Euler-Bernoulli and Timoshenko beams as well as their combination to generalized beam elements. Focusing on one-dimensional elements reduces the complexity of the mathematical framework and the resulting matrix equations can still be displayed with all components, and not only in a symbolic representation. The use of a computer algebra system and the incorporated functions, e.g. for equation solving, highlights the methodology of the finite element method rather than standard procedures.

Abstrak :
This book is intended as an essential study aid for the finite element method. Based on the free computer algebra system Maxima, the authors offer routines for symbolically or numerically solving problems in the context of plane truss and frame structures, allowing readers to check classical ‘hand calculations’ on the one hand and to understand the computer implementation of the method on the other. The mechanical theories focus on the classical one-dimensional structural elements, i.e. bars, Euler–Bernoulli and Timoshenko beams, and their combination to generalized beam elements. Focusing on one-dimensional elements reduces the complexity of the mathematical framework, and the resulting matrix equations can be displayed with all components and not merely in the form of a symbolic representation. In addition, the use of a computer algebra system and the incorporated functions, e.g. for equation solving, allows readers to focus more on the methodology of the finite element method and not on standard procedures.