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"In order to characterize the distribution patten of rotifers Brachionus spp.in North Sulawesi,sample collections have been conducted at four locations,two at east coast and other two at West coast of North Sulawesi peninsula,which are connected to Maluku and Sulawesi Seas,respectively...."

"Satu parameter distribusi Lindley (𝜃) telah banyak digunakan di berbagai bidang seperti Biologi, teknik, medis, dan industri. Distribusi Lindley mampu memodelkan data dengan tingkat bahaya monoton yang meningkat. Namun, dalam kehidupan nyata, ada situasi di mana tingkat bahaya bukan monoton. Oleh karena itu, untuk meningkatkan kemampuan distribusi Lindley untuk pemodelan data, suatu modifikasi dapat digunakan dengan menggunakan metode transformasi Alpha Power. Hasil dari modifikasi distribusi Lindley biasa disebut distribusi Alpha Power Transformed Lindley (APTL) yang memiliki dua parameter (𝛼, 𝜃). Distribusi APTL baru ini sesuai dalam memodelkan data dengan bentuk pdf menurun atau unimodal dan meningkatkan, mengurangi, dan bak terbalik berbentuk tingkat bahaya. Berbagai sifat dari distribusi yang diusulkan dibahas termasuk kepadatan probabilitas fungsi, fungsi distribusi kumulatif, fungsi survival, fungsi tingkat bahaya, fungsi momen, dan momen r.Parameter model diperoleh dengan menggunakan metode kemungkinan maksimum. Data waktu tunggu digunakan "sebagai ilustrasi untuk menggambarkan kegunaan distribusi APTL"Satu parameter distribusi Lindley (𝜃) telah banyak digunakan di berbagai bidang seperti Biologi, teknik, medis, dan industri. Distribusi Lindley mampu memodelkan data dengan tingkat bahaya monoton yang meningkat. Namun, dalam kehidupan nyata, ada situasi di mana tingkat bahaya bukan monoton. Oleh karena itu, untuk meningkatkan kemampuan distribusi Lindley untuk pemodelan data, suatu modifikasi dapat digunakan dengan menggunakan metode transformasi Alpha Power. Hasil dari modifikasi distribusi Lindley biasa disebut distribusi Alpha Power Transformed Lindley (APTL) yang memiliki dua parameter (𝛼, 𝜃). Distribusi APTL baru ini sesuai dalam memodelkan data dengan bentuk pdf menurun atau unimodal dan meningkatkan, mengurangi, dan bak terbalik berbentuk tingkat bahaya. Berbagai sifat dari distribusi yang diusulkan dibahas termasuk kepadatan probabilitas fungsi, fungsi distribusi kumulatif, fungsi survival, fungsi tingkat bahaya, fungsi momen, dan momen r.Parameter model diperoleh dengan menggunakan metode kemungkinan maksimum. Data waktu tunggu digunakan " sebagai ilustrasi untuk menggambarkan kegunaan distribusi APTL. Satu parameter distribusi Lindley (𝜃) telah banyak digunakan di berbagai bidang seperti Biologi, teknik, medis, dan industri. Distribusi Lindley mampu memodelkan data dengan tingkat bahaya monoton yang meningkat. Namun, dalam kehidupan nyata, ada situasi di mana tingkat bahaya bukan monoton. Oleh karena itu, untuk meningkatkan kemampuan distribusi Lindley untuk pemodelan data, suatu modifikasi dapat digunakan dengan menggunakan metode transformasi Alpha Power. Hasil dari modifikasi distribusi Lindley biasa disebut distribusi Alpha Power Transformed Lindley (APTL) yang memiliki dua parameter (𝛼, 𝜃). Distribusi APTL baru ini sesuai dalam memodelkan data dengan bentuk pdf menurun atau unimodal dan meningkatkan, mengurangi, dan bak terbalik berbentuk tingkat bahaya. Berbagai sifat dari distribusi yang diusulkan dibahas termasuk kepadatan probabilitas fungsi, fungsi distribusi kumulatif, fungsi survival, fungsi tingkat bahaya, fungsi momen, dan momen r.Parameter model diperoleh dengan menggunakan metode kemungkinan maksimum. Data waktu tunggu digunakan sebagai ilustrasi untuk menggambarkan kegunaan distribusi APTL.

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One Lindley distribution parameter (𝜃) has been widely used in fields such as Biology, engineering, medical, and industry. The Lindley distribution is able to model data with an increased level of monotonous danger. However, in real life, there are situations where the level of danger Therefore, to improve Lindleys distribution capabilities for data modeling, a modification can be used using the Alpha Power transformation method. The results of the Lindley distribution modification are commonly called the Alpha Power Transformed Lindley distribution (APTL) which has two parameters (𝛼 , 𝜃) This new APTL distribution is suitable for modeling pdf data in a declining or unimodal form and increasing, reducing, and inverted body in the form of hazard level.The various properties of the proposed distribution are discussed including probability density functions, cumulative distribution functions, survival functions, functions danger level, moment function, and moment r. Parameter model is obtained uh using the maximum likelihood method. Wait time data is used as an illustration to illustrate the usefulness of the APTL distribution. One Lindley distribution parameter (𝜃) has been widely used in fields such as Biology, engineering, medical, and industry. Distribution Lindley is capable modeling data with an increased level of monotonous danger. However, in real life, there are situations where the level of danger is not monotonous. Therefore, to improve Lindleys distribution capabilities for data modeling, a modification can be used using the Alpha Power transformation method. The result of the modification of the Lindley distribution is called the Alpha Power Transformed Lindley (APTL) distribution which has two parameters (𝛼, 𝜃). This new APTL distribution is suitable in modeling data in pdf format in a declining or unimodal form and increasing, reducing, and inverted like a hazard level. Various properties of the proposed distribution are discussed including the probability density function, cumulative distribution function, survival function, hazard level function, moment function, and moment r. Parameter models are obtained using the maximum likelihood method. The waiting time data is used as an illustration to illustrate the usefulness of the APTL distribution. One Lindley distribution parameter (𝜃) has been widely used in fields such as Biology, engineering, medical, and industry. The Lindley distribution is able to model data with an increased level of monotonous danger. However, in real life, there are situations where the level of danger is not monotonous. Therefore, to improve Lindleys distribution capabilities for data modeling, a modification can be used using the Alpha Power transformation method. The result of the modification of the Lindley distribution is called the Alpha Power Transformed Lindley (APTL) distribution which has two parameters (𝛼, 𝜃). This new APTL distribution is suitable in modeling data in pdf format in a declining or unimodal form and increasing, reducing, and inverted like a hazard level. Various properties of the proposed distribution are discussed including the probability density function, cumulative distribution function, survival function, hazard level function, moment function, and moment r. Parameter models are obtained using the maximum likelihood method. Wait time data is used as an illustration to illustrate the usefulness of the APTL distribution. "

"Distribusi Generalized Exponential diperkenalkan oleh Rameshwar D. Gupta dan Debasis Kundu pada tahun 2007. Distribusi Generalized Exponential tersebut merupakan hasil transformasi generalized dari distribusi Exponential. Skripsi ini menjelaskan distribusi Generalized Exponential Marshall Olkin yang merupakan hasil dari perluasan distribusi Generalized Exponential menggunakan metode Marshall Olkin. Distribusi Generalized Exponential Marshall Olkin lebih fleksibel dari distribusi sebelumnya terutama pada fungsi hazardnya yang memiliki berbagai bentuk, baik monoton (naik atau turun) maupun non monoton (bathub atau upside down bathup) sehingga dapat memodelkan data survival dengan lebih baik. Sifat fleksibelitas ini disebabkan karena penambahan parameter baru ke dalam distribusi Generalized Exponential. Selanjutnya dijelaskan beberapa karakteristik dari distribusi Generalized Exponential Marshall Olkin antara lain fungsi kepadatan peluang (fkp), fungsi distribusi kumulatif, fungsi survival, fungsi hazard, momen ke-n, mean, dan variansi. Penaksiran parameter dilakukan dengan metode maximum likelihood. Pada bagian aplikasi ditunjukkan data survival yang berasal dari data Aarset (1987) berdistribusi Generalized Exponential Marshall Olkin. Selanjutnya distribusi Generalized Exponential Marshall Olkin dibandingkan dengan distribusi Alpha Power Weibull untuk mencari distribusi mana yang lebih cocok dalam memodelkan data Aarset (1987). Dengan menggunakan AIC dan BIC distribusi Generalized Exponential Marshall Olkin lebih cocok dalam memodelkan data Aarset (1987).

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Generalized Exponential distribution was introduced by Rameshwar D. Gupta and Debasis Kundu in 2007. Generalized Exponential distribution was generated by generalized transformation of the Exponential distribution. This thesis explained the Generalized Exponential Marshall-Olkin distribution which is the result of the expansion of the Generalized Exponential distribution using the Marshall-Olkin method. The Generalized Exponential Marshall Olkin distribution has a more flexible form than the previous distribution, especially in its hazard function which has various forms that it can represent survival data better. The flexibility characteristic is due to the addition of new parameters to the Generalized Exponential distribution. Futhermore, some characteristics of the Generalized Exponential Marshall Olkin distribution was explained such as, the probability density function (PDF), cumulative distribution function, survival function, hazard function, moment, mean, and variance. Parameter estimation was conducted by using the maximum likelihood method. In the application section was shown survival data from Aarset data (1987) which distributed Generalized Exponential Marshall-Olkin distribution. Futhermore, Generalized Exponential Marshall Olkin distribution was compared with Alpha Power Weibull distribution to decided the prominent distribution in modeling Aarset data (1987). Using AIC and BIC, Generalized Exponential Marshall Olkin distribution more suitable in modeling Aarset data (1987)."

"In order to understand biodiversity,distribution and abundance among the tropical anguillid eels in the Indonesian waters,inshore migration mechanism of the juvenile anguillid eel (glas eel) to the estuaries of western,central and eastern region of Indonesian waters were examined using both morphology and genetic analysis...."

"This study was conducted in PT. Coca-Coca Distribution Indonesia (PT.CCDI) that evaluated the change from direct selling, in 2007, to become Distribution Center Matahari Balaraja, in 2008. It uses SCOR 9.0 process mapping and performance metrics to analyze whether this change is better for PT. CCDI. The advantages are reducing outbound transportation cost about 204 million after subtracting with distribution fee for Matahari DC, centralized discount management. The disadvantages are decreasing in Service Level and Perfect Order Fulfillment, and increasing cash-to-cash cycle time. Due to some advantages, this study also recommends evaluation with other parameter such as upside supply chain flexibility and upside supply chain adaptability."

"Ten surface sediment samples were collected from Jakarta bay to study the horizontal distribution of dinoflagellate resting cysts in this area.Overall results had shown unique species composition and diversity of dinoflagellate cyst assemblages...."

"Distribusi Weibull-Poisson merupakan distribusi kontinu yang dapat memodelkan beberapa macam bentuk hazard yaitu monoton naik, monoton turun dan increasing upside-down bathtub shape yang mempunyai bentuk bathtub shape terbalik dan monoton naik. Distribusi ini merupakan suatu distribusi lifetime yang dapat memodelkan kegagalan dalam suatu sistem seri dan merupakan pengembangan dari distribusi EksponensialPoisson. Distribusi ini diperoleh dengan melakukan metode compounding terhadap distribusi Weibull dan distribusi ZT-Poisson. Untuk mendapatkan bentuk akhir dari distribusi tersebut digunakan beberapa sifat matematis seperti order statistik dan ekspansi deret taylor. Selain pembentukan distribusi Weibull-Poisson, skripsi ini menjelaskan fungsi kepadatan peluang, fungsi distribusi, momen ke-r, momen sentral ke-r, mean, dan variansi. Sebagai ilustrasi, dibahas pula aplikasi distribusi Weibull-Poisson pada data survival marmut setelah terinfeksi virus Turblece Bacilli.

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The Weibull-Poisson distribution is a continuous distribution that can be modeled various forms of hazard namely monotone up, monotone down and upside-down down bathtub shape which is shaped up. This distribution is a lifetime-distribution that can model failures in a series system and is development of the Exponential-Poisson distribution. This distribution is obtained by perform the compounding method on the Weibull distribution and the ZT-Poisson distribution. To obtain the final form of the distribution, several mathematical properties are used such as statistical order and Taylor's number expansion. In addition to the formation of Weibull-Poisson distribution, this thesis includes the probability density function, distribution function, moment rth, rth central moment, mean, and variance. As an illustration, Weibull-Poisson distribution is applied on guinea pig survival data after being infected with Turblece virus Bacilli."

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