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"On 14 July 2013, India closed down its telegraph service, drawing the curtain over an important chapter in its history of telecommunications. Introduced during the British colonial period, the telegraph was opened for public use on 1 February 1855. The beginning of the service, much like its end, was marked by strikingly similar scenes of people rushing to the telegraph office in order to send messages. The similarity with the contemporary scenario does not end here. Like the internet today, the electric telegraph came to play an important role in the conduct of journalism in nineteenth-century India. This book is an attempt to reconstruct this interconnected history of telegraphy and journalism and the first systematic account of the development of English-language news reporting in nineteenth-century India. Drawing on a wide range of historical material and an in-depth analysis of the newspaper press, it questions grand narratives of media revolutions, arguing instead that the use of telegraphy in journalism was gradual and piecemeal. News itself emerged as the site of many contestations, as imperial politics, capitalist enterprise, and individual agency shaped not only access to technologies of communication, but also the content and form of reporting."

"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. "

"Analisis data waktu tunggu memiliki peran penting pada berbagai disiplin ilmu. Distribusi yang sering digunakan untuk memodelkan data waktu tunggu adalah distribusi Weibull, hal ini karena pola penyebarannya yang menceng. Akan tetapi, distribusi Weibull tidak mampu memodelkan data waktu tunggu dengan fungsi hazard berbentuk non-monoton. Pada kenyataanya, data waktu tunggu sering kali memiliki fungsi hazard non-monoton. Pada skripsi ini, dibahas pembentukan distribusi Alpha Logarithmic Transformed Weibull yang merupakan generalisasi dari distribusi Weibull. Distribusi Alpha Logarithmic Transformed Weibull dikenalkan oleh Nassar (2018) menggunakan metode Alpha Logarithmic Transformation. Metode ini menambahkan suatu parameter bentuk pada fungsi survival distribusi Weibull dengan tujuan meningkatkan fleksibilitas fungsi hazard-nya. Salah satu karakteristik dari distribusi ini adalah fungsi hazard-nya memiliki berbagai macam bentuk, yaitu konstan, monoton naik, monoton turun, bathtub dan upside-down bathtub. Beberapa karakteristik dari distribusi Alpha Logarithmic Transformed Weibull seperti fungsi kepadatan probabilitas, fungsi distribusi, fungsi survival, fungsi hazard dan momen ke-r juga dibahas. Penaksiran parameter distribusi dilakukan dengan metode maximum likelihood. Pada bagian akhir, diberikan ilustrasi dengan data waktu hingga kerusakan alat industri yang dimodelkan dengan distribusi Weibull dan distribusi Alpha Logarithmic Transformed Weibull. Hasil uji Kolmogorov-Smirnov menunjukan bahwa distribusi Weibull tidak mampu memodelkan data tersebut, sementara distribusi Alpha Logarithmic Transformed Weibull mampu memodelkan data tersebut dengan baik.

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Lifetime analysis has an important role in various disciplines. The most common distribution used to model lifetime data is the Weibull distribution, this is because of its skewed distribution. However, the Weibull distribution cannot model lifetime data with a non-monotone hazard function. Most lifetime data have a non-monotone hazard function. This thesis discusses the formation of the Alpha Logarithmic Transformed Weibull distribution, which is a generalization of the Weibull distribution. The Alpha Logarithmic Transformed Weibull was first introduced by Nassar (2018) using the Alpha Logarithmic Transformation. This method adds a shape parameter to the survival function of the Weibull distribution with the purpose of increasing the flexibility of its hazard function. This distribution features a hazard function with various shapes such as constant, increasing, decreasing, bathtub and upside-down bathtub. Some properties of this distribution such as its probability density function, cumulative distribution function, survival function, hazard function and the r-th moment is discussed. Parameter estimation is done with the maximum likelihood method. On the last part, an illustration using time to failure data of industrial devices is modeled by the Weibull distribution and the Alpha Logarithmic Transformed distribution. Results of the Kolmogorov-Smirnov test shows that the Weibull distribution is unable to model the data, while the Alpha Logarithmic Transformed Weibull distribution can model the data well."