Menurut data BPS, tingkat pertumbuhan populasi penduduk di Indonesia secara konsisten meningkat setiap tahun. Kondisi pertumbuhan populasi penduduk yang tidak dapat ditekan akan menyebabkan berbagai masalah. Salah satu masalah yang mungkin terjadi di Indonesia dan sulit diselesaikan adalah kesehatan masyarakat Indonesia. Provinsi Papua menjadi provinsi dengan persentase rumah tangga kumuh perkotaan tertinggi di Indonesia, persentase rumah tangga yang memiliki akses terhadap layanan sanitasi layak dan berkelanjutan terendah, menempati peringkat kelima persentase terendah yang memiliki akses terhadap layanan sumber air minum layak dan berkelanjutan, serta menjadi provinsi dengan angka kematian balita per 1000 kelahiran hidup tertinggi di Indonesia. Salah satu penyebabnya adalah rendahnya persentase balita yang pernah mendapatkan imunisasi. Penelitian ini melakukan pemodelan untuk mendapatkan faktor-faktor yang memengaruhi kesehatan di Provinsi Papua. Penelitian ini melibatkan pengaruh spasial (model panel spasial) dan membandingkannya dengan model panel biasa untuk mendapatkan model terbaik. Model panel spasial yang dipilih dalam penelitian ini adalah model SAR, SEM, dan GSM. Hasil menunjukkan bahwa model SAR dengan pengaruh tetap adalah model terbaik dalam penelitian ini.According to BPS data, the rate of population growth in Indonesia consistently increasing every year. Conditions of population growth that cannot be suppressed will cause several problems. One of them and difficult to solve is the public health problem. Papua Province is the province with the highest percentage of urban slum households, the lowest percentage of households that has access to decent and sustainable sanitation services, ranks fifth lowest who has access to decent and sustainable drinking water services, and the highest number infant mortality per 1000 live births in Indonesia. One of the reasons is the low percentage of children under five who have been immunized. This research is modeling to find the factors that influence health in Papua Province. This research involves spatial influence (spatial panel model) and compares it with the ordinary panel models to get the best model. The spatial panel models selected in this research are SAR, SEM, and GSM models. The results show that the SAR model with the fixed effect is the best in this research.
According to data from the BPS, the rate of population growth in Indonesia consistently increases every year. Conditions of population growth that cannot be suppressed will cause several problems. One of the problems in Indonesia and difficult to solve is the public health problem. Until now, the number of sick people in Indonesia is still quite high. The spatial panel model is the result of the development of the panel data model. This development was carried out because of the influence of spatial or location on panel data. Spatial information is very important because it can determine the relationship of an area with other regions that are close together. Data containing spatial elements will not be accurate if only using panel regression analysis because there are assumptions that are violated, one of which will produce heterogeneous errors. This is due to the inter-regional linkages. The purpose of this study was to determine the factors that influence the percentage of sick people in Papua Province based on several selected spatial panel models, namely SAR, SEM, GSM models. Next, an analysis was conducted to obtain the best spatial panel model.
The attacks on cloud-based networks have increased and could lead to various disadvantages such as the inaccessibility of services until the loss of user’s trust. Owncloud is one cloud implementation that runs on a network with more than 200 million users. The aims of these researches are to find digital evidence from DoS attacks. Some DoS attacks are SSH brute force, SYN flood, ping of death, and port scanning on the Owncloud network and then finding the digital evidence such as the attacker's IP, time occurred of the attack, types of the attack, also the resource usage of CPU and RAM. This research uses Wireshark and Snort tools to analyze the network and the method of Generic Framework for Network Forensic (GFNF) as a framework during the simulation process until performing the evidence. The simulation was carried out for 1 minute with 30 trials for each attack. The results of this study found the attacker’s IP, time of the attack occurred, types of attack, and also the increase of the resource usage on CPU and RAM when an attack occurred. The found of results digital evidence such as the attacker's IP, the time occurred of attack, and the types of attack were visualized as a table and presented on the ELK Stack dashboard.
Menghitung Cadangan Klaim pada suatu perusahaan asuransi merupakan hal penting untuk dilakukan. Cadangan Klaim dapat merepresentasikan seberapa besar kewajiban perusahaan terhadap tertanggung di masa yang akan datang serta menjadi tolak ukur kondisi keuangan dari suatu perusahaan. Cadangan Klaim terbagi menjadi dua, yaitu IBNR (Incurred But Not Reported) dan RBNS (Reported But Not Settled). Pada dasarnya, ada banyak metode dalam menghitung Cadangan Klaim, salah satu yang umum dikenal adalah Metode Chain Ladder. Oleh karena itu, penelitian ini menggunakan Metode Chain Ladder dalam menghitung Cadangan Klaim. Dalam penerapannya, hampir segala jenis perhitungan akan dipermudah menggunakan beberapa software yang belakangan dikembangkan, diantaranya adalah Microsoft Excel dan RStudio. Tujuan penelitian ini adalah untuk menghitung nilai Cadangan Klaim yang dihasilkan dari kedua software tersebut dengan menggunakan metode yang sama, yaitu Chain Ladder, serta mengetahui efektifitas serta efisiensi dalam perhitungan. Hasil menunjukkan bahwa hasil perhitungan antara kedua software adalah sama tetapi dari sisi kecepatan pengerjaan, penggunaan RStudio lebih unggul dibandingkan dengan Microsoft Excel.
Based on data from Badan Pusat Statistik (BPS), the number of Indonesia's population has consistently increased every year. This will cause several problems, one of them is public health. Health problems that occur cause some risks that cause losses. Therefore, one way to reduce the impact of these risks is insurance. This study analyzes how the process of forming premium prices with loading assumptions that are in accordance with existing assumptions in order to meet the criteria of equitable to the client, deliverable by the agent, and profitable to the company. This research uses a quantitative research approach. The population in this study was all Sum Insured (SI) while the sample was 1000 SI male sex. The data used in this study are secondary data that is data from an ABC insurance company in Indonesia, literature study through books, journal references, and previous research. The results of this study are the calculation of premiums by the methods used by the company more efficiently when compared with formulas in accordance with the theory. Although the price of a premium is more expensive using the company method, it is not significantly different. If using the company method, the sum insured used is the sum insured from death benefit whereas the theory is the average of claims incurred at that age. The fees charged are still fairly reasonable in accordance with the rules of setting the price of insurance premiums. In addition, the use of CSO 80 tables and Reinsurance Rates that have been adjusted to the company's interest rate of 5% in accordance with the expected cash value desired by the company.
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