Judul: Forecasting the Average Cost of Health Insurance Claims using the Seasonal ARIMA and Holt-Winters Methods, Forecasting the Average Cost of Health Insurance Claims using the Seasonal ARIMA and Holt-Winters Methods
Abstrak: Banyaknya penduduk di Indonesia meningkat setiap tahunnya. Hal ini berbanding lurus dengan peningkatan jumlah penduduk yang mengikuti asuransi kesehatan serta berdampak kepada biaya rata-rata klaim asuransi. Biaya rata-rata klaim asuransi merupakan salah satu indikator penting dalam penentuan premi asuransi. Semakin tepat penentuan biaya rata-rata klaim asuransi, semakin kompetitif premi suatu perusahaan asuransi dipasarkan. Pada penelitian ini diramalkan biaya rata-rata klaim asuransi kesehatan dari suatu produk pada periode tertentu dengan menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dan metode Holt-Winters. Hasil penelitian menunjukkan bahwa metode SARIMA memiliki nilai MAPE forecasting yang lebih kecil dibandingkan nilai MAPE forecasting Holt-Winters., The number of people in Indonesia increases every year. This is directly proportional to the increase in the number of people participating in health insurance and impacts the average cost of insurance claims. The average cost of insurance claims is one of the essential indicators in determining insurance premiums. The more precise the determination of the average cost of insurance claims, the more competitive the premium of an insurance company is marketed. This study predicts the average cost of health insurance claims from a certain product in a certain period using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method and the Holt-Winters method. The results showed that the SARIMA method has a smaller forecasting MAPE value than the Holt-Winters forecasting MAPE value.
Keyword: forecasting, health insurance, Holt-Winters, SARIMA, the average cost of claims

Judul: Pemodelan Klaim Asuransi Kesehatan Menggunakan Hidden Markov Kontinu
Abstrak: Salah satu tahapan penting yang harus dilakukan perusahaan asuransi agar terhindar dari kerugian akibat risiko klaim yaitu dengan memodelkan data klaim. Pada asuransi kesehatan, ukuran risiko klaim memerlukan status kondisi kesehatan pasien yang tidak selalu teramati. Jika kondisi kesehatan pasien yang memengaruhi biaya perawatan rumah sakit diasumsikan tidak teramati secara langsung dan membentuk rantai Markov, maka pasangan kondisi kesehatan pasien dan biaya perawatan rumah sakit dapat dimodelkan menggunakan model hidden Markov. Pada tugas akhir ini dilakukan pemodelan klaim asuransi kesehatan untuk memprediksi klaim perawatan jalan di masa depan. Dari hasil perhitungan, model HMM terbaik pada pemodelan klaim asuransi kesehatan dengan data rataan mingguan untuk rata-rata total biaya rawat jalan per orang per hari terdiri atas empat state yang masing-masing state menyebar lognormal dengan parameter berbeda. Hasil pembangkitan data menggunakan model terbaik memberikan nilai prediksi yang tergolong sangat akurat untuk sepuluh minggu ke depan dengan nilai MAPE sebesar delapan persen., One of the important steps that insurance companies must take to avoid losses due to claim risk is modeling claim data. In health insurance, the measure of claim risk requires the patient's health condition status, which is not always observed. If it is assumed that the patient's health condition that affects hospital care costs is not directly observable and forms a Markov chain, then the patient's health condition and hospital care cost pair can be modelled using the hidden Markov model. In this final project, claim modeling is carried out to predict future claims. From the calculation results, the best model in modelling health insurance claims using weekly average data from average total outpatient costs per person per day consists of four states that are lognormally distributed with different parameters for each state. The results of data generation using the best model provide predictive values that are classified as very accurate for the next ten weeks with a MAPE value of eight percent.
Keyword: claim modelling, health insurance, hidden Markov model

Judul: Enzymatic Deinking Of Pulp From Laser Print Paper
Abstrak: The objective of this research is to evaluate the physical and chemical properties of pulp from waste laser print paper. The waste paper was pretreated with 1 % lipase (based on oven-dried pulp) for repulping. After repulping, pulp slurry was floated at two conditions : alkaline flotation and acidic flotation. The result of the research shows that brightness increased in the added lipase, reach the maximum brightness after flotation by increased ash content. Residual ink area and particle size distribution decreased in the lipase addition. Furthermore, they can increase deinkability factor. The enzymatic deinking can change cellulose structure with the formation of carboxyl groups and increasing index of crystallinity. The Water Retention Value (WRV) with centrifuge method at 3000 G, increased in the added lipase with existence of carboxyl groups. Lipase and acidic treatment can increase the viscosity of pulp. Enzymatic treatment can increase strength properties of deinked pulp.
Keyword: