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Journal : Eksponensial

Metode Hierarchical Density-Based Spatial Clustering of Application with Noise (HDBSCAN) Pada Wilayah Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara Nanda Anggun Wahyuni; Memi Nor Hayati; Nanda Arista Rizki
EKSPONENSIAL Vol 12 No 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (778.141 KB) | DOI: 10.30872/eksponensial.v12i1.758

Abstract

The underdeveloped areas are generally the districts which are relatively underdeveloped compared to other regions on a national scale. Determination of underdeveloped villages is often done in order to determine the distribution of government assistance so that assistance can be distributed appropriately. The identification is based on facilities, infrastructure, access, social, population and economy provided in the Village Potential data (PODES). The concept of grouping based on regional or spatial is done to find out certain characteristics in an area. HDBSCAN is a grouping concept with a parameter called Mpts. The purpose of this study is to know the number of clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method. The Mpts parameters that is used in this study is from 2 to 6. Based on the results of the analysis, the clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method, were 3 clusters. Cluster 0 consists of 19 villages / urban areas , cluster 1 consists of 4 villages / urban areas and cluster 2 consists of 61 villages / urban areas. Based on the analysis, villages / urban areas included in cluster 1 could be the main target of the government in providing assistance and development of regional facilities / infrastructure.
Penggunaan Metode Seven New Quality Tools dan Metode DMAIC Six Sigma Pada Penerapan Pengendalian Kualitas Produk Yurin Febria Suci; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.816 KB)

Abstract

Product quality control is a technique and activities or planned actions undertaken to achieve, maintain, and improve the quality of products and services to meet with customers standards and satisfaction. This study aim to address the product quality at a company using statistical methods of products control. The methods are Seven New Quality Tools and DMAIC Six Sigma which are used on a product with a brand of Roti Durian Panglima, produced by PT. Panglima Roqiiqu Group in June 2016. Based on the result by using Seven New Quality Tools method, there are five factors that caused defect on Roti Durian Panglima product, which are : human factor, materials, environmental, machine, and work method, which makes the priority of the product improvement lays on human factor. Meanwhile, the use of DMAIC Six Sigma method has obtained performance baseline values at 4,48 Sigma with four kinds of defects on Roti Durian Panglima products, and based on improvement phase using PFMEA method, the priority on product improvement also lays on human factor.
Analisis Value At Risk Portofolio Saham Menggunakan Metode Varian-Kovarian Nur Rizki Wahidah; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.206 KB)

Abstract

Investing is a human effort to save a certain amount of money in time, in the hope of gaining some profit in the future. Investment decisions are fundamentally related to the management of funds in a given period, in which investors have hope to earn income or profit from the funds invested. Almost all investors do not want losses when investing. Various ways are done to avoid loss, or at least maximize profits with minimal risk. The value of risk that is often used is Value At Risk (VaR). Values ​​At Risk (VaR) is one of the statistical tools used to measure the maximum loss of an asset or investment over a certain period with a certain degree of confidence to reduce the occurrence of the risk. This study aims to determine how the risk of stock portfolio of PT. Astra Agro Lestari Tbk (AALI) and PT.PP London Sumatra Indonesia Tbk (LSIP) use Value at Risk analysis using Varian-Covariance method at closing price of shares incorporated in Jakarta Islamic Index (JII) and Asset Value at Risk PT. Astra Agro Lestari Tbk (AALI) and PT.PP London Sumatra Indonesia Tbk (LSIP) to Value at Risk Portfolio. The results showed that if the initial fund invested to PT. Astra Agro Lestari Tbk. and PT.PP London Sumatra Indah Tbk. Rp. 10.000.000, - with a 95% confidence level obtained Value at Risk (VaR) of Rp. 369.682. this can be interpreted there is a 95% confidence that the losses received by investors will not exceed from Rp. 369.682..The result of PT. Astra Agro Lestari tbk. against portfolio risk at 6% and PT. PP London Sumatra Indonesia Tbk. of portfolio risk is 46%.
Pengelompokkan Data Runtun Waktu menggunakan Analisis Cluster Andrea Tri Rian Dani; Sri Wahyuningsih; Nanda Arista Rizki
EKSPONENSIAL Vol 11 No 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.538 KB)

Abstract

The export value of East Kalimantan Province has big data conditions with time series and multivariable data types. Cluster analysis can be applied to time series data, where there are different procedures and grouping algorithms compared to grouping cross section data. Algorithms and procedures in the cluster formation process are done differently, because time series data is a series of observational data that occur based on a time index in sequence with a fixed time interval. The purpose of this research is to obtain the best similarity measurement using the cophenetic correlation coefficient and get the optimal c-value using the silhouete coefficient. In this study, the grouping algorithm used is a single linkage with four measurements of similarity, namely the Pearson correlation distance, euclidean, dynamic time warping and autocorrelation based distance. The sample in this study is the data on the export value of oil and non-oil commodities in East Kalimantan Province from January 2000 to December 2016 consisting of 10 variables. Based on the results of the analysis, the distance of the best similarity measurement in clustering the export value of oil and non-oil commodities in East Kalimantan Province is the dynamic time warping distance with the optimal c-value of 3 clusters.
Analysis of (M/G/c): (GD/∞/∞) Menggunakan Software Lazarus Akbar Maulana; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (972.023 KB)

Abstract

Queueing theory is a theory that concerns in the mathematical study of queue or row of waiting. The formation of the queue is occurs when the need for a service exceed the capacity of the service. In this study, an analysis is done to determine whether the queue model (M/G/c) :(GD/∞/∞) can be applied to the Workshop of Utomo Motor Yamaha Samarinda Seberang. Primary data is used and is taken for 3 days in a random busy period selected in May to August 2017. The result is the queue system on Utomo Motor Yamaha using FCFS queue discipline with 4 parallel mechanics, and follows the (M/G/4) :(GD/∞/∞) model. The average of the waiting time in the queue on Monday is 0,38 hours and Wednesday is 0,35 hours. The average of the customers in the queue on Monday and Wednesday is the same as much 2 customer. The average of the customers in the system on Monday and Wednesday is the sama as 5 customers. The average of the waiting times that customers spend on the system on Monday is 0,93 hour and Wednesdayis 0,94 hours and on May 22nd, 2017 is 0,81 hours. In order to calculate the queue model more quickly, a program is made using Lazarus software to search the queue model on daily data.
Klasifikasi Persediaan Barang Menggunakan Analisis Always Better Control (ABC) dan Prediksi Permintaan dengan Metode Monte Carlo Ricca Noviani; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.284 KB)

Abstract

ABC analysis is a method of inventory control to manage a small number of item but has a high utilization. Inventories are categorized into three classes A, B, and C. The objective of the research is to manage the inventories using ABC analysis, EOQ, ROP, and to provide an overview of the next-year demand of the drug items using Monte Carlo method. ABC analysis results show that out of 79 drug items, class A consists of 19 drug items with usage value 69,11%, class B consists of 19 drug items with usage value 20,29%, and class C consists of 41 drug items with usage value 10,60%. Based on economic order quantity method, minimum ordering quantity of drug are two items and maximum ordering quantity of drug are 96 items.Based on reorder point method, the minimum quantity of drug for reordering is zero item and the maximum quantity of drug for reordering are seven items. Monte Carlo method results show that Fludane Plus 60 ml has the minimum demand on January - Desember 2017 which is only one bottle a month and Actifed Cough Merah 60 ml has the maximum demand which is 78 - 81 bottles a month. Lapisiv 100 ml, Kamulvit B12 Sirup 120 ml, Fludane Plus 60 ml and Miconazole 2% has the highest accuration with the percentage of error 0% and Ikadryl DMP Sirup 100 mlhas the lowest accuration with the percentage of error 0,22%.
Model Geographically Weighted Univariat Weibull Regression pada Data Indikator Pencemaran Air Dissolve Oxygen di Daerah Aliran Sungai Mahakam Kalimantan Timur Tahun 2018 Sugiarto Sugiarto; Suyitno Suyitno; Nanda Arista Rizki
EKSPONENSIAL Vol 12 No 2 (2021): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1241.218 KB)

Abstract

Geographically Weighted Univariat Weibull Regression (GWUWR) model is a regression model applied to spatial data. Parameter estimation of GWUWR model is performed at every observation location using spatial weighting. The purpose of this study is to determine the GWUWR model at the water pollution indicator data namely dissolved oxygen (DO) at Mahakam river in East Kalimantan and to find out the factors that influence DO in Mahakam river. The research data are secondary from the environmental services East Borneo. The research response variable was DO, meanwhile the predictor variables were pH, Total Dissolve Solid, Total Suspended Solid, Nitrate and Amonia. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting was determined using the Adaptive Gaussian weighting function and optimum bandwidth determination criteria used Generalized Cross-Validation (GCV). Based on the result of the parameter testing of GWUWR model it was concluded the factors influencing DO locally were pH, Total Dissolve Solid and ammonia concentrations, while the factors influencing globally were Total Dissolve Solid and ammonia concentration
Penentuan Besaran Premi Asuransi Jiwa dengan Model Apportionable Fractional Premiums Berdasarkan Tabel Mortalita dengan Metode Interpolasi Kostaki Muhammad Nor Abdul Rajak; Yuki Novia Nasution; Nanda Arista Rizki
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (595.108 KB) | DOI: 10.30872/eksponensial.v9i1.272

Abstract

Insurance is an agreement between the customer and insurance company, at which the insurance company bears some loss in the future and the customer pays the premium according to the agreement. Insurance company determines the amount of premiums based on mortality tables. The purpose of the research is to determine the characteristics of Indonesia mortaliy table with Kostaki interpolation method, to determine whole life insurance premium with apportionable fractional premiums model, and to determine the amount of the premium return. The results of the research indicate that in the mortality table of Indonesia in 2014, the number of female deaths tend to be lower than male at 1-74 years, but the number of deaths increased over the age of 75 years. The premiums paid by a 30 year-old male with a semester payment is Rp 2.358.988, quarterly payment is Rp 1.186.823, and monthly payment is Rp 397.253. The premiums paid by a 30 year-old female with a semester payment is Rp 2.044.666, quarterly payment is Rp 1.028.669, and monthly payment is Rp 344.242. Premium return of 30 years-old male is Rp 84.204.338 and of 30 years-old female is Rp 72.968.560.