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All Journal Eksponensial Geosains Kutai Basin

Yuki Novia Nasution, Yuki Novia

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Author-ID : 621307

Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Mathematics Other

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Pengklasifikasian Status Gizi Balita di Puskesmas Sempaja Samarinda menggunakan Probabilistic Neural Network (PNN) Tahun 2019 Lestari, Putri Ayu Dwi; Hayati, Memi Nor; Nasution, Yuki Novia
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Probabilistic Neural Network (PNN) is a model in Artificial Neural Networks (ANN) that is used for classification. PNN depends on the smoothing parameter (α). PNN has the advantage of being able to value of problems that previously existed in the back propagation method of ANN. The PNN method in this study was applied to the nutritional status of toddlers. Assessment of the nutritional status of toddlers can be determined through measurements of the human body known as anthropometry. Parameters for determining nutritional status based on anthropometry are age, weight and height. Therefore, in this study, a classification of the nutritional status of children under five is carried out to determine whether the toddler is experiencing good nutrition or poor nutrition. It was found that PNN with the best classification accuracy rate on the nutritional status of toddlers, namely the proportion of training data and testing data of 50%: 50% with α = 1, with accuracy results between training data and training data of 85% and accuracy results between data testing of the training data by 70%.

Peramalan Pendapatan Asli Daerah Kota Samarinda Menggunakan Metode Double Exponential Smoothing Dari Brown Devira, Annisa Suci; Nasution, Yuki Novia; Suyitno, Suyitno
EKSPONENSIAL Vol. 14 No. 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1138

Forecasting is a technique for estimating a value in the future by paying attention to past data and current data. One of the forecasting methods for exponentially increasing or decreasing data patterns is Exponential Smoothing. Exponential Smoothing is a method that shows the weighting decreases exponentially with respect to the older observation values. The linear model of the Exponential Smoothing method that uses a two-time smoothing process is Brown's Double Exponential Smoothing method. This study aims to get a forecast of Regional Original Income (PAD) in Samarinda with the double exponential smoothing method. Research data is secondary data from the Samarinda City Regional Revenue Agency (BAPENDA) file. The conclusion of the study is that the results of forecasting PAD in the city of Samarinda in 2021 are IDR 3.374.750.000.000 with an accuracy rate of Mean Absolute Percentage Error (MAPE) of 0,41%.

Analisis Sistem Antrean Untuk Optimalisasi Jumlah Server Menggunakan Model Keputusan Tingkat Aspirasi: (Studi Kasus : Restoran Cepat Saji di Samarinda Central Plaza) Felysia, Novia; Wahyuningsih, Sri; Nasution, Yuki Novia
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

This research aims to obtain information on the number of cashiers with optimal performance in the fast food restaurant at Samarinda Central Plaza queue system using R Studio software. The queue model applied in this research is (M1/M2/c):(FCFS/∞/∞) with First Come First Served (FCFS) queue discipline. The optimal number of cashiers are determinated by using the aspiration level decision model. The results of the analysis showed that the optimal number of cashiers was 3 cashiers with an average number of customers in the queue as many as 1 customer every 15 minutes, the average number of customers in the queue system as many as 2 customers every 15 minutes, the average waiting time for customers in the queue for 1.2 minutes, the average waiting time of customers in the queue system for 6 minutes, and the percentage of idle time is 2.26% that is about 20.34 seconds

Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Dengan Fuzzy C-Means Berdasarkan Indikator Kemiskinan Ningtyas, Retno Ayu; Nasution, Yuki Novia; Syaripuddin, Syaripuddin
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis is a branch of statistical science that is used to grouping data that have similar characteristics between each other. The grouping method used in this research is Fuzzy C-Means. Fuzzy C-Means method is one of the grouping methods developed from the C-Means method by applying the properties of fuzzy sets. With the existence of each data is determined by the degree of membership. This method is applied to data from 56 districts/cities on Borneo based on poverty indicators with variables namely the percentage of average length of schooling, life expectancy, percentage of the poor, percentage of open unemployment rate, percentage of households with proper sanitation, and percentage of households with proper drinking water. This study aims to obtain the results of grouping districts/cities on Borneo based on poverty indicators and to obtain optimal cluster results based on three validity indices, namely Connectivity, Dunn, and Silhoutte values. Based on the results of the study, it was found that there were 2 optimal clusters, namely the first cluster consisted of 36 regencies/cities while the second cluster consisted of 20 regencies/cities.

Perbandingan Metode K-Means Dan Metode Fuzzy C-Means (FCM) Pada Analisis Kinerja Pegawai PT. Cemara Khatulistiwa Persada Bontang Rakhmawaty, Nurul; Nasution, Yuki Novia; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Discipline assessment of employee performance is one of the factors to improve the situation of the quality of human resources. Monitoring and assessment of employee discipline must be carried out continuously as some of the characteristics of management that have gone well as a benchmark for considering the targets that have been set. The K-Means method and the Fuzzy C-Means (FCM) method are non-hierarchical cluster methods. Both methods attempt to partition data into one or more clusters, so that data with the same characteristics are grouped into the same cluster or groups and data with different characteristics are grouped into other groups. This study discusses the comparison of the K-Means method and the Fuzzy C-Means (FCM) method in analyzing employee performance at PT. Cemara Khatulistiwa Persada Bontang, where groups of employees with high, medium, and low levels of employee performance will be determined based on the clustering results of the two methods and determine the best method. The grouping of data for the two methods was obtained from employee attendance data in 2020. Based on the results, it was found that clustering using the K-Means method in the first cluster (low performance level) had 23 employees, the second cluster (medium performance level) had 27 employees, and cluster the third (high performance level) there are 30 employees. Then based on the results of clustering using the FCM method in the first cluster (medium performance level) there are 26 employees, the second cluster (high performance level) there are 31 employees, and the third cluster (low performance level) there are 23 employees. Based on the results of the standard deviation ratio, it was obtained that the K-Means method with a value of 2.4944 was better than the FCM method with a value of 2.7323 in clustering employee performance at PT. Cemara Khatulistiwa Persada.

Estimasi Parameter Model Regresi Linier Berganda dengan Pendekatan Bayes Menggunakan Prior Pseudo: (Studi Kasus Indeks Pembangunan Manusia (IPM) di Kalimantan Timur) Isgiarahmah, Afryda; Goejantoro, Rito; Nasution, Yuki Novia
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The parameter estimation of a regression model can use the Ordinary Least Square (OLS) method which must fulfill the assumption of BLUE. Besides OLS, there is another method that can be used to estimate the regression parameters, namely the Bayes method. Parameter estimates using the OLS method and the Bayes method have been widely used in the field of development. One of them is on economic development, namely the Human Development Index (HDI). The purpose of this study is to know multiple linear regression models and interpretations that state the relationship between per capita expenditure, average length of school, life expectancy, and school length for the Human Development Index (HDI) with the Bayes approach using pseudo priors.

Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing: Studi Kasus: Jumlah Wisatawan Mancanegara yang Berkunjung Ke Indonesia Aryati, Ayu; Purnamasari, Ika; Nasution, Yuki Novia
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Forecasting is a technique for estimating a value in the future by looking at past and current data. Foreign tourists are everyone who visits a country outside their place of residence, driven by one or several needs without intending to earn income in the place visited and the duration of the visit is no more than twelve months. The method used in this study is the Holt-Winters smoothing smoothing method. In this study used data of foreign tourists visiting Indonesia in January 2014 - September 2018. The purpose of this study was to determine the pattern of data forecasting the number of foreign tourists, the value of the accuracy of forecasting, and the results of forecasting. Based on the Holt-Winters smoothing method, the data pattern for the number of foreign tourists is the multiplicative Holt-Winters data pattern. The value of the smoothing parameter combination with the smallest MAPE of 0,938% is α = 0,9; β = 0,1; and γ = 0,9. The results of forecasting the number of foreign tourists visiting Indonesia in October 2018 and November 2018 were 1.410.157 and 1.362.473 people respectively

Optimasi Algoritma Naïve Bayes Menggunakan Algoritma Genetika Untuk Memprediksi Kelulusan: Studi Kasus: Mahasiswa Jurusan Matematika FMIPA Universitas Mulawarman Feronica, Elisa; Nasution, Yuki Novia; Purnamasari, Ika
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The Naïve Bayes algorithm is classification method that uses the principle of probability to create predictive models. Naïve Bayes is based on the assumption that all its attributes are independent which can be optimized by genetic algorithms. Genetic algorithm is an optimization technique which works by imitating the process of evaluating and changing the genetic structure of living creatures. In this study, the Naive Bayes algorithm was optimized using by genetic algorithm to predict student graduation with attributes, namely gender, regional origin, admission path and employment status. The data used is the students of the Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University who graduated in March 2018 to December 2020. The results of this study indicate the accuracy value generated by Naïve Bayes of 50% increased by 16,67% after the attributes were optimized by using the genetic algorithm to 66,67% with 3 selected attributes, namely regional origin, admission path and employment status

Numerical Study of Hydrodynamics in the waters of Balikpapan Bay using Finite Volume Method Rahman, Suci Erniya; Yusuf, Mustaid; Nasution, Yuki Novia
GEOSAINS KUTAI BASIN Vol 5 No 1 (2022)
Publisher : Geophysics Study Program, Faculty of Mathematics and Natural Sciences, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/geofisunmul.v5i1.793

Suci Erniya Rahman. 2021. Numerical Study Hydrodynamic in the waters of Balikpapan Bay using Finite Volume Method. Supervised by Dr. Sc. Mustaid Yusuf, M.Si. and Yuki Novia Nasution, S.Si., M.Sc. Faculty of Mathematics and Natural Sciences, Mulawarman University. The hydrodynamic model FVCOM (Finite Volume Coastal Ocean Model) using the approach method finite volume has been applied to determine the distribution of tidal current patterns in the Balikpapan Bay area. The simulation was carried out for 30 days using river discharge and tides as generating forces. The simulation results has verified by using the Root Mean Square Error (RMSE) method on observation data in the Balikpapan Bay area. The results of the simulation and observation verification have a good agreement, which is 0.09917 meters. The smaller (closer to zero) RMSE value, is the better results of the observation and simulation match. The results of study showed that the pattern and average current velocity in Balikpapan Bay for 15 days (11-26 October 2012) was dominant out of the bay.

Co-Authors Aryati, Ayu Devira, Annisa Suci Felysia, Novia Feronica, Elisa Fidia Deny Tisna Amijaya Ika Purnamasari Isgiarahmah, Afryda Memi Nor Hayati Mochammad Imron Awalludin Ningtyas, Retno Ayu Putri Ayu Dwi Lestari, Putri Ayu Dwi Rahman, Suci Erniya Rakhmawaty, Nurul Rito Goejantoro, Rito Suyitno Suyitno Syaripuddin Syaripuddin Yusuf, Mustaid

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