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Meiliyani Siringoringo
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Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Mulawarman Jl. Barong Tongkok, Kampus Gunung Kelua Kota Samarinda, Provinsi Kalimantan Timur 75123
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INDONESIA
Eksponensial
Published by Universitas Mulawarman
ISSN : 20857829     EISSN : 27983455     DOI : https://doi.org/10.30872/
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Jurnal Eksponensial is a scientific journal that publishes articles of statistics and its application. This journal This journal is intended for researchers and readers who are interested of statistics and its applications.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 205 Documents
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Perbandingan Metode C-Means dan Fuzzy C-Means Dalam Pengelompokkan Wilayah Desa/Kelurahan di Kabupaten Kutai Kartanegara Nissa Irabawati; Sri Wahyuningsih; Rudy Ramadani Syoer
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (192.417 KB)

Abstract

Cluster analysis is a multivariate statistical technique that has the main purpose to classify objects based on common characteristics. With this analysis, the object will be grouped such that each object is the closest similarity to other objects are in the same group. In the clustering process by using no hierarchical C-Means formation of partition is done such that each object explicitly declared as a member of one group and not a member of any other group. But sometimes can not put an object just in one partition, because in fact the object is located between two or more other partitions, so it needs to be weighted based on its fuzzy membership level. In this way, it is to define a method in the formation of the group will be more flexible. The concept is called fuzzy clustering, the fuzzy way each object can be members of multiple groups. The difference lies in the assumptions used as a basis for allocation. One technique that is not part of the method of using the hierarchical nature of fuzzy clustering technique is using Fuzzy C-Means (FCM). This study will examines comparative method C-Means and FCM clustering in a case study, namely the grouping of the village/urban village in Kutai Kartanegara regency based on the characteristics of facilities/infrastructure and socio-economic factors of the population. The results showed that in some respects, FCM was superior than the C-Means, especially in generating the minimum of objective function, the computation time and ratio value Sw and Sb. Based on the similarity matrix eigen value and the index value Xie and Beni (XB) concluded that the most optimal number of groups is 5 (five) groups.
Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur Fathurahman, M
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (671.081 KB) | DOI: 10.30872/eksponensial.v13i1.888

Abstract

Negative Binomial Regression (NBR) is an alternative regression model to model the relationship between the dependent variable in overdispersion count data and one or more independent variables. Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson regression model has a standard error value that is not under-estimated. The NBR model was applied to modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in 2019 indicated overdispersion. Infant mortality is an indicator that can measure the progress of development outcomes in the health sector in a region. In the last three years, from 2017 to 2019, infant mortality data in East Kalimantan has increased. Therefore, it is necessary to do modeling to get the factors that cause it. The modeling results with NBR show that the percentage of the complete neonatal visit of KN3, the percentage of infant health services, and the percentage of visits by pregnant women K4 significantly affect infant mortality in East Kalimantan in 2019.
Upaya Pencegahan Pencemaran Air Sungai Mahakam melalui Pemodelan Geographically Weighted Logistic Regression pada Data BOD Inayah, Ulfah Resti; Suyitno, Suyitno; Siringoringo, Meiliyani
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 (830.508 KB) | DOI: 10.30872/eksponensial.v12i1.755

Abstract

Since the early years, Mahakam River has important roles in supporting human needs in East Kalimantan province. Activities around Mahakam watershed such as restaurants, fishery, and industries were in the potential of generating waste around the flow area. The waste consisted of domestic and nondomestic waste. The waste was a threat to the Mahakam River water quality. Water pollution around the Mahakam River was a threat to public health, and therefore, there’s a need for precaution. One of the precautions is to give the public information regarding the factors that influence the chances of polluted water in the Mahakam River increased through logistic regression modeling. One way to detect water pollution is to indicate by using Biochemical Oxygen Demand (BOD). BOD data was suspected spatial, therefore the appropriate statistical modeling is Geographically Weighted Logistic Regression (GWLR). GWLR is a regression model that developed from a logistic regression in which parameter estimation is done locally at every observation location. The purpose of the research is to determine the GWLR model on the BOD data of Mahakam River and to find out the factors that influence water pollution at 27 observation points along with the Mahakam River flow. The parameter estimation method is the Maximum Likelihood Estimation (MLE). The spatial weighting is calculated by using the Adaptive Bisquare weighting function and the optimum bandwidth is determined by using Generalized Cross-Validation (GCV) criteria. Research shows that the closed-form of the Maximum Likelihood estimator can’t be obtained analytically and the approximation is obtained by using Newton-Raphson (N-R) iterative method. Based on parameter testing of the GWLR model result, it was concluded that the factors were influences the probability of Mahakam River water were polluted based on the BOD indicator was locally and different in each 27 observation locations. The factors that influence locally were water temperature, acidity, Total Dissolved Solids (TDS), ammonia concentration, and water debit, meanwhile, the factors which influence globally were acidity and TDS.
Perbandingan Pengelompokan K-Means dan K-Medoids Pada Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas Athifaturrofifah Athifaturrofifah; Rito Goejantoro; Desi Yuniarti
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.777 KB)

Abstract

The cases of forest/land fires in Indonesia seem endless, almost every year in the dry season similar problems always occur. Some areas in Indonesia often occur in forest fires and result in losses of up to trillions of rupiah. Various ways have been made to help the government in minimizing the potential for forest or land fires, one of them is by monitoring hot spots. In this study using data hot spots with parameters of latitude, longitude, brightness, fire radiation power and confidence by using the method of grouping K-Means and K-Medoids. The difference between these two methods is that the K-means method uses the mean as the center of the cluster, while K-Medoids uses representative objects (medoids) as the center of the cluster. This study aims to compare the results of the grouping of K-Means method with K-Medoids by using 42 data. The results of this study indicate that the K-Means method produces Silhouette Coefficient scores greater than K-Medoids. So that, K-Means can provide more accurate grouping results with a greater Silhouette Coefficient value.
Peramalan Dengan Metode Fuzzy Time Series Markov Chain Yenni Safitri; Sri Wahyuningsih; Rito Goejantoro
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 (418.6 KB)

Abstract

Forecasting is an activity to predict what will happen in the future with certain methods. Fuzzy time series is a method known as artificial intelligence used to predict the problem which the actual data is formed in linguistic values using fuzzy principles as its basis. This study discusses the method of fuzzy time series developed by Ruey Chyn Tsaur to predict the closing price of the shares of PT. Radiant Utama Interinso Tbk April 2017. Markov Chain fuzzy time series method is used to analyze a time series data which is a combination of fuzzy time series model with Markov Chain. Forecasting of closing stock price based on data from January 2011 to March 2017 for April 2017 is Rp 224,29,00. Markov Chain's fuzzy time series method to forecast the closing stock prices data from January 2011 to March 2017 has a 3,48% of MAPE value or has a 96,52% of precision forecast. The results show that the Markov Chain fuzzy time series has an excellent level of accuracy for forecasting the closing stock prices.
Perbandingan Hasil Analisis Cluster Dengan Menggunakan Metode Average Linkage Dan Metode Ward: Studi Kasus : Kemiskinan Di Provinsi Kalimantan Timur Tahun 2018 Imasdiani, Imasdiani; Purnamasari, Ika; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (829.126 KB) | DOI: 10.30872/eksponensial.v13i1.875

Abstract

Hierarchical cluster analysis is an analysis used to classify data based on its characteristics. The average linkage method and the Ward method are methods of hierarchical cluster analysis. Grouping data from various aspects, one of which is poverty. This study uses poverty indicator data in East Kalimantan in 2018. The average linkage method is based on the average distance size, while the Ward method is based on the size of the distance between clusters by minimizing the number of squares. The purpose of this study was to determine the best method based on the average value of the standard deviation ratio. The results of the study using the average linkage method obtained two clusters, both the average linkage method and the Ward method both obtained two clusters. Where in the average linkage method, the first cluster consists of 7 districts / cities and the second cluster consists of 3 districts / cities. Whereas in the Ward method, the first cluster consists of 6 districts / cities and the second cluster consists of 4 districts / cities. For the best method based on the average standard deviation ratio in groups (Sw) and the standard deviation between groups (Sb), it is found that the ratio in the Ward method is smaller than the average linkage method, which is 2,681 which indicates that the average linkage method is the best method.
Perbandingan Metode Klasifikasi Naïve Bayes Dan Jaringan Saraf Tiruan: Studi Kasus: Pt Asuransi Jiwa Bersama Bumiputera Tahun 2018 Ardyanti, Hesti; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.239 KB) | DOI: 10.30872/eksponensial.v11i2.657

Abstract

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new object. Naïve Bayes is a classification technique for predicting future probability based on past experiences with a strong assumption of independence. Artificial neural network is one of the data mining analysis tools that can be used to create data on classification. Model selection in artifial neural networks requires various factors such as the selection of optimal number of hidden neuron. This research has a goal to compare the level of classification accuracy between the Naïve Bayes method and artificial neural network on payment status of the insurance premium. The data used is insurance costumer’s data of PT AJB Bumiputera Samarinda in 2018. The result of the comparison of accuracy calculation from the two analyzes indicate that artificial neural network has a higher level of accuracy than naïve Bayes method. Classification accuracy result of Naïve Bayes is 82,76% and artificial neural network is 86,21%.
Analisis Pengendalian Kualitas Produk Amplang Menggunakan Peta Kendali Kernel Rahmad Fahreza Adiyasa; Desi Yuniarti; Ika Purnamasari
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Quality control is the use of techniques and activities to maintain and improve the quality of products or services. One of the quality control methods is epanevhnikov kernel control chart. The epanevhnikov kernel control chart is a control chart used to evaluate nonparametric product quality characteristic data because it does not require certain assumptions. The purpose of this research is to find out whether the 1 kg packaged Amplang product in UD. H. Icam Samarinda is within the control limit and what factors can cause the weight of the product becomes uncontrollable. The result shows that there is no sample point outside the control limits in the control chart with kernel density function estimation. So it can be concluded that the weight of the product is within a controlled condition. The factors that can cause the products uncontrolable are environmental factors, human factors, machine factors and material factors.
Klasifikasi Batubara Berdasarkan Jenis Kalori Dengan Menggunakan Algoritma Modified K-Nearest Neighbor Imalita Agustin; Yuki Novia Nasution; Wasono Wasono
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Coal is a sedimentary rock containing the main elements Carbon (C), Hydrogen (H), and Oxygen (O). Examination of coal samples in the laboratory according to company operational standard based on Air Dried Basis (ADB) are the amount of water, ash content, flying substance, solid carbon, sulfur, and Gross Calorific Value. At PT. Pancaran Surya Abadi Anggana Subdistrict Kutai Kartanegara, coal is classified based on its calorie type namely Antrachite, Bituminous, and Sub-Bituminous. In this research Modified K-Nearest Neighbor (MKNN) Algorithm is used to predict the classification. The k-Fold Cross Validation technique is used to obtain the optimal K value on MKNN Algorithm for accuracy. A measurement based on this research, the K-Optimal value used in MKNN Algorithm for coal classification in PT.Pancaran Surya Abadi is 3-NN. The value of K = 3 produces the prediction accuracy of Coal Classification based on the type of calories in PT.Pancaran Surya Abadi on 100% testing data
Proses Optimasi Masalah Penugasan One-Objective dan Two-Objective Menggunakan Metode Hungarian Diang Dewi Tamimi; Ika Purnamasari; Wasono Wasono
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 (324.556 KB)

Abstract

Assignment problem is a situation where m workers are assigned to complete n tasks/jobs to minimize costs and time or maximize profits and quality by setting the proper task to each worker. Many researches have been focused to solve assignment problem, but most of them only consider one-objective such as minimizing the cost of operation. Two-objectiveassignment problem is the assignment problem that has two objectives optimization of some of the resources owned by each worker to complete every task/job which are cost and time for this case. Case in this research use primary data drawn from the interviews of Rattan furniture craftman in Rotan Sejati store, Samarinda. This research will optimize the one-objective and two-objective assignment problem by using Hungarian Method. The analysis result revealed that the optimization proccess of one-objective assignment problem only considering operation cost is Rp. 2.950.000,- with total time is 63 days. The optimization proccess of one-objective assignment problem only considering operation time is Rp. 3.290.000,- with total time is 52 days. The optimization proccess of one-objective assignment problem only considering quality is Rp. 3.550.000,- with total time is 59 days. The optimization proccess of two-objective assignment problem only considering operation cost and operation time is Rp. 3.170.000,- with total time is 52 days. The optimization proccess of two-objective assignment problem only considering operation cost and quality is Rp. 3.380.000,- with total time is 61 days. The optimization proccess of two-objective assignment problem only considering operation time and quality is Rp. 3.350.000,- with total time is 59 days.

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