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Ika Purnamasari
Laboratorium Statistika Ekonomi Dan Bisnis FMIPA Universitas Mulawarman
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Peramalan Menggunakan Model Hybrid ARIMAX-NN untuk Total Transaksi Pembayaran Nontunai Nuning Kusumaningrum; Ika Purnamasari; Meiliyani Siringoringo
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm57

Abstract

Non-cash payment transactions in Indonesia continue to experience an increase marked by the high consumptive behavior of the people. This consumptive behavior is based on the many attractive offers, especially on year-end holidays which are the effect of calendar variations. ARIMAX is a time series method that is able to detect the effects of calendar variations. Meanwhile, to increase the level of forecasting accuracy, it can be combined with other methods such as Neural Networks (NN). This study aims to predict the total non-cash payment transactions in Indonesia in the period January to December 2022 using the ARIMAX-NN hybrid model. Based on the forecasting results, four highly accurate models were obtained, namely the hybrid model ARIMAX(0,1,2)-NN 1 neuron, ARIMAX(0,1,2)-NN 2 neurons, ARIMAX(1,1,0)-NN 1 neurons, and ARIMAX(1,1,0)-NN 2 neurons with MAPE values ​​for each model below 5%. Based on the four models formed, the results of forecasting in the period January to December 2022 as a whole the data tends to fluctuate and has an upward trend pattern, especially in December, which is the month when year-end holidays occur.
ANALISIS FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP STATUS PEMBAYARAN KREDIT BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN REGRESI LOGISTIK Memi Nor Hayati; Surya Prangga; Rito Goejantoro; Darnah; Ika Purnamasari
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm66

Abstract

Electronic goods and furniture for some people are currently seen as basic needs that must be met. High prices make it difficult for people to meet their needs with cash purchases, so they choose credit purchases using the services of finance companies in purchasing goods. This study aims to determine the factors that influence the status of credit payments for electronic goods and furniture at PT. KB Finansia Multi Finance Bontang 2020 uses logistic regression. Based on the results of the analysis, it was found that the predictor variables that had a significant effect on the credit payment status response variable were length of stay (domicile) at the address borne by the debtor when applying for credit (X3) and the amount of credit payments charged by the debtor per month (X6). The value of the Apparent Error Rate (APER) of 29.323% indicates that the logistic regression model obtained is also good for solving cases of current and non-current classification of credit payment status.
Peramalan Jumlah Wisatawan Mancanegara di Provinsi Kalimantan Timur Menggunakan Fuzzy Backpropagation Neural Network Rina Aprilianti; Ika Purnamasari; Surya Prangga
Jurnal Statistika dan Komputasi Vol. 2 No. 1 (2023): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v2i1.1592

Abstract

Latar   Belakang: Pariwisata merupakan salah satu bidang ekonomi yang menjadi sumber penerimaan devisa bagi negara. Banyaknya wisatawan merupakan salah satu faktor yang dapat berpengaruh terhadap perkembangan pariwisata. Sepanjang tahun 2021, jumlah wisatawan mancanegara di Provinsi Kalimantan Timur mengalami penurunan. Penurunan tersebut merupakan dampak dari mewabahnya COVID-19. Peneliti melakukan peramalan jumlah wisatawan mancanegara di Kalimantan Timur menggunakan Fuzzy Backpropagation Neural Network (FBPNN) guna mengantisipasi kenaikan maupun penurunan jumlah wisatawan di masa mendatang. FBPNN adalah metode peramalan Neural Network (NN) yang menggunakan algoritma pembelajaran backpropagation dimana nilai input dan output-nya berupa derajat keanggotaan himpunan fuzzy. Tujuan: Meramalkan jumlah wisatawan mancanegara di Kalimantan Timur pada bulan Januari 2022 sampai dengan Mei 2022. Metode: Metode yang digunakan adalah Fuzzy Backpropagation Neural Network (FBPNN). Hasil: Berdasarkan hasil prediksi FBPNN dengan proporsi 80%:20% untuk data training diperoleh Root Mean Square Error (RMSE) sebesar 113,61 sedangkan untuk RMSE data testing dipeoleh adalah sebesar 108,45. Kesimpulan: Adapun kesimpulan penelitian yaitu metode Fuzzy Backpropagation Neural Network dapat digunakan untuk meramalkan jumlah wisatawan dengan nilai RMSE yang dihasilkan oleh data testing lebih kecil jika dibandingkan dengan nilai RMSE yang dihasilkan oleh data training.
Aplikasi Critical Path Method (CPM) dengan Crashing Program untuk Mengoptimalkan Waktu dan Biaya Proyek Try Hardini Rahayu Mukti; Ika Purnamasari; Wasono Wasono
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (488.402 KB)

Abstract

The management of small and large-scale projects requires good planning, scheduling and coordination. Critical Path Method (CPM) is one of the method that has been developed to overcome the problem of managing a project. Time and cost greatly affect the success and failure of a project. A desired project is completed within a predetermined time, it can accelerate the duration of the activity with the consequence of an increase in cost. Acceleration of project duration at the lowest possible cost is called crashing program. The purpose of this study is to determine the optimal time and cost in completing the project. The data used is the type of work and time of completion of project work and wage costs of workers wages in the project development of SMP Negeri 24 Jalan Pangeran Suryanata Samarinda. Based on the analysis using the time efficient CPM method for completion of the project is 185 days. Acceleration of project completion time by using crashing program, project can be done for 157 days with increase of worker wage equal to Rp 473,802,785.32.
Penerapan Metode Fuzzy Time Series Using Percentage Change Nurul Hidayah; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (107.311 KB)

Abstract

In 1993, Song and Chissom introduce fuzzy times series is capable of handling the problem of data forecasting if historical data are the values ​​of linguistic. The study uses the modeling outline by way of fuzzy relation equations and approximate reasoning to predict the number of students. In this study, the approach to the theory of fuzzy time series used is fuzzy time series using percentage change developed by Stevenson and Porter in 2009. The case studies used in this study is the population of East Kalimantan Province. This study aims to determine how the application of fuzzy time series method using percentage change in the population of East Kalimantan from 1980 until 2013. Forecasting is done menggukan linguistic value of the fuzzy set which is formed of the differences and converted into a percentage of the universe of discourse as a value data. Based on the results of the application of the method using fuzzy time series of the percentage change obtained 12 fuzzy set which is linguistics of the data, the accuracy of forecasting value from 1981 to 2013 using MAPE (Avarage Forcasting Error Rate) that is equal to 0.557%.
Optimasi Pendistribusian Barang Dengan Menggunakan Vogel’s Approximation Method dan Stepping Stone Method Yuli Ratnasari; Desi Yuniarti; Ika Purnamasari
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 (958.795 KB)

Abstract

The development of era and technology are getting shopisticated which impacts the increasing of company in service area. Distribution and transportation are important aspects that can affect the success of the company’s performance.Vogel’s Approximation Method the first solution to solve the transportation problem and also Stepping Stone Method for the optimum solution to get the minimum operational cost. The aim of this research is to see the difference distribution operational cost of LPG gas 3 Kg in PT. Tri Pribumi Sejati before and after applying Vogel’s Approximation Method (VAM) and Stepping Stone Method. The result shows that Vogel’s Approximation Method (VAM) spent transportation cost Rp 24.353.568,- so it saved the transportation cost for 45,9% and made difference Rp 20.646.432,-. Next, applying Stepping Stone Method optimum solution spent transportation cost Rp 24.031.104,- so it also saved the transportation cost for 46,6% and made difference Rp 20.968.896,- of total cost of PT. Tri Pribumi Sejati Rp 45.000.000,-. To sum up that using Vogel’s Approximation Method the first solution and Stepping Stone Method optimum solution are exact method to minimize the distribution operational cost of 3kg gas tube in PT. Tri Pribumi Sejati.
Penerapan Metode Complete Linkage dan Metode Hierarchical Clustering Multiscale Bootstrap Lisda Ramadhani; Ika Purnamasari; Fidia Deny Tisna Amijaya
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 (566.958 KB)

Abstract

Cluster analysis is an analysis that has a purpose to grouping the data (object). The multiscale bootstrap method in cluster analysis is used as a manner for looking at the validity from the result of cluster analysis. The working process of multiscale bootstrap in cluster analysis is taking a sample that has been bootstrapped and then take the one of bootstrap resampling result that has been reputed to represent the distribution in East Kalimantan 2016. The purpose of this research is looking at the result of data agglomeration in poverty indicator in East Kalimantan 2016 in using a multiscale bootstrap method that produces four cluster types. The first cluster consists of two regencies/cities who has the low percentage of poverty indicator 49,32%. Additionally, the second cluster contains of five regencies/cities with the high percentage of poverty indicator 53,39%. In addition, the third cluster involves of two regencies/cities with the percentage of poverty indicator in high sufficient 51,46%. Finally, the fourth cluster consists of a regency/city which has a percentage of poverty indicator low adequate 51,02%.
Peramalan dengan Menggunakan Metode Holt-Winters Exponential Smoothing Ayu Aryati; Ika Purnamasari; Yuki Novia Nasution
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 (591.792 KB)

Abstract

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
Metode Regresi Robust Dengan Estimasi Method of Moment (Estimasi-MM) Pada Regresi Linier Berganda Hisintus Suban Hurint; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.811 KB)

Abstract

Method of Ordinary Least Square (OLS) on the regression analysis is a method which is often used to estimate the parameters. In the OLS method, there are several assumptions that must be fulfilled, these assumptions are often not fulfilled when the data contains outlier, so need a method that are robust to the presence of outliers. In this research, studied method of robust regression with MM-estimation. MM-estimation is a combination of estimation methods that have a high breakdown point, namely the Scale estimation(S-estimation) and Least Trimmed Square estimation (LTS estimation) and the method that have higher efficiency point, namely the Maximum Likelihood Type estimation (M-estimation). The first step in the MM-estimation is to find the S-estimator, then set the parameter regression using the M-estimation. The purpose of this study was to determine the effect of price index of foodstuffs ( ), the price index of education ), and the price index of health ) to the CPI for the province of east borneo, where the CPI data contains outliers, namely observation to 13, 31,and 32.
Optimasi Self-Organizing Map Menggunakan Particle Swarm Optimization untuk Mengelompokkan Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara Provinsi Kalimantan Timur Nanda Yopan Kusrahman; Ika Purnamasari; Fidia Deny Tisna Amijaya
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 (494.677 KB)

Abstract

Self-Organizing Maps (SOM) is an efficient cluster analysis in handling high dimensional and large dataset. Particle Swarm Optimization (PSO) is an effective in nonlinear optimization problems and easy to implement. A clustering process occurs if all data can clustered into 1 cluster, however if one or two data did not join then the data have a deviant behavior called outliers or noise. PSO is used to evolve the weights for SOM to improve the clustering result and to cluster some social aspect in society, for example is poverty. Development strategies are prioritized to regions with largest population lived in poverty. Kutai Kartanegara regency (Kukar) are recorded as the biggest contributor on population lived in poverty at East Kalimantan in 2017. Development of underdeveloped villages is requires Village Potential data, which focus on visualizing the situation in the regions. This study aims to determine the number of clusters formed and to find the value of Davies Bouldin Index (DBI) from clustering underdeveloped villages in Kukar region using PODES 2018 data. This study uses 9 particle which are the final weight of the SOMs algorithm with different learning rate each particle. Based on the analysis, the optimal number of clusters is 2 clusters with DBI value of 0.7803, where cluster 1 consists of 82 underdeveloped villages and the cluster 2 consist of underdeveloped villages.
Co-Authors Aisyah Aisyah Alfian Hidayat Annisa Hayatunnufus Arjun Pratama Ayu Aryati Bayu Iswahyudi Noor Bayu Nanda Permana Chairunnisa Chairunnisa Cristine Uli Artha Darnah Darnah Darnah Desi Yuniarti Diang Dewi Tamimi Dini Elizabeth Dominggus Norvindes Dellas Dwi Kartika Damayanti Elisa Feronica Emi Harmianti Fahrullah, Fahrullah Fidia Deny Tisna Amijaya Franklin Peter Anton Karundeng Gracia Indah Fajarini Grassella Gunsyang Hidaya Annur Hisintus Suban Hurint Imasdiani Imasdiani Khairun Nida Lisda Ramadhani Marisa Nanda Rahmaniah Meiliyani Siringoringo Melisa Arumsari Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Muhammad Al-Firdaus Erdian Muhammad Fathurahman Muhammad Jainudin Musmirani Musmirani Nanda Arista Rizki Nanda Yopan Kusrahman Nariza Wanti Wulan Sari Nuning Kusumaningrum Nur Afifah Salsabila Nur Annisa Roziya Nur Eka Fitria Nurul Hidayah Prangga, Surya Rahmad Fahreza Adiyasa Reyham Nopriadi Gurianto Rina Aprilianti Riska Veronika Rito Goejantoro Rito Goejantoro Rito Goejantoro Rito Goejantoro Roy Tridoni Situmorang Sekar Nur Utami Siti Rahmah Binaiya Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Suyitno Suyitno Suyitno Suyitno Syeli Ariessela Try Hardini Rahayu Mukti Wasono Wasono Wenny Kristina Yudha Muhammad Faishol Yuki Novia Nasution Yuki Novia Nasution Yuki Purnamasari Yuli Ratnasari