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VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Published by Universitas Negeri Makassar
ISSN : -     EISSN : 26847590     DOI : http://dx.doi.org/10.35580/variansiunm26374
Core Subject : Economy, Science, Education,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics
VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian pustaka.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 69 Documents
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REGRESI PANEL SPASIAL UNTUK PEMODELAN INDEKS PEMBANGUNAN MANUSIA DI KABUPATEN/KOTA SE-KALIMANTAN Muh. Gunadil Ukra; Muhammad Nusrang; Bobby Poerwanto
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.811 KB) | DOI: 10.35580/variansiunm34

Abstract

MUH. GUNADIL UKRA 2022. Regresi Panel Spasial Untuk Pemodelan Indeks Pembangunan Manusia Di Kabupaten/Kota Se-Kalimantan Tahun 2017-2021, Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar, (dibimbing oleh Muhammad Nusrang dan Bobby Poerwanto). Penelitian ini membahas tentang Indeks Pembangunan Manusia di Kabupaten/Kota Se-Kalimantan dengan membandingkan model regresi panel spasial SAR-FE dan SEM-FE. Pembangunan manusia merupakan faktor penting dalam meningkatkan kesejahteraan penduduk. United Nations Development Programme (UNDP) yang juga menyatakan bahwa manusia yang bermartabat adalah manusia yang dapat menikmati umur panjang, sehat, dan menjalankan kehidupan yang produktif. Untuk melihat faktor apa saja yang berpengaruh terhadap IPM di Kabupaten/Kota Se-Kalimantan dan model apa yang cocok untuk menggambarkan IPM di Kabupaten/Kota Se-Kalimantan tahun 2017-2021. Maka dari itu dilakukan analisis regresi panel spasial dengan menggunakan Dimensi IPM yaitu umur panjang dan hidup sehat, pengetahuan dan standar hidup layak. Analisis regresi data panel adalah analisis regresi dengan struktur data merupakan data panel dengan data cross section dan time series. Pengujian asumsi dengan kenormalan galat, multikolinieritas dan autokorelasi spasial perlu dilakukan sebagai syarat menentukan model regresi panel spasial pengaruh tetap. Selanjutnya dilakukan uji kebaikan model dengan menggunakan R-Square yang di mana dari nilai SAR-FE sebesar 0,9997669 dan nilai SEM-FE sebesar 0,9997541. Dengan kesimpulan nilai tertinggi merupakan model terbaik, SAR-FE merupakan model yang paling baik digunakan dalam memodelkan IPM di Kabupaten/Kota Se-Kalimantan karena memiliki nilai lebih besar dibandingkan dengan SEM-FE. Pengaruh kedekatan spasial dipengaruhi oleh rata-rata IPM di Kabupaten/Kota lain yang berdekatan. Rata-rata lama sekolah merupakan faktor yang paling berpengaruh terhadap IPM di Kabupaten/Kota Se-Kalimantan tahun 2017-2021. Hasil penelitian dapat dijadikan informasi dan evaluasi bagi pemerintah untuk memperhatikan perubahan IPM setiap Kabupaten/Kota terutama di Kabupaten/Kota terutama di Kabupaten/Kota yang berdekatan.
Hybrid Hierarchical Clustering dalam Pengelompokan Daerah Rawan Bencana Tanah Longsor di Sulawesi Selatan Fithriyah Azzahrah; Suwardi Annas; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.161 KB) | DOI: 10.35580/variansiunm38

Abstract

This study aims to describe and classify areas prone to landslides in South Sulawesi. The method used is Hybrid Hierarchical Clustering. The data used is landslide disaster data sourced from the National Disaster Management Agency (BNPB) for 2018-2020 in South Sulawesi. The variables used are the number of landslides, deaths, damaged houses, injured victims, and damaged public facilities. Grouping using the Hybrid Hierarchical Clustering method with mutual clusters using bottom-up and top-down methods. Grouping with bottom-up method produces 2 groups, top-down method produces 2 groups and 1 best mutual cluster. The ratio results in the bottom-up method is 0.84, the top-down method is 1.07 and the mutual cluster is 0.84. The grouping results obtained were 2 groups.
PENGGUNAAN METODE DOUBLE EXPONENTIAL SMOOTHING BROWN UNTUK MERAMALKAN KASUS POSITIF COVID-19 DI PROVINSI PAPUA Ratu Huriyah Ali; M. Nadjib Bustan; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.18 KB) | DOI: 10.35580/variansiunm39

Abstract

Forecasting is an activity to predict events that will occur in the future. The data used in this study is data on the addition of positive cases of COVID-19 per day in Papua Province from March 21, 2020 to November 25, 2020. The forecasting method used for data that has an element of trend is the double exponential smoothing brown method. The number of additional positive cases of COVID-19 which tends to increase is assumed to be a trend. In this study, the used is = 0.10 which is obtained based on the smallest SSE, MSE, and MAE values. Forecasting the addition of positive cases of COVID-19 in Papua Province for the next 7 days, namely November 26, 2020 to December 2, 2020, obtained additional positive cases of COVID-19 per day as many as 81, 82, 82, 83, 83, 84, and 84.
Penerapan Metode Singular Spectrum Analysis dalam Peramalan Jumlah Produksi Beras di Kabupaten Gowa Rezki Amalia Idrus; Ruliana Ruliana; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.022 KB) | DOI: 10.35580/variansiunm40

Abstract

Total rice production in Gowa Regency from January 2018 to December 2020 has decreased which is not significant every month, so it is necessary to do a forecast to anticipate food shortages in the future. This study aims to determine the yield of rice production in Gowa Regency and to model data from October 2021 to September 2022 using the Singular Spectrum Analysis (SSA) method. Based on the results of the analysis, the MAPE value obtained is 6.32% so it can be said that forecasting using the SSA method is very accurate
Analisis Sensitivitas Dalam Metode Analytic Hierarchy Process dan Pengaruhnya Terhadap Urutan Prioritas Pada Pemilihan Smartphone Android Yakoba Yusina Muanley; Aloisius Loka Son; Grandianus Seda Mada; Nugraha K. F. Dethan
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm32

Abstract

Smartphones nowadays have become a basic need for everyone because they provide many benefits and conveniences for users. People always want to have a smartphone with good quality. However, due to the lack of information along with the many types of smartphones in circulation, it often makes it difficult for users to choose a smartphone that suits their needs. To overcome these problems, it is necessary to have a method that can provide recommendations for appropriate decision-making for users. This study aims to apply the Analytic Hierarchy Process (AHP) method and sensitivity analysis in determining the priority order of smartphone selection by comparing one smartphone to another. The criteria for consideration are Facilities, Price, Battery, and RAM with alternative choices in the form of Xiaomi, Oppo, and Vivo brand smartphones. Data collection in this study was carried out by distributing questionnaires to 100 students of the Mathematics Study Program. The data were processed using the AHP method and sensitivity analysis. AHP is used to produce a more consistent ranking order of each alternative, while the sensitivity test is carried out to measure the stability of the calculation results if there is a change in decision-making. From the results of the analysis with AHP, it was found that Xiaomi was the first priority of the respondent's choice, followed by Vivo, and the last priority was Oppo with an inconsistency level of 0.02. Meanwhile, sensitivity testing shows that RAM is the most influential criterion for changing the order of alternative priorities, where Xiaomi remains the first priority, followed by Oppo, and Vivo is the last priority.
Peramalan Jumlah Produksi Kelapa Sawit Provinsi Kalimantan Timur Menggunakan Metode Singular Spectrum Analysis Siringoringo, Meiliyani; Wahyuningsih, Sri; Purnamasari, Ika; Arumsari, Melisa
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm46

Abstract

Singular spectrum analysis (SSA) is a nonparametric method that does not rely on assumptions such as stationary nature or residual normality. SSA separates time series data into its components, which are trend, seasonality, and error (noise). This study aimed to obtain forecasting results for the amount of oil palm production in East Kalimantan Province for the period January 2021 to December 2021 using SSA. Based on the results of the data analysis, in the process of forming the forecasting model with in-sample data, the parameter window length (L) was 24, which produced a MAPE value of 0.464%, and while the forecasting model validation process used out-sample data, it produced a MAPE value of 41.172%.
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.
Analisis Pengaruh Profitabilitas, Ukuran Perusahaan, dan Reputasi Auditor terhadap Audit Delay pada Perusahaan Otomotif yang Terdaftar di Bursa Efek Indonesia Tahun 2015-2020 Menggunakan Regresi Logistik Hardianti Hafid; Ansari Saleh Ahmar; Zulkifli Rais
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/variansiunm71

Abstract

This research aims to determine whether profitability, company size, and auditor reputation significantly influence audit delay using binary logistic regression analysis. The research results indicate that profitability has a significant individual (partial) effect on audit delay, while company size and auditor reputation do not have a significant individual (partial) effect on audit delay
Pemodelan Regresi Data Panel pada IPM di Sulawesi Selatan Zakiyah Mar'ah; Ruliana Ruliana; Ansari Saleh Ahmar; Zulkifli Rais
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/variansiunm72

Abstract

HDI is an important indicator to measure success in efforts to build the quality of human life (community/population). HDI can determine the rank or level of development of a region/country. For Indonesia, HDI is strategic data because apart from being a measure of government performance, HDI is also used as an allocator for determining the General Allocation Fund (DAU). The development of HDI in Indonesia has always increased from year to year. In South Sulawesi, the HDI has increased significantly in the last 10 years. Where in 2012 the HDI of South Sulawesi was at 67.26 to 72.82 in 2022. This is measured based on three essential aspects, namely longevity and healthy living, knowledge, and a decent standard of living. Along with HDI, other indicators also show an increase from year to year. To find out how much these variables affect the increase in HDI during the 2018-2022 period, the panel data regression method is used which is a combination of time series data and cross section data. The regression model that is suitable for South Sulawesi HDI data from 2018-2022 is a panel data regression model with one-way random effects, namely individual effects. The model is written as follows IPM=(-1.9360e+01) + (1.0734e+00) UHH + (1.4014e-03) PPK + e

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