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All Journal Variansi : Journal of Statistics and Its Application on Teaching and Research Indonesian Journal of Fundamental Sciences
Muhammad Arif Tiro
Program Studi Statistika FMIPA UNM
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health

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Metode Vector Autoregressive (VAR) dalam Menganalisis Pengaruh Kurs Mata Uang Terhadap Ekspor Dan Impor Di Indonesia Dwi Reskiyani Febrianti; Muhammad Arif Tiro; S. Sudarmin
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 1 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm14645

Abstract

Abstrak. Metode Vector Autoregressive (VAR) adalah salah satu analisis yang digunakan untuk menganalisis data deret waktu. Data deret waktu dikategorikan menurut interval waktu yang sama, baik dalam harian, mingguan, bulanan, kuartalan, ataupun tahunan. Vector Autoregressive (VAR) merupakan pemodelan yang tidak perlu menentukan variabel endogen dan variabel eksogen. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh kurs mata uang terhadap ekspor dan impor di Indonesia. Data yang digunakan dalam penelitian ini adalah data kurs, ekspor, dan impor dari bulan Januari 2014 hingga Desember 2018. Uji stasioneritas dalam penelitian ini menggunakan metode Augmented Dickey Fuller (ADF). Dalam penelitian ini menggunakan differencing terhadap data karena data tidak stasioner pada level. Penentuan panjang lag optimal diperoleh dari nilai Akaike Information Criterion (AIC) yang paling minimum. Estimasi model VAR diperoleh setelah penentuan panjang lag optimal. Uji kausalitas dilakukan dengan uji Causality Granger untuk melihat pengaruh timbal balik antar variabel yang diuji dalam penelitian ini. Terakhir menggunakan uji Impulse Response Function (IRF) untuk menelusuri guncangan atau shock suatu variabel terhadap variabel lainnya. Adapun hasil analisis yang diperoleh menunjukkan terdapat dua hubungan satu arah yaitu kurs mempengaruhi ekspor dan ekspor mempengaruhi impor.Kata Kunci: VAR, Kurs, Ekspor, Impor.
Metode Analisis Diskriminan dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Indeks Pembangunan Manusia Novi Afryanthi S.; Muhammad Arif Tiro; Ansari Saleh Ahmar
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 2, No 1 (2020)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm14978

Abstract

Abstract. Discriminant analysis is a method in multivariat statistic analysis that related with object which have separated into the defined group defined and see the accuracy  of the formed group. In this research, clustera analysis is used for the first grouping,  cluster  analysis is a statistical analysis which aims to classify some objects based on the characteristics similarity among the object. Data for this study is HDI (Human Development Index)  of indicator in south sulawesi in 2016. The result of this research are 1st cluster (lower  HDI indicator) which have 21 city/ distric and the 2nd cluster (higher  HDI indicator) which have 3 city/distric as the closeness value between the cluster that formed is 0.902 which shows the closeness between the cluster is high . Furthermore, the discriminant function that have formed explains that if the life expectancy increase, the HDI indicator in city/distric in south sulawesi province will decrease but if school  expectation duration in school , average of duration in school, and parity of pur hasing power is increasing, the HDI indicator in city/distric in aouth sulawesi will also increase.Keywords: Cluster analysis, Discriminant analysis , Human development index indicator.
Model Regresi Logistik Terboboti Georafis pada Status Kemiskinan Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2016 Sadriana Rustan; Muhammad Arif Tiro; Muhammad Nadjib Bustan
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 1, No 3 (2019)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm14624

Abstract

Abstrak. Analisis regresi logistik digunakan untuk menentukan hubungan antara peubah respon bersifat kategori dengan satu atau lebih peubah penjelas dengan asumsi bahwa respon tidak dipengaruhi oleh lokasi geografis (data spasial). Salah satu metode analisis spasial adalah Model Regresi Logistik Terboboti Geografis (RLTG). Model RLTG adalah bentuk regresi logistik lokal di mana lokasi geografis diperhatikan dan diasumsikan memiliki distribusi Bernoulli. Pendugaan parameter model RLTG menggunakan metode Maximum Likelihood Estimation (MLE) dengan memberikan bobot yang berbeda pada lokasi yang berbeda. Data dalam penelitian ini diperoleh dari publikasi Badan Pusat Statistik, yaitu data dan Informasi Kemiskinan di Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan dengan menggunakan model regresi logistik terboboti geografis dengan fungsi pembobot Kernel bisquare. Hasil penelitian menunjukkan bahwa peubah penjelas yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan adalah persentase penduduk tidak bekerja dan persentase rumah tangga pengguna jamban bersama.Abstract. Logistic regression a analysis is used to determine the relationship between categorical response variables with one or more predictor variable assuming that the response is not influenced by geographical location (spatial data). One method of spatial analysis is Geographically Weighted Logistic Regression (GWLR). The GWLR model is a local form of logistic regression where the geographical location is considered and assumed to have a Bernoulli distribution. Estimating parameters of the RLTG model uses the Maximum Likelihood Estimation (MLE) method by giving different weights to different locations. The data were obtained from BPS publications, namely Data and Information on Poverty in South Sulawesi Province. This study aims to determine the factors that influence poverty status in South Sulawesi Province using a geographically weighted logistic regression model with kernel bisquare weighting function. The results showed that the explanatory variables that influence the status of poverty in the province of South Sulawesi were the percentage of the population not working and the percentage of common household toilet users.Keywords: logistic regression, kernel bisquare, GWLR and poverty.
Analisis Cluster Ensemble dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kinerja Pembangunan Ekonomi Daerah Adrian Aqil Yusfar; Muhammad Arif Tiro; S. Sudarmin
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 1 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm14626

Abstract

Abstract. Cluster analysis or group analysis is an analysis method to classify objects of observation into several groups based on their characteristics. Conventional methods namely Hierarchy and Non-Hierarchy are used in the formation of the initial group. However, the results of the grouping formed had mixed results so that the Cluster Ensemble analysis was then used to obtain a good final grouping. The Cluster Ensemble with the Link-Based Cluster Ensemble approach with the Connected Triple Based Similarity (CTS) method resulted in three final group divisions. The evaluation of the grouping performance used, namely Compactness and Davies-Bouldin, stated that the Cluster Ensemble was better than the hierarchical and non-hierarchical methods. The final group that has been formed is described using the average value for each variable in the district / city in South Sulawesi Province. The first group has the characteristics of regional economic development performance that is better than the second and third groups, but for the third group has the lowest characteristics of regional economic development performance from the first and second groups.Keywords : Cluster, Cluster Ensemble, Group Performance Evaluation, Performance, Regional Economic
Temporal Analysis of the Influence of the Number of Vaccinations on the Number of Covid-19 Cases in South Sulawesi Province Using ARIMAX Model Aswi Aswi; Muhammad Arif Tiro; Sudarmin Sudarmin; Ruliana Ruliana; Andi Gagah Palarungi Taufik; Zulhijrah Zulhijrah
Indonesian Journal of Fundamental Sciences Vol 8, No 2 (2022)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.515 KB) | DOI: 10.26858/ijfs.v8i2.40561

Abstract

South Sulawesi is a province that has the highest number of Covid-19 cases in the Sulawesi island region. Vaccination is one way that is considered effective in controlling the infection of a disease. Covid-19 vaccination in Indonesia was carried out in January 2021. This study aims to obtain the best Autoregressive Integrated Moving Average X (ARIMAX) model in modeling the effect of the number of vaccinations on the number of Covid-19 cases in South Sulawesi Province. Data on the number of vaccinations and Covid-19 cases in South Sulawesi Province (October 1, 2021 - January 31, 2022) were used. The best ARIMAX model in modeling Covid-19 in relation to the number of vaccinations is ARIMAX (2,1,0). The results showed that the number of vaccinations had a negative effect on the number of Covid-19 cases at the significant level of 10%. This indicates that if the number of vaccinations increases then the number of Covid-19 cases will decrease.
Co-Authors Adrian Aqil Yusfar Ahmar, Ansari Saleh Andi Gagah Palarungi Taufik Aswi Aswi Aswi, Aswi Dwi Reskiyani Febrianti Muhammad Nadjib Bustan Novi Afryanthi S. Ruliana Ruliana S. Sudarmin Sadriana Rustan Sudarmin Sudarmin Zulhijrah Zulhijrah