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INDONESIA
Statistika
Published by Universitas Islam Bandung
ISSN : 14115891     EISSN : 25992538     DOI : https://doi.org/10.29313/jstat.v19i2.4898
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics
STATISTIKA published by Bandung Islamic University as pouring media and discussion of scientific papers in the field of statistical science and its applications, both in the form of research results, discussion of theory, methodology, computing, and review books.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 6 Documents
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Search results for , issue "Vol 19, No 1 (2019)" : 6 Documents clear
ANALISIS DISKRIMINAN LINEAR ROBUST PADA BERAT BAYI LAHIR DI RSUD LUWUK Nur'eni Nur'eni; Surni’a Surni’a; Lilies Handayani
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4759

Abstract

Analisis diskriminan linear robust digunakan untuk mengklasifikasikan suatu pengamatan apabila dalam pengamatan tersebut terdapat pencilan. Pencilan akan menyebabkan matriks varians kovarians menjadi tidak robust. Minimum Covariance Determinant (MCD) digunakan untuk menduga sebagian pengamatan dengan meminimumkan determinan matriks kovariansi. Berat bayi lahir menurut WHO (1961) terbagi menjadi dua kategori yaitu berat bayi lahir rendah (BBL  2500 gram) dan berat bayi lahir normal (BBL > 2500 gram). Hasil dari klasifikasi berat bayi lahir di RSUD Luwuk Kabupaten Banggai dengan menggunakan metode analisis diskriminan linear robust diperoleh tingkat akurasi sebesar 81%.
ANALYSIS OF THE IMPACT OF IMPROVING COAL PRODUCTION ON ECONOMIC STRUCTURE OF KABUPATEN MUARA ENIM, SOUTH SUMATERA triswan suseno
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4531

Abstract

Starting in 2022, the State-owned coal company in South Sumatera plans to produce coal 5.72 million tonnes per year. 3.43 million tons of which will be processed to produce 450,000 tons of polypropylene, 1.47 million tons to produce 400,000 tons of dymethyl ether, and 811,885 tons to produce 570,000 tons of urea fertilizer. The amount of coal to steam power plant needs as much as 3.45 million tons, bringing the total addition of coal production per year as much as 9.17 million tons.The coal downstreaming sector will able to absorb labour of 1,604 people, the size of the value of money which will be obtained by these workers is estimated at USD 26,459,273, an the company's profit from the results of the processing of the coal downstreaming of USD 298,476,000. The existence of the industry turned out taxes, generating government revenue to USD 110.819.765. Using input output method, the increase in coal production amounted to 9.1 million tonnes or IDR 3.44 trillion worth encourages other sectors to improve the output of Rp. 3.62 trillion. The impact of investment for processing coal in Muara Enim in 2022 results on increasing of MuaraEnim GDP to IDR 114,38 trillion and creating jobs for the various sectors as much as 5,578 people.Keywords: GDP, coal, downstream, input, output.
Pendekatan Regresi Nonparametrik dengan Fungsi Kernel untuk Indeks Harga Saham Gabungan Nur Azizah Komara Rifai
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4775

Abstract

Stock is one of the investment instruments that is very popular among investors. One indicator of stock price movements in Indonesia is the Jakarta Composite Index (JCI). JCI data is a time series data about joint stock prices which can be analyzed by time series analysis method. However, with this method there are assumptions that cannot be fulfilled. In this study, JCI data will be analyzed by a nonparametric method namely kernel regression with Nadaraya-Watson estimator. The weekly JCI closing price data from January 2015 to December 2015 is applied using various kernel functions that minimize the value of cross validation to get the optimal bandwidth. The results show that the biweight kernel regression with Mean Square Error = 9030,63 and bandwidth = 108,2 is the best model for predictions.
HUBUNGAN NEGATIF ANTARA TINGKAT INFLASI DENGAN TINGKAT KEMISKINAN DI WILAYAH PERDESAAN PROVINSI LAMPUNG Andra Wina
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4587

Abstract

Menurut teori, tingkat inflasi akan berpengaruh positif pada angka kemiskinan. Namun pada beberapa penelitian, hal itu bisa saja bertentangan. Itulah sebabnya maka penting untuk menguji hubungan kedua variabel tersebut di beberapa lokasi di Indonesia. Penelitian ini mengkaji pengaruh inflasi terhadap angka kemiskinan di wilayah perdesaan di Provinsi Lampung. Metode yang digunakan adalah statistika analitik menggunakan teknik regresi linear sederhana dengan menggunakan data sekunder hasil survei sosial ekonomi dan survei harga perdesaan dari BPS Provinsi Lampung tahun 2012 – 2017. Hasil analisis telah memenuhi uji asumsi klasik sehingga model regresi linear sederhana dapat digunakan dalam memprediksi variabel kemiskinan di perdesaan. Temuan awal penelitian ini menyimpulkan bahwa inflasi perdesaan berpengaruh negatif terhadap tingkat kemiskinan perdesaan di Provinsi Lampung sebesar 0,061. Artinya semakin tinggi pertumbuhan inflasi perdesaan, maka semakin rendah tingkat kemiskinannya. Hal itu berbanding terbalik dengan landasan teori yang mungkin disebabkan oleh adanya fenomena sosial tertentu atau karena inflasi merupakan determinan dari makro ekonomi, sehingga bisa saja tidak berpengaruh langsung pada tingkat kemiskinan. Hal ini bukan saja terjadi pada kajian ini, tetapi juga pada beberapa kajian sebelumnya. Pada akhirnya, hal ini membutuhkan penelitian lanjutan yang lebih komprehensif untuk wilayah-wilayah lain di Indonesia, untuk dapat lebih memahami hubungan antara dua variabel tersebut.Kata kunci : Inflasi Perdesaan, Pertumbuhan, Regresi Linear Sederhana
Membangun Interval Kepercayaan Proporsi Dengan Menggunakan Metode Jackknife Terhapus-1 Wahyu Suryadi Suryadi; Epha Diana Supandi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4721

Abstract

Metode Jackknife merupakan teknik resampling nonparametric. Prinsip dari metode Jackknife yaitu membangkitkan data dari sampel asli untuk mendapatkan sampel tiruan. Tujuan penelitian ini adalah mengestimasi prosentase suara pada pemilihan umum Kepala Daerah Kabupaten Bantul tahun 2015 dengan menggunakan sampel asli dan metode Jackknife sampel terhapus-1.  Keakuratan kedua metode dihitung dengan menggunakan standar error. Hasil penelitian menunjukan bahwa selang kepercayaan menggunakan metode Jackknife sampel terhapus-1 memiliki tingkat keakuratan yang lebih baik dibandingan dengan menggunakan sampel asli, hal ini ditunjukan dengan nilai standar error yang lebih kecil.
Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi Selma Yulistiani; Suliadi Suliadi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4740

Abstract

Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there are criteria that must be considered in order to produce the best model. The good criteria for models selection  can use the Bayesian Information Criterion (BIC) derived by Schwarz (1978). Galeano and Pena (2011) proposed a modified Bayesian Information Criterion for model selection and detect potential outliers. The modified Bayesian Information Criterion for outlier detection will be applied to the data OutStanding Loan PT.Pegadaian Cimahi year 2013-2017. So that the best model is obtained that the model with adding 2 potential outliers with the ARIMA model (1.0,0), that outliers at observations 48, and 58 because it has a minimum BICUP value of 1064.95650.

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