cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Wardhani Utami Dewi
Contact Email
dewiutamiwardhani@gmail.com
Phone
+62895379324824
Journal Mail Official
scncstatistics@gmail.com
Editorial Address
Jl. Ki Hajar Dewantara No.116, Iringmulyo, Metro Timur, Kota Metro, Lampung 34111
Location
Kota metro,
Lampung
INDONESIA
Sciencestatistics: Journal of Statistics, Probability, and Its Application
Published by Universitas Muhammadiyah Metro
ISSN : 29642884     EISSN : 29639875     DOI : https://doi.org/10.24127
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Sciencestatistics: Journal of Statistics, Probability, and Its Application is an Open Access journal in the field of statistical inference, experimental design and analysis, survey methods and analysis, research operations, data mining, statistical modeling, statistical updating, time series and econometrics, multivariate analysis, statistics education, simulation and modeling, numerical analysis, algebra, combinatorics, and applied mathematics.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): JULY" : 5 Documents clear
Analisis Komponen Utama pada Data Diabetes Irfan, Miftahul
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 2 (2024): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i2.5729

Abstract

Permasalahan dalam penelitian ini adalah tingginya jumlah variabel yang saling berkorelasi, sehingga menyulitkan pemahaman terhadap struktur data. Tujuan penelitian ini untuk mereduksi dimensi variabel yang saling berkorelasi dan memperoleh pemahaman yang lebih baik terhadap struktur data. Data yang digunakan terdiri dari 768 sampel dengan 8 variabel bebas dan 1 variabel terikat pada Data Diabetes. Langkah-langkah analisis meliputi penentuan jumlah komponen utama, uji Bartlett dan uji Keiser-Meyer-Olkin (KMO) untuk memastikan kecocokan data, perhitungan koefisien komponen utama, serta visualisasi grafik AKU. Hasil analisis menunjukkan bahwa terdapat 5 komponen utama yang mampu menangkap lebih dari 80% keragaman data, serta hubungan yang beragam antar variabel yang diamati. The problem in this research is the high number of variables that weaken each other, making it difficult to understand the data structure. The aim of this research is to reduce the dimensions of mutually burdening variables and gain a better understanding of the data structure. The data used consists of 768 samples with 8 independent variables and 1 dependent variable in Diabetes Data. The analysis steps include determining the number of principal components, Bartlett's test and Keiser-Meyer-Olkin (KMO) test to ensure data suitability, performance of principal component coefficients, and visualization of the AKU graph. The results of the analysis show that there are 5 main components that are able to capture more than 80% of the diversity of the data, as well as various relationships between the observed variables.
Implementation Of Artificial Neural Network (ANN) Classification In Type 2 Diabetes Mellitus Cases Naflah Faulina
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 2 (2024): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i2.5951

Abstract

Machine learning is a type of artificial intelligence that provides computers with the ability to learn from data. There are three main branches of machine learning, namely supervised machine learning, unsupervised learning, and reinforcement learning. One of the categories in supervised machine learning is classification. An example of a classification algorithm is Artificial neural networks are information processing systems that have characteristics and capabilities that are generally similar to human neural networks. A neural network consists of an arrangement of connections between neurons which is called architecture, a method for determining weights on connections which is called a training process or algorithm, and an activation function. This algorithm is used to classify type 2 diabetes mellitus patients as having complications and no complications by dividing training data and testing data, namely 70:30, to get the best results, namely multi layer (3 Hidden Layers with number of nodes/neurons= 5,4,3) . Machine Learning adalah jenis kecerdasan buatan yang memberi komputer kemampuan untuk belajar dari data. Ada tiga cabang utama pembelajaran mesin, yaitu pembelajaran mesin yang diawasi, pembelajaran tanpa pengawasan, dan pembelajaran penguatan. Salah satu kategori dalam pembelajaran mesin yang diawasi adalah klasifikasi. Contoh algoritma klasifikasi adalah Jaringan syaraf tiruan merupakan sistem pengolah informasi yang mempunyai karakteristik dan kemampuan yang umumnya mirip dengan jaringan syaraf manusia. Jaringan saraf terdiri dari susunan koneksi antar neuron yang disebut arsitektur, metode penentuan bobot koneksi yang disebut proses pelatihan atau algoritma, dan fungsi aktivasi. Algoritma ini digunakan untuk mengklasifikasikan pasien diabetes melitus tipe 2 memiliki komplikasi dan tanpa komplikasi dengan membagi data latih dan data uji yaitu 70:30, untuk mendapatkan hasil terbaik yaitu multi layer (3 Hidden Layer dengan jumlah node/neuron= 5,4,3).
Penggunaan ARIMA Box-Jenskin dalam Meramalkan Harga Emas Antam Tahun 2025-2027 di Indonesia Sholiha, Sangidatus; Wardhani Utami Dewi
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 2 (2024): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i2.5958

Abstract

Penelitian ini sangat penting mengingat volatilitas pasar global dan ketidakpastian ekonomi yang semakin meningkat, yang mendorong kebutuhan untuk memiliki alat peramalan yang andal bagi aset-aset lindung nilai seperti emas. Penelitian ini bertujuan untuk meramalkan harga emas Antam di Indonesia untuk periode 2025-2027 menggunakan model ARIMA. Metode kuantitatif dengan desain deret waktu digunakan, dengan data harga emas dari tahun 2021 hingga 2024. Hasil analisis menunjukkan bahwa model ARIMA (1,1,1) adalah yang terbaik dalam meramalkan harga emas Antam, dengan nilai MSE, AIC, dan BIC yang rendah. Peramalan menunjukkan tren kenaikan harga emas dari awal 2025 hingga akhir 2027, mencerminkan kepercayaan pasar terhadap emas sebagai aset lindung nilai yang aman. Kesimpulan dari penelitian ini adalah bahwa peramalan harga emas Antam dapat memberikan wawasan yang penting bagi investor dan pembuat kebijakan untuk merencanakan strategi investasi dan langkah-langkah ekonomi di masa depan. This research is especially important given the increasing global market volatility and economic uncertainty, which drives the need to have reliable forecasting tools for hedging assets such as gold. This research aims to predict the price of Antam gold in Indonesia for the 2025-2027 period using the ARIMA model. A quantitative method with a time series design was used, with gold price data from 2021 to 2024. The analysis results show that the ARIMA (1,1,1) model is the best in estimating Antam's gold price, with MSE, AIC and BIC values ​​that are low . Forecasts show an upward trend in gold prices from the beginning of 2025 to the end of 2027, reflecting market confidence in gold as a safe hedging asset. The conclusion of this research is that Antam's gold price forecasting can provide important insights for investors and policy makers to plan investment strategies and economic steps in the future.
Pendekatan Multivariate Analysis of Variance (MANOVA) terhadap Pengaruh Blended learning Berbasis Google classroom pada Kemampuan Berpikir dan Minat Belajar Indah Resti Ayuni Suri; Farida
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 2 (2024): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i2.6119

Abstract

Berdasarkan hasil pra penelitian yang dilakukan SMP Al-Huda Jati Agung, terlihat bahwa kemampuan berpikir kritis dan minat belajar masih rendah. Berdasarkan indikator kemampuan berpikir kritis dan minat belajar diketahui kemampuan berpikir kritis dan minat belajar juga masih rendah. Selain itu kurang efektifnya model pembelajaran konvensional yang diterapkan di sekolah tersebut, yaitu pembelajaran langsung. Penelitian ini bertujuan untuk mengetahui pengaruh model pembelajaran Blended learning berbasis Google classroom terhadap kemampuan berpikir kritis dan minat belajar peserta didik dengan pendekatan MANOVA. Penelitian ini menggunakan jenis penelitian Quasy Experimental Design. Pengambilan sampel pada penelitian ini menggunakan teknik Cluster Random Sampling. Populasi pada penelitian ini adalah peserta didik kelas VIII SMP Al-Huda Jati Agung. Sampel penelitian ini yaitu Kelas VIIIA (model pembelajaran direct instruction), Kelas VIIIB (model pembelajaran blended learning berbasis google classroom). Instrumen yang digunakan untuk mengumpulkan data yaitu instrumen tes kemampuan berpikir kritis dan angket minat belajar. Analisis data pada penelitian ini adalah Multivariate Analysis of Varian (Manova). Berdasarkan perhitungan yang telah dilakukan mendapatkan hasil bahwa p-value dari masing masing kemampuan kurang dari 0,05. Sehingga dapat disimpulkan, terdapat pengaruh model pembelajaran blended learning berbasis google classroom terhadap kemampuan berpikir kritis dan minat belajar peserta didik Based on the results of pre-research conducted by Al-Huda Jati Agung Middle School, it appears that critical thinking skills and interest in learning are still low. Based on indicators of critical thinking ability and interest in learning, it is known that critical thinking ability and interest in learning are also still low. Apart from that, the conventional learning model applied at the school is less effective, namely direct learning. This research aims to determine the effect of the Google classroom-based Blended learning learning model on students' critical thinking skills and interest in learning using the MANOVA approach. This research uses the Quasy Experimental Design type of research. Sampling in this study used the Cluster Random Sampling technique. The population in this study was class VIII students at Al-Huda Jati Agung Middle School. The samples for this research are Class VIIIA (direct instruction learning model), Class VIIIB (Google classroom based blended learning model). The instruments used to collect data were critical thinking ability test instruments and learning interest questionnaires. Data analysis in this research is Multivariate Analysis of Variant (Manova). Based on the calculations that have been carried out, the results show that the p-value of each ability is less than 0.05. So it can be concluded, there is an influence of the Google classroom-based blended learning model on students' critical thinking skills and interest in learning.
Perbandingan Pembobot Welsch dan Tukey Bisquare pada Regresi Robust S-estimator Nurhafifah, Fifi; Khoirin Nisa; Nusyirwan; Rizki Agung Wibowo
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 2 (2024): JULY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i2.6156

Abstract

Regresi robust merupakan sebuah metode yang dikembangkan untuk memiliki kinerja yang baik ketika data yang dianalisis menyimpang dari asumsi yang mendasari, misalnya terdapat pencilan yang dapat menyebabkan galat menjadi tidak berdistribusi normal. Salah satu metode estimasi pada regresi robust adalah S-estimator, metode ini memiliki fungsi pembobot antara lain pembobot Welsch dan Tukey Bisquare. Pada penelitian ini, kami membandingkan bobot-bobot pada metode S-estimator pada data berukuran: 30, 60, 100 dan 200 yang diberikan kontaminasi pencilan sebesar: 5%, 10%, 15%, 20%, 25% dan 30%. Berdasarkan hasil simulasi diperoleh bahwa kedua pembobot menghasilkan nilai MSE (Mean Square Error) dan bias yang serupa. Sehingga dapat disimpulkan bahwa kedua pembobot memberikan hasil yang sesuai dan sama baiknya pada regresi S-estimator. Robust regression is a method developed to have good performance when the analyzed data deviates from the underlying assumptions, for example, there are outliers that can cause errors to be not normally distributed. One of the estimation methods in robust regression is the S-estimator, this method has weighting functions, including the Welsch and Tukey Bisquare weights. In this study, we compared the weights in the S-estimator method on data sizes: 30, 60, 100 and 200 which were given outlier contamination of: 5%, 10%, 15%, 20%, 25% and 30%. Based on the simulation results, it is found that the two weights produce similar MSE (Mean Square Error) and bias values. So it can be concluded that the two weights provide appropriate and equally good results in the S-estimator regression

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2026): JANUARY Vol. 3 No. 2 (2025): JULY Vol. 3 No. 1 (2025): JANUARY Vol. 2 No. 2 (2024): JULY Vol. 2 No. 1 (2024): JANUARY Vol. 1 No. 2 (2023): JULY Vol. 1 No. 1 (2023): JANUARY