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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 29 Documents
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Naive Bayes Classification of Bullying and Non-Bullying Comments in Instagram Social Media Posts Naflah Faulina
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5527

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. Classification is the process of assessing a data object where the object is put into a certain class from the number of classes available. An example of a classification algorithm is Naive Bayes with classification using probability and statistical methods. This algorithm is used to classify Bullying and Non-bullying with a division of training data and testing data, namely 60:40, 70:30, and 80:20 resulting in the best accuracy value for training data and testing data 60:40 of 66%.
Loglinier Model on Immunization of Baduta Juanda, Juanda
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5529

Abstract

This study aims to analyze the relationship between the type of immunization and the province of origin of children under two years of age (BADUTA) in Indonesia in 2020. This study uses a loglinear model approach to analyze DPT-HB-Hib4 and Measles/MR2 immunization data based on gender and home province. Data was obtained from the Directorate General of Disease Prevention and Control of the Indonesian Ministry of Health. Through statistical analysis using IBM SPSS Statistics 22 software, this research found that there is a relationship between the type of immunization and the child's province of origin. The chi-square test results show that there is a significant relationship between the type of immunization and the child's province of origin. Of the several loglinear models tested, the (J, VP) model is considered the best model based on Goodness-of-fit criteria. Thus, this study concludes that there is a relationship between the type of immunization and the province of origin of children under two years old in Indonesia in 2020.
Partial Derivatives of Gompertz, Logistic, and Weibull Non-Linear Growth Models on Confirmed COVID-19 Cases Utami Dewi, Wardhani; Warsono
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5641

Abstract

. The epidemiological picture of COVID-19 is still unknown, and the number of confirmed cases of COVID-19 varies every day. Researchers have studied COVID-19 a lot, and many of them have used statistical models to estimate the growth of the outbreak. Non-linear statistical models can be used to describe growth behavior, as it varies in time. The aim of this research is to analyze, compare, and find the best model from the Gompertz, Logistic, and Weibull non-linear models. Daily cumulative data on confirmed COVID-19 viruses in Indonesia for 2020-2021 will be used in this research. The results obtained by the Logistic model proved to be very effective in describing the COVID-19 epidemic curve and estimating epidemiological parameters. The Logistic Model provides the best results compared to other growth models applied by Gompertz and Weibull. The R-Square of the logistic model is 0.9990, meaning that the model is able to explain or predict 99.90% of the data and 0.10% is explained by other factors. However, this research cannot explain the turning point of the curve, because there are many factors other than the model. One of them is the nature of the virus carrier from one place to another, then the behavior of the carrier who has not fully implemented the health protocol rules.
Factor Analysis on Possum Dataset to Simplify Many Independent Variables Into Fewer Factors Larasati, Ayu; Suminto
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5649

Abstract

This research aims to apply factor analysis to possum data with the aim of simplifying many independent variables into fewer factors. The factor analysis steps begin by grouping the variables to be analyzed and compiling a correlation matrix using the Bartlett test and the Kaiser-Meyer-Olkin (KMO) test. From the test results, it was found that the variables had sufficient correlation to proceed to factor analysis. After that, factor extraction was carried out using three criteria, namely eigenvalues, diversity percentage, and scree plot, which concluded that the number of factors formed was two. Next, factor rotation was carried out using the varimax method to simplify interpretation. The results show that certain variables have high loadings on certain factors, making it easier to identify patterns. In conclusion, factor analysis succeeded in simplifying the relationship between variables into two factors that can be interpreted more easily.
Penerapan Metode Geographically Weighted Panel Regression Pada Indeks Pembangunan Manusia di Indonesia Tahun 2017-2022 Deta Erviana; Mustofa Usman; Widiarti; Khoirin Nisa
Sciencestatistics: Journal of Statistics, Probability, and Its Application Vol. 2 No. 1 (2024): JANUARY
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sciencestatistics.v2i1.5669

Abstract

Regresi linier merupakan metode statistik untuk memeriksa hubungan antara variabel respons dan satu atau lebih variabel prediktor. Dalam sebuah penelitian, satu unit observasi harus diteliti selama beberapa periode waktu, karena mempelajari satu unit dalam satu periode waktu tidaklah cukup. Oleh karena itu, sebuah pendekatan statistik yang disebut analisis regresi panel diciptakan untuk mengintegrasikan data cross-section dan data time series. Namun pada kenyataannya, perbedaan kondisi antar lokasi dipengaruhi oleh efek spasial yang menyebabkan terjadinya heterogenitas spasial. Dikembangkanlah metode Geographically Weighted Regression (GWR) untuk mengatasi masalah heterogenitas spasial. Berdasarkan kelebihan kedua metode tersebut maka berkembanglah suatu metode yang menggabungkan antara regresi data panel dan GWR yaitu Geographically Weighted Panel Regression (GWPR). Tujuan dari penelitian ini adalah untuk mengetahui faktor-faktor yang mempengaruhi indeks pembangunan manusia (IPM) di Indonesia tahun 2017-2022 dan menentukan model terbaik dengan membandingkan model regresi global dan GWPR. Model GWPR dengan pembobot adaptive bisquare merupakan model terbaik dengan nilai AIC terkecil dan R^2 terbesar. Secara keseluruhan semua variabel prediktor yang digunakan dalam penelitian berpengaruh signifikan terhadap IPM pada taraf signifikansi α=0,05. Persamaan model dan variabel yang berpengaruh signifikan yang dihasilkan dalam pemodelan GWPR berbeda untuk setiap provinsi. Berdasarkan kesamaan variabel yang mempengaruhi IPM di provinsi yang letaknya berdekatan membentuk 8 kelompok.
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

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