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All Journal International Journal of Advances in Applied Sciences PROSIDING SEMINAR NASIONAL Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Statistika Universitas Muhammadiyah Semarang Bioeksperimen: Jurnal Penelitian Biologi BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) SEMAR Komatika: Jurnal Pengabdian Kepada Masyarakat Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Konferensi Nasional PKM-CSR Urecol Journal. Part D: Applied Sciences Proceeding of International Conference on Humanity Education and Society Radiant : Journal of Applied, Social, and Education Studies
Muhammad Bayu Nirwana
Department Of Statistics, Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Mardia’s Skewness and Kurtosis for Assessing Normality Assumption in Multivariate Regression Wulandari, Dewi; Sutrisno, Sutrisno; Nirwana, Muhammad Bayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 1, April 2021
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.901 KB) | DOI: 10.20885/enthusiastic.vol1.iss1.art1

Abstract

In Multivariate regression, we need to assess normality assumption simultaneously, not univariately. Univariate normal distribution does not guarantee the occurrence of multivariate normal distribution [1]. So we need to extend the assessment of univariate normal distribution into multivariate methods. One extended method is skewness and kurtosis as proposed by Mardia [2]. In this paper, we introduce the method, present the procedure of this method, and show how to examine normality assumption in multivariate regression study case using this method and expose the use of statistics software to help us in numerical calculation. Received February 20, 2021Revised March 8, 2021Accepted March 10, 2021
Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature Nirwana, Muhammad Bayu; Wulandari, Dewi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 1 Issue 2, October 2021
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.417 KB) | DOI: 10.20885/enthusiastic.vol1.iss2.art3

Abstract

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021
Pelatihan Manajemen dan Visualisasi Data Menggunakan Excel untuk Guru Matematika SMP di Kabupaten Karanganyar: Data Management and Visualization Training using Excel for Junior High School Mathematics Teacher in Karanganyar Regency Muhammad Bayu Nirwana; Hasih Pratiwi; Yuliana Susanti; Respatiwulan Respatiwulan; Sri Sulistijowati Handayani; Andreas Rony Wijaya; Alfito Putra Fajar Pratama; Kiki Ferawati
Komatika: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2024): November 2024
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat, Institut Informatika Indonesia Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/komatika.v4i2.1023

Abstract

Literasi statistik merupakan kemampuan untuk memahami beragam informasi statistik yang dimunculkan di berbagai media. Kemampuan ini meliputi keterampilan dalam menginterpretasikan grafik dan tabel, serta mampu membaca dan memahami statistik dalam berita, media, jajak pendapat, dan lain-lain. Kabupaten Karanganyar merupakan salah satu kabupaten di Provinsi Jawa Tengah yang berbatasan dengan Kota Surakarta dan termasuk sebagai wilayah Karesidenan Surakarta. Pengetahuan mengenai literasi statistik dan implementasinya di wilayah Kabupaten Karanganyar merupakan hal yang penting untuk disampaikan kepada masyarakat, karena berkaitan langsung dengan pemahaman mengenai informasi data statistika dan bagaimana merepresentasikannya. Sebagai ilmu yang mempelajari tentang cara pengumpulan, analisis, dan pengambilan keputusan dari data, pengetahuan tentang statistika merupakan ilmu penunjang yang penting untuk dimiliki oleh masyarakat. Sebagai sasaran peningkatan literasi statistika kali ini Grup Riset Statistika dan Sains Data Bidang Lingkungan dan Kesehatan Program Studi Statistika FMIPA UNS akan melaksanakan pengabdian masyarakat dengan bentuk pelatihan untuk guru dan siswa SMP di Kabupaten Karanganyar melalui forum Musyawarah Guru Mata Pelajaran (MGMP) Matematika. Literasi statistik memerlukan pengetahuan tentang analisis dan visualisasi data yang diberikan untuk meningkatkan pemahaman terkait penerapan metode statistika dengan menggunakan Excel yang sudah banyak dikenal oleh masyarakat.
A Comparative Study of PCA-Based Dimensionality Reduction and Best Subset Selection in Disease Classification Andreas Rony Wijaya; Atika Ratna Dewi; Muhammad Bayu Nirwana; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38265

Abstract

Real-world datasets often contain many variables, some of which may be irrelevant or redundant. To build an effective classification model, it is important to simplify the data by keeping only the most influential features. One common approach that can be used for selecting the most influential variables is feature selection. However, when dealing with many variables, removing some may result in the loss of information. Hence, it is also necessary to consider methods that can simplify the model while retaining most of the information from the original variables. Dimensionality reduction is one such approach that effectively addresses this issue. This study employs a comparative quantitative research approach to evaluate the effectiveness of principal component analysis (PCA) as a dimensionality reduction method and best subset selection as a feature selection method in improving classification performance. The study utilizes a heart disease dataset from the UCI Machine Learning Repository consisting of 303 observations and 13 predictor variables as a case study. Both approaches are applied to reduce the number of predictor variables and make the model more interpretable. After applying both methods, three classification models — logistic regression, naïve Bayes, and linear discriminant analysis — are trained and evaluated using accuracy, recall, precision, and F1-score, and the results are further illustrated through ROC curves. Feature selection using best-subset selection yields seven variable combinations with the most significant predictors, whereas PCA requires eight principal components to explain 80% of the total variation.  The best classification performance was obtained using the feature-selected dataset, achieving an accuracy of 87% and an AUC of 0.93, outperforming both the original dataset model and the PCA-reduced dataset model. These results show that feature selection using best subset selection provides a better balance between simplicity and classification performance. Furthermore, the models obtained after feature reduction, both from best subset selection and PCA, still maintain good predictive ability as indicated by their relatively high AUC values.
Application of Proportional Hazard and Additive Models in the Survival Analysis of Breast Cancer Patients Muhammad Bayu Nirwana; Tiara Fitri Adani; Kayla Argya Puruhita; Andreas Rony Wijaya; Hasih Pratiwi; Silvina Rosita Yulianti
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.37028

Abstract

Breast cancer is the most common type of cancer among women and one of the highest causes of death among other types of cancer. This study aims to evaluate the methodological advantages of additive hazard models over the multiplicative Cox model in identifying temporal risk factors for breast cancer survival. Using secondary data from 1458 patients and 10 covariates, applying three methods, Cox proportional hazards model, Lin-Ying additive hazard model, and Aalen additive hazard model. The proportional hazard assumption test indicated that Cox regression model did not fully satisfy the assumption; therefore, the Lin–Ying and Aalen additive models were applied. In the Lin–Ying models, hormonal therapy, radiotherapy, the Nottingham Prognostic Index (NPI), and tumor size were identified as significant predictors of survival, whereas in the Aalen model, significant factors also included age and chemotherapy in addition to those four covariates. These findings highlight that while the Cox model provides efficient estimation and interpretable hazard ratios, the Lin–Ying and Aalen models offer more robust alternatives when the proportional hazard assumption is violated. The Aalen model was selected based on the results of the Aalen plot. Overall, risk control efforts in breast cancer patients should focus on managing NPI scores and tumor size as well as ensuring appropriate therapies, particularly hormonal therapy and radiotherapy, which have been demonstrated to provide protective effects.
Markov-switching and noise-to-signal ratio approach for early detection of currency crises Sugiyanto Sugiyanto; Muhammad Bayu Nirwana; Isnandar Slamet; Etik Zukhronah; Syifa’ Salsabila Gita Parahita
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp42-54

Abstract

Economic instability can easily lead to a currency crisis. Therefore, observing a number of crisis indicators is crucial for building an early warning system (EWS). However, selecting the indicators most responsive to the crisis is the best choice. For this purpose, the noise-to-signal ratio (NSR) method was used. Monthly data from 1990-1925 were used in the autoregressive moving average (ARMA), generalized autoregressive moving average with generalized autoregressive conditional heteroscedasticity (GARMACH), and Markov-switching (MS)-GARMACH hybrid models to explain the crisis. Model interpretation indicates that there will be no crisis from May 2025-April 2026.
LITERASI STATISTIK DAN IMPLEMENTASINYA PADA OPTIMALISASI MICROSOFT EXCEL DALAM PEMBUATAN DAN PENGELOLAAN BANK SOAL ANDREAS RONY WIJAYA; Muhammad Bayu Nirwana; Kiki Ferawati; Hasih Pratiwi; Respatiwulan Respatiwulan; Sri Sulistijowati Handayani; Yuliana Susanti; Frencilia Paulina Agustin; Aqila Khansa Hartanto; Bilqies Syafina Wardati
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 8 (2025): Penguatan Ekonomi Masyarakat Berbasis Ekologis untuk Mencapai Keberlanjutan Menuju Ind
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v8i0.2733

Abstract

In today’s digital era, the integration of technology in education has become increasingly important. One key aspect that can be enhanced through technology is its application in teaching and learning activities. The use of technology enables the learning process to become more efficient. One such technology that can be applied is Microsoft Excel. Traditionally, Excel has been widely recognized as data processing software. In Karanganyar Regency, particularly within the Mathematics Teachers’ Working Group Forum (MGMP Matematika), teachers already possess basic skills in using Excel. Strengthening statistical literacy through the use of Excel among teachers is expected to improve the quality of teaching, enabling them to better understand, analyze, and present data. Until now, in the preparation and development of question banks, many teachers have created exam questions manually without taking advantage of technological tools. In fact, Excel provides features that can assist teachers in designing question banks in a more systematic, efficient, and organized manner. These features include the creation, categorization, and analysis of questions. As a discipline focused on data collection, analysis, and data-driven decision-making, statistics plays a vital role in enhancing society’s understanding of quantitative information. To promote statistical literacy, the Statistics and Data Science Research Group in the Field of Environment and Health, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Sebelas Maret, organized a community service program in the form of training for junior high school teachers in Karanganyar through the MGMP Matematika forum. The training aimed to optimize the use of Microsoft Excel in developing question banks, enabling teachers to design and manage questions more systematically and efficiently. Through this training, teachers are expected to apply data-based techniques in test item development to improve the effectiveness of learning evaluation.
Pelatihan Penggunaan Excel untuk Evaluasi Pembelajaran bagi Guru Matematika SMP di Kabupaten Karanganyar Respatiwulan Respatiwulan; Kiki Ferawati; Muhammad Bayu Nirwana; Andreas Rony Wijaya; Elvin Agustiyan Nugroho; Aprilia Saniatul Rahmawati
SEMAR (Jurnal Ilmu Pengetahuan, Teknologi, dan Seni bagi Masyarakat) Vol 14, No 2 (2025): November
Publisher : LPPM UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/semar.v14i2.110468

Abstract

Seiring dengan perkembangan teknologi, kebutuhan peneglolaan data dengan cepat dan akurat sangat penting termasuk dalam dunia pendidikan. Banyak perangkat lunak yang dapat digunakan untuk mengelola data termasuk Microsoft Excel. Microsoft Excel adalah program spreadsheet yang dikembangkan oleh Microsoft. Microsoft Excel digunakan untuk perhitungan sederhana, penyimpanan data, mengolah data dalam bentuk tabel, perhitungan matematis, analisis statistik, pembuatan grafik, dan berbagai keperluan administratif lainnya. Bagi guru matematika, Excel adalah alat yang sangat powerful untuk membantu dalam berbagai aspek pengelolaan data, dari mencatat nilai, mengolah statistik, hingga merancang laporan dan perencanaan pembelajaran. Menguasai Excel dapat sangat meningkatkan efisiensi kerja dan kualitas pengajaran. Forum Musyawarah Guru Mata Pelajaran (MGMP) Matematika SMP di Kabupaten Karanganyar berfungsi sebagai tempat bagi guru-guru Matematika untuk saling berbagi pengetahuan dan meningkatkan kompetensi dalam proses pembelajaran. Para guru di forum MGMP Matematika  telah memiliki keterampilan dasar penggunaan Excel. Oleh karena itu pelatihan analisis statistika menggunakan Excel di kalangan guru MGMP Matematika Karanganyar dapat meningkatkan kualitas pembelajaran  sehingga lebih baik dalam memahami, menganalisis, dan menyajikan data. Peningkatan kemampuan analisis statistik ini dapat dilakukan dengan memberikan pelatihan yang mencakup pengenalan data, persiapan data, visualisasi data, analisis dan interpretasi hasil. Pelatihan ini memberikan ruang konsultasi setelah kegiatan sehingga peserta tetap dapat pendampingan belajar dan meningkatkan kemampuannya. Menggunakan Excel untuk mengelola soal dan hasil evaluasi pembelajaran adalah cara yang efisien bagi guru untuk menyajikan hasil belajar untuk keperluan evaluasi hasil pembelajaran.
Co-Authors Alfito Putra Fajar Pratama Andreas Rony Wijaya Andreas Rony Wijaya Andreas Rony Wijaya Andreas Rony Wijaya Anggraira, Attilah Suci Aprilia Saniatul Rahmawati Aqila Khansa Hartanto Atika Ratna Dewi Attilah Suci Anggraira Bilqies Syafina Wardati Dewi Wulandari Dewi Wulandari Elvin Agustiyan Nugroho Frencilia Paulina Agustin Handajani Handajani Handajani, Sri Sulistijowati Harmajati, Firdaus Hasih Pratiwi Hasih Pratiwi hasih pratiwi Hilyatul Hilwy, Sarah Isnandar Slamet Kayla Argya Puruhita Kiki Ferawati Mahmudah, Arik Nareswari, Lintang Pramesti NISWATUL QONA’AH Niswatul Qona’ah pratiwi, hasih Pratiwi, Hasih Respatiwulan Respatiwulan Respatiwulan Respatiwulan Respatiwulan Respatiwulan Respatiwulan Respatiwulan Respatiwulan, Respatiwulan Sri Subanti Sri Sulistijowati Handajani Sri Sulistijowati Handajani Sri Sulistijowati Handayani Sri Sulistijowati Handayani SUGIYANTO Sugiyanto Sugiyanto Sutrisno Sutrisno Syifa’ Salsabila Gita Parahita Tiara Fitri Adani Umi Farida Yuliana Susanti Yuliana Susanti Yuliana Susanti Yuliana Susanti, Yuliana Yulianti, Silvina Rosita Zaenal Fanani Zukhronah, Etik