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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Mechatronics, Electrical Power, and Vehicular Technology Al-Jabar : Jurnal Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Elektronika dan Telekomunikasi Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Ilmiah Matematika Prosiding Konferensi Nasional PKM-CSR Prosiding University Research Colloquium Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya STATISTIKA
pratiwi, hasih
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation

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EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION Nafi'udin, Fauzi; Pratiwi, Hasih; Zukhronah, Etik
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2387-2396

Abstract

In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. With the rapid growth in the amount of image data, efficiency in data processing is becoming increasingly important. In this study, the performance of neural network models using the convolution layer and Fourier transform layer in medical image data classification was compared. The results show that models with a Fourier transform layer tend to provide higher accuracy and better Area Under Curve (AUC) compared to models using a convolution layer. In addition, the model with the Fourier transform layer also shows faster execution time per epoch, which indicates efficiency in data processing. However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. In conclusion, the Fourier transform layer has an advantage in the classification of medical image data.
TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION TO HANDLE MIXED PATTERN DATA IN MODELING THE RICE PRODUCTION IN EAST JAVA PROVINCE Handajani, Sri Sulistijowati; Pratiwi, Hasih; Respatiwulan, Respatiwulan; Susanti, Yuliana; Nirwana, Muhammad Bayu; Nareswari, Lintang Pramesti
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2597-2608

Abstract

Climate change can affect rice production through changes in temperature, precipitation patterns, extreme weather events, and atmospheric carbon dioxide levels. A statistical model can be used to understand the correlation between rice production and factors that affect it. The existence of some patterns that are formed from independent variables and others that do not show data patterns due to volatility in weather element data makes semiparametric regression modeling more appropriate. In forming a parametric model, the data pattern needs to be regular to make the model more precise. Irregular data patterns are more appropriately modeled with nonparametric regression models. The existence of several patterns formed from independent variables to their dependent variables, and several others, does not show a particular pattern due to the volatility in climate data, making truncated spline semiparametric regression modeling more appropriate to use. This research aims to model rice production in several regions in East Java Province in 2022 using a semiparametric regression model. The data used were from the Meteorology, Climatology, and Geophysics Agency and the Central Statistics Agency for East Java Province in 2022. The response variable is the rice production (tons) in 2022 in Tuban, Gresik, Nganjuk, Malang, Banyuwangi, and Pasuruan Regency (Y). The predictor variables are paddy harvested area (hectares), average temperature (℃), humidity (percent), and rainfall (mm). The semi-parametric spline truncated regression model is obtained by combining the parametric and non-parametric models based on truncated splines. The analysis showed a spline truncated semiparametric regression model with a combination of knot points (3,3,1) with a minimum GCV value of 12,642,272. The variables significantly affecting rice production were rice harvest area, temperature, air humidity, and rainfall, with an adjusted value of 98.522%.
Parameter Estimation Robust Regression Method of Moment (MM) in Cases of Maternal Death in Indonesia Pramesti, Putri Ayu; Susanti, Yuliana; Pratiwi, Hasih
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Regression analysis is used to determine the relationship between the dependent and independent variables with a parameter estimator. The parameter estimator that is usually used is the Least Squares Method (LSM), this requires a classical assumption test. Some cases have normality assumptions that are unfulfilled because there are outliers so the result regression parameter estimates are not accurate so that robust regression is used in the analysis. Robust regression is a regression analysis method that can withstand outliers. The purpose of this study is the application of robust regression estimation Method of Moment (MM) with Tukey Bisquare weighting in the case of data on the number of maternal deaths in Indonesia 2020 with the number of maternal deaths as a dependent variable, and with independent variables such as the number of pregnant women who experience bleeding, the number of diabetics in pregnancy, and the number of HIV positive in pregnancy. The result showed that every one unit increase of three independent variables had a positive effect on the number of cases of maternal deaths, each of which was 2,8064; 2,5014; 1,1577.
Peramalan curah hujan di kota bandung menggunakan singular spectrum analysis Febrianti, Tri Kartika; Sulandari, Winita; Pratiwi, Hasih
Jurnal Ilmiah Matematika Vol 8, No 2 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v0i0.21461

Abstract

Curah hujan merupakan fenomena alam yang selalu terjadi di Indonesia setiap tahunnya. Fenomena ini bisa saja menyebabkan bencana seperti banjir dan tanah longsor. Adanya peramalan sangat dibutuhkan sebagai bentuk peringatan dini mengenai kondisi di waktu yang akan datang. Singular Spectrum Analysis (SSA) merupakan suatu teknik analisis deret waktu dan peramalan. SSA bertujuan untuk menguraikan deret waktu asli menjadi sejumlah kecil komponen yang dapat diinterpretasikan menjadi tren, osilasi dan noise. Tujuan dari penelitian ini yaitu menyajikan model peramalan curah hujan di Kota Bandung menggunakan metode Singular Spectrum Analysis (SSA). Berdasarkan penelitian ini, diketahui bahwa data curah hujan di Kota Bandung memiliki pola musiman. Penentuan window length (L) dilakukan dengan trial and error, yang dalam kasus ini diperoleh window length 17. Melalui dekomposisi dan rekonstruksi dengan window length 17 diperoleh 4 pengelompokan, yaitu satu kelompok tren dan tiga kelompok musiman. Pada penelitian ini digunakan RMSE untuk mengukur kesalahan hasil peramalan. Berdasarkan hasil pengujian dengan metode Singular Spectrum Analysis (SSA) diperoleh RMSE sebesar 167,510.
LITERASI STATISTIK DAN IMPLEMENTASINYA PADA OPTIMALISASI MICROSOFT EXCEL DALAM PEMBUATAN DAN PENGELOLAAN BANK SOAL WIJAYA, ANDREAS RONY; Nirwana, Muhammad Bayu; Ferawati, Kiki; Pratiwi, Hasih; Respatiwulan, Respatiwulan; Handayani, Sri Sulistijowati; Susanti, Yuliana; Agustin, Frencilia Paulina; Hartanto, Aqila Khansa; Wardati, Bilqies Syafina
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.
Co-Authors Ade Ramdan Agustin, Frencilia Paulina Ahmad Afif Supianto Andreas Rony Wijaya Apsari, Nindya Eka Budiyono Budiyono Dewi, Arnetta Listiana Dewi, Margareta Aprilia Kusuma Evitasari, Niken Evitasi, Niken Febrianti, Tri Kartika Handajani, Sri Sulistijowati Handayani, Sri Sulistijowati Hartanto, Aqila Khansa Heryana, Ana Kadar, Jimmy Abdel Kiki Ferawati Kumarahadi, Brigitta Melati Kusumo, Budiarianto Suryo Mahani, Ibud Mahmudah, Arik mutiah, siti Nafi'udin, Fauzi Nareswari, Lintang Pramesti Nirwana, Muhammad Bayu Pardede, Hilman F. Pardede, Hilman Ferdinandus Putri Ayu Pramesti, Putri Ayu Qona’ah, Niswatul Ramadhania, Monica Respatiwulan, Respatiwulan Rifa, Isna Hidayatur Sri Subanti Suryawati, Endang Wardati, Bilqies Syafina Wati, Rosita Kurnia Winita Sulandari Yuliana Susanti, Yuliana Yuliani, Asri R Yuwana, Raden Sandra Zilfan, Vicky Zukhronah, Etik