Radiasi: Jurnal Berkala Pendidikan Fisika
Vol. 18 No. 2 (2025) pp. 93 - 102
http://jurnal.umpwr.ac.id/index.php/radiasi/index
p-ISSN: 2302-6111 e-ISSN: 2549-0826

Analysis of Rainfall Patterns in Papua Using The Empirical Orthogonal
Function
1

Amelia Putri Sayyendra

, 1Melly Ariska, 1Hamdi Akhsan

Universitas Sriwijaya
Jl. Raya Palembang - Prabumulih Km. 32 Indralaya, Ogan Ilir, 30662, Indonesia

|mellyariska@fkip.unsri.ac.id

| DOI: https://doi.org/10.37729/radiasi.v18i2.6197 |

Abstract
Indonesia, as a tropical maritime country, experiences dry and rainy seasons influenced by
geographical location, topography, winds, and climate phenomena such as ENSO and IOD.
Although extensive research on rainfall patterns in Indonesia exists, specific studies on Papua remain
limited. This gap leads to insufficient understanding of Papua's rainfall characteristics and
dynamics, including dominant patterns and variability factors. This study aims to analyze rainfall
patterns in Papua using the Empirical Orthogonal Function (EOF) method to identify dominant
patterns and key modes of variability systematically. The EOF method is applied to extract dominant
patterns from complex datasets, enabling spatial and temporal analysis of rainfall variability in the
region. Analyzing rainfall patterns in Papua (1981–2015) using EOF combined with Machine
Learning reveals that average rainfall intensity in Papua ranges from 0–9 mm per month. The EOF
analysis identifies three principal modes explaining 62.53 percent, 11.39 percent, and 7.01 percent
of total variance, respectively. EOF1 indicates dominant positive anomalies, while EOF2 and EOF3
show mixed positive and negative anomalies. Furthermore, rainfall variability in Papua is
significantly influenced by interactions between the Indian Ocean Dipole (IOD) and El NiñoSouthern Oscillation (ENSO), where positive IOD and El Niño phases contribute to dry conditions
and reduced rainfall, while negative IOD and La Niña phases lead to wet conditions and increased
rainfall.

Article Info:
Recieved:
05/03/2025
Revised:
05/07/2025
Accepted:
25/08/2025

Keywords: Rainfall, Empirical orthogonal function, EOF, Papua

1. Introduction
One of the weather components that most affects human activities is rainfall [1]. Indonesia, as an
archipelago flanked by two continents and two large oceans, has a tropical climate with two main
seasons: dry and rainy. Rainfall patterns in Indonesia are not only influenced by the monsoon; there
are also local and equatorial rainfall patterns [2][3]. Changes in the average weather patterns on earth
or in a particular region over the long term are called climate change. Studying local climate, it is
important to understand how local atmospheric conditions and the overall climate system affect it [4].
Climate change can result in erratic rainfall patterns, with some regions experiencing extreme rainfall
and flooding, while others experience prolonged drought [5]. These natural disasters have the potential
to destroy homes, infrastructure, and cause loss of life [6]. As a result, it is imperative to take action to
reduce the impact of these events and prepare for whatever may happen.
Indonesia has conducted numerous studies on rainfall analysis, such as the research by Setiawan,
Dedi in 2021, which states that the regions of Papua and West Papua are areas with the highest levels
of rainfall [7].
93

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

The research by Tulak, Noper et al. [8] on daily rainfall patterns in Jayapura City (2001-2018) utilized
MATLAB and Excel. The objective was to identify cycles, trends, and extreme rainfall events, as well
as to understand changes in rainfall patterns in this region. The findings indicate that the increasing
trend of rainfall is accompanied by occurrences of extreme rainfall at the beginning and end of the year,
with a frequency of 1 to 5 times per year. The research conducted by Edowati, Desi N in 2023 [9] in
Nabire Regency, Central Papua Province, reveals that the results of the linear regression analysis on
rainfall and temperature indicate annual changes. Rainfall has decreased by 0.782 mm per year, while
temperature has increased by 0.1436 °C each year. Overall, the intensity of rainfall and temperature
tends to change annually, contributing to climate change. Based on several studies, each region has
different levels of rainfall. High and extreme rainfall often occurs in certain areas, which can lead to
hydrometeorological disasters in Papua. Therefore, research on rainfall is essential to understand the
climatic conditions in this region.
Papua is located between latitudes 0° 20′ and 10° 42′ N, and longitudes 130° and 151° E. Papua is
a tropical and island region with an average rainfall of 2,015.8 mm, with rainfall between 1,500 and
7,500 mm per year. The north coast and central mountains receive the highest rainfall, while the south
coast receives the lowest. With increasing altitude, temperatures vary. Rainfall levels on the island of
Papua are highly variable and different. Therefore, it is very important to always check the latest
weather forecasts.
Indonesia has conducted many studies on rainfall pattern analysis. However, no one has
examined and analyzed rainfall patterns specifically on the island of Papua. This research is important
because the Papua region has unique and complex rainfall characteristics, yet there is still a lack of indepth studies related to rainfall patterns. The absence of comprehensive information leads to a limited
understanding of the spatial and temporal variability of rainfall, which is crucial for mitigating
hydrometeorological disasters such as floods and landslides, as well as for climate-based development
planning. Furthermore, the available rainfall data is often limited in scope due to an inadequate number
of observation stations, necessitating new approaches such as the utilization of modern analytical
methods. Therefore, based on the explanation above, the author is interested in conducting research on
"Analysis of Rainfall Patterns in Papua Island Using the Empirical Orthogonal Function (EOF)
Method." The Empirical Orthogonal Function (EOF) method is applied as a multivariate analysis
technique aimed at identifying and extracting dominant patterns that explain the main variability in
complex datasets. Thus, the application of the EOF method is expected to provide a deeper and more
thorough understanding of the characteristics and dynamics of complex rainfall patterns in the Papua
region, as well as identify key modes of variation influencing rainfall distribution in this area. The
expected benefits of this research include identifying areas with high, moderate, and low rainfall
patterns on Papua Island. This study not only identifies rainfall patterns but also analyzes their spatial
and temporal variability, providing new insights into how global climate factors such as ENSO and
IOD influence rainfall in Papua. The availability of updated data and information on rainfall patterns
can serve as a reference for the people and the government of Papua Province in developing
commodities that align with the current climatic conditions of Papua Island.

2. Methods
This study employs a descriptive quantitative approach, chosen for its emphasis on numerical
data derived from the research location. The focus is on analyzing rainfall patterns in Papua over a
span of 408 months, covering the period from January 1981 to December 2015. Rainfall data utilized in
the research was sourced from the website https://sacad.bmkg.go.id/ID/ . The data in this study uses
NeTCDF format.

94

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

2.1 Empirical Orthogonal Function (EOF)
The Empirical Orthogonal Function (EOF) method is a time series data analysis technique used
to separate the signal into two components, the spatial function and the time function. The aim is to
identify the dominant spatial patterns in the time series data and sort these patterns by their variance,
from largest to smallest [10]. EOF is utilized to identify the primary weather patterns in rainfall data by
employing singular value decomposition (SVD) to reduce the data's dimensionality. Before applying
principal component analysis (PCA) or empirical orthogonal function (EOF), a crucial initial step is to
standardize the data, especially if the variables being analyzed have heterogeneous units or ranges of
values.

𝑍=

𝑋−𝜇
𝜎

In this case, X is the original data matrix, µ is the mean vector of each variable, and σ is the standard
deviation of each variable used for scaling in data standardization. After standardizing the data, the
next step is to calculate the covariance matrix of the standardized data. This covariance matrix measures
the shared variation between variables, providing information on how changes in a variable correlate
with other variables.

𝐶=

1
𝑍𝑇 𝑍
𝑁−1

In this formulation, C represents the covariance matrix, Z represents the data that has undergone the
standardization process, and N indicates the cardinality of the observation set.
The eigen decomposition of the covariance matrix yields the eigenvalue and eigenvector. The
eigenvalue indicates the amount of variance explained by each principal component (PC), while the
eigenvector determines the direction of that component in the original feature space.

𝐶𝑣 = 𝜆𝑣
In eigenanalysis, λ (eigenvalue) indicates the amount of variance explained by each principal
component, and v (eigenvector) defines the direction or pattern of variation captured by the principal
component in the original data. The overall variance accounted for by all principal components is equal
to the total of their eigenvalues.
𝑛

𝑉𝑎𝑟𝑖𝑎𝑠𝑖 𝑇𝑜𝑡𝑎𝑙 = ∑ 𝜆𝑖
𝑖=1

The fraction of variance accounted for by each principal component (PC) can be quantified as a
percentage of the total variance of the dataset.

𝑉𝑎𝑟𝑖𝑎𝑛𝑠𝑖 𝑃𝐶1 =

𝜆𝑖
𝑋 100%
𝑛
∑𝑖=1 𝜆𝑖

2.2 Analisis Komposit
Composite analysis is an easy and efficient method for recognizing conditions that occur under
particular climatic circumstances. Composite Analysis can show the relationship between a
phenomenon and key areas around it [11]. ENSO (El Niño-Southern Oscillation) and IOD (Indian
Ocean Dipole) composite analysis is a statistical technique used to identify rainfall patterns across
different areas of the globe, related to the ENSO and IOD phenomena.

95

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

3. Results and Discussion
This research goes in-depth on rainfall patterns in the Papua region by relying on official rainfall
data from SACA&D accessed through the website https://sacad.bmkg.go.id/id/. The main focus of this
study is to understand how the distribution of rainfall varies across the Papua region within a given
time span, as well as to identify possible seasonal trends or patterns. The study area is bounded by 0°
to 10° south latitude and 130° to 151° east longitude. This study takes into account the typical
geographical and climatic conditions of Papua. Monthly rainfall data collected from the Southeast Asia
Climate Assessment and Dataset Store (SACA&D) website for a period of 35 years, between 1981-2015,
has been carefully selected to provide a comprehensive picture of rainfall fluctuations over a long
period of time.
EOF is used to identify the spatial pattern of rainfall in Papua after the data is cleaned from NaN
and missing values are filled using singular value decomposition. The results are visualized as annual
rainfall distribution maps. The analysis results are then visualized in the form of rainfall distribution
maps (Figure 1).
3.1 Mean of Precipitation

Figure 1. Average Rainfall on Papua Island
Based on the BMKG classification, rainfall intensity in Indonesia is categorized into four groups as
shown in Table 1 (Source: https://www.bmkg.go.id/)
Table 1. Categorization of rainfall intensity in Indonesia
Rainfall Intensity (mm/month)

Criteria

0-100
100-300
300-500
>500

Low
Intermediate
High
Very High

Figure 1 shows significant spatial variations. The rainfall distribution map uses a color scale to illustrate
the spatial variation of rainfall, with dark blue indicating high rainfall and dark red indicating low
rainfall. The analysis shows that most areas in Papua experience intermediate rainfall, with Mimika
Regency in Central Papua Province identified as the region with the highest monthly rainfall based on
the data displayed on the map. This result aligns with previous research conducted by Faisol and his
team in 2022, which stated that the average monthly rainfall in West Papua is more than 100 mm [12].

96

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

Then research conducted by Presli and the team in 2018 showed that the average rainfall in Papua was
120-200 mm [13]. Both studies show that Papua experiences medium levels of rainfall, which can be
seen Table 1 on the classification of rainfall intensity in Indonesia.
3.2 Std of Precipitation

Figure 2. Standard Deviation of Rainfall in Papua Island
The visualization in Figure 2 uses a color scale to show the level of uncertainty of rainfall in
Papua. The standard deviation in the Papua region is classified as low to medium level with a range of
values of 8-1 mm/month. Mampi district in South Papua and Mimika district in Central Papua show
the highest level of rainfall uncertainty. Dark blue color indicates very high rainfall variability, which
is mainly found in Mimika district in Central Papua and Mampi district in South Papua, indicating
more extreme rainfall fluctuations in these two regions. The standard deviation of rainfall in Papua
shows a heterogeneous spatial distribution. Some areas have much higher levels of rainfall variability,
indicating more extreme rainfall fluctuations over time than others.
3.3 Monthly Climatology

Figure 3. Monthly Climatological Rainfall in Papua Island

97

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

Figure 3 shows that most areas in Papua experience relatively low rainfall throughout the year,
especially in the June to September period which is the dry season. However, there are exceptions in
Merauke Regency which records high rainfall in December and January.
3.4 Identification of Rainfall Patterns Using the EOF Method
Principal Component Analysis (PCA) was applied to the rainfall data matrix in mainland Papua
to simplify the complex data, identify dominant geographic and seasonal patterns, and find patterns
that explain most of the rainfall variability. Marine rainfall data were excluded. PCA modes with higher
variance have a greater contribution to the total variability of the data. Dominant variance of SACA&D
data described in Figure 4.

Figure 4. Dominant Variance of SACA&D Data
Figure 4 shows that the total variance is 73.93%. A larger PCA mode variance value indicates a greater

contribution to the observed event. As shown by BMKG's SACA&D data, PCA components with high
mode variance values have a significant impact on explaining the variation in the data.
Figure 4 (a) shows the first principal mode (PC1) in the data analysis, i.e. its spatial and temporal
patterns, and its contribution to the data variability. The variance of the first EOF mode, determined
using the first 15 principal components, shows that this mode explains 62.53% of the data variability,
making it the most dominant mode. The EOF 1 analysis shows that almost the entire Papua region
experiences a positive anomaly pattern with a range of values between 1.029-8.037 mm. Figure 4 (b)
(PC2) in the data analysis has a distinctive spatial and temporal pattern. The variance value of the
second EOF mode shows a contribution of 11.39%. EOF2 analysis indicates that Papua dominantly
experiences negative and positive anomaly patterns. The second principal component (PC2) of areas
experiencing negative anomaly patterns are Papua, South Papua and Papua Mountains with a range
between -0.93 to -7.56 mm. Then areas with positive anomaly patterns occur in Southwest Papua
Province, West Papua, parts of Papua and Central Papua with a range from 0.72 to 9.00 mm. Figure 4
(c) (PC3) shows that the PC3 mode explains 7.01% of the data variability. EOF3 indicates that Papua
dominantly experiences positive and negative anomaly patterns. Areas with positive anomaly patterns
occur in Papua, Papua Mountains, Central Papua, parts of South Papua and West Papua with a range
of 0.67-6.87 mm. Then areas with negative anomaly patterns occur in Southwest Papua, parts of South
Papua and West Papua with a range of -0.88 to -8.62 mm.

98

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

(a)

(b)

(c)
Figure 5. Spatial Plots of the First, Second and third PCA Modes and
Temporal Plots of the PCA Modes
Three main orthogonal modes (EOF1, EOF2, EOF3) account for the majority of the temporal variability
of the data (80.93% of the variance of rainfall in Papua), with EOF1 being the most dominant. The
unexplained variability (11.47%) is assumed to come from other variables that have little impact
individually.
The EOF research in Papua using Google Colab identified three EOF modes, with the first mode
explaining the largest variance, in line with previous research showing a decrease in variance
contribution in subsequent modes. Such as research from Ariska, et al which resulted in three main
modes, which explained 38.57% (first mode), 13.92% (second mode), and 8.93% (third mode) of the total
variance with the first mode being the most dominant mode [14].
3.5 Analisis Komposit
This research examines the effects of the El Niño-Southern Oscillation (ENSO) and the Indian
Ocean Dipole (IOD), phenomena on climate and rainfall in Papua for 35 years (1981-2015), especially
in the ASO and NDJ seasons. During El Niño, Papua experiences a significant decrease in rainfall in
the ASO season (-3.7 mm in almost all areas), but this decrease is reduced in the NDJ season (-1.2 mm).
In contrast, during La Niña, there is an increase in rainfall in almost all affected areas.

99

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

In the ASO season, rainfall increases almost all over Papua (3.0 mm), in the NDJ season, the increase in
rainfall in Papua is less than in the ASO season (1.4 mm). Based on the results of the composite analysis,
the classification of the influence of El Niño and La Niña phenomena on climatic conditions in Papua
can be seen clearly in the table presented below (Table 2).
Table 2. Classification of the Influence of El- Nino and La Nina Phenomena on the Decrease and
Increase of Rainfall in Papua
Climate
Anomalies

Region

ASO Season
Papua Barat Daya,
Papua Barat, Papua,
El Niño
Papua Tengah, Papua
(Decreased rainfall)
Pegunungan, Papua
Selatan
Papua Barat Daya,
La Niña
Papua Barat, Papua
(Increased rainfall) Selatan, Papua, Papua
Pegunungan

NDJ Season
Papua Barat Daya,
Papua Barat, Papua,
papua Tengah, Papua
Selatan
Papua Barat Daya,
Papua Barat, Papua
Tengah, Papua Selatan

During the positive IOD phase, most of Papua experiences decreased rainfall (negative anomalies)
during the ASO and NDJ seasons, with the most pronounced impact seen in the drought-inducing ASO
season (-2.6 mm). In contrast, during the negative IOD phase, most of Papua experienced increased
rainfall (positive anomalies) during the ASO and NDJ seasons. In the ASO season, the increase in
rainfall reached 4.0 mm. During the NDJ season, the increase in rainfall decreases to 1.7 mm but more
areas experience rainfall (Table 3).
Table 3. Classification of The Influence of Positive and Negative IOD Phenomena on The Decrease
And Increase of Rainfall in Papua
Region
Climate
Anomalies
ASO Season
NDJ Season
IOD Positif
Papua Barat Daya,
Papua Barat Daya,
(Decreased rainfall) Papua Barat, Papua
Papua Barat, Papua
Tengah, Papua
Tengah, Papua, Papua
Pegunungan, Papua
Selatan
Selatan
IOD Negatif
Papua Barat Daya,
Papua Barat, Papua
(Increased rainfall) Papua Barat, Papua
Tengah, Papua
Tengah, Papua Selatan
Pegunungan, Papua
Selatan
Many studies have investigated the impact of ENSO and IOD on rainfall in Indonesia, for example
Anggraini et al. in 2024 [15] found that El Niño and IOD are positively correlated with dry conditions
in Nusa Tenggara, while La Niña and IOD are negatively correlated with wet conditions. The results
of this study show that La Niña and the negative phase of the Indian Ocean Dipole (IOD) contribute to
an increase in rainfall intensity in different parts of Indonesia. In contrast, El Niño and the positive
phase of the IOD are associated with a decrease in rainfall in Indonesia.

100

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

4. Conclusion
The analyzed rainfall averages show that the Papua region as a whole experiences medium
intensity rainfall, which ranges from 0-9 mm per month. Analysis using the Empirical Orthogonal
Function (EOF) method reveals three main patterns that contribute to this rainfall variation. These
modes represent different spatial and temporal patterns of rainfall anomalies. EOF1 indicates that
Papua predominantly experiences positive anomalies, EOF2 and EOF3 indicate that Papua
predominantly experiences negative and positive anomalies. The EOF mode identifies three main
modes that explain 62.53%, 11.39% and 7.01% of the total variance, respectively. Composite analysis
shows that rainfall variability in Papua is strongly influenced by the interaction of IOD and ENSO.
Positive IOD and El Niño trigger dry conditions, while negative IOD and La Niña cause wet conditions.

References
[1]

[2]

[3]

[4]

[5]
[6]
[7]

[8]
[9]

[10]

[11]

M. Ariska, H. Akhsan, and M. Muslim, “Impact Profile of Enso and Dipole Mode on Rainfall As
Anticipation of Hydrometeorological Disasters in the Province of South Sumatra,” Spektra J. Fis.
dan Apl., vol. 7, no. 3, pp. 127–140, 2022, doi: 10.21009/spektra.073.02.
M. Ariska, H. Akhsan, M. Muslim, Sudirman, and Kistiono, “Pengaruh El Niño Southern
Oscillation (ENSO) dan Indian Ocean Dipole (IOD) Terhadap Curah Hujan dan Korelasinya
dengan Consecutive Dry Days (CDD) Provinsi Sumatera Selatan dari Tahun 1981-2020,” JIFP
(Jurnal Ilmu Fis. dan Pembelajarannya), vol. 6, no. 2, pp. 31–41, 2022, [Online]. Available:
http://jurnal.radenfatah.ac.id/index.php/jifp/
S. Nurdiati, E. Khatizah, M. K. Najib, and R. R. Hidayah, “Analysis of rainfall patterns in
Kalimantan using fast fourier transform (FFT) and empirical orthogonal function (EOF),” IOP
Conf. Ser. Earth Environ. Sci., vol. 1796, no. 1, 2021, doi: 10.1088/1742-6596/1796/1/012053.
S. Ainurrohmah and S. Sudarti, “Analisis Perubahan Iklim dan Global Warming yang Terjadi
sebagai Fase Kritis,” J. Phi J. Pendidik. Fis. dan Fis. Terap., vol. 3, no. 3, p. 1, 2022, doi: 10.22373/pjpft.v3i3.13359.
A. Hidayat, “Dampak Perubahan Iklim Terhadap Pertanian Dan Strategi Adaptasi Yang
Diterapkan Oleh Petani (2),” Univ. Medan Area, pp. 1–11, 2023.
Faturohman, L. Hidayanto, and M. Fahruroji, “Analisis Dampak Perubahan Iklim Terhadap Hak
Pada Manusia,” vol. 1, no. 3, 2024.
D. Setiawan, “Analisis Curah Hujan di Indonesia untuk Memetakan Daerah Potensi Banjir dan
Tanah Longsor dengan Metode Cluster Fuzzy C-Means dan Singular Value Decompotition
(SVD),” Eng. Math. Comput. Sci. J., vol. 3, no. 3, pp. 115–120, 2021, doi:
10.21512/emacsjournal.v3i3.7428.
N. Tulak and Y. B. dan H. Huda, “Periodicity and Trend Analysis of Rainfall in Jayapura City,
Papua in the Period 2001-2018,” J. Meteorol. dan Geofis., vol. 23, no. 1, pp. 47–54, 2022.
D. N. Edowai and B. O. Paga, “Analisis Curah Hujan dan Temperatur untuk Identifikasi
Perubahan IklimDi Kabupaten Nabire Provinsi Papua Tengah,” J. Agritechnology, vol. 6, no. 1,
pp. 50–60, 2023.
R. Albeta, I. Iskandar, H. Akhsan, and Suhadi, “Analisis Pola Curah Hujan di Wilayah Sumatera
Bagian Utara Menggunakan Fast Fourier Transform ( FFT ) Analysis of Rainfall Patterns in The
Northern Sumatra Region Using Fast Fourier Transform ( FFT ),” vol. 8, no. 1, pp. 9–13, 2024.
G. Boschat, I. Simmonds, A. Purich, T. Cowan, and A. B. Pezza, “On the use of composite
analyses to form physical hypotheses: An example from heat wave - SST associations,” Sci. Rep.,
vol. 6, no. July, pp. 1–10, 2016, doi: 10.1038/srep29599.

101

Amelia Putri Sayyendra, Melly Ariska, Hamdi Akhsan

[12]

[13]

[14]

[15]

A. Faisol, B. O. Paga, and D. N. Edowai, “Pemutakhiran Zona Iklim Schmidt – Ferguson Melalui
Pemanfaatan Data Climate Hazards Group Infrared Precipitation with Stations untuk
Mendukung Pengembangan Pertanian di Provinsi Papua Barat,” Pros. Semin. Nas. Pembang. dan
Pendidik. Vokasi Pertan., vol. 3, no. 1, pp. 546–556, 2022, doi: 10.47687/snppvp.v3i1.338.
P. P. Simanjuntak, S. I. Sugiarto, and ..., “Variografi dalam Analisis Variabilitas dan Perencanaan
Observasi Curah Hujan Bulanan di Kalimantan, Sulawesi, dan Papua Variography of Variability
…,” … Penginderaan Jauh ke …, no. January, 2018.
M. Ariska, S. Suhadi, and D. K. Herlambang, “Empirical Orthogonal Function (Eof) Analysis
Based on Google Colab on Sea Surface Temperature (Sst) Dataset in Indonesian Waters,” Indones.
Phys. Rev., vol. 6, no. 1, pp. 20–32, 2023, doi: 10.29303/ipr.v6i1.187.
P. Anggraini, I. Iskandar, and Suhadi, “Dampak anomali iklim di wilayah indo-pasifik terhadap
curah hujan ekstrem di kepulauan nusa tenggara,” vol. 11, no. 2, pp. 171–181, 2024.

102