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Analisis Statistik Faktor-faktor yang Mempengaruhi Angka Stunting di Kalimantan Barat Warsidah, Warsidah; Ayyash, Muhammad Yahya; Priani , Wina; Satyahadewi , Neva
Empiricism Journal Vol. 4 No. 2: December 2023
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/ej.v4i2.1563

Abstract

Stunting merupakan satu di antara permasalahan gizi utama pada anak yang dihadapi Indonesia hingga saat ini. Stunting merupakan permasalahan kurang gizi yang disebabkan oleh kurangnya asupan gizi sehingga mengakibatkan gangguan pada pertumbuhan dan perkembangan anak yaitu tinggi badan anak lebih rendah atau pendek dari standar usianya. Kalimantan Barat merupakan provinsi ke-8 dari 10 provinsi dengan angka stunting tertinggi nasional. Penelitian membahas mengenai faktor-faktor yang memengaruhi angka stunting di Kalimantan Barat menggunakan metode  analisis regresi linear berganda dengan mengukur besarnya pengaruh antara dua variable atau lebih variabel independen terhadap satu variable dependen. Data yang digunakan yaitu data angka stunting ( ), melahirkan pertama di usia kurang dari 20 tahun (MHPK20) ( ), pernikahan dini <19 tahun ( ), balita yang mendapatkan imunisasi lengkap ( ), rumah tangga yang memiliki akses terhadap sanitasi layak ( ), dan kemiskinan ( ) yang diperoleh dari laman BPS Kalimantan Barat dan Kementerian Kesehatan Republik Indonesia pada tahun 2022. Hasil penelitian ini menunjukkan bahwa angka stunting di Kalimantan Barat dipengaruhi oleh angka melahirkan pertama pada usia kurang  dari 20 tahun (MHPK20) ( ), pernikahan dini <19 tahun ( ), balita berimunisasi lengkap ( ), serta rumah tangga yang memiliki sanitasi layak ( ), dengan koefisien determinasi sebesar 80,42%. Hal ini menunjukkan bahwa ada 4 faktor yang berpengaruh terhadap angka stunting di Kalimantan Barat yaitu, melahirkan pertama di usia kurang dari 20 tahun, pernikahan dini, balita berimunisasi lengkap dan akses terhadap sanitasi. Statistical Analysis of Factors Affecting Stunting Rates in West Kalimantan Abstract Stunting is one of the main nutritional problems faced by Indonesia to date. Stunting is a problem of malnutrition caused by a lack of nutritional intake, which results in disruption to the growth and development of children, namely the child's height is lower or shorter than the age standard. West Kalimantan is the 8th province out of 10 provinces with the highest national stunting rate. The research discusses the factors that influence the stunting rate in West Kalimantan using the multiple linear regression analysis method by measuring the magnitude of the influence between two or more independent variables on one dependent variable. The data used are data on stunting rates (Y), first birth at the age of less than 20 years (MHPK20) (X_1), early marriage <19 years (X_2), toddlers who receive complete immunization (X_3), households that have access to adequate sanitation (X_4), and poverty (X_5) obtained from the BPS West Kalimantan and Ministry of Health of the Republic of Indonesia pages in 2022. The results of this study show that the stunting rate in West Kalimantan is influenced by the number of first births at the age of less than 20 years (MHPK20 ) (X_1), early marriage <19 years (X_2), children with complete immunization (X_3), and households that have proper sanitation (X_4), with a coefficient of determination of 80.42%. This shows that there are 4 factors that influence the stunting rate in West Kalimantan, namely, first birth at the age of less than 20 years, early marriage, fully immunized toddlers and access to sanitation.
DEGRADATION OF HUMIC ACID BY FLOATING PHOTOCATALYST TiO2/Cu-ARECA FIBER Sugandi, Didiek; Agustiawan, Deri; Wijayanto, Ericco; Marpaung, Maria Oktavia Putri; Ayyash, Muhammad Yahya; Wahyuni, Nelly
Jurnal Kimia Riset Vol. 9 No. 1 (2024): June
Publisher : Universitas Airlangga, Campus C Mulyorejo, Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jkr.v9i1.50850

Abstract

 The photocatalyst method is effective in degrading humic acid into O2 and H2 compoundsthat are more environmentally friendly. The photocatalysis process involves light andsemiconductors such as TiO2 to accelerate the reaction rate. Therefore, modification of TiO2is needed to shift light absorption to visible light by using Cu2+ doping and areca fiber. XRDcharacterization shows that Merck's TiO2 has shifted at 2θ, indicating that Cu has enteredthe TiO2 structure, and several peaks have reduced in intensity after being embedded withareca fiber, indicating that TiO2/Cu has successfully attached to areca fiber. FTIR resultsshow that TiO2/Cu has been attached to the areca fiber, which is marked by shifting andweakening the intensity of the Ti-O-Cu wave number absorption. The test results show thatTiO2/Cu embedded in areca fiber had higher degradation activity than TiO2/Cu withoutembedded, with a percent degradation of 54% for 180 minutes of irradiation. These resultsprove that TiO2/Cu floated to the surface of the solution can optimize irradiation so that itis effective in the degradation process.
COMMUNICATION ORGANIZATIONAL PATTERNS TO REGIONAL DISASTER MITIGATION AFTER THE COVID-19 PANDEMIC Adhrianti, Lisa; Ayyash, Muhammad Yahya; Alfarabi, Alfarabi; Warsah, Idi; Firmansyah, Mas Agus
Interaksi: Jurnal Ilmu Komunikasi Vol 13, No 2 (2024): December 2024
Publisher : Master of Communication Science Program, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/interaksi.13.2.387-403

Abstract

The COVID-19 pandemic, classified as a social disaster, continues to present challenges even in the post-pandemic era, especially in regions where public awareness and belief in the existence of COVID-19 remain low. This study focuses on identifying and analyzing the communication patterns employed by the Regional Disaster Management Agency (RDMA) of North Bengkulu Regency, Bengkulu Province, in mitigating the impact of the pandemic. The research aims to determine the organizational communication patterns within RDMA post-COVID-19 pandemic. A qualitative descriptive method was applied to explore the types and effectiveness of communication patterns—vertical, horizontal, and diagonal—used within the organization. The findings reveal that RDMA adopts a "wheel communication pattern" to coordinate disaster mitigation efforts, emphasizing the strategic use of digital platforms such as WhatsApp groups for instructions, reporting, and coordination among team members. This study provides insights into effective organizational communication strategies for regional disaster management and offers recommendations for future research to explore disaster mitigation efforts through digital communication channels and social media dissemination
The GSTAR (1;1) Modelling with Three Combination of the Grid Sizes and Spatial Weight Matrix in Forest Fires Cases Ayyash, Muhammad Yahya; Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27543

Abstract

One of the models that is utilized in spatio-temporal analysis is known as the Generalized Space-Time Autoregressive (GSTAR). This model incorporates two dimensions, namely the geographical and temporal aspects of the situation. This approach assists in the identification of patterns and correlations between data by taking into account both spatial and temporal elements. From modeling the confidence level of forest fire hotspot cases in Kubu Raya and its surrounds using the GSTAR (1;1) model with three different combinations of grids and special weight matrices, the purpose of this study is to discover which combination of grids and spatial weight matrices is the most effective. The results of diagnostic tests and the degrees of MAPE accuracy are used to determine which model is the most suitable. The data was obtained from the FIRMS-NASA platform, ranging from January 2014 to August 2024. A grid with a dimension of 1.25 x 1.25 degrees and a rook contiguity weight matrix is a combination of grids and spatial weight matrices that meet the white noise assumption, according to the findings of the study. This conclusion is based on the diagnostic test. As a result, the combination of a grid with a size of 1.25 x 1.25 and a rook contiguity weight matrix is the best in this modeling. This combination has a MAPE of 11.797%, which indicates that this model has a good level of accuracy. 
COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Ayyash, Muhammad Yahya
Jurnal Matematika UNAND Vol 14, No 1 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.1.31-45.2025

Abstract

This research aims to investigate the usefulness of the Generalized Space- Time Autoregressive (GSTAR) approach in predicting the number of fire hotspots in West Kalimantan Province. Specifically, the study compares the performance of the Queen contiguity method and the uniform weight matrix. Fires in the forests and on the land in West Kalimantan are severe problems that cause harm to the environment and other adverse effects. Data on fire hotspots were collected from four different regencies in West Kalimantan between January 2018 and March 2023 to provide the information used in this study. Compared to the uniform weight matrix, the study results reveal that the Queen contiguity weight matrix produces more accurate results. This is evidenced by the fact that the Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) values are lower in the Queen contiguity weight matrix. Based on these findings, more effective techniques for preventing forest and land fires are anticipated to be considered for planning purposes.
COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Ayyash, Muhammad Yahya
Jurnal Matematika UNAND Vol. 14 No. 1 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.1.31-45.2025

Abstract

This research aims to investigate the usefulness of the Generalized Space- Time Autoregressive (GSTAR) approach in predicting the number of fire hotspots in West Kalimantan Province. Specifically, the study compares the performance of the Queen contiguity method and the uniform weight matrix. Fires in the forests and on the land in West Kalimantan are severe problems that cause harm to the environment and other adverse effects. Data on fire hotspots were collected from four different regencies in West Kalimantan between January 2018 and March 2023 to provide the information used in this study. Compared to the uniform weight matrix, the study results reveal that the Queen contiguity weight matrix produces more accurate results. This is evidenced by the fact that the Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) values are lower in the Queen contiguity weight matrix. Based on these findings, more effective techniques for preventing forest and land fires are anticipated to be considered for planning purposes.
Forest Fires in Peatlands Analyzed from Various Perspectives: Spatial, Temporal, and Spatial-Temporal Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri; Ayyash, Muhammad Yahya; Pratiwi, Hesty
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28884

Abstract

Peatland fires are characterized by the compaction of organic matter below the soil surface. If dry conditions occur, the organic matter can burn, making it difficult to extinguish the fire. This study aims to analyze peatland forest fires with three perspectives, namely temporal, spatial, and spatial-temporal. The data used is the confidence level data of hotspots in forest fires in Kubu Raya Regency, West Kalimantan from January 2014 to December 2023. The methodology used includes collecting fire data from satellite imagery and prepocessing the data. Furthermore, three different data analyzes were carried out, namely temporal, spatial, and spatial-temporal analysis. The results of the study obtained three perspectives, namely from the time period, handling of forest fire cases because they have an impact on the future as seen from the ARIMA model. Regarding spatiality, the distribution of hotspots spread to surrounding areas that were heavily affected by hotspots as seen from the contour map using Kriging interpolation. Finally, regarding spatiality and temporality, forest fire projections show that locations that are close together and have a history of being affected by forest fires have a strong potential for the distribution of forest fire cases as seen from the GSTAR space-time model.
A Hybrid ARIMA-Intervention Modelling for Forest Fire Risk in The Dry Season Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Pratiwi, Hesty; Ayyash, Muhammad Yahya
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36741

Abstract

This study explores the time-related patterns of forest fires and assesses the impact of measures implemented during the dry season. Special focus is directed towards the effects of these interventions on the frequency and intensity of fires. This study highlights the importance of combining temporal analysis with spatial data to identify high-risk locations and optimize resource allocation for fire prevention. This study develops an ARIMA model to forecast fire risk before intervention. The findings indicate that integrating intervention factors into the ARIMA model will enhance the model's accuracy. The satisfactory MAPE values and the value data plots effectively demonstrate the data patterns. This method establishes a solid basis for predicting and reducing the risk of forest fires in the dry season, thereby enhancing the fire resilience of ecosystems considered at risk. The findings indicate that the onset of the dry season significantly elevates the risk of forest fires, especially in areas near bodies of water.