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Hasih Pratiwi
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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INDONESIA
Indonesian Journal of Applied Statistics
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 2621086X     DOI : https://doi.org/10.13057/ijas
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 3 Documents
 1 
Search results for , issue "Vol 8, No 2 (2025)" : 3 Documents clear
Pemetaan Daerah Rawan Bencana di Pulau Sulawesi menggunakan Metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Havinka Angel Salsabilla; Nandini Lova Diani; Abimanyu Arya Ramadhan; M. Al Haris
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.106040

Abstract

Indonesia terletak pada pertemuan tiga lempeng tektonik aktif sehingga memiliki tingkat kerawanan yang tinggi terhadap bencana alam seperti gempa bumi, banjir, letusan gunung api, dan tanah longsor. Pulau Sulawesi merupakan salah satu wilayah dengan aktivitas seismik dan hidrometeorologi yang tinggi, sehingga identifikasi daerah rawan bencana menjadi penting dalam upaya pengurangan risiko dan perencanaan mitigasi yang efektif. Penelitian ini bertujuan untuk memetakan daerah rawan bencana di Pulau Sulawesi menggunakan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN merupakan metode klasterisasi berbasis kepadatan yang mampu mengidentifikasi pola spasial tanpa harus menentukan jumlah klaster di awal serta dapat mendeteksi data pencilan (outlier). Data yang digunakan adalah data sekunder dari Badan Nasional Penanggulangan Bencana (BNPB) tahun 2020–2024 yang mencakup kejadian bencana di seluruh kabupaten/kota di Pulau Sulawesi. Variabel yang dianalisis meliputi frekuensi kejadian banjir, tanah longsor, cuaca ekstrem, kekeringan, gempa bumi, letusan gunung api, dan gelombang pasang. Sebelum proses klasterisasi, data dinormalisasi menggunakan metode Min–Max. Hasil terbaik diperoleh pada parameter ε = 0,28 dan MinPts = 5, yang menghasilkan dua klaster utama dan satu kelompok noise. Klaster 1 menunjukkan wilayah dengan tingkat kejadian bencana tertinggi, terutama banjir, tanah longsor, dan cuaca ekstrem. Klaster 0 mencakup wilayah dengan intensitas bencana sedang, sedangkan kelompok noise terdiri atas wilayah dengan tingkat kejadian bencana yang rendah atau pola bencana yang tidak jelas. Penerapan algoritma DBSCAN terbukti efektif dalam pemetaan kerawanan bencana karena mampu menangani distribusi spasial yang tidak merata serta mengungkap pola tersembunyi. Hasil penelitian ini diharapkan dapat menjadi dasar dalam pengembangan strategi mitigasi bencana yang lebih terarah. Penelitian selanjutnya disarankan untuk menambahkan indikator kerentanan sosial-ekonomi serta memperluas cakupan data.Kata kunci: DBSCAN; Sulawesi; Klasterisasi Spasial; Pemetaan Bencana; Mitigasi RisikoIndonesia is located at the confluence of three active tectonic plates, making it highly vulnerable to natural disasters such as earthquakes, floods, volcanic eruptions, and landslides. Sulawesi Island is one of the regions with the highest seismic and hydro-meteorological activity in Indonesia, so identifying its disaster-prone areas is crucial for effective risk reduction and mitigation planning. This study aims to map disaster-prone areas in Sulawesi Island using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN is a density-based clustering method that is able to identify spatial patterns without determining the number of clusters from the start, as well as detect outlier data. The data used is secondary data from National Disaster Management Authority (BNPB) for 2020–2024 covering disaster events in all districts/cities in Sulawesi. The variables analyzed include the frequency of floods, landslides, extreme weather, droughts, earthquakes, volcanic eruptions, and tidal waves. The data was normalized using the Min-Max method before the clustering process. The best results were obtained at parameters ε = 0.28 and MinPts = 5, resulting in two main clusters and one noise group. Cluster 1 shows areas with the highest disaster occurrences, especially floods, landslides, and extreme weather. Cluster 0 includes areas with moderate disaster intensity, while the noise group consists of areas with low or unclear disaster patterns. The application of DBSCAN has proven effective for disaster vulnerability because it is able to handle uneven spatial distribution and reveal hidden patterns. These results are expected to be the basis for developing more targeted disaster mitigation strategies. Further research is recommended to add socio-economic vulnerability indicators and expand data coverage.Keywords: DBSCAN; Sulawesi; Spatial Clustering; Disaster Mapping; Risk Mitigation 
Penerapan GWR dan MGWR dengan Pembobot Kernel Adaptive Tricube pada Pemodelan Prevalensi Stunting di Provinsi Jawa Tengah Imas Fitri Ningrum; Sri Sulistijowati Handajani; Respatiwulan Respatiwulan
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.94657

Abstract

Jawa Tengah merupakan salah satu provinsi dengan prevalensi stunting yang tinggi di Indonesia pada tahun 2022 sebesar 20,8% dan hampir mendekati prevalensi stunting di Indonesia sebesar 21,6%. Wilayah di Jawa Tengah beragam dalam hal geografi, ekonomi, sosial budaya, kualitas sumber daya manusia, dan ketersediaan sumber daya alam. Regresi spasial digunakan untuk memodelkan faktor-faktor yang mempengaruhi prevalensi stunting di Jawa Tengah dengan mempertimbangkan pengaruh lokasi. Perbedaan karakteristik antar lokasi menyebabkan heterogenitas spasial, sehingga masalah tersebut diatasi dengan pemodelan menggunakan geographically weighted regression (GWR). Pemodelan dengan GWR memberikan hasil bahwa tidak ada heteroskedastisitas pada salah satu variabel, sehingga pemodelan juga dilakukan menggunakan mixed geographically weighted regression (MGWR) dengan kernel tricube tertimbang adaptif. Namun, dari analisis, model GWR memberikan hasil yang lebih baik daripada model MGWR. Unit sampel dalam penelitian ini adalah 35 kabupaten/kota di Provinsi Jawa Tengah. Model GWR untuk memodelkan prevalensi stunting di Jawa Tengah menghasilkan model yang lebih baik dengan nilai AIC yang lebih kecil dibandingkan dengan model MGWR. Hasil penelitian menunjukkan bahwa balita yang kekurangan gizi memiliki pengaruh positif terhadap stunting, sedangkan bayi baru lahir yang menerima IMD, balita yang menerima vitamin A, dan pengeluaran riil per kapita memiliki pengaruh negatif terhadap stunting.kata kunci: tricube adaptif; GWR; MGWR; stuntingCentral Java is one of the provinces with a high prevalence of stunting in Indonesia in 2022 at 20.8% and is almost close to the prevalence of stunting in Indonesia at 21.6%. The regions in Central Java are diverse in terms of geography, economy, socio-culture, quality of human resources, and availability of natural resources. Spatial regression was used to model the factors that influence the prevalence of stunting in Central Java by considering the influence of location. The characteristics between locations cause heterogeneity, so the modeling used is Geographically Weighted Regression (GWR). Because one variable is not locally significant, modeling is also carried out using Mixed Geographically Weighted Regression (MGWR) with adaptive tricube kernel weighted. However, from the analysis, the GWR model gave better results than the MGWR model. The GWR model for modeling stunting prevalence in Central Java produces a better model with an AIC value of 148.883 and R^2 of 88.01% compared to the MGWR model, which only provides an AIC value of 190.371 and R^2 value of 47.66%. Based on the analysis results with the GWR model using adaptive tricube weighted, the factors influencing the prevalence of stunting in Central Java Province are newborns getting early breastfeeding initiation (IMD), toddlers with malnutrition, toddlers getting vitamin A, and real expenditure per capita.Keywords: adaptive tricube; GWR; MGWR; stunting
Exploring Statistical Power and Mediation Analysis: Understanding the Impact of Antecedent-Mediator-Outcome Relationships Szilárd Nemes
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.93376

Abstract

This paper explores the phenomenon of statistical power stagnation and decline in mediation analysis, specifically focusing on the interplay between the antecedent variable, mediator, and outcome. Mediation analysis is a critical statistical tool used to understand the causal pathways between variables. However, statistical power may not always increase with stronger relationships between the antecedent and mediator, often stagnating or even declining due to variance inflation caused by multicollinearity. We provide a in detail examination of this issue, including key theoretical concepts, the mathematical foundations of variance inflation, and the impact of mediator-antecedent correlations on power. A simulation study further illustrates how varying these correlations affects statistical power, variance estimates, and possible bias in mediation effects. Our findings indicate that while increasing the strength of the relationship between the antecedent and mediator improves mediation detection initially, beyond a certain threshold, it results in inflated variance estimates, leading to decreased precision and power. Variance inflation of the mediated effect is more accentuated than variance inflation of regression coefficients.Keywords: mediation; variance inflation; Sobel test

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