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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Indonesian Journal of Applied Statistics Jambura Journal of Mathematics Jurnal Pengabdian kepada Masyarakat
Husna Afanyn Khoirunissa
Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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PENINGKATAN LITERASI STATISTIKA : MEWUJUDKAN SANTRI CERDAS SEBAGAI UPAYA OPTIMALISASI ZAKAT DAN PEMBERDAYAAN POTENSI UMMAT Slamet, Isnandar; Zukhronah, Etik; Sulandari, Winita; Subanti, Sri; Sugiyanto, Sugiyanto; Susanto, Irwan; Isnaini, Bayutama; Afanyn Khoirunissa, Husna; Adi Wicaksono, Nanda; Indra Raditya , Dionisius
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3: Agustus 2025
Publisher : Bajang Institute

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Abstract

Pemberdayaan Potensi Ummat”. Tujuan utama kegiatan adalah membekali peserta dengan pengetahuan dasar statistika sebagai alat berpikir rasional dan analitis, serta memperkuat kesadaran akan kewajiban dan keutamaan (fadhilah) zakat dalam kehidupan sosial-keagamaan. Kegiatan diikuti oleh 114 peserta, terdiri dari 102 santri dan 12 ustadz. Materi yang disampaikan meliputi statistika dasar, konsep kewajiban zakat menurut syariat Islam, serta fadhilah zakat dalam rangka pemberdayaan umat. Tim pengabdian berasal dari Grup Riset Statistika dan Sains Data Bidang Industri dan Ekonomi, Universitas Sebelas Maret (UNS). Metode pelaksanaan meliputi pre-test, penyampaian materi secara interaktif, praktik pengolahan data sederhana, diskusi aplikatif, dan post-test. Hasil evaluasi menunjukkan peningkatan signifikan pada pemahaman peserta terhadap materi yang disampaikan. Kegiatan ini diharapkan menjadi langkah awal dalam membentuk generasi santri yang cerdas secara statistik, sadar zakat, dan siap berkontribusi dalam penguatan ekonomi umat berbasis pesantren.
Exploring Spatial Nonstationarity in the Number of Motor Vehicles in East Java Using Robust Geographically Weighted Regression with an MM-Estimator Isnaini, Bayutama; Isnandar Slamet; Sulandari, Winita; Khoirunissa, Husna Afanyn
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 20 No. 2 (2025)
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v20i2.90866

Abstract

The number of motor vehicles in each region is quite diverse, not least in East Java Province. The variety of number of motor vehicles can be affected by local factors that are important to study so that vehicle growth can be adequately anticipated. On the other hand, economic growth incentives are influenced by people's purchasing power. In addition, motor vehicles are one of the essential things in the sustainability of economic activities. This study aims to evaluate a robust, geographically weighted regression model with an MM-estimator (RGWR MM-estimator), which is considered suitable for analyzing the number of motor vehicles in East Java. The results showed that the RGWR MM-estimator model generates an estimate of the number of motor vehicles based on the HDI explanatory variables, road length, sex ratio, poverty gap index, and the number of colleges that is accurate compared to other models formed. In addition, there are significant differences in the influence of the five explanatory variables in each region. Districts/cities located near the capital of East Java Province tend to have many explanatory variables that have a significant effect compared to regencies/cities far from the provincial capital.
Comparative Analysis of Hierarchical Cluster Methods in Inflationary Cities in Indonesia Based on Sectoral Inflation Patterns Khoirunissa, Husna Afanyn; Safitriani, Nur Rezky; Widyaningrum, Erlyne Nadhilah; Putri, Rizka Amalia; Fathan, Morina A.; Nisa, Nabilla Rida Tri
Jambura Journal of Mathematics Vol 8, No 1: February 2026
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v8i1.35105

Abstract

This study aims to assess the performance of single linkage, complete linkage, and average linkage hierarchical clustering algorithms in grouping cities used as inflation benchmarks in Indonesia into clusters based on sectoral inflation patterns. The data utilized are 150 regencies/cities divided into 11 sectors that drive inflation, identified by BPS Indonesia. Prior to clustering, a distance analysis using Euclidean distances was conducted to measure similarity between regions. Evaluation of the optimal number of clusters was conducted by applying the stability measure approach (APN, AD, ADM, and FOM), which showed that creating five clusters produced the most stable results. The results of the analysis revealed that the single linkage approach had the lowest within-cluster to between-cluster standard deviation ratio compared to the other two approaches, which revealed a greater level of homogeneity between the clusters. From an economic perspective, this clustering pattern revealed impressive differences in sectoral inflation pressures between provinces, even between cities within a province. Consequently, the single linkage method is proposed as the optimal method for identifying spatial variations in sectoral inflation in Indonesia.
Analisis Faktor-Faktor Penyebab Inflasi di Indonesia Menggunakan Regresi Ridge, LASSO, dan Elastic-Net Khoirunissa, Husna Afanyn; Wijaya, Andreas Rony; Isnaini, Bayutama; Ferawati, Kiki
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.96921

Abstract

The economic condition of a country can be measured using one of the indicators, the inflation rate. Therefore, the inflation needs to be maintained so that its rate can be controlled. To support this, it is necessary to pay attention to several factors that influence the inflation rate. These factors include the amount of exports, imports, narrow money (M1), broad money (M2), the rupiah exchange rate against the USD, interest rates, rice prices in wholesale trade, farmer exchange rates (NTP), world crude oil prices, bank investment credit, GDP, and foreign exchange reserves. In this study, we analyze the significant factors influencing the inflation rate in Indonesia using the best model of the Ridge regression, LASSO regression, and Elastic-Net methods. In this modeling, the γ and λ values from the three methods are optimized first. The data used in this study consist of inflation data in Indonesia and its factors for 2020-2024, sourced from the BPS. Among the three high-dimensional data methods, the LASSO regression is the best method with the smallest MSE for modeling inflation data in Indonesia. The LASSO regression model produces 8 predictor variables that significantly influence inflation data, i.e., imports, M1, interest rates, and world crude oil prices with positive coefficient signs, as well as rice price variables in wholesale trade, NTP, GDP, and foreign exchange reserves with negative coefficient signs.Keywords: inflation; ridge regression; lasso regression; elastic-net.
Co-Authors Adi Wicaksono, Nanda Amanda Rizky Widyaningrum Andreas Rony Wijaya Annisa Priliya Ayu Maharani Erlyne Nadhilah Widyaningrum Fathan, Morina A. Indra Raditya , Dionisius Irwan Susanto Isnaini, Bayutama Isnandar Slamet Isnandar Slamet, Isnandar Kiki Ferawati Nisa, Nabilla Rida Tri Rizka Amalia Putri Safitriani, Nur Rezky Sri Subanti Sugiyanto - Sugiyanto Sugiyanto Winita Sulandari Zukhronah, Etik