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INDONESIA
Jurnal Statistika dan Matematika (Statmat)
Published by Universitas Pamulang
ISSN : 26553724     EISSN : 27209881     DOI : 10.32493
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
P-ISSN : 2655-3724 E-ISSN : 2720-9881 Jurnal Statmat UNPAM: Jurnal Statistika dan Matematika Universitas Pamulang is a means of publication of scientific articles and research with concentrations of Statistics, Pure Mathematics, Applied Mathematics, Computational Mathematics, Educational Mathematics, and other research articles related to Statistics and Mathematics. Mathematics Department, Faculty of Mathematics and Natural Sciences, University of Pamulang publishes this journal, since 2019, which scheduled periodically every six months (twice a year).
Arjuna Subject : Matematika - Matematika Terapan
Articles 150 Documents
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Analysis of Public Satisfaction Levels Using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA): (Case Study: Public Satisfaction Survey on Services at the Pontianak City Inspectorate) Afifah Diah Afianti; Pitriani
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.54887

Abstract

Public service delivery serves as a primary indicator of successful governance that emphasizes accountability, transparency, and citizen satisfaction. Poor-quality public services can diminish public trust in governmental performance and hinder the realization of good governance. This study aims to analyze the level of public satisfaction with services provided by Inspectorate of Pontianak City during the second semester of 2024 using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The CSI method is employed to measure overall satisfaction levels, whereas the IPA method is utilized to evaluate service attributes more comprehensively by comparing the importance and performance of each attribute and categorizing them into the IPA quadrants. The findings indicate that the overall public satisfaction level falls within the “very satisfied” category, with a CSI score of . This result reflects that the community generally perceives the quality of services delivered by Inspectorate of Pontianak City as highly satisfactory and able to meet user expectations. Furthermore, the IPA results reveal several attributes that require priority improvement, namely requirements, service completion time, service products, and staff competence, all of which fall into Quadrant I (high importance but low performance). These attributes are considered highly important by the public, yet their performance still requires enhancement to achieve optimal service delivery. Meanwhile, the attributes of service fees/tariffs, procedures, staff behavior, and complaint handling fall within Quadrant II (maintain performance), as they demonstrate strong performance and high importance, thus necessitating continued consistency.
Perbandingan Kinerja Model Regresi Logistik, Support Vector Machine, dan Naive Bayes Dalam Memprediksi Kepuasan Mahasiswa Terhadap Proses Pembelajaran Randa, Trigarcia Maleachi; Loria Amisah Lubis
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 7 No 3 (2025)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v7i3.54904

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja tiga metode klasifikasi Regresi Logistik, Support Vector Machine (SVM), dan Naïve Bayes dalam memprediksi kepuasan mahasiswa terhadap proses pembelajaran pada semester gasal 2025/2026 di Universitas Papua. Data penelitian diperoleh melalui survei yang berisi delapan variabel prediktor kategorik yang berkaitan dengan aspek proses pembelajaran, sementara variabel respon terdiri atas dua kategori, yaitu puas dan tidak puas. Analisis dilakukan dengan membagi data menjadi training dan testing dengan proporsi 80:20. Hasil penelitian menunjukkan bahwa metode Regresi Logistik dan SVM memberikan akurasi tertinggi, masing-masing sebesar 95.24%, sedangkan metode Naïve Bayes menghasilkan akurasi sebesar 87.88% meskipun telah dilakukan penentuan parameter terbaik menggunakan validasi silang 5-fold dan penerapan Laplace smoothing. Temuan ini menunjukkan bahwa Regresi Logistik dan SVM merupakan metode yang paling efektif untuk memprediksi kepuasan mahasiswa pada dataset ini, sementara Naïve Bayes tetap menjadi alternatif yang efisien untuk pemodelan yang sederhana dan cepat. Hasil penelitian ini diharapkan dapat mendukung pengambilan keputusan dalam evaluasi dan peningkatan kualitas pembelajaran di perguruan tinggi.
Pengelompokan Kecamatan di Kabupaten Banyumas Berdasarkan Produktivitas Hasil Tanaman Pangan Menggunakan Metode K-Means Clustering Retno Dwi Astuti; Puspita, Dian
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.51667

Abstract

Significant differences in food crop productivity in each sub-district in Banyumas are a major concern that needs to be addressed. This handling is specifically given to sub-districts with low productivity categories. However, the sub-district groups according to their categories are not yet known. Therefore, this study aims to group sub-districts in Banyumas Regency based on food crop productivity. Grouping is done using the K-Means Clustering method. The data used are data on the harvest productivity of five food crop commodities according to sub-districts in Banyumas Regency in 2023. The results obtained that there are five clusters with three clusters having at least one type of food crop that is less productive and two clusters which all types of food crops are productive.
Penerapan Generalized Additive Model Spline Dalam Analisis Dinamika Tingkat Pengangguran Terbuka di Provinsi Sulawesi Selatan Zalzabila, Jelita; Anna Islamiyati
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.52430

Abstract

Analisis regresi adalah metode yang sangat penting dalam memodelkan hubungan antara variabel, di mana variabel prediktor berpengaruh terhadap variabel respon. Namun, regresi linear konvensional memiliki beberapa keterbatasan, terutama ketika asumsi linearitas dan normalitas tidak terpenuhi. Dalam konteks ini, Generalized Additive Model (GAM) muncul sebagai alternatif yang lebih fleksibel. GAM mampu menangani hubungan non-linear antara variabel prediktor dan respon, serta tidak mengharuskan variabel respon untuk berdistribusi normal. GAM menggunakan pendekatan regresi nonparametrik, seperti penalized spline, untuk mengestimasi fungsi smoothing yang dapat menyesuaikan pola data secara otomatis. Pendekatan ini juga membantu mencegah overfitting, yang sering menjadi masalah dalam pemodelan data yang kompleks. Penelitian ini bertujuan untuk menerapkan GAM spline dalam memodelkan faktor-faktor yang memengaruhi Tingkat Pengangguran Terbuka (TPT) di Provinsi Sulawesi Selatan. Tahapan penelitian mencakup pengujian linearitas, penentuan titik knot, dan parameter penghalus optimal. Selanjutnya, pemodelan GAM dilakukan dengan menggunakan penalized spline pada variabel non-linear. Hasil pemodelan menunjukkan bahwa variabel Rata-Rata Lama Sekolah (RLS) dan Produk Domestik Regional Bruto (PDRB) memiliki pengaruh signifikan terhadap TPT, sementara variabel lainnya tidak menunjukkan signifikansi. Dengan demikian, model GAM penalized spline berhasil memodelkan hubungan antara faktor-faktor yang memengaruhi TPT secara efektif. Penelitian ini memberikan pemahaman yang lebih dalam bahwa peningkatan RLS dan PDRB dapat berkontribusi dalam menurunkan TPT di Sulawesi Selatan, yang merupakan informasi penting bagi pengambil kebijakan dalam merumuskan strategi pengurangan pengangguran.
Comparison of Moving Average and Double Exponential Smoothing Methods in Rice Production Forecasting Based on NTB Satu Data Arbyati, Asri Mustika; Maharani, Andika Ellena Saufika Hakim
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.52806

Abstract

The rapid development of technology in the industry 4.0 era has led to the emergence of the data literacy era, where data is no longer merely supporting information but has become a strategic asset in decision-making. Recognizing the importance of data utilization, the West Nusa Tenggara/Nusa Tenggara Barat (NTB) Provincial Government launched the NTB Satu Data program through Governor Regulation No. 45 of 2021. This program provides an open, standardized, and integrated sectoral data platform managed by the NTB Communication, Information, and Statistics Agency. Through this platform, data is collected, validated, analyzed, and visualized so that it can be utilized for evidence-based policy planning. This study utilizes annual rice production data from 2001 to 2024 from the NTB Satu Data portal to forecast rice production in 2025. Two commonly used time series forecasting methods, Moving Average (MA) and Double Exponential Smoothing (DES), are applied and compared in terms of accuracy. Evaluation was conducted using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) metrics. The analysis results show that the DES (Holt) method produced a MAPE of 9.455% and a MAD of 158.533, outperforming the MA order 2 method with a MAPE of 9.746% and a MAD of 161.700. These findings indicate that DES is more adaptive in capturing historical trend patterns and more effective in modeling production changes over time. The results of this study are expected to provide input for local governments in designing food security policies that are responsive to production dynamics. Utilizing these forecasts will enable more appropriate allocation of resources, increased preparedness for potential food supply disruptions, and strengthening of sustainable food security in NTB Province.
Pemodelan Tren Pencarian Google Penyakit Penyebab Kematian Tertinggi di Indonesia Dengan Metode Loop Prophet Qurani, Anggun; Chandra Sari Widyaningrum
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.52858

Abstract

The Loop Prophet method is a time series forecasting approach that utilizes the Facebook Prophet model iteratively (looping) to automatically process multiple variables or keywords. This study aims to predict the weekly search trend on Google for the leading causes of death in Indonesia, taking into account trend, yearly seasonality, and weekly seasonality components, as well as to evaluate model performance using error metrics such as NMAE, NRMSE, and MAPE. The dataset consists of weekly Google Trends search data for selected diseases during the period from September 2020 to July 2025. The results indicate that the Loop Prophet model successfully captures both trend and seasonal patterns. Based on the evaluation criteria, models categorized as “very good” were obtained for the keywords “Stroke”, “Diabetes”, and “Diare”. The “good” category was obtained for “Serangan Jantung”, “Sirosis Hati”, “Penyakit Paru”, “COPD”, dan “Neonatal”. The keyword of “Tuberkulosis” was categorized as “good enough”. Meanwhile, the “poor” category was found for “Kanker Paru-Paru” dan “Pneumonia”, which tend to have fluctuating patterns influenced by incidental events. These findings demonstrate that the Loop Prophet method is effective for time series analysis with complex seasonal patterns, although its performance decreases for diseases with highly irregular search trends.
Generalized LASSO Regression Menggunakan K-Nearest Neighbors Pada Data Persentase Penduduk Miskin Indonesia Sulistyo, Sheilla Amanda; Anna Islamiyati
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.53420

Abstract

Generalized LASSO Regression merupakan pengembangan dari metode LASSO dengan memberikan penalti tidak hanya pada setiap parameter secara terpisah, tetapi juga pada kelompok parameter yang saling terkait. Pendekatan K-Nearest Neighbors (KNN) menentukan besaran penalti berdasarkan kedekatan data dalam ruang fitur sehingga model dapat memilih variabel yang berelasi secara lokal, meningkatkan kemampuan mengenali pola berurutan atau kelompok pada data berdimensi tinggi tanpa mengurangi kemudahan interpretasi. Penelitian ini bertujuan untuk memperoleh parameter tuning optimal pada model Generalized LASSO Regression dengan KNN dan mendapatkan variabel yang berpengaruh terhadap persentase penduduk miskin. Metode penelitian ini terdiri dari dua tahap umum, yakni penentuan tetangga menggunakan KNN dan pemodelan Generalized LASSO Regression dengan pendekatan KNN untuk pendugaan persentase penduduk miskin. Hasil analisis menunjukkan bawa model terbaik diperoleh pada KNN K=3dengan nilai parameter tuning sebesar 0,028 menghasilkan koefisien determinasi 91,5% dan RMSE sebesar 0,130. Model Generalized LASSO Regression dengan pendekatan KNN terbukti dapat menangani masalah multikolinearitas dan efek wilayah untuk mengetahui variabel yang berpengaruh terhadap persentase penduduk miskin. Model ini dapat menjadi alat bantu dalam perencanaan kebijakan di Indonesia dengan fokus wilayah.
Model Struktural Faktor Sosio-Ekonomi Yang Memengaruhi Tingkat Kesehatan di Indonesia: Pendekatan Partial Least Squares Priscillia Maharani, Cinta; Gunawan, Risky
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.53694

Abstract

Pembangunan manusia merupakan tolok ukur fundamental dalam menilai keberhasilan pembangunan suatu wilayah, dengan kesehatan sebagai variabel hasil yang krusial. Penelitian ini bertujuan untuk menganalisis model struktural pengaruh variabel laten Pendidikan dan Ekonomi terhadap Kesehatan, dengan Kemiskinan sebagai variabel mediasi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode Partial Least Squares Structural Equation Modeling (PLS-SEM). Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik untuk 38 provinsi di Indonesia pada tahun 2024. Evaluasi model awal menunjukkan perlunya revisi akibat adanya masalah multikolinearitas, loading factor yang rendah, dan jalur yang tidak signifikan. Model akhir yang direvisi menunjukkan tingkat kecocokan yang baik dengan nilai SRMR sebesar 0,041. Hasil penelitian menunjukkan bahwa variabel laten Ekonomi berpengaruh negatif signifikan terhadap Kemiskinan (koefisien jalur = -0,851) dan berpengaruh positif signifikan terhadap Kesehatan (koefisien jalur = 0,765). Variabel Ekonomi mampu menjelaskan 71,7% varians Kemiskinan dan 57,4% varians Kesehatan. Dalam model akhir, variabel Pendidikan serta peran mediasi Kemiskinan tidak terbukti signifikan. Studi ini menyimpulkan bahwa faktor ekonomi merupakan pendorong utama yang secara langsung memengaruhi penurunan angka kemiskinan dan peningkatan status kesehatan di Indonesia.
Spatial Modeling in Public Health: A Review of Geographically Weighted Poisson Regression (GWPR) Applications Raihannabil, Syfriza Davies; Halma, Karini; Hutagalung, Reghita Maria
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.54205

Abstract

Geographically Weighted Poisson Regression (GWPR) is an important approach in analyzing spatial count data, especially in the field of public health. However, its application in Indonesia still has various methodological weaknesses. This study aims to critically review the suitability and application of the GWPR model, as well as analyze aspects of statistical assumptions, weighting function selection, model evaluation, and methodological innovation. This study uses an article review approach of 10 research articles that apply GWPR in public health. The review results show that most studies have not explicitly articulated research gaps and novelty, and have ignored crucial offset variables in count data. Spatial heterogeneity testing is often performed incorrectly using the BP test instead of visual exploration through thematic maps. The selection of weighting functions and bandwidths is often not based on objective evaluation. Additionally, many studies have not conducted multicollinearity checks and tests of the assumption of equidispersion, which directly impact model validity. Descriptive analysis and visualization of local parameters through maps remain limited, hindering contextual interpretation. Finally, some studies fail to include model goodness-of-fit evaluations such as AIC or pseudo-R², making it impossible to demonstrate the superiority of GWPR over global models objectively. These findings underscore the importance of upholding statistical validation principles and methodological transparency in GWPR modeling to produce accurate and relevant spatial analyses for regional policy-making.
Analysis of Factors Influencing Poverty in South Sumatra Using the Poisson Regression Model Manik, Jolius Saut Mangaraja; Fitri Maya Puspita; Sisca Octarina
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.54763

Abstract

Poverty remains a key development issue for the government, including in South Sumatra Province. This study aims to analyze the factors influencing poverty levels at the district/city level using a Poisson regression model. The independent variables used include the open unemployment rate, education level, Gross Regional Domestic Product (GRDP) per capita, and the percentage of the population working in the informal sector. The data used are secondary data obtained from the Central Statistics Agency (BPS) for the most recent available year. The analysis shows that several variables significantly influence the number of poor people in South Sumatra, where increasing open unemployment and a high proportion of informal workers tend to increase the number of poor people. Conversely, increasing education and GRDP per capita contribute to reducing poverty levels. The Poisson regression model proved appropriate for modeling the number of incidents (count) data in this study. These findings are expected to provide input for local governments in formulating more targeted poverty alleviation policies.

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