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All Journal Jurnal Aplikasi Statistika & Komputasi Statistik Multica Science and Technology Prosiding Seminar Nasional Official Statistics
Andrea Tri Rian Dani
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Library & Information Science Mathematics Physics Social Sciences

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ANALISIS KLASTER KASUS AKTIF COVID-19 MENURUT PROVINSI DI INDONESIA BERDASARKAN DATA DERET WAKTU Raditya Novidianto; Andrea Tri Rian Dani
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 2 (2020): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v12i2.280

Abstract

Corona Virus Disease 2019 (COVID-19) merupakan masalah yang sangat serius yang dihadapi oleh negara-negara di dunia, lebih dari 240 negara terjangkit virus ini. Pada 11 Maret 2020 WHO mengumumkan COVID-19 sebagai pandemi. Saat ini, penyebaran wabah COVID-19 terus bergerak signifikan, khususnya di Indonesia. Sejak mulai diumumkan pada awal Maret lalu hingga menjelang pertengahan Juli, jumlah kasus positif COVID-19 sudah mencapai 80.094 pasien yang dinyatakan positif, terkonfirmasi 3.797 kasus kematian dan 39.050 pasien yang dinyatakan sembuh. Salah satu kesulitan yang dialami pemerintah dalam penanganan COVID-19 yaitu tingkat kedaruratan dan kebijakan yang diterapkan oleh pemerintah daerah. Setiap daerah memiliki karakteristik yang berbeda-beda sehingga diperlukan pengetahuan mengenai kesamaan karakteristik daerah dalam penanganannya berdasarkan kasus COVID-19 yang berkelanjutan setiap harinya. Oleh karena itu, tujuan dari penelitian ini adalah menganalisis secara deskriptif mengenai kasus aktif COVID-19 berdasarkan data deret waktu dari setiap Provinsi di Indonesia. Selanjutnya melakukan proses pengelompokkan menggunakan data kasus aktif COVID-19 di Indonesia. Proses pengelompokkan menggunakan metode agglomerative hierarchical clustering, yaitu algoritma single, complete dan average linkage. Pengukuran kemiripan menggunakan Euclidean Distance dan Dynamic Time Warping (DTW). Berdasarkan hasil analisis, dengan menggunakan ukuran kebaikan yaitu koefisien korelasi cophenetic menunjukkan bahwa pengukuran kemiripan yang terbaik dari ketiga algoritma yang digunakan adalah Euclidean Distance. Dendogram yang didapat dari hasil pengelompokkan menunjukkan bahwa dengan ketiga algoritma yang digunakan menghasilkan anggota pengelompokkan yang sama. Pentingnya informasi tentang hasil pengelompokkan ini dapat membantu pemerintah pusat dan daerah untuk membuat strategi pencegahan penyebaran rantai virus COVID-19.
Aplikasi Pendekatan Agglomerative Hierarchical Time Series Clustering untuk Peramalan Data Harga Minyak Goreng di Indonesia Muhammad Aldani Zen; Sri Wahyuningsih; Andrea Tri Rian Dani
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.973 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1394

Abstract

Cooking oil is a strategic staple food commodity in Indonesia. The high consumption of cooking oil every year is accompanied by increasing demand from the market, causing the price of cooking oil to increase every year. The purpose of this study is to group provinces in Indonesia based on time series patterns of cooking oil prices and evaluate group-level forecasts. The clustering algorithm used is Agglomerative Hierarchical Clustering (AHC) with dynamic time warping (DTW, autocorrelation function (ACF), and Euclidean distance similarity measures. The optimal algorithm and number of clusters is selected based on the cophenetic correlation coefficient and silhouette coefficient. Then, each cluster that is formed will be modeled Autoregressive Integrated Moving Average (ARIMA) with the Auto ARIMA approach. Forecasting evaluation using Mean Absolute Percentage Error (MAPE). This study shows that the optimal algorithm chosen is the average linkage with a euclidean distance’s similarity measures. Cophenetic correlation and silhouette coefficient obtained respectively 0.8281 and 0.4296. The MAPE values ​​obtained were 0.3399 and 0.0793 respectively.
COMPARISON OF MEAN CENTERING REGRESSION AND SPLINE TRUNCATED NONPARAMETRIC REGRESSION ON FACTORS AFFECTING THE NUMBER OF CRIMES IN INDONESIA Felicia Joy Rotua Tamba; Liana Oklas Ranly; Andrea Tri Rian Dani; Meirinda Fauziyah; Narita Yuri Adrianingsih; Mislan Mislan
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/fpp74f96

Abstract

Crime remains one of the major challenges facing Indonesia, with the national crime rate showing an upward trend in 2022. This increase is driven by various social, economic, and demographic factors. To investigate these influences, this study applies the nonparametric truncated spline regression method to identify the determinants of crime rates across provinces in Indonesia. The response variable is the number of recorded crimes, while the predictor variables include the percentage of people living in poverty, mean years of schooling, average monthly per capita expenditure on food and non-food items, number of beneficiary households, budget for food social assistance, liberty aspects from the Indonesia Democracy Index, and the percentage of people with mental disorders. The analysis reveals that the linear truncated spline regression model with three knot points provides the best fit, achieving a coefficient of determination (R²) of 87.31%. These findings highlight the model’s capability to capture complex, nonlinear relationships between socio-economic indicators, democratic freedoms, mental health, and crime incidence in Indonesia.
MAPPING CRIME-PRONE AREAS USING PRINCIPAL COMPONENT ANALYSIS (PCA) – CENTROID LINKAGE Yossy Candra; Andrea Tri Rian Dani
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/57qngy96

Abstract

Cluster analysis is a method employed to categorize data or objects according to their degree of resemblance. Centroid linkage is an algorithm that can be utilized in the grouping process. Centroid Linkage employs a hierarchical methodology that categorizes things into tiers according to their degree of similarity. Nevertheless, multicollinearity issues frequently arise in cluster analysis scenarios. Optimization of the centroid linkage technique through principal component analysis (PCA) diminishes research variables and generates a new principal component to address the issue of multicollinearity. To assess the validity of the clusters, the Silhouette Coefficient (SC) was utilized. The case study included characteristics deemed pertinent to crime issues in 34 provinces in Indonesia in 2021. The analysis yielded six principal components (PCs) with eigenvalues of one or above. The results from the Centroid Linkage algorithm indicated that the optimal number of clusters is 2, with a silhouette coefficient (SC) value of 0.61, signifying a well-structured and effective clustering arrangement. The attributes and delineation of each established cluster can yield insights for identifying crime-prone regions.
Co-Authors Felicia Joy Rotua Tamba Liana Oklas Ranly Meirinda Fauziyah Mislan Muhammad Aldani Zen NARITA YURI ADRIANINGSIH Raditya Novidianto Sri Wahyuningsih Yossy Candra