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All Journal Media Statistika Jurnal Aplikasi Statistika & Komputasi Statistik Building of Informatics, Technology and Science Inferensi Pattimura Proceeding : Conference of Science and Technology Jurnal Perspektif Pembiayaan dan Pembangunan Daerah STATISTIKA
Irlandia Ginanjar
Departemen Statistika, Fakultas MIPA, Universitas Padjadjaran, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Materials Science & Nanotechnology Mathematics Physics Social Sciences

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ANALISIS OBYEK DAN KARAKTERISTIK DARI MATRIKS INDIKATOR MENGGUNAKAN HYBRID ANALISIS KELAS LATEN DENGAN BIPLOT ANALISIS KOMPONEN UTAMA (BIPLOT AKU) Ginanjar, Irlandia; Pravitasari, Anindya Apriliyanti; Martuah, Aleknaek
MEDIA STATISTIKA Vol 6, No 2 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (933.108 KB) | DOI: 10.14710/medstat.6.2.81-90

Abstract

Analysis of the object and the characteristics will be much easier, efficient, and informative when based on a perceptual map, which can display objects and characteristics. Indicator matrix is a matrix where the rows represent objects and the columns is a dummy variable representing characteristics. This article writes about techniques to make perceptual map from indicator matrix, where that can provide information about the similarity between objects, the diversity of each characteristic, correlations between the characteristics, and characteristic values ​​for each object, the techniques we call Hybrid Latent Class Cluster with PCA Biplot, where Latent Class Cluster Analysis is used to transform the indicator matrix to cross section matrix, where rows represent the objects and columns represent the characteristics, the observation cells is the probability of characteristic for each object, next the cross section matrix mapped using Principal Component Analysis Biplot (PCA Biplot).   Key Words: Hybrid Latent Class Cluster with PCA Biplot, Latent Class Cluster Analysis, Biplot Principal Component Analysis, Indicator Matrix.
ANALISIS KORESPONDENSI BERGANDA UNTUK MENGETAHUI INDIKATOR-INDIKATOR YANG MEMPENGARUHI KEJADIAN LOW BACK PAIN PADA KUSIR KUDA/DELMAN DI KOTA CIMAHI TAHUN 2019 Dhita Diana Dewi; Fajriatus Sholihah; Rosa Rosmanah; Lucy Fitria Dewi; Mochamad Yudhi Afrizal; Irlandia Ginanjar
Pattimura Proceeding 2021: Prosiding KNM XX
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (895.97 KB) | DOI: 10.30598/PattimuraSci.2021.KNMXX.351-358

Abstract

Low Back Pain (LBP) merupakan nyeri yang dirasakan di area punggung bagian bawah. Pekerjaan yang dapat menimbulkan LBP salah satunya kusir kuda/delman. Dampak dari LBP yaitu dapat menurunkan produktivitas kerja. Hasil survei pendahuluan tahun 2019 di Kota Cimahi didapatkan 10 dari 10 orang atau 100% kusir kuda/delman mengeluhkan nyeri punggung bagian bawah. Penelitian ini bertujuan untuk mengetahui indikator-indikator yang memiliki dependensi dengan kejadian LBP serta mengetahui kategori variabel yang berpengaruh terhadap kejadian LBP pada kusir kuda/delman di Kota Cimahi tahun 2019. Metode yang digunakan dalam penelitian ini yaitu Uji Chi Kuadrat dan analisis korespondensi berganda. Hasil Uji Chi Kuadrat menunjukan bahwa variabel kelompok umur (p-value = 0,004) dan masa kerja (p-value = 0,005) berhubungan dengan kejadian LBP. Tiga indikator lainnya yaitu lama kerja (p-value = 0,171), status gizi (p-value = 0,672), dan posisi kerja (p-value = 1,000) tidak berhubungan dengan kejadian LBP. Selain itu, terdapat hubungan antara kejadian LBP, kelompok umur, dan masa kerja (p-value = 0,14765). Selanjutnya dengan analisis korespondensi berganda diperoleh hasil bahwa kusir kuda/delman yang mengalami kejadian LBP berasal dari kelompok umur tua dengan masa kerja lama. Sedangkan kusir kuda/delman yang tidak mengalami LBP berasal dari kelompok umur muda dengan masa kerja lama. Berdasarkan observasi yang digunakan, disarankan bagi kusir kuda untuk meningkatkan kesadaran mengenai pentingnya menjaga kesehatan dan mencegah kejadian LBP dengan sedapat mungkin memenuhi posisi kerja yang ergonomi agar dapat meningkatkan produktivitas kerja secara optimal
KLASTERISASI KABUPATEN/KOTA TERDAMPAK COVID-19 DI SEKTOR KETENAGAKERJAAN DENGAN PENDEKATAN K-MEANS NONHIERARCHICAL CLUSTERING Armalia Desiyanti; Devi Yanti; Hamim Tsalis Soblia; Irlandia Ginanjar
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 14 No 2 (2022): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i2.367

Abstract

Sepanjang 2020, pandemi Covid-19 merupakan masalah terbesar yang dihadapi dunia, termasuk Indonesia. Pandemi Covid-19 memberikan akibat buruk di hampir semua sektor, salah satunya sektor ketenagakerjaan. BPS mencatat bahwa terdapat 29,12 juta orang atau 14,28 persen penduduk usia kerja yang terdampak Covid-19. Tujuan penelitian ini adalah memetakan kabupaten/kota di Indonesia berdasarkan dampak Covid-19 pada sektor ketenagakerjaan sehingga dapat mempermudah pemerintah dalam menentukan kebijakan-kebijakan untuk mengatasi masalah ketenagakerjaan sebagai dampak dari pandemi Covid-19 di Indonesia. Metode analisis yang digunakan yaitu Principal Component Analysis dan K-Means Clustering. Hasil penelitian menunjukkan bahwa terdapat delapan klaster yang terbentuk dengan karakteristik yang berbeda di masing-masing klaster.
Analisis Biplot Pada Pengelompokan Kecamatan Di Kabupaten Tasikmalaya Berdasarkan Indikator Kemiskinan Annisa Siti Utami; Anindya Apriliyanti Pravitasari; Irlandia Ginanjar
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19128

Abstract

Poverty is a social problem that continues to exist in people's lives according to Nurwati, 2008. Therefore, the problem of poverty is the center of attention of the Tasikmalaya Regency government. In the National Long-Term Development Plan (RPJPN) 2005-2025 the problem of poverty is seen in a multidimensional framework, therefore poverty is not only related to income measurement, but related to several things. This is because poverty is not only related to the size of income but involves several things. In the Tasikmalaya Regency Regional Medium-Term Development Plan (RPJMD), the target for achieving the poverty rate in 2021 is 10.23%. Based on BPS publications, there are 10.75% of the population of Tasikmalaya Regency who are categorized as poor, meaning that the Tasikmalaya Regency government's target has not been achieved. So it is necessary to make efforts to overcome the problem of poverty. This study aims to group sub-districts in Tasikmalaya Regency based on the similarity of poverty indicators owned by each sub-district by using biplot analysis. The data used is poverty indicator data for 39 sub-districts in Tasikmalaya Regency in 2021. From the research results it is known that the amount of variation that can be described is 97%, meaning that the plots formed can best describe actual conditions. data information. In addition, three clusters have the same poverty indicators. Cluster 1 contains sub districts that have an indicator in the form of a high student to school ratio in SMA/SMK/MA. Cluster 2 contains sub districts that have moderate to low indicators on all variables except the ratio of SMP/MTs students and the ratio of SMA/SMK/MA students. Meanwhile, Cluster 3 consists of sub-districts that have an indicator in the form of a high ratio of SMP/MTs students.
Pemodelan Kasus Gizi Buruk Balita di Indonesia Menggunakan Panel Quantile Regression Model Harifa Hananti; I Gede Nyoman Mindra Jaya; Irlandia Ginanjar
Statistika Vol. 23 No. 2 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i2.2025

Abstract

ABSTRAK Kasus gizi buruk balita di Indonesia masih menjadi suatu masalah yang harus diperhatikan secara konsisten setiap tahunnya karena balita yang berusia 0-59 bulan adalah generasi penerus bangsa. Untuk itu perlu dilakukan pemodelan kasus gizi buruk balita dengan faktor yang mempengauhinya, yaitu kemiskinan dengan juga perlu diketahui bagaimana dampak kasus gizi buruk balita di masing-masing level kuantil (kecil, sedang, dan tinggi). Penelitian ini menggunaka pemodelan regresi data panel dengan pendekatan fixed effcts model  (FEM) yang mengandung outlier. Oleh karena itu, untuk mengatasi hal tersebut maka panel quantile regression model dengan penalizes fixed effects dapat digunakan. Hasil penelitian menunjukkan bahwa dampak kasus gizi buruk balita dengan faktor kemiskinan di level kuantil rendah (0,25) akan menyebabkan kasus gizi buruk juga rendah, sedangkan pada level kuantil yang sedang (0,5) akan menyebabkan kasus gizi buruk juga sedang, begitu juga untuk level kuantil yang tinggi (0,75) akan menyebakan kasus gizi buruk yang tinggi. ABSTRACT The case of malnutrition among toddlers in Indonesia is still a problem that must be paid attention to consistently every year because toddlers aged 0-59 months are the nation's next generation. For this reason, it is necessary to model cases of malnutrition under five with the factors that influence it, namely poverty. Apart from that, it is also necessary to know the impact of cases of under-five malnutrition at each quantile level (small, medium and high). This research uses panel data regression modeling using a fixed effects model (FEM) approach which contains outliers. So, the solution to overcome this is that a panel quantile regression model with fixed effects penalization can be used. The results of the research show that the impact of cases of under-five malnutrition with poverty factors at the low quantile level (0.25) will cause cases of malnutrition which are also low, while at the medium quantile level (0.5) it will cause moderate cases of malnutrition, and so do A high quantile level (0.75) will cause high cases of malnutrition.
Predicting future inflation in Indonesia using Dynamic Model Averaging (DMA) Sari, Shania Puspita; Ginanjar, Irlandia; Noviyanti, Lienda
Jurnal Perspektif Pembiayaan dan Pembangunan Daerah Vol. 12 No. 2 (2024): Jurnal Perspektif Pembiayaan dan Pembangunan Daerah
Publisher : Program Magister Ilmu Ekonomi Pascasarjana Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/ppd.v12i2.31817

Abstract

The features of Indonesia's inflation data, which make it extremely susceptible to shocks like those felt in 2005 and 2008, as well as extensive potential influencing factors, lead to problems in forecasting inflation. These problems include time variation in coefficients, models that can change over time, and many predictors to consider. Dynamic Model Averaging (DMA) solves these problems since it has evolved coefficients and models that change over time. This study uses DMA to predict future inflation by involving eight macroeconomic indicators as exogenous variables. The results of the in-sample analysis show that six predictors are significant in forecasting inflation, with posterior inclusion probability (PIP) being above 40%. Although the remaining predictors have PIP means below 40%, they can still be considered important. The out-of-sample results suggest that DMA performs better than dynamic model selection and models that don’t include exogenous variables, such as autoregressive models. The forecast results indicate a consistent pattern over the 12 months studied. The attempt to control inflation can be achieved by prioritizing the money supply factor, which has the highest PIP value, indicating that it is the most important factor.
Stacking-Correspondence Analysis for Fuzzy Data: Computational Framework for Analyzing Complex Qualitative Survey Data Kirana, Disa Rahma; Ginanjar, Irlandia; Tantular, Bertho
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9054

Abstract

Bandung Regency faces a significant challenge in achieving Sustainable Development Goal (SDG) 12, marked by a critically low score of 14.53 out of 100. Uniform policies are often ineffective due to regional diversity and uncertainty in categorical survey data, which inadequately reflects real-world conditions. This study aims to identify sub-district characteristics based on consumption and production patterns to provide precise policy recommendations. The research utilizes data from the 2024 Supporting Area Survey (SWP), covering 280 villages across 31 sub-districts. A computational framework combining stacking techniques and Correspondence Analysis for Fuzzy Data (CAFD) is implemented to analyze four qualitative variables. The stacking phase transforms the multi-way data structure into a two-way structure, while CAFD effectively handles qualitative uncertainty using membership degrees. Analysis results indicate that two principal dimensions capture 73.35% of the total information variance and successfully identify 17 sub-district clusters with similar problem profiles. The fuzzy approach unveils multi-characteristic profiles, identifying both dominant and secondary traits. This research contributes a two-dimensional perceptual map, enabling the government to transition from generic policies to tailored interventions for each sub-district. This computational solution represents a concrete step toward improving the SDG 12 achievement score through data-driven strategic planning.
Co-Authors Aleknaek Martua Anindya Apriliyanti Pravitasari Annisa Siti Utami Armalia Desiyanti Bertho Tantular Devi Yanti Dhita Diana Dewi Fajriatus Sholihah Hamim Tsalis Soblia Harifa Hananti I Gede Nyoman Mindra Jaya Kirana, Disa Rahma Lienda Noviyanti Lucy Fitria Dewi Mochamad Yudhi Afrizal Rosa Rosmanah Sari, Shania Puspita