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All Journal Media Statistika Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Kesehatan Masyarakat
Shofi Andari
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Decision Sciences, Operations Research & Management Public Health

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APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG Anggraeni, Dian; Prahutama, Alan; Andari, Shofi
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 (625.794 KB) | DOI: 10.14710/medstat.6.2.61-70

Abstract

Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834. Key words: GSTAR, Vehicle Volume, Space-Time Model
Assessing the Quality of Life Among Commuting Workers and Uncomfortable Travel Kusmawan, David; Andari, Shofi; Susilowati, Indri H
KEMAS: Jurnal Kesehatan Masyarakat Vol 16, No 3 (2021)
Publisher : Department of Public Health, Faculty of Sport Science, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/kemas.v16i3.25467

Abstract

Many studies conclude commuting that has an impact on the quality of life of the commuter both in the physical, psychological, health, and environmental aspects of the commuter. Increased risk of musculoskeletal disorder (MSD), obesity, increased blood pressure, and low physical health conditions are found in prolonged commuting activities as the existing problem in public health. This study using cross sectional design with WHO QOL BREF questionnaire.  The total sample 155 respondents of commuting working using KRL Commuter Line Bogor to Jakarta in 2018. The initial model for assessing the relationship directly and indirectly between quality of life among commuting workers and travel uncomfortable, health complaint, psychological condition, bad experience, and income was constructed on the basis of severe hypotheses Based on the results of the path analysis it was found that income has a direct effect on quality of life. Psychological conditions have a direct effect on quality of life. Psychological condition is intervening variable for travel uncomfortable and health complaints as indirect effect. These results may help to identify the direct factor to improve the quality of life among commuting workers and as a basis for developing policies to improve the quality of public transportation services for commuting workers, and as a basis for formulating policies related to housing development locations that are integrated with public transportation facilities.
SMOOTH SUPPORT VECTOR MACHINE DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE UNTUK MENDIAGNOSIS KANKER PAYUDARA Shofi Andari; Santi W. Purnami; Bambang W. Otok
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 1, No 2 (2013): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.431 KB) | DOI: 10.26714/jsunimus.1.2.2013.%p

Abstract

Kanker payudara merupakan kanker yang paling umum menyerang wanita dan menjadi kanker penyebab kematian utama bagi wanita di seluruh dunia. Penyebab dari kanker payudara masih belum dapat dipastikan sehingga metode preventif yang spesifik untuk penyakit ini juga belum dapat ditentukan, oleh karena itu diagnosis terhadap kanker payudara sedini mungkin menjadi sangat penting bagi para dokter dan tenaga medis untuk menyelamatkan pasien maupun orang-orang yang memiliki faktor risiko kanker payudara. Beberapa penelitian telah dikembangkan dengan ide dasar mengklasifikasikan kanker payudara berdasarkan rekaman gambar radiologi dan usia pasien terhadap hasil biopsi. Berdasarkan keunggulan smooth SVM (SSVM) serta potensi MARS dalam menyelesaikan permasalahan diagnosis kanker payudara, tulisan ini mengkaji dan memaparkan kedua metode tersebut digunakan untuk mengklasifikasikan kanker payudara ke dalam dua kelompok yaitu kelompok malignant dan kelompok benign. Secara umum baik SSVM maupun MARS mampu menghasilkan tingkat akurasi yang sama-sama tinggi. Tingkat akurasi kedua metode dalam mendiagnosis kanker payudara ke dalam kelompok benign dan malignant yang cukup tinggi dipercaya dapat mendukung prosedur pemeriksaan dan diagnosis kanker payudara.Kata Kunci : kanker payudara, klasifikasi, smooth SVM, MARS
Co-Authors Alan Prahutama Anak Agung Gede Sugianthara Bambang W. Otok David Kusmawan Indri Hapsari Susilowati Santi W. Purnami