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Front Matter Vol 3 No 1 2020 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 3, No 1 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i1.43192

Abstract

Front Matter Vol 4 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 4, No 1 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i1.51602

Abstract

Back Matter Vol 1 No 1 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24686

Abstract

Back Matter Vol 3 No 1 2020 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 3, No 1 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i1.43193

Abstract

Analisis Cluster Intensitas Kebencanaan di Indonesia Menggunakan Metode K-Means Hafiz Yusuf Heraldi; Nabila Churin Aprilia; Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34911

Abstract

Indonesia is one of the most prone countries to natural disasters in the world because of the climate, soil, hydrology, geology, and geomorphology. There are many different natural disasters, but the three most common natural disasters in Indonesia are flood, landslide, and tornado. This research aimed to cluster the provinces in Indonesia based on the flood, landslide, and tornado’s intensity in 2018. The results of clustering by K-Means method in this research divided the provinces in Indonesia into four clusters. The second cluster contained West Java, Central Java, and Bali, the third cluster contained DKI Jakarta, the fourth cluster contained DI Yogyakarta, and the first cluster contained the other 29 provinces. The result of this research hopefully can help the government in order to make decision and improve the natural disaster management system, such as preparedness, disaster response, and disaster recovery based on the most common disaster in each province. Furthermore, the society is expected to be more aware on natural disaster management based on the most common natural disaster in province that they lived.Keywords : natural disaster, cluster, k-means
A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia Hasih Pratiwi; Yuliana Susanti; Sri Sulistijowati Handajani
Indonesian Journal of Applied Statistics Vol 1, No 1 (2018)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v1i1.24090

Abstract

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator
Front Matter Vol 2 No 2 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.38495

Abstract

Front Matter Vol 4 No 2 2021 Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 4, No 2 (2021)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v4i2.56839

Abstract

Klasifikasi Jenis Pencabutan Layanan oleh Pelanggan Indihome Menggunakan Metode Chi-Square Automatic Interaction Detection Siti Khodijatunnuriyah; Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34526

Abstract

Market segmentation is a classic topic in marketing which is never loss its attractiveness. In addition to market segmentation, customer satisfaction is important in the field of marketing. Customer satisfaction is a person's feelings after using goods or services produced by a company. High customer satisfaction shows a company's success in producing goods or services. Statistics provides many tools for segmentation research. One of statistical tool for segmentation research which takes the dependency method as an approach is Chi-Squared Automatic Interaction Detection (CHAID) analysis. CHAID analysis would provide decision tree like diagram which provide information about degree of association from dependent variable to the independent variables and the information about segments characteristic. In this case, the CHAID analysis is used to determine the type of service revocation segmentation by Indihome customers. Based on CHAID analysis, 25 segmentations were obtained, which consisted of revocation of the downgrade category of 45314 customers and the number of revocation of the Churn Out category by 11137 customers.Keywords : market segmentation, customer satisfaction, CHAID, Indihome
Penggunaan Geoda untuk Pemetaan Bencana Alam di Kabupaten Karanganyar Hasih Pratiwi; Niswatul Qona’ah; Kiki Ferawati; Sri Sulistijowati Handajani; Handajani Handajani; Yuliana Susanti; Muhammad Bayu Nirwana
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 3 (2020): Peran Perguruan Tinggi dan Dunia Usaha Dalam Pemberdayaan Masyarakat Untuk Menyongsong
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.354 KB) | DOI: 10.37695/pkmcsr.v3i0.817

Abstract

Kemampuan mengolah data menjadi kebutuhan di masa kini, apalagi dengan banyaknya data yang tersedia yang dapat diakses secara bebas. Statistika dapat digunakan untuk membantu masyarakat dalam menjelaskan dan memahami gambaran tentang kejadian bencana alam. Karanganyar, yang terletak di Provinsi Jawa Tengah, merupakan salah satu kabupaten di Indonesia yang rawan bencana alam. Oleh karena itu, diperlukan visualisasi data sebagai upaya untuk memberikan pemahaman kepada masyarakat tentang bencana alam yang terjadi di wilayah Kabupaten Karanganyar. Pemetaan bencana alam dengan Geoda dapat memberikan informasi kondisi kecamatan-kecamatan di Karanganyar yang rawan bencana alam. Untuk menyusun peta, diperlukan data bencana alam serta file peta wilayah. Setelah program Geoda terinstal, peta dapat disusun melalui menu toolbar, mengurutkan kolom kode kabupaten, create project file, dan map. Peta spasial menunjukkan bahwa tanah longsor sering terjadi di wilayah Kabupaten Karanganyar bagian timur yang berbatasan dengan Kabupaten Magetan di Jawa Timur, kebakaran di bagian tengah, dan angin ribut di bagian utara.