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All Journal Jurnal Mahasiswa Statistik Journal of Innovation and Applied Technology Erudio: Journal of Educational Innovation Jurnal POINTER JOIN (Jurnal Online Informatika) Science and Technology Indonesia ComTech: Computer, Mathematics and Engineering Applications Indonesian Journal of Electrical Engineering and Computer Science Aksara: Jurnal Ilmu Pendidikan Nonformal
Ani Budi Astuti
Departemen Statistika FMIPA Universitas Brawijaya
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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PERBANDINGAN METODE PARTIAL LEAST ABSOLUTE DEVIATION REGRESSION DAN METODE PARTIAL ROBUST M REGRESSION PADA KASUS DATA OUTLIER Maghfiroh, Aulia; Astuti, Ani Budi; Pramoedyo, Henny
Jurnal Mahasiswa Statistik Vol 1, No 2 (2013)
Publisher : Jurnal Mahasiswa Statistik

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Abstract

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THE EFFECTIVENESS OF DEMONSTRATION AND EXPERIMENTATION LEARNING METHODS FOR EMPOWERING PINE FOREST COFFEE FARMERS BENDOSARI VILLAGE PUJON-MALANG INDONESIA Ani Budi Astuti; N Nurjannah; Luthfatul Amaliana; Wenny Bekti Sunarharum
Erudio Journal of Educational Innovation Vol 6, No 2 (2019): Erudio Journal of Educational Innovation
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18551/erudio.6-2.4

Abstract

An effective learning method is very important for the success of a learning goal, especially for the success of learning that involves the village community. The village communities have very diverse abilities and knowledge, in general an activity is carried out in accordance with daily habits, so it is not easily changed. However, the villagers already have a lot of experience, especially related to their daily livelihoods. The characteristics of such objects require an effective learning innovation in order to increase the knowledge they do not yet have. An effective learning method is a form of activity to improve the culture of literacy in the community. The demonstration learning methods and field experiments proposed as learning methods are expected to be in accordance with the characteristics of coffee farmers in Bendosari Village in the context of empowering coffee farmers in Bendosari Village. The purpose of this study is to identify how effective the demonstration learning methods and field experiments for empowering coffee farmers in Bendosari Village, Pujon-Malang Indonesia. The results showed that the demonstration learning method and field experiment very effective in increasing the knowledge and field ability of coffee farmers in Bendosari Village. Increase farmers knowledge and ability by 20%.
Aplikasi Fuzzy Ordered Weighted Averaging (OWA) Dalam Multi Kriteria Analisis Untuk Penentuan Kelas Area Bencana Lumpur Lapindo Candra Dewi; Ani Budi Astuti; Putra Pandu Adikara
Jurnal POINTER Vol 2, No 1 (2011): Jurnal Pointer - Ilmu Komputer
Publisher : Ilmu Komputer, Universitas Brawijaya

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Abstract

ABSTRAK Penentuan area yang berbahaya dari suatu sumber bencana sangat penting untuk memberikan informasi tentang area yang mungkin terdampak bencana tersebut. Dalam analisa tingkat bahaya terhadap bencana alam kadang timbul fuzziness yang terkait dengan adanya informasi yang tidak lengkap terhadap lingkungan sistem dan ketidakpastian dalam pengukuran. Untuk kasus analisa tingkat bahaya yang memiliki informasi yang tidak presisi, metode standarisasi yang bisa digunakan adalah dengan metode fuzzy. Tujuan dari penelitian ini adalah untuk menerapkan metode fuzzy Ordered Weighted Averaging (OWA) dalam evaluasi multi kriteria daerah berbahaya Lumpur Lapindo. Hasil yang diperoleh dari penerapan fuzzy dari masing-masing kriteria ini kemudian digunakan untuk menampilkan informasi area berdasarkan tingkat bahaya dengan menggunakan analisa spasial. Sedangkan untuk analisa sensitifitas, digunakan nilai kriteria pada batas bawah, batas tengah dan batas atas. Berdasarkan hasil analisa spasial diketahui bahwa sekitar 56,35% area dikategorikan kelas kerawanan rendah (Z3), daerah dengan kelas kerawanan sedang (Z2) mencakup luas sekitar 28,07%, kelas keranan tinggi (Z1) dan daerah tidak rawan (Z4) secara berturut-turut hanya menempati luasan 15,33% dan 0,24% dari total area pemukiman. Dari hasil overlay peta kerawanan ini dengan peta terdampak dapat diketahui bahwa terdapat daerah diluar daerah terdampak yang termasuk dalam kelas kerawanan sedang dan tinggi. Dan dari hasil analisa sensitifitas diketahui bahwa zona Z1 dan Z4 tidak begitu sensitif untuk nilai dari batas tengah sampai atas, zona Z2 dan Z3 tidak begitu sensitif untuk nilai dari batas bawah sampai batas tengah.   Kata kunci: analisa multi kriteria, analisa spasial, fuzzy OWA   ABSTRACT Determining vulnerable area of hot mud volcano is important to provide information on the extent of the areas affected by the hazard. Vulnerability analysis for natural hazard deals with uncertainty arises due to the lack of information about system behavior (vagueness, ambiguity, fuzziness) and inexactness of measurement (impreciseness, fuzziness). Fuzzy approach can be used to overcome this fuzziness. The objectives of this paper are to implement fuzzy Ordered Weighted Averaging (OWA) for multi-criteria evaluation of mud volcano vulnerable area and to develop mud volcano vulnerable map using proposed method in Lapindo Mud area. The calculation of membership degree used sigmoid and triangular membership function. The sensitivity analysis was done at lower, middle and upper value of the range. Base on the spatial analysis was found that low hazardous area (Z3) covered about 56.35% area, while 28.07% considered as moderate hazardous area (Z2), 15.33% as high hazardous area (Z1) and 0.24% as not impacted area (Z4). Base on sensitivity analysis also found that classes Z1 and Z4 were not enough sensitive between the middle and upper range, while classes Z2 and Z3 were not enough sensitive between the lower and middle range.   Keywords: multi criteria analysis, spatial analysis, fuzzy OWA
Finding Biomarkers from a High-Dimensional Imbalanced Dataset Using the Hybrid Method of Random Undersampling and Lasso Masithoh Yessi Rochayani; Umu Sa'adah; Ani Budi Astuti
ComTech: Computer, Mathematics and Engineering Applications Vol. 11 No. 2 (2020): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v11i2.6452

Abstract

The research conducted undersampling and gene selection as a starting point for cancer classification in gene expression datasets with a high-dimensional and imbalanced class. It investigated whether implementing undersampling before gene selection gave better results than without implementing undersampling. The used undersampling method was Random Undersampling (RUS), and for gene selection, it was Lasso. Then, the selected genes based on theory were validated. To explore the effectiveness of applying RUS before gene selection, the researchers used two gene expression datasets. Both of the datasets consisted of two classes, 1.545 observations and 10.935 genes, but had a different imbalance ratio. The results show that the proposed gene selection methods, namely Lasso and RUS + Lasso, can produce several important biomarkers, and the obtained model has high accuracy. However, the model is complicated since it involves too many genes. It also finds that undersampling is not affected when it is implemented in a less imbalanced class. Meanwhile, when the dataset is highly imbalanced, undersampling can remove a lot of information from the majority class. Nevertheless, the effectiveness of undersampling remains unclear. Simulation studies can be carried out in the next research to investigate when undersampling should be implemented.
Knowledge discovery from gene expression dataset using bagging lasso decision tree Umu Sa'adah; Masithoh Yessi Rochayani; Ani Budi Astuti
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 2: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i2.pp1151-1159

Abstract

Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.
Community Empowerment of Ngingit Village, Tumpang District, Malang Regency through Suweg Processed Products in the Framework of Raising the Existence of Local Food Ingredients Towards a Food Self-Sufficiency Village Mochammad Yosi Pratikno; Ani Budi Astuti; Yasma Aziza Ul Akrimah; Endryana Seftyaningtyas; Siva Nur Alvia; Bayu Hangga Trio Eko Putro
Journal of Innovation and Applied Technology Vol 8, No 2 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

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Abstract

The program carried out to overcome food problems is CAMPA. The CAMPA program provides training on the processing of local food ingredients, namely suweg into several preparations such as noodles, meatballs, cookies, and cereals. The method used in this study is the Participatory Action Research (PAR) method, a method that involves the dominant role of the community in the concept of empowerment
Covid-19 Pandemic Disaster And Inflation Rate In East Java By Statistical Perspective Using Robust Factor Analysis Nindi Dwike Bella Astari; Ani Budi Astuti; Niel Ananto
Aksara: Jurnal Ilmu Pendidikan Nonformal Vol 9, No 1 (2023): January 2023
Publisher : Magister Pendidikan Nonformal Pascasarjana Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/aksara.9.1.549-556.2023

Abstract

Bencana pandemi COVID-19 telah menghantam semua sektor kehidupan di seluruh dunia termasuk di Indonesia, khususnya pada sektor ekonomi. Jawa Timur sebagai salah satu provinsi di Indonesia dengan jumlah penduduk terbesar kedua setelah Provinsi Jawa Barat merupakan provinsi yang terimbas besar dalam sektor ekonomi akibat bencana Pandemi COVID-19. Salah satu indikator bencana ekonomi adalah angka inflasi yang tinggi dan hal ini berakibat pada pertumbuhan ekonomi yang akan terus melambat sehingga mengancam kesejahteraan masyarakat. Oleh karena itu, pemerintah Indonesia berupaya terus menerus berupaya untuk menjaga angka inflasi yang rendah dan stabil. Deskripsi tingkat inflasi harga barang/jasa kebutuhan rumah tangga dapat dilihat melalui perubahan Indeks Harga Konsumen (IHK), yaitu variabel makanan, minuman, dan tembakau; sandang, perumahan, perlengkapan rumah tangga, kesehatan, transportasi, informasi dan komunikasi, pendidikan, dan rekreasi. Keterkaitan antara bencana pandemi Covid-19 dan tingkat inflasi dalam perspektif statistika akan dikulik melalui pendekatan analisis faktor kekar dengan metode pendugaan parameter Minimum Volume Ellipsoid (MVE). Tujuan penelitian ini adalah membentuk faktor-faktor yang dominan pengaruhnya terhadap tingkat inflasi di Jawa Timur berdasarkan sembilan variabel indikator IHK saat terjadi bencana pandemi Covid-19. Hasil penelitian menunjukkan bahwa dampak bencana pandemi Covid-19 terhadap tingkat inflasi di Jawa Timur adalah masyarakat lebih mengutamakan faktor kebutuhan primer, yaitu komponen makanan, minuman, dan tembakau daripada komponen yang lain serta kurang terpenuhinya faktor kebutuhan sekunder.
Ketahanan Ekonomi Produksi Padi Di Wilayah Jawa Timur Melalui Pemodelan Regresi Robust Data Panel Dengan Penduga Least Trimmed Square Ani Budi Astuti; Aprilia Nurul Azizah; Niel Ananto
Aksara: Jurnal Ilmu Pendidikan Nonformal Vol 9, No 1 (2023): January 2023
Publisher : Magister Pendidikan Nonformal Pascasarjana Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/aksara.9.1.567-580.2023

Abstract

Produksi padi merupakan salah satu hal yang terus ditingkatkan untuk memenuhi kebutuhan konsumsi di Indonesia. Sektor pertanian memiliki fungsi sebagai penyedia pangan dan telah memberikan peran strategis dalam pembangunan nasional. Beras sebagai bahan pangan utama penduduk Indonesia merupakan komoditas yang berperanan dalam penetapan ketahanan pangan dan ketahanan ekonomi untuk mewujudkan stabilitas nasional. Jawa Timur merupakan provinsi dengan tingkat produksi padi terbesar kedua setelah provinsi Jawa Barat. Metode Kuadrat Terkecil merupakan salah satu metode pendugaan parameter regresi yang mudah terpengaruh adanya pencilan. Pencilan dapat menyebabkan penduga parameter regresi bersifat bias. Oleh karena itu, diperlukan metode yang kekar terhadap adanya pencilan. Regresi robust digunakan ketika asumsi normalitas sisaan tidak terpenuhi karena adanya pencilan. Salah satu jenis data yang banyak ditemui adalah data panel yang merupakan gabungan antara data cross section dan time series. Pada penelitian ini dimodelkan data panel produksi Padi dari 38 kabupaten/kota di Provinsi Jawa Timur dengan regresi robust penduga-LTS (Least Trimmed Square) dan diidentifikasi faktor-faktor yang mempengaruhi produksi padi di Jawa Timur. Hasil penelitian menunjukkan bahwa faktor-faktor yang mempengaruhi produksi padi di Jawa Timur adalah luas panen padi dan luas serangan organisme penggangu tanaman padi dengan validasi model berdasarkan nilai koefisien determinasi sebesar 99,3%. Performa penduga LTS model random effect model pada regresi robust data panel yang mengandung pencilan sangat baik untuk data produksi padi di wilayah Jawa Timur. 
Bayesian Mixture Statistical Modeling Perspective in the Series of Diabetes Mellitus Disaster Mitigation in Malang Regions Ani Budi Astuti; Nur Iriawan; Suci Astutik; Viera Wardhani; Ari Purwanto Sarwo Prasojo; Tiza Ayu Virania
Science and Technology Indonesia Vol. 8 No. 1 (2023): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2023.8.1.71-83

Abstract

Statistical modeling is one of the most important activities in Statistics in order to simplify complex problems in society, to make it easy, simple, and useful. The perspective of statistical modeling is very useful for society in various fields. Probabilistic-based statistical modeling concept is strongly influenced by the shape of the data distribution, data validity, and data availability. Bayesian concept approach in the statistical modeling has advantages compared to the non-Bayesian approach, which is any sample and any distribution of the data and in this case it often occurs in data in the community. In particular, the Bayesian mixture concept discusses the Bayesian approach with data specifications having a mixture (multimodal) distribution. Diabetes Mellitus (DM) is a disease that is not contagious but the side effects are very dangerous for humans and require large costs to handle. Indonesia ranks seventh in the world for the number of DM sufferers and it is estimated that in 2045, the number of DM sufferers in Indonesia will reach approximately 16.7 million people. Mitigation of DM disease in various regions in Indonesia continues to be pursued, including Malang regions. One of the efforts made is through the statistical modeling perspective of the Bayesian approach which can be used for efforts to control, prevent, treat, and overcome DM. The purpose of the study was to build a suitable Bayesian model for DM cases in Malang regions in order to map the DM case areas in Malang. The results showed that in each district area in the city of Malang it was divided into three groups based on the severity of DM sufferers. The three groups are DM sufferers in the categories of not yet severe, moderate, and severe with the model validation indicator using the smallest Kolmogorov-Smirnov value. Sukun District and Klojen District in the Malang region are two districts that need serious attention from the local government of Malang City in dealing with DM cases. Through the perspective of Bayesian statistical modeling, DM cases in five districts in the Malang area showed a mixture distribution with a different number of mixture components as the basis for regional mapping.
Two-stage Gene Selection and Classification for a High-Dimensional Microarray Data Masithoh Yessi Rochayani; Umu Sa'adah; Ani Budi Astuti
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.569

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Microarray technology has provided benefits for cancer diagnosis and classification. However, classifying cancer using microarray data is confronted with difficulty since the dataset has high dimensions. One strategy for dealing with the dimensionality problem is to make a feature selection before modeling. Lasso is a common regularization method to reduce the number of features or predictors. However, Lasso remains too many features at the optimum regularization parameter. Therefore, feature selection can be continued to the second stage. We proposed Classification and Regression Tree (CART) for feature selection on the second stage which can also produce a classification model. We used a dataset which comparing gene expression in breast tumor tissues and other tumor tissues. This dataset has 10,936 predictor variables and 1,545 observations. The results of this study were the proposed method able to produce a few numbers of selected genes but gave high accuracy. The model also acquired in line with the Oncogenomics Theory by the obtained of GATA3 to split the root node of the decision tree model. GATA3 has become an important marker for breast tumors.
Co-Authors Aprilia Nurul Azizah Ari Purwanto Sarwo Prasojo Bayu Hangga Trio Eko Putro Candra Dewi Endryana Seftyaningtyas Luthfatul Amaliana Maghfiroh, Aulia Masithoh Yessi Rochayani Mochammad Yosi Pratikno N Nurjannah Niel Ananto Nindi Dwike Bella Astari Nur Iriawan Pramoedyo, Henny Putra Pandu Adikara Siva Nur Alvia Suci Astutik Tiza Ayu Virania Umu Sa'adah Umu Sa'adah Viera Wardhani Wenny Bekti Sunarharum Yasma Aziza Ul Akrimah