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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Media Infotama Journal of the Indonesian Mathematical Society KEUNIS JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) SINTECH (Science and Information Technology) Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics International Journal of Remote Sensing and Earth Sciences (IJReSES) Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Building of Informatics, Technology and Science ESTIMASI: Journal of Statistics and Its Application Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Natural Xplore: Journal of Statistics STATISTIKA Scientific Journal of Informatics Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications The International Journal of Remote Sensing and Earth Sciences (IJReSES)

Anang Kurnia

School Of Data Science, Mathematics And Informatics, IPB University, Indonesia

Author-ID : 2479473

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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Transportation Other

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Perbandingan Hasil Pengelompokan menggunakan Analisis Cluster Berhirarki, K-Means Cluster, dan Cluster Ensemble (Studi Kasus Data Indikator Pelayanan Kesehatan Ibu Hamil) Cici Suhaeni; Anang Kurnia; Ristiyanti Ristiyanti
Jurnal Media Infotama Vol 14 No 1 (2018)
Publisher : UNIVED Press

Pengelompokan merupakan kegiatan di bidang riset yang banyak digunakan hingga saat ini. Terlebih di era big data seperti sekarang. Banyak metode yang berkembang untuk keperluan tersebut. Penelitian ini membandingkan hasil pengelompokan menggunakan metode cluster hierarki, k-means cluster, dan cluster ensemble pada pengelompokan provinsi di Indonesia berdasarkan indikator pelayanan kesehatan ibu hamil. Hasil analisis menunjukkan bahwa cluster ensemble merupakan metode yang paling tepat dalam mengelompokkan provinsi-provinsi tersebut. Cluster yang dihasilkan adalah 3 (tiga) cluster. Kata Kunci: analisis cluster, cluster ensemble, cluster hierarki, k-means cluster.

PENDEKATAN MARGINAL PADA ANALISIS DATA SURVIVAL “BERKORELASI” Dian Handayani; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.523

Didalam konteks data survival yang berkorelasi, yaitu pada saat objek terkelompok (misal karena perlakuan, ikatankeluarga atau karena pengamatan berulang), maka peubah respon didalam kelompok pada dasarnya akan berkorelasi, sehinggakita akan menganggap dan mengasumsikan bahwa data tersebut berkorelasi.Shoukri dan Pause (1998) telah menunjukan bahwa metode penduga maksimum likehood (MLE) memberikan hasil yang tidakkonsisten. Sedangkan Liang dan Zeger (1986) serta Zeger dan Liang (1986) telah mengembangkan metode GEE untukmengkoreksi kasus data berjorelasi. Telah banyak penulis yang memberikan evaluasi terhadap GEE dan memberikan kesimpulanbahwa GEE adalah salahsatu pendekatan yang robust dalam menduga ragam untuk data terkelompok. Selain itu alternatif lainyang bisa digunakan adalah GJE yang dikembangkan oleh Therneau (1993).Dalam makalah ini akan dicoba pendekatan GEE dalam analisis survival untuk kasus data terkelompok yang dikenal sebagaipendekatan marginal.Pendekatan GEE dikembangkan serupa dan berlandaskan pada model Cox Proportional Hazards.Pendekatan margianl membeikan hasil pendugaan ragam yang cukup baik sehingga cukup efektif mengoreksi pengaruh dataterkelompok. Namun demikian masih terdapat kelemahan yang sangat mengganggu yaitu makna dari pengelompokan data,dimana tidak semua kelompok mempunyai makna yang berarti.

Penerapan Algoritma Tree Augmented Naive Bayesian pada Penentuan Peubah Penting Pingkan Awalia; Aji Hamim Wigena; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 11, No 2 (2011)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v11i2.1053

In the era of free market competition today, improving product quality is very important. Consumerpreferences through product level of analysis is one method that many manufacturers conducted toevaluate the product. Multivariable regression is a statistical method used to determine the importantvariables. The weakness of this method is the strict assumption. This problem will be completed bythe method of bayesian networks. There are several algorithms to build the BN. This study uses TANand NB because of its simplicity. This study shows that the most accurate method at the chosen levelof classification accuracy is the TAN by 83%. The importance variable is the aspect liking of strengthof after taste.

ANALYZING THE CONSUMER’S RICE PRICE USING MULTIPLE LINEAR REGRESSION AND X-12 ARIMA Dian Kusumaningrum,; Asep Saefuddin; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.876

Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. Onthe other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to therice price, as well as forecasting the consumer’s rice price are needed in order to maintain the stabilization of rice price.The second objective is to find key factors which influence the rice price by using multiple linear regression models. Theparameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which arethe consumer’s rice price at the previous period, rice production at the current and previous period, farmer’s GKP price,realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domesticstock increase whereas total rice import and the consumer’s rice price at the previous period have negative influencestowards the rice price. In this model rice production at the current and previous period have positive signs, contradictory tothe microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop.That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but inIndonesia, rice is affected by the government’s policy and the rice productivity is left behind by the demand. Forecastingthe consumer’s rice price for the next five years was the last objective of this research. ARIMA Box–Jenkins Method, X-12ARIMA, Winter’s Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer’srice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. Thisresult is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal andtrading day factors which usually causes fluctuations in an economic time series data. Besides that, the X-12 ARIMAmethod also enhances the lack of other forecasting techniques used in this research to add regression effects. TheregARIMA makes it possible to add the user defined parameters, in this case the length of month parameter. The length ofmonth parameter rescales the monthly observation by a weight corresponding to the month relative length with respect tothe average length. The seasonal adjusted data from the original time series data is aimed to simplify the data withoutloosing important information.

Generalized Multilevel Linear Model dengan Pendekatan Bayesian untuk Pemodelan Data Pengeluaran Perkapita Rumah Tangga Azka Ubaidillah; Anang Kurnia; Kusman Sadik
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Household per capita expenditure data is one of the important information as an approach to measure the level of prosperity in an area. Such data is needed by the government, both at the central and regional levels in formulating, implementing and evaluating the implementation of development programs. This research is aimed at modeling the household per capita expenditure data which takes into account the specificity of BPS data which has a hierarchical structure, and data distribution pattern which has the right skewed characteristic. The modeling is done by using the three parameters of Log-normal distribution (LN3P) and the three parameters of Log-logistics (LL3P) with a single level (unilevel) and two levels (multilevel) structure. The parameter estimation process is done by Markov Chain Monte Carlo (MCMC) method and Gibbs Sampling algorithm. The results showed that on the unilevel model, the LL3P model is better than the LN3P model. While in multilevel model, LN3P model is better than LL3P model. The results also show that the best model for modeling household per capita expenditure data is the LN3P multilevel model with the smallest Deviance Information Criterion (DIC) value.

ANALISIS PENDUGAAN UKURAN KEMISKINAN MONETER PADA CONTOH BERUKURAN KECIL Nurul Hidayati; Asep Saefuddin; Anang Kurnia
FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Vol 5, No 1 (2019): FIBONACCI: Jurnal Pendidikan Matematika dan Matematika
Publisher : Fakultas Ilmu Pendidikan Universitas Muhammadiyah Jakarta

Kemiskinan merupakan sebagian dari masalah pembangunan yang berkaitan dengan berbagai dimensi yang meliputi sosial, ekonomi, budaya, politik, regional dan waktu.Penelitian ini bertujuan untuk menilai ukuran sampel dari ukuran kemiskinan moneter yang digunakan oleh Badan Pusat Statistik Indonesia (BPS) dan juga untuk mencari solusi alternatif dalam mengestimasi ukuran kemiskinan moneter dalam contoh berukuran kecil. Metode yang digunakan dalam penelitian ini adalah estimasi langsung yang dilengkapi dengan simulasi yang bertujuan untuk mengevaluasi ukuran sampel dalam perhitungan estimasi pengukuran kemiskinan dan Bayes empiris sebagai solusi alternatif dalam mengestimasi ukuran kemiskinan moneter dengan ukuran sampel kecil. Hasil penelitian ini menunjukkan nilai estimasi memiliki varians kecil dan tidak bias jika ukuran sampel yang digunakanbesar, dan sebaliknya. Ini terbukti dalam perbandingan ukuran sampel, seperti yang ditunjukkan padaperilaku indeks bias Relative Bias (RB), Absolute Relative Bias, dan Relative Mean Square Error (RMSE). Dengan demikian, estimasi langsung dapat dikoreksi dengan estimasi Bayesian empiris dalam menangani masalah ukuran sampel yang kecil.

PENANGANAN OVERDISPERSI PADA PEMODELAN DATA CACAH DENGAN RESPON NOL BERLEBIH (ZERO-INFLATED) Viarti Eminita; Anang Kurnia; Kusman Sadik
FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Vol 5, No 1 (2019): FIBONACCI: Jurnal Pendidikan Matematika dan Matematika
Publisher : Fakultas Ilmu Pendidikan Universitas Muhammadiyah Jakarta

Overdispersi pada data cacah yang disebabkan karena kasus nol berlebih tidak dapat ditangani dengan metode model linier umum biasa seperti Poisson dan Binomial Negatif. Penanganan overdispersi karena nol berlebih dapat dilakukan dengan menggunakan model Zero-Inflated. Zero-Inflated Poisson (ZIP) dan Zero-Inflated Binomial Negatif (ZIBN) telah diyakini performanya dalam menangani masalah ini. Selain menangani masalah tersebut kedua model ini juga dapat memberikan informasi mengenai penyebab nol berlebih pada data respon. Performa ke Empat model tersebut dibandingkan dalam menduga model dari jumlah anak yang tidak sekolah dalam keluarga di Provinsi Jawa Barat pada tahun 2017. Berdasarkan nilai dari ukuran Pearson Chi-Squares, Likelihood Ratio Chi-Square, dan Akaike Information Crieteria (AIC). Pearson Chi-Squares, model ZIP lebih baik dibandingkan ZIBN dan model lainnya, walaupun berbeda sedikit dengan ZIBN.

PEMODELAN KEMISKINAN DI JAWA MENGGUNAKAN BAYESIAN SPASIAL PROBIT PENDEKATAN INTEGRATED NESTED LAPLACE APPROXIMATION (INLA) Retsi Firda Maulina; Anik Djuraidah; Anang Kurnia
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few. The Bayes Method is a parameter estimation method that utilizes initial information (prior) and sample information so that it can provide predictions that have a higher accuracy than the classical methods. Bayes inference using INLA approach provides faster computation than MCMC and possible uses large data sets. This study aims to model Javanese poverty using the Bayesian Spatial Probit with the INLA approach with three weighting matrices, namely K-Nearest Neighbor (KNN), Inverse Distance, and Exponential Distance. Furthermore, the result showed poverty analysis in Java based on the best model is using Bayesian SAR Probit INLA with KNN weighting matrix produced the highest level of classification accuracy, with specificity is 85.45%, sensitivity is 93.75%, and accuracy is 89.92%.

Metode AdaBoost dan Random Forest untuk Prediksi Peserta JKN-KIS yang Menunggak Ikhlasul Amalia Rahmi; Farit Mochamad Afendi; Anang Kurnia
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

The contribution of participants, employers, and/or the government is one of the most important things in the National Health Insurance Program-Healthy Indonesia Card (JKN-KIS) implementation. All Indonesian residents were required to participate in the JKN-KIS program which is divided into four types of participation, one of which is Non-Wage Recipient Participants (PBPU) whose contributions are paid independently. However, based on December 2021 data, 60% of PBPU participants were late in paying monthly until they were in arrears. Arrears in payment of contributions cause several problems, including payment of claims to deficits. This research utilized big data owned by the Healthcare and Social Security Agency (BPJS Kesehatan) and machine learning based on ensemble trees, namely AdaBoost and random forest to get the predictions of participants in arrears. The results showed that machine learning based on an ensemble tree was able to predict PBPU participants in arrears with high accuracy, as evidenced by the AUC values in both models above 80%. The random forest model has an F1-score and the AUC value is better than the AdaBoost, namely the F1-score of 85,43% and the AUC value of 87,20% in predicting JKN-KIS participants who are in arrears in payment of contributions.

CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Triscowati, Dwi Wahyu; Sartono, Bagus; Kurnia, Anang; Dirgahayu, Dede; Wijayanto, Arie Wahyu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.

Co-Authors . Hanniva . Marzuki . Sutriyati Abdullah Ilman Fahmi Achmad Fauzan Achmad Fauzan, Achmad Agus Buono Agus M Soleh Agus Mohamad Soleh Ahmad Ansori Mattjik Ajeng Bita Alfira Aji Hamim Wigena Alkahfi, Cahya Amalia Pasaribu, Asysta Amin, Yudi Fathul Anik Djuraidah Ardiansyah, Muhlis Arie Anggreyani Arief Gusnanto ASEP SAEFUDDIN Astri Fatimah Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Susetyo Budi Waryanto Cici Suhaeni Citra Jaya Dede Dirgahayu Dede Dirgahayu Deiby T Salaki Dewi Juliah Ratnaningsih Dhea Dewanti Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Dwi Agustin Nuriani Sirodj Dwi Wahyu Triscowati Efriwati Efriwati Erfiani Erfiani Erfiani Erwan Setiawan, Erwan Farit Mochamad Afendi Farit Mohamad Afendi Fauzi, Fatkhurokhman Fauziah, Ghina Febryna Sembiring Fitri Dewi Shyntia Fitrianto, Anwar Fitriyani Sahamony, Nur Gerry Alfa Dito Hamid, Assyifa Lala Pratiwi Hamim Wigena, Aji Haq, Irvanal Hari Wijayanto Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Agus Sofian Eka Hidayat, Muhammad I Made Sumertajaya I Wayan Mangku Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Ita Wulandari Iwan Kurniawan Khairani, Fitri Khairil Anwar Notodiputro Kristuisno Martsuyanto Kapiluka Kusman Sadik Loly, Joao Ferreira Rendes Bean Matualage, Dariani Maulana Achiar, Anshari Luthfi Muhammad Nur Aidi Mulianto Raharjo Nashir, Husnun Newton Newton Nurul Hidayati Pardomuan Robinson Sihombing Pasaribu, Asysta Amalia Pingkan Awalia Pramana, Setia Purba, Widyo Pura Purwanto, Arie Putri, Christiana Anggraeni Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rahman, Gusti Arviana Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Ryska Putri Madyasari Sahamony, Nur Fitriyani Santoso, Andrianto Santoso, Zein Rizky Sari Agustini Hafman Septiani, Adeline Vinda Setyowati, Indah Rini Siregar, Jodi jhouranda Siskarossa Ika Oktora Siti Muchlisoh Suhaeni, Cici Suprayogi, Muhammad Azis Suprayogi, Muhammad Aziz Teguh Prasetyo Thooriq Ghaith Topan . Ruspayandi Triscowati, Dwi Wahyu Tyas, Maulida Fajrining Utami Dyah Syafitri Viarti Eminita Widiyanto, Rhendy K. P. Widoretno, Widoretno Yani Nurhadryani Yenni Angraini Yenni Kurniawati Yudistira Yudistira Yully Sofyah Waode

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