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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Signifikan : Jurnal Ilmu Ekonomi Media Statistika Jurnal Statistika Universitas Muhammadiyah Semarang Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Matematika Sains dan Teknologi Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal Information System Development Jambura Journal of Informatics Jurnal Aplikasi Statistika & Komputasi Statistik Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems International Journal of Business, Law, and Education JUSTIN (Jurnal Sistem dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Prosiding Seminar Nasional Official Statistics
Robert Kurniawan, Robert
Jurusan Komputasi Statistik Sekolah Tinggi Ilmu Statistik (STIS) Jakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Optimasi Algoritma Fuzzy Clustering dengan Menggunakan Algoritma Forest Optimization Kurniawan, Robert; Prabowo, Edhi
Journal Information System Development (ISD) Vol 4, No 1 (2019): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.364 KB)

Abstract

Fuzzy C-Means (FCM) adalah salah satu teknik clustering yang sering digunakan, tetapi memiliki kelemahan yaitu sensitif terhadap local optima dan sensitif terhadap pusat cluster awal. Forest Optimization Algortihm mampu mengatasi kelemahan dari FCM. FOFCM dibangun dengan 2 jenis jarak yaitu Euclidean dan Mahalanobis. FOFCM memiliki performa yang lebih baik dari FCM, karena sebagian besar iterasi FOFCM lebih sedikit dari FCM. FOFCM Mahalanobis menghasilkan nilai fungsi objektif paling kecil pada sebaran data hyperspherical dibandingkan dengan FOFCM Euclidean maupun FCM. Oleh karena itu, dapat disimpulkan bahwa FOFCM Mahalanobis cocok untuk data hyperspherical.
ANALISIS PERBANDINGAN METODE FUZZY C-MEANS DAN SUBTRACTIVE FUZZY C-MEANS Haqiqi, Baiq Nurul; Kurniawan, Robert
MEDIA STATISTIKA Vol 8, No 2 (2015): 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 (200.309 KB) | DOI: 10.14710/medstat.8.2.59-67

Abstract

Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent. Subtractive Clustering (SC) is an alternative method that can be used when number of clusters are unknown. Moreover, SC produces consistent clustering result. A hybrid method of FCM and SC called Subtractive Fuzzy CMeans (SFCM) is proposed to overcome FCM’s disadvantages using SC. Both SFCM and FCM are implemented to cluster generated data and the result of the two methods are compared. The experiment shows that generally SFCM produces better clustering result than FCM based on six validity indices.Keywords : Clustering, Fuzzy C-Means, Subtractive Clustering, Subractive Fuzzy C-Means
PEMODELAN DATA KEMATIAN BAYI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION Ramadhan, Riza F.; Kurniawan, Robert
MEDIA STATISTIKA Vol 9, No 2 (2016): 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 (238.539 KB) | DOI: 10.14710/medstat.9.2.95-106

Abstract

Overdispersion phenomenon and the influence of location or spatial aspect on data are handled using Binomial Geographically Weighted Regression (GWNBR). GWNBR is the best solution to form a regression analysis that is specific to each observation’s location. The analysis resulted in parameter value which different from one observation to another between location. The Weighting Matrix Selection is done before doing The GWNBR modeling. Different weighting  will resulted in different model. Thus this study aims to  investigate the best fit model using infant mortality data that is produced by some kind of weighting such as fixed kernel Gaussian, fixed kernel Bisquare, adaptive kernel Gaussian and adaptive kernal Bisquare in GWNBR modeling. This region study covers all the districts/municipalities in Java because the number of observations are more numerous and have more diverse characteristics. The study shows that out of four kernel functions, infant mortality data in Java2012, the best fit model is produced by fixed kernel Gaussian function. Besides that GWNBR with fixed kernel Gaussian also shows better result than the poisson regression and negative binomial regression for data modeling on  infant mortality based on the value of AIC and Deviance.                                                                                    Keywords:   GWNBR, infant mortality, fixed gaussian, fixed bisquare, adaptive gaussian, adaptive bisquare.
KOMPARASI METODE PERAMALAN AUTOMATIC CLUSTERING TECHNIQUE AND FUZZY LOGICAL RELATIONSHIPS DENGAN SINGLE EXPONENTIAL SMOOTHING Endaryati, Betik; Kurniawan, Robert
MEDIA STATISTIKA Vol 8, No 2 (2015): 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 (345.696 KB) | DOI: 10.14710/medstat.8.2.93-101

Abstract

Automatic clustering technique and fuzzy logical relationships(ACFLR) is one of the forecasting method that used to predict time series data that can be applied in any data. Several previous studies said that this method has a good accuracy. Therefore, this study aims to compare the ACFLR methods with single exponential smoothing method and apply it to simulation data with uniform distribution. The performance of the method is measured based on MSE and MAPE. The results of the comparison of the methods showed that ACFLR has a higher forecasting accuracy than single exponential smoothing. This is evidenced by the value of MSE and MAPE of ACFLR is lower than single exponential smoothing.Keywords: Fuzzy, Forecasting, Automatic Clustering-Fuzzy Logic Relationships, Single Exponential Smoothing
The Linkage of Employment to Poverty in Central Java at 2010-2017 Sartika Andari Murti; Robert Kurniawan
Signifikan: Jurnal Ilmu Ekonomi Vol 9, No 2 (2020)
Publisher : Faculty of Economic and Business Syarif Hidayatullah State Islamic University of Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/sjie.v9i2.14466

Abstract

In 2017, Central Java was the second largest contributor to the GDP in Java but still has poverty and employment problems. In this research, wellbeing can approach with per capita expenditure. Empirically per capita expenditure has spatial relevance. This study aims to determine the relationship between employment indicators and the spending spatially in Central Java. The variables used are expenditure, labor force participation, labor productivity, minimum wage of regency/city, and the average length of the school. The analytical method used to determine the relationship is spatial panel regression with the Spatial Error Model fixed effect. The results obtained are labor force participation, labor productivity, minimum wage of regency/city, and the average length of the school, and spatial error dependencies have a significant positive effect on expenditure. Suggestions proposed are to increase employment through investments, especially in education, and to increase cooperation between regencies/cities in poverty alleviation efforts.JEL Classification: E2, E22, I3, I32, I38, J2, J21, J23.How to Cite:Murti, S. A., & Kurniawan, R. (2020). The Linkage of Employment to Poverty in Central Java. Signifikan: Jurnal Ilmu Ekonomi, 9(2), 195-206. https:// doi.org/10.15408/sjie.v9i2.14466.
PEMODELAN ANGKA KEMATIAN IBU DI INDONESIA DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED POISSON REGRESSION Rivan Destyanugraha; Robert Kurniawan
Jurnal Matematika Sains dan Teknologi Vol. 18 No. 2 (2017)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (824.929 KB) | DOI: 10.33830/jmst.v18i2.131.2017

Abstract

Maternal Mortality Rate (MMR) is one of the important indicators of a country's health development and is one of the targets of achieving Sustainable Development Goals (SDGs). This study aims to develop a model on the relationship of MMR with provincial health development variables using the Geographically Weighted Poisson Regression (GWPR) method; as well as mapping the model to the provincial map. Estimation of model parameters using PODES data for 2011 and the projected health and projection profile of 2010-2013. The obtained model consists of four variables that influence the number of maternal deaths: (1) the ratio of health facilities, (2) the ratio of midwives, (3) the percentage of deliveries assisted by health personnel, and (4) the percentage of pregnant women received Fe tablets. The mapping of the four variables into the provincial map yields three groups of regions with different levels of significance of variables. The AIC value and the GWPR model deviance are lower than Poisson regression, indicated that the AKI model with GWPR is better than Poisson regression. Angka Kematian Ibu (AKI) merupakan salah satu indikator penting pembangunan kesehatan suatu negara danmenjadi salah satu target pencapaian Sustainable Development Goals (SDGs). Penelitian ini bertujuan menyusun model hubungan AKI dengan variabel-variabel pembangunan kesehatan provinsi menggunakan metode Geographically Weighted Poisson Regression (GWPR) dan memetakan model tersebut kedalam peta provinsi. Estimasi parameter model menggunakan data PODES tahun 2011 dan profil kesehatan dan proyeksi penduduk tahun 2010-2013. Model yang diperoleh terdiri dari empat variabel yang mempengaruhi jumlah kematian ibu yaitu rasio sarana kesehatan, rasio bidan, persentase persalinan ditolong tenaga kesehatan, dan persentase ibu hamil mendapat tablet Fe. Pemetaan empat variabel tersebut ke dalam peta provinsi menghasilkan tiga kelompok wilayah dengan tingkat signifikansi variabel yang berbeda-beda. Nilai AIC dan deviance model GWPR lebih rendah dari regresi Poisson menunjukkan bahwa model AKI dengan GWPR lebih baikdari regresi Poisson.
PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TIMUR BERDASARKAN KASUS STUNTING BALITA MENGGUNAKAN ALGORITME FUZZY PARTICLE SWARM OPTIMIZATION-FUZZY C-MEANS Sepnita Wulandari; Robert Kurniawan
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 7, No 1 (2019): 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 (309.05 KB) | DOI: 10.26714/jsunimus.7.1.2019.%p

Abstract

Stunting is a condition that describe the presence of chronical malnutrition problem caused by various condition. East Java Province is a region that has the highest percentage of short toddler in Java Island. Moreover, there is high disparity in cross regency/city and the prevalence rate of stunting in the East Java Province is same as national prevalence rate. Meanwhile, Rencana Pembangunan Jangka Menengah Nasional (RPJMN) 2015-2019 sets the target of national prevalence rate of stunting toddler decreasing in 2019. Based on that problem, this research is clustering regency/city in East Java Province based on stunting toddler case. The clustering uses Fuzzy Particle Swarm Optimization-Fuzzy C-Means (FPSO-FCM). From the clustering result, this research obtains 2 cluster which are cluster of low stunting potential region (cluster 1) and high stunting potential region (cluster 2).
PENGELOMPOKAN MENGGUNAKAN METODE SUBTRACTIVE FUZZY C-MEAN (SFCM), STUDI KASUS DEMAM BERDARAH DI JAWA TIMUR Robert Kurniawan; Baiq Nurul Haqiqi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 3, No 2 (2015): 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 (539.283 KB) | DOI: 10.26714/jsunimus.3.2.2015.%p

Abstract

Metode pengelompokan yang sering digunakan dalam penelitian adalah Fuzzy C-Mean Cluster (FCM). Dalam perkembangannya, FCM dikombinasikan metode Subtractive Clustering (SC) sehingga didapatkan Hybrid Subtractive Fuzzy C-Mean (SFCM).Metode SFCM memiliki keunggulan dari tingkat kecepatan, dalam hal iterasi, dan menghasilkan partisi data yang lebih stabil dan akurat bila dibandingkan dengan metode sebelumnya. Pada penelitian ini, metode SCFM diaplikasikan dengan 13 variabel dari data demam berdarah. Studi kasus demam berdarah pada penelitian ini dilakukan di Provinsi Jawa Timur. Berdasarkan pengolahan dengan metode SFCM didapat hasilpengelompokan dengan 2 kelompok, 3 kelompok, dan 4 kelompok. Dari 6 indeks validasi untuk mengetahui jumlah pengelompokan yang tepat, menunjukkan bahwa pengelompokan menjadi 2 kelompok memberikan hasil pengelompokan yang lebih bagus dibandingkan dengan pengelompokan yang lainnya. Seluruh kabupaten di Pulau Madura menjadi daerah endemi demam berdarah yang perlu diperhatikan olehPemerintah Provinsi Jawa Timur. Dan hal ini senada dengan fakta yang dirilis oleh dinas kesehatan Provinsi Jawa Timur, bahwa beberapa wilayah di Madura menjadi daerah KLB yang memerlukan perhatian serius dalam penanganannya.Kata Kunci : Fuzzy C-Mean Cluster (FCM), Subtractive Clustering (SC), Subtractive Fuzzy C-Mean (SFCM), Demam Berdarah.
APLIKASI CIRCOS PLOT SEBAGAI ALTERNATIF EKSPLORASI DATA: MIGRASI KOMUTER JABODETABEK TAHUN 2014 Robert Kurniawan
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 4, No 2 (2016): 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 (1092.084 KB) | DOI: 10.26714/jsunimus.4.2.2016.%p

Abstract

Mobilitas nonpermanen yang sering dilakukan penduduk pinggiran kota besar disebut juga dengan kegiatan commuting. Kegiatan commuting ini ternyata memberikan dampakbagi kedua daerah, baik asal maupun daerah tujunnya. Untuk mengetahui seberapa besar dan bagaimana pola perpindahan penduduknya bisa dilakukan dengan eksplorasi datamenggunakan plot, diagram, atau pun cross tabuation untuk memberikan informasi lebih terhadap pola sebaran data. Oleh karena itu, dalam penelitian ini bertujuan untuk menginvestigasi pola data migrasi komuter di Jabodetabek tahun 2014 dengan Circos Plot. Berdasarkan hasil penelitian didapatkan bahwapola kegiatan komuter di Jabodetabek jika dilihat berdasarkan jenis kelamin ternyata tidak menunjukkan pola yang berbeda. Dan pola perpindahannya pun terjadi pada jarak dekat, yang berarti bahawa sebagian besar migran yang melakukan kegiatan komuternya pada daerah tujuan yang terdekat dengan daerah asal.Kata Kunci : Commuting, Migrasi Komuter, Circos Plot
FUZZY CLUSTERING MENGGUNAKAN ALGORITHM FIREFLYFUZZY C-MEANS DENGAN JARAK MAHALANOBIS Joshua Ariel Perkasa; Robert Kurniawan
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 2 (2018): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.332 KB) | DOI: 10.26714/jsunimus.6.2.2018.%p

Abstract

Fuzzy C-Means (FCM) adalah salah satu teknik clustering yang cukup sering digunakan, tetapi memiliki kelemahan yaitu mudah terjebak ke dalam local optima. Hal ini dikarenakan adanya faktor pengambilan pusat cluster yang awalnya random sehingga terjadi inkonsistensi pada saat memulai FCM. Firefly Algorithm (FA) mampu mengatasi ketidak konsistenan dari FCM. Penelitian ini bertujuan untuk melihat performa dari Firely Algorithm Fuzzy C-Means (FAFCM) dengan pendekatan jarak mahalanobis dibandingkan dengan jarak euclidean. Algoritme FAFCM ini dibangun dengan 2 jenis jarak tersebut untuk mengakomodir berbagai jenis persebaran data. FAFCM memiliki performa yang lebih baik dikarenakan sebagian besar nilai iterasi dari FAFCM lebih kecil dari FCM. FAFCM Mahalanobis sendiri menunjukan nilai fungsi objektif palingminimum untuk jenis data hyperspherical sehingga dapat disimpullkan FAFCM Mahalanobis cocok untuk data hyperspherical.Kata kunci : Clustering, Fuzzy C-Means, Mahalanobis, Firefly Algorithm Optimization-Fuzzy C-Means.
Co-Authors Agung Purwanto Agustini, Peni Akbar, Vicha Amalia Alif Andika Putra Amalia Noviani Arie Wahyu Wijayanto Asikin, Munawar Asshidiq, Isna Aissatussiri Azhar, Daris Bagus Sumargo, Bagus Baiq Nurul Haqiqi Baiq Nurul Haqiqi, Baiq Nurul Betik Endaryati, Betik Dini Arifatin Dora, Rika Fadhlullah Fadhlullah Fakhrizal Adam, Rifqi Fella Ulandari Frans Judea Samosir Hasabi, Rafif Hidayat, Arief Ramadhan Rifky Hilmianto, Rizky Hutabarat, Rizky Theofilus Ignatius Sandyawan Ilmi Aulia Akbar Irman Firmansyah Joshua Ariel Perkasa Kadek Angga Wicaksana Kadir Kadir Kamilia Wafa Pakuani Khamila, Azzahra Dhisa Marsisno, Waris Muhammad Iqbal Muhammad Irsad Arief Muhammad Yusuf Aristyanto Murti, Sartika Andari Mustika Sari, Evandha Nashir Wahyudi Neli Agustina Nilam Novita Sari Nugroho, Yoga Dwi Nurmawati, Erna Nurmawiya - Parina, Okta Prabowo, Edhi Pratama, Ahmad R. Putri, Salwa Rizqina Ratu Kintan Karina Ribut Nurul Tri Wahyuni Rivan Destyanugraha Riza F. Ramadhan, Riza F. Sabila, Cahya Safariza, Dena Sartika Andari Murti Sepnita Wulandari Singrapati, Lalu Riza Siregar, Dania Sitorus, Agnes Vera Yanti Sugiarto S Sukim, Sukim Syaifudin Syaifudin, Syaifudin Umbara, Danu Victor Trismanjaya Hulu Wahyu Hassapni Wahyuni, Krismanti Tri Wardani, Martha Budi Wilantika, Nori Yuniarto, Budi Zalukhu, Bill Van Ricardo