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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika Jurnal Riset Statistika Pattimura Proceeding : Conference of Science and Technology Bandung Conference Series: Statistics STATISTIKA
Nusar Hajarisman
Program Studi Statistika, Fakultas MIPA Universitas Islam Bandung (S1 - Akreditasi B/2014)
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Journal : STATISTIKA

 1 
Exploring Pattern Recognition for Bearing Fault Diagnosis Sutawanir Darwis; Nusar Hajarisman; Suliadi; Achmad Widodo; Rejeki Wulan Islamiyati
Statistika Vol. 22 No. 2 (2022): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v22i2.1128

Abstract

Traditional bearing sensory diagnostic include touching and hearing rely on personal experience, and for more complex system are unable to meet the needs of equipment fault diagnosis. The research on bearing fault diagnosis is developing significantly. Bearings are used in rotating machinery and most machinery failures are caused by bearing failures. The fault diagnosis of bearings is an important research area. The core of bearing fault diagnosis is the pattern recognition of fault features. The key of pattern recognition is to develop a reasonable classifier. Intelligent pattern recognition has been developed such as principal components, support vector machine, neural network. In this study, a bearing fault diagnosis based on exploring pattern recognition is proposed. The key to pattern recognition is to design a significant classifier. A number of features from bearing vibration of normal and fault bearing are extracted and processed using principal components of correlation matrix. Plot of principal components shows the visualization of normal and fault bearing and the classifier is chosen subjectively. The principal components exploration will be confirmed using least squares support vector machine. The parameter of support vector machine estimated using heuristic optimization particle swarm optimization. The proposed method can be applied in the detection of faults of bearing
Law of Total Probability of Aftershocks in Earthquake Insurance Darwis, Sutawanir; Hajarisman, Nusar; Suliadi; Fatiha Nurfauzan, Arsyi; Aulia, Githa
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.3886

Abstract

Abstract. Seismic hazard expressed in annual rate of exceedance of a peak ground acceleration traditionally refers to mainshock. A similar seismic hazard, APSHA, has been adopted for aftershock probabilistic seismic hazard. Probabilistic seismic hazard assessment (PSHA) refers to a homogeneous Poisson process to describe mainshock while APSHA models aftershock occurrence as a nonhomogeneous Poisson process whose rate modeled as Omori law. It shown that the combination of PSHA and APSHA results seismic hazard for mainshock-aftershock seismic sequence/cluster (SPSHA/CPSHA). This study shows how to combine results of APSHA and PSHA and proposes a method for earthquake insurance. The study was carried out for West Java region with 206 occurrences consist of 11 clusters. One cluster with 74 aftershocks was chosen for further study. The parameters of SPSHA/CPSHA was estimated using maximum likelihood. The results of SPSHA/CPSHA combined with damage probability matrix (DPM) yields an expected annual damage ratio (EADR) as an indicator of earthquake insurance. The proposed method in this study can be used as a method for computing earthquake insurance premium. Due to limited data further study is needed to obtain accurate and reasonable results.
Monte Carlo Based Sampling Distribution of Annual Rate of Exceedance for Earthquake Insurance Darwis, Sutawanir; Hajarisman, Nusar; Suliadi; Widodo, Achmad; Diahsty Marasabessy, Munira; Arya Ramadhan, Iqbal
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3173

Abstract

ABSTRACT Seismic hazard expressed in terms of annual rate of exceedance and is used to calculate the earthquake insurance premium. Annual rate of exceedance is a complicated function of magnitudes, distances from site to earthquake sources and attenuation. Due to its complexity, determination of exact sampling distribution of earthquake insurance premium is not an easy task. This research proposes Monte Carlo simulation approach to determine the sampling distribution of earthquake insurance premium. Annual rate of exceedance was simulated first and then the insurance premium calculated based on simulation of annual rate of exceedance. The simulation involves quantifying synthetic catalogue similar to historical catalogue. Its simulation is conducted in order to construct annual rate of exceedance as an indicator of earthquake risk used in earthquake insurance. The simulation of 25 iteration and sample size of 100 shows that the sampling distribution of insurance premium is skewed to the right. The idea of using Monte Carlo simulation to study the sampling distribution of annual rate of exceedance is the originality of the study and the study contributes to the methodology of earthquake insurance.
Co-Authors 10060116101, Tanti Rudiawan Putri Aceng Komarudin Mutaqin Achmad Widodo Achmad Widodo Achmad Widodo Anggi Savitri Ani Ressa Nuryaningsih Anneke Iswani A. Anneke Iswani Ahmad Annisa Lisa Nurjanah Arya Ramadhan, Iqbal ASEP SAEFUDDIN Aulia, Githa Budiman, Aulia Sabila Dewi Rosiana Diahsty Marasabessy, Munira Erfina Agustin Hidayat Esa Nurtiara Fadia Wahyu Utami Fatiha nurfauzan, Arsyi Fera Shintarini Hardiana Prasasti Jasmine Wildani Arisa Kamilatul 'Aini Khairi A N Kusman Sadik Mardha Tri Meilani Muh. Fahmi Nur Akbar Muhammad Bahry Baidhowy Muhammad Bangkit Riksa Utama Neng Marlina Nopita Sari Murtafiah Rejeki Wulan Islamiyati Riyani Desriawati Siti Magfiratul Hasanah Suliadi Suliadi Suliadi Sutawanir Darwis Sutawanir Darwis Sutawanir Darwis Teti Sofia Yanti Winda Aya Haliza Wulan Putri Prihartini Yayat Karyana