Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

2.59

P-Index

This Author published in this journals

All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmu Komputer dan Informasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer UNEJ e-Proceeding Indonesian Journal of Applied Statistics Journal of Mathematics and Mathematics Education (JMME) International Journal of Computing Science and Applied Mathematics Jurnal Aplikasi Statistika & Komputasi Statistik Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Pattimura Proceeding : Conference of Science and Technology Prosiding University Research Colloquium

Irwan Susanto

Universitas Sebelas Maret

Author-ID : 3756568

Agriculture, Biological Sciences & Forestry Humanities 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 Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 18 Documents Claim Missing Document

Claim Missing Document

Articles

1 2

PENGELOMPOKAN DAERAH PENGHASIL BAHAN DASAR TEPUNG KOMPOSIT DI INDONESIA MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS (LCCA) Budiati, Shinta; Susanto, Irwan; Wibowo, Supriyadi
MEDIA STATISTIKA Vol 7, No 1 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Wheat as a base substance of flour, is a source of carbohydrate which is most used for the manufacturing of variety of foodstuffs. Substitution a part of flour with composite flour for manufacturing food will decrease dependency of imported wheat.This research aims to classify the area which produce base substance of composite flour in Indonesia.For this research we will know a group of provinces which become center of production and development target of local resources potency. One way that is used to grouping the object is cluster analysis. In development, there is another grouping technique used, namely Latent Class Cluster Analysis (LCCA).The results show that the selected model from grouping using LCCA is 3groups. The first group is the enough potential area as a production development center. While the second group have the greatest potential area. Meanwhile the last group is the less potentially area. Â Keywords: Composite Flour, Cluster Analysis, Latent Class Cluster Analysis (LCCA) Â

Bayesian Bernoulli Mixture Regression Model for Bidikmisi Scholarship Classification NUR Iriawan; Kartika Fithriasari; Brodjol Sutija Suprih Ulama; Wahyuni Suryaningtyas; Irwan Susanto; Anindya Apriliyanti Pravitasari
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Bidikmisi scholarship grantees are determined based on criteria related to the socioeconomic conditions of the parent of the scholarship grantee. Decision process of Bidikmisi acceptance is not easy to do, since there are sufficient big data of prospective applicants and variables of varied criteria. Based on these problems, a new approach is proposed to determine Bidikmisi grantees by using the Bayesian Bernoulli mixture regression model. The modeling procedure is performed by compiling the accepted and unaccepted cluster of applicants which are estimated for each cluster by the Bernoulli mixture regression model. The model parameter estimation process is done by building an algorithm based on Bayesian Markov Chain Monte Carlo (MCMC) method. The accuracy of acceptance process through Bayesian Bernoulli mixture regression model is measured by determining acceptance classification percentage of model which is compared with acceptance classification percentage of the dummy regression model and the polytomous regression model. The comparative results show that Bayesian Bernoulli mixture regression model approach gives higher percentage of acceptance classification accuracy than dummy regression model and polytomous regression model

Model Variasi Kalender pada Regresi Runtun Waktu untuk Peramalan Jumlah Pengunjung Grojogan Sewu Etik Zukhronah; Winita Sulandari; Isnandar Slamet; Sugiyanto Sugiyanto; Irwan Susanto
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.v4i2.47163

Abstract. Grojogan Sewu visitors experience a significant increase during school holidays, year-end holidays, and also Eid al-Fitr holidays. The determination of Eid Al-Fitr uses the Hijriyah calendar so that the occurrence of Eid al-Fitr will progress 10 days when viewed from the Gregorian calendar, this causes calendar variations. The objective of this paper is to apply a calendar variation model based on time series regression and SARIMA models for forecasting the number of visitors in Grojogan Sewu. The data are Grojogan Sewu visitors from January 2009 until December 2019. The results show that time series regression with calendar variation yields a better forecast compared to the SARIMA model. It can be seen from the value of root mean square error (RMSE) out-sample of time series regression with calendar variation is less than of SARIMA model.Keywords: Calendar variation, time series regression, SARIMA, Grojogan Sewu

Profile Of Tourist Visits In Sangiran Site Area, Sragen Regency Sri Subanti; Isnandar Slamet; Winita Sulandari; Etik Zukhronah; Sugiyanto Sugiyanto; Irwan Susanto
Journal of Mathematics and Mathematics Education Vol 11, No 1 (2021): Journal of Mathematics and Mathematics Education (JMME)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jmme.v11i1.52744

Tourism activities are chain activities that involve various sectors and related institutions. Tourism is one of the fields in the lives of the people of Sragen Regency, which has become one of the priorities in development in recent years. This is based on the local government's awareness that tourism development can support regional income while at the same time increasing the standard of living of people living in tourist areas. For this reason, evaluating the impact of tourism in an area on the socioeconomic conditions of the community is an important thing to know. Sangiran is one of the most complete paleontological sites in Indonesia. Sangiran has also been designated as a cultural heritage by UNESCO on December 5, 1996, with the designation number C.593. The Sangiran site itself is located in Sragen Regency and Karanganyar Regency, Central Java Province. In general, the background of the population in the Sangiran Site area comes from the Javanese ethnic group, who in daily life communicate using the Javanese language. The Sangiran site has been known as an ancient human area from the Pleistocene. Not only storing archaeological wealth, but Sangiran is also very rich in artistic potential, both from prehistoric times and the present. Many things can be enjoyed in Sangiran. Apart from the museum that presents archaeological findings full of meaning, the public can also enjoy the local culture, including traditional arts, traditional ceremonies, local architecture, and folk crafts, adding value to the site. This study aims to determine the profile of tourist visits in the Sangiran Site Area. This study found that the factors that influence the number of visits to the Sangiran Site Area are travel costs, age, gender, and monthly income of respondents related to visiting the Sangiran Site Area. Furthermore, the factors that influence the respondents' willingness to accept ticket offers in the market hypothesis scenario in the Sangiran Site Area are the nominal price of the entrance ticket to a market hypothesis given to respondents, age, gender, monthly income of respondents, education level of respondents, and origin of the respondent.

ANALISIS KLASTER KABUPATEN/KOTA INDONESIA BERDASARKAN INDEKS PEMBANGUNAN MANUSIA DENGAN MODEL MIXTURE SKEW-T Kristoforus Exelsis Pratama; Irwan Susanto; Yuliana Susanti
Pattimura Proceeding 2021: Prosiding KNM XX
Publisher : Pattimura University

Indonesia merupakan salah satu negara dengan jumlah penduduk yang besar. Penduduk Indonesia yang besar dapat menjadi modal kemajuan bangsa. Indeks pembangunan manusia (IPM) merupakan ukuran yang dapat digunakan untuk mengetahui kualitas manusia di suatu wilayah. Capaian IPM Indonesia dinilai cukup rendah jika dibandingkan negara lainnya. Hal itu terjadi karena adanya disparitas pembangunan manusia antar wilayah. Diperlukan pengelompokkan wilayah sehingga terjadi peningkatan dan pemerataan dalam pembangunan manusia di Indonesia. Penelitian ini akan menggunakan data indeks pembangunan manusia kabupaten/kota di Indonesia pada tahun 2019. Model finite mixture dengan distribusi skew-t tepat digunakan karena dapat mengatasi karakteristik multimodal, kemencengan, heavy-tailed, serta outlier yang sering ditemukan pada data. Estimasi parameter model dilakukan dengan metode maksimum likelihood menggunakan algoritma Expectation-Maximization. Ukuran berbasis Akaike Information Criterion digunakan untuk memilih jumlah komponen mixture. Berdasarkan hasil penelitian diperoleh jumlah komponen optimal model finite mixture distribusi skew-t sebanyak tiga komponen mixture. Hal itu menunjukan kabupaten/kota di Indonesia berdasarkan indeks pembangunan manusia dapat dibagi menjadi tiga klaster. Klaster pertama berisi 80 kabupaten/kota dengan rata-rata IPM sebesar 78,317, klaster kedua berisi 415 kabupaten/kota dengan rata-rata IPM sebesar 70,856, dan klaster ketiga berisi 19 kabupaten/kota dengan rata-rata IPM sebesar 56,247

PENGELOMPOKAN RUMAH TANGGA DI INDONESIA BERDASARKAN PENDAPATAN PER KAPITA DENGAN MODEL FINITE MIXTURE Irwan Susanto; Sri Sulistijowati Handajani
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of household income per capita in Indonesia based on The Fifth Wave of the Indonesia Family Life Survey (IFLS5) 2014-2015. The mixture components of the finite mixture model have been build based on the heavy-tailed statistical distributions, i.e., gamma, lognormal, and Weibull distributions. The estimation of the fitting finite mixture model was conducted using the maximum-likelihood estimation method through the expectation-maximization (EM) algorithm. The suitable finite mixture models were verified with the bootstrap likelihood ratio statistics test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Based on the results, the distribution of household income per capita in Indonesia can be modeled by the four components-lognormal mixture model.

Robust Regression Generalized Scale (GS) Estimation On Profit Data Of Poultry Farm Companies Safira Callisa; Yuliana Susanti; Irwan Susanto
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Bidang MIPA dan Kesehatan
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.496 KB)

Poultry farming is the business of cultivating poultry such as breeding chickens, laying hens, and broilers to obtain meat and eggs. Robust regression is a regression method that is used when some outlier data affect the model so that the distribution of the error is not normal. Estimates on robust regression that can overcome outliers such as Generalized Scale (GS) estimation, GS estimation is seen as an extension of S estimation. GS estimation is a solution for minimizing M estimation with paired scale error. This estimate is applied to poultry data companies in 2020 as an indicator to determine the robust regression model. It is concluded that the factors that affect the total profit of poultry farming companies in Indonesia in 2020 are wages for workers and electricity and water.

PEMODELAN INDEKS PEMBANGUNAN KESEHATAN MASYARAKAT DENGAN METODE GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Quraini Septy Wardhani; Sri Sulistijowati Handajani; Irwan Susanto
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 14 No 1 (2022): Jurnal Aplikasi Statistika dan Komputasi Statistik
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i2.333

Geographically weighted logistic regression (GWLR) adalah metode yang dapat memodelkan data bersifat kategorik dengan mempertimbangkan efek spasial. Pada penelitian ini, metode GWLR digunakan untuk memodelkan indeks pembangunan kesehatan masyarakat (IPKM) di Provinsi Jawa Timur. Variabel yang digunakan adalah prevalensi balita stunting, hipertensi, pneumonia, persalinan ditangani nakes, pengguna KB MKJP, dan penduduk dengan perilaku cuci tangan benar. Hasil penelitian ini menunjukkan bahwa pemodelan GWLR dengan pembobot adaptive Gaussian Kernel lebih baik daripada pembobot lain dengan faktor yang berpengaruh signifikan secara lokal adalah prevalensi balita stunting dan hipertensi. Nilai akurasi, sensitivity, dan specitivity yang dihasilkan berturut-turut sebesar 97,4% , 100% dan 85,71%.

PENGELOMPOKAN NEGARA BERDASARKAN KASUS STUNTING DENGAN MODEL FINITE MIXTURE NORMAL MENGGUNAKAN PENDEKATAN BAYESIAN Adella Okky Herashanti; Irwan Susanto; Isnandar Slamet
UNEJ e-Proceeding 2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)
Publisher : UPT Penerbitan Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Stunting (short stature) is a chronic condition characterized by stunted growth due to malnutrition over a long period of time. Stunting needs special attention because it can hamper children's physical and mental development and is associated with an increased risk of illness and death. The high incidence of stunting in the world is still a health problem that is still being faced by WHO [7]. Therefore, it is necessary to group countries based on stunting cases in order to facilitate appropriate policy making in overcoming and preventing stunting. In this study, 151 countries were grouped based on stunting cases with a normal finite mixture model. The model is estimated using Bayesian estimation through Birth-Death Markov Chain Monte Carlo (BD-MCMC) method. The results of the study show that there are five groups of countries based on stunting cases, where the first group consists of 30 countries and the second group consists of 32 countries which are groups with low stunting rates. While the third group has 33 members, the fourth group has 34 countries and the fifth group has 22 countries which is a group with a high stunting rate. Keywords: BD-MCMC, Finite Mixture, Pengelompokan, Stunting.

PENINGKATAN KEMAMPUAN GURU PEMBINA KOMPETISI SAINS NASIONAL MATA PELAJARAN MATEMATIKA SMP DI KABUPATEN KARANGANYAR Etik Zukhronah; Winita Sulandari; Sugiyanto Sugiyanto; Isnandar Slamet; Sri Subanti; Irwan Susanto
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 8: Januari 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v2i8.4526

Dalam upaya meningkatkan kemampuan guru pembina kompetisi sains nasional, Dinas Pendidikan dan Kebuadayaan Kabupaten Karanganyar bekerjasama dengan tim pengabdi dari Program Studi Statistika FMIPA Universitas Sebelas Maret mengadakan bimbingan teknis materi KSN mata pelajaran Matematika pada guru-guru Pembina KSN mata pelajaran Matematika SMP di Kabupaten Karanganyar. Kegiatan bimbingan teknis yang dilaksanakan selama dua hari telah mampu meningkatkan kemampuan peserta hingga 33 %. Untuk hasil yang lebih optimal, kegiatan serupa perlu dilakukan secara rutin dan terstruktur.

Co-Authors Adella Okky Herashanti Anindya Apriliyanti Pravitasari Aprilia, Nabila Churin Brodjol Sutija Suprih Ulama Fhadilla, Nahdatul Fithriasari, Kartika Handajani, Sri Sulistijowati Isnandar Slamet Isnandar Slamet Isnandar Slamet Isnandar Slamet, Isnandar Jelita, Nadia Aulia Kadar, Jimmy Abdel Kristoforus Exelsis Pratama Nugraha, Titis Jati Nur Iriawan Pardede, Hilman Ferdinandus Quraini Septy Wardhani Safira Callisa Shinta Budiati Sri Subanti Sri Sulistijowati Handajani Sri Sulistijowati Handajani Sugianto Sugianto Sugiyanto - Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto, Sugiyanto Sulandari, Winita Supriyadi Wibowo Susilotomoa, Dhestahendra Citra Wahyuni Suryaningtyas, Wahyuni Winita Sulandari Winita Sulandari Winita Sulandari Yuliana Susanti Yuliana Susanti, Yuliana Zukhronah, Etik

Title

Found 18 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 18 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 18 Documents
Search