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All Journal PROSIDING SEMINAR NASIONAL Jurnal Statistika Universitas Muhammadiyah Semarang Value Added : Majalah Ekonomi dan Bisnis Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Journal of Community Dedication INTERNATIONAL JOURNAL OF ECONOMIC LITERATURE INTERNATIONAL JOURNAL OF SOCIETY REVIEWS (INJOSER)
Moh. Yamin Darsyah
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Journal : PROSIDING SEMINAR NASIONAL

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SMALL AREA ESTIMATION PADA TINGKAT KEMISKINAN DI PROVINSI JAWA TENGAH DENGAN PENDEKATAN EMPIRICAL BEST LINIER UNBIASED PREDICTION Wijaya, Arianto; Darsyah, Moh. Yamin; Suprayitno, Iswahyudi Joko
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Poverty is a complex problem for every country, similar to Indonesia. Poverty isone of the important measures to determine the level of welfare of a household.Factors that cause poverty include low income, the number of familydependents, health, and education levels that characterize poor families inIndonesia. The purpose of this research is to know the level of impact atdistricts level in Central Java Province by using Small Area Estimation (SAE)method with Empirical Best Linier Prediction (EBLUP) approach. The dataused in this research are poverty data obtained from SUSENAS of Central JavaProvince with the response variable that is the number of poor population,while as the participant variable is selected gross enrollment rate (X1), schoolparticipation rate (X2), health insurance (X3), goods per capita (X4) and lifeexpectancy (X5). The results of the MSE study of the SAE model were smallerthan the direct predicted MSE, indicating the SAE model was better than thedirect estimates in the estimated number of poor people in each district inCentral Java Province.Keywords : Poverty Rate, SAE and EBLUP.
ANALISIS PENGARUH STATUS BEKERJA TERHADAP JENIS KELAMIN DAN UMUR DENGAN PENDEKATAN BINARY LOGISTIC REGRESSION Syamsul Rizal; Imaroh Izzatun Nisa; Moh. Yamin Darsyah
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

One of method of Data Analysis Category used to determine the effect of the relationship with the response variable of type nominal is using Binary LogisticRegression (BLR) approach. BLR is used for data whose response variable isdata consisting of two categories, with one predictor variable or more, bothcategorical and continuous. In determining the degree of workforce inIndonesia, one indicator that can be used is the status of working. Status workdefined into two, namely the status is still working and the status does not work.Variabel used in this study is the working status ( Y) as the response variablewith category 1 is still working, category 0 does not work, while the predictorvariables are education level (X1) and gender (X2). There are 2 variables thataffect the model of education variables (X1) and age (X2). The accuracyclassification is 87%. Keywords: Working Status, BLR,Classification
PENGELOMPOKAN KABUPATEN/KOTA DI JAWA TENGAH MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS Rahman Hidayat; Rochdi Wasono; Moh. Yamin Darsyah
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

The Poverty is still a serious problem, especially in Indonesia. Central Java is the province with the highest percentage of poor people in Java Island 13, 19%,the figure is above the national poverty rate. In this research will be an analysisof the factors affecting poverty in Central Java. The statistical approach used inthis case is cluster analysis. Cluster analysis is an analysis that aims to classifyan object (region) based on similarity characteristics of data. The method usedis the method of K-Means and Fuzzy C-Means. The object of research isgrouped into 4 clusters. The result of grouping shows that K-Means method isthe best method based on SW and SB ratio of 0.124. Keywords: Data Mining, K-means, Fuzzy C-Means
SMALL AREA ESTIMATION UNTUK PEMETAAN KEMISKINAN DI KABUPATEN DEMAK Moh. Yamin Darsyah; Setia Iriyanto; Iswahyudi Joko S
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2014: PROSIDING SEMINAR NASIONAL HASIL - HASIL PENELITIAN & PENGABDIAN
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Small area statistic is very interested in various fields at present. Estimation of small area is needed to obtain information on a small area, such as the scope of the district, subdistrict, or village. Such information becomes very important with the development of regional autonomy in Indonesia because it can be used as reference to construct a system ofplanning, monitoring, and other government policies without the cost of large to collect the data itself. The method being developed to predict small area statistics is the estimation of small area (small area estimation). Estimation for small areas in this study is applied to estimate poverty mapping of distric level in Demak. The result of poverty mapping in demak shows that population density become dominan factor poverty in some areas of demak.
LASIFIKASI INDEKS PEMBANGUNAN MANUSIA (IPM) DENGAN PENDEKATAN K-NEARSET NEIGHBOR (K-NN) Moh. Yamin Darsyah
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Human development index (HDI) is one of measuring instrument of achieving quality of life of one region even country. There are three basic components of the Human Development Index compilers: health dimension, knowledge dimension, and decent living dimension. To measure the health dimension, we use life expectancy at birth, knowledge dimension is used combination of indicator of old school expectation and mean of school length, and life dimension suitable for use indicator ability of people purchasing power to some basic requirement seen from mean of expense per customized capita. Data mining works to gather information from a large amount of data. Jobs that are closely related to data mining are prediction models, group analysis, association analysis, and anomaly detection. One of the classification methods contained in data mining and is often used and produces a fairly good accuracy is the K-Nearset Neighbor (k-NN) method. The absence of research on the classification or grouping of Human Development Index withK-Nearset Neighbor (k-NN) method will be done by using k-NN method with k value of 1, 5, and 10. With the ultimate goal of comparing the accuracy of kaslifikasi between value k on the k-NN method. The result of classification of IPM by using k-NN method with k value of 5 and 10 obtained classification accuracy of 91.43% which is the best classification accuracy, with sensitivity of 100% and 83.33%. Key words: HDI , Classification, K-Nearset Neighbor, Accuracy
KLASIFIKASI INDEKS PEMBANGUNAN MANUSIA KABUPATEN/KOTA SE-INDONESIA DENGAN PENDEKATAN SMOOTH SUPPORT VECTOR MACHINE (SSVM) KERNEL RADIAL BASIS FUNCTION (RBF) Fatkhurokhman Fauzi; Moh. Yamin Darsyah; Tiani Wahyu Utami
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: Prosiding Seminar Nasional Pendidikan, Sains dan Teknologi
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Human Development Index (HDI) is a measure of human development achievementbased on basic components of quality of life. The human development index is low ifthe HDI is less than 60, moderate HDI between 60 to less than 70, high HDI between70 to less than 80, and equal to 80 and more than 80 are high. Smooth SupportVector Machine (SSVM) is a classification technique that is new. The algorithm usedis Radial Basis Function (RBF). The result of human development sperm using SSVMmethod with RBF kernel is 100%. With 41 districts / cities including low HDI. While332 districts / cities are included in medium HDI coverage, 134 districts / cities areincluded in the high HDI, and 12 districts / cities including HDI is very high. Keywords : Human Development Index, Smooth Support Vector Machine (SSVM), Radial Basis Function (RBF), accuracy, classification.
PERENCANAAN PROGRAM BANTUAN OPERASIONAL SEKOLAH (BOS) DI PROVINSI JAWA TENGAH BERBASISKAN MODEL SPATIAL AUTOREGRESSIVE (SAR) DAN SPATIAL ERROR MODEL (SEM) Rochdi Wasono; Abdul Karim; Moh. Yamin Darsyah; Suwardi Suwardi
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Formulasi penyaluran dana Bantuan Operasional Sekolah (BOS) merupakan masalah yang kompleks, karena setiap daerah memiliki karakteristik yang berbeda.  Penelitian  ini  bertujuan  untuk  mengetahui  perbandingan model spatial autoregressive (SAR) dan spatial error model (SEM) serta menentukan model terbaik diantara kedua model. Hasil penelitian menunjukan bahwa model SEM lebih baik disbanding dengan model SAR. Kata Kunci : Bantuan operasional sekolah (BOS), SAR, SEM
PENDUGAAN TINGKAT KEMISKINAN DI KABUPATEN SUMENEP DENGAN PENDEKATAN SAE Moh. Yamin Darsyah; Rochdi Wasono
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2013: PROSIDING SEMINAR NASIONAL STATISTIKA 2013
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Small area estimation (SAE) merupakan suatu teknik statistika untuk menduga parameter-parameter subpopulasi yang ukuran sampel nya kecil. Teknik pendugaan ini borrowing information memanfaatkan data dari domain besar (seperti data sensus, data susenas) untuk menduga variabel yang menjadi perhatian pada domain yang lebih kecil yang selanjutnya dikenal pendugaan tidak langsung. Adapun pendugaan langsungtidak mampu memberikan ketelitian yang cukup bila ukuran sampel dalam area kecil, sehingga statistik yang dihasilkan akan memiliki varian yang besar atau bahkan menghasilkan pendugaan yang bias. SAE dalam penelitian ini menggunakan pendekatan nonparametrik yang digunakan untuk menduga tingkat kemiskinan pada level kecamatan di Kabupaten Sumenep. Kecamatan Bluto merupakan wilayah dengan mayoritas penduduk miskin di Kabupaten Sumenep dengan rata-rata pengeluaran per kapita jauh dibawah garis kemiskinan. Kata Kunci : SAE, Kemiskinan, Nonparametrik
PENGARUH TINGKAT PENDIDIKAN TERHADAP JUMLAH PENGANGGURAN DI KOTA SEMARANG Iswahyudi Joko Suprayitno; Moh. Yamin Darsyah; Ujiati Suci Rahayu
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2017: PROSIDING IMPLEMENTASI PENELITIAN PADA PENGABDIAN MENUJU MASYARAKAT MANDIRI BERKEMAJUAN
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Penduduk adalah orang yang mendiami suatu wilayah. Penduduk yang sudah memasuki usia kerja, baik yang sudahbekerja, belum bekerja, atau sedang mencari pekerjaan disebut angkatan kerja. Jumlah angkatan kerja yang tidak sebanding dengankesempatan kerja mengakibatkan tidak semua angkatan kerjadapat diserap oleh lapangan kerja disebut pengangguran. Pekerja tidak hanya dari Warga Negara Indonesia saja tetapi Warga Negara Asing yang bekerja di Indonesia juga disebut sebagai pekerja. Pendidikan merupakan salah satu hal yang sangat penting untuk anak-anak di Indonesia. Dari kasus ini akan dianalisis pengaruh jumlah Pekerja Warga Negara Asing dan Jumlah penduduk yang berpendidikan (dari SD sd S1) terhadap jumlah pengangguran di Kota Semarang. Dari hasil penelitian didapatkan hasil bahwa pendidikan seorang pekerja sangat berpengaruh terhadap jumlah pengangguran di Kota Semarang. Jadi untuk mendapatkan pekerjaan dibutuhkan pendidikan dan keahlian dari calon pekerja agar bisa terserap dalam dunia kerja. Kata kunci : Penduduk, Tenaga Kerja, Pengangguran, Regresi Linier Berganda
Small Area Estimation For Mapping Human Development Index Moh. Yamin Darsyah; Rochdi Wasono
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2016: Proceeding of International Seminar on Education Technology (ISET) 2016
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Abstract.Human Development Index (HDI) is one ofthe indicators that used to determine the human developmentofa country. The calculation of the value of HDI in Indonesia is carried out until the scale of the districteach year. Since the implementation of regional autonomy policy,the calculation of the HDI value is required with a smaller scale in the district. The calculation of HDI values with sub-scale is difficult because the sample is toosmall to estimate the value of HDI perdistrict. One of the component stocalculate the value of HDI is an indexof purchasing power that approximated by the value of percapita expenditure. Small Area Estimation is one of the indirect estimates that used to estimate the parameter values of the subpopulation. On this research, Small Area Estimation (SAE) is a statistics methode for estimate small sampel .The research purpose to estimate per capita expenditurefor HDI in Demak District. The results of the estimation with SAE methods in Demak District indicates that the Demak Sub-district has the largest per capita expenditure that can be said have the highest HDI value while Kebonagung Sub-district has the smallest per capita expenditure that can be said have the lowest HDI value.
Co-Authors - Pristiyani Al-Amin Caroline Caroline Devi Sumayya Sara Dwi Haryo Ismunarti Eyad Saleh Bani-Domi Fauzi, Fatkhurokhman Felina C Young Imaroh Izzatun Nisa Indah Manfaati Nur Iswahyudi Joko S Iswahyudi Joko Suprayitno Karim, Abdul Maulana Arafat Lubis Mega Kartika Lestari Melyana R Pugu Mercy Reinette Handayani Mantur Mohammad Taqiuddin Mohamad Mohd Syahrin Monica Firda Agustina MUHAMMAD AGUS SUPRAYUDI Muhammad Saifudin Nur Nashran Azizan Prisca Shery Camelia Rahman Hidayat Redi Yana Kapisa Rena Muntafiah Ria Muslikah Rochdi Wasono Setia Iriyanto Sidderatul Akbar Sidderatul Akbar Tiani Wahyu Utami Ujiati Suci Rahayu Widjanarko Wijaya, Arianto