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All Journal FORUM STATISTIKA DAN KOMPUTASI Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma Journal of Mathematical and Fundamental Sciences JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Socioeconomics and Development Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan Indonesian Journal on Learning and Advanced Education (IJOLAE) Jurnal Jamu Indonesia Jurnal Penelitian Kebijakan Pendidikan Indonesian Journal of Educational Assesment Jurnal Pendidikan dan Kebudayaan Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Penelitian Kebijakan Pendidikan Aceh International Journal of Science and Technology Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Xplore: Journal of Statistics Indonesian Journal of Jamu Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications
Budi Susetyo
Departemen Statistika, IPB University
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Pemodelan Tingkat Kriminalitas di Indonesia Menggunakan Analisis Geographically Weighted Panel Regression Endah Febrianti; Budi Susetyo; Pika Silvianti
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.539 KB) | DOI: 10.29244/xplore.v12i1.950

Abstract

Crime is one of the socio-economic problems that Indonesia has not yet resolved. Although Indonesia is categorized as a safe country to visit, in reality, there are still many Indonesian people who experience crime. The resolution of this socio-economic problem is very important because it involves the safety and comfort of the community. This study aims to identify the factors that influence the crime rate in Indonesia and determine the best model for each province by comparing the panel data regression model and the Geographically Weighted Panel Regression (GWPR) model. This research data consists of 34 provinces in Indonesia from 2016 to 2020. The analysis used is panel data regression analysis and GWPR. The result is that the adaptive kernel gaussian GWPR is the best model with of 69,89% and AIC of 167,4585. The GWPR modeling produces model equations and significant variables for each province. In general, five variables have a significant effect on the crime rate, namely percentage of poor population, open unemployment rate, Gross Regional Domestic Product at the constant price per capita, human development index, and mean years of schooling.
Perbandingan Metode Hot-deck, Regression dan K-Nearest Neighbor Imputation dalam Pendugaan Data Hilang pada Dapodik Tahun 2020 Inayatul Izzati Diana Yusuf; Budi Susetyo; La Ode Abdul Rahman
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.396 KB) | DOI: 10.29244/xplore.v12i1.1056

Abstract

Data Pokok Pendidikan (Dapodik) is a nation-wide data collection system that contains data on education units. Missing value in Dapodik cause the loss of important information. To solve this problem can use imputation. Imputation is a procedure to predict the missing value with a certain method. This study aims to compare three imputation methods which are Hot-deck imputation, Regression Imputation and K-Nearest Neighbor imputation (KNNI). Simulation for generating missing value was carried out by dividing the percentage of 2%, 3%, 4% and 5%, then imputed with the three methods. The best model is determined based on the lowest value of RMSE and MAPE. The best imputation method based on the lowest RMSE and MAPE values is a regression imputation
Comparing Several Missing Data Estimation Methods in Linear Regression;Real Data Example and A Simulation Study Anwar Fitrianto; Jap Ee Jia; Budi Susetyo; La Ode Abdul Rahman
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.20548

Abstract

Analysis on incomplete could lead to biased estimation when using standard statistical procedure since it ignores the missing observations. The disadvantage of ignoring missing data is that the researcher might not have enough data to conduct an analysis. The main objective of the study is to compare the performance between listwise deletion (LD), mean substitution (MS) and multiple imputation (MI) method in estimating parameters. The performance will be measured through bias, standard error and 95% confidence interval of interested estimates for handling missing data with 10% missing observations. A complete empirical data set was used and assumed as population data. Ten percent of total observations in the population ere set as missing arbitrarily by generating random numbers from a uniform distribution,  . Then, bias of parameter estimates and confidence interval of parameter estimates are calculated to compare the three methods. A Monte Carlo simulation was carried out to know the properties of missing data and investigated using simulated random numbers. Simulation of 1000 sampled data with 20, 50, and 100 observations and each sample is set to have 10% missing observations. Standard statistical analyses are run for each missing data and get the average of parameter estimates to calculate the bias and standard error of parameter estimates for every missing data method. The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. And, MS method is strongly recommended not to use for handling missing data because it will result in large bias and standard error of parameter estimates.
ANALISIS MULTIVARIAT UNTUK PEMETAAN KARAKTERISTIK MUTU PENDIDIKAN SEKOLAH MENENGAH KEJURUAN (SMK) DI INDONESIA Kristuisno Martsuyanto Kapiluka; Dhea Dewanti; Budi Susetyo
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i3.328

Abstract

Evaluation of the education quality in Indonesia, especially in Vocational High Schools (VHS), carried out through the Presidential Instruction 2016. The implementation of these instructions is not easy because of the different characteristics of each region both geographically and socio-culturally. This study aims to analyze related to the mapping of the characteristics of the quality of vocational education from 34 provinces in Indonesia. The methods used are correspondence analysis, biplot analysis, and cluster analysis which are multivariate statistical analysis methods. The correspondence analysis results show that there are differences in the characteristics of the quality of vocational education from each province based on school accreditation. The biplot analysis show that Teacher Quality is greatest diversity variable compared to other variables. In addition, the provinces of Aceh, East Nusa Tenggara, West Sulawesi and Central Kalimantan have a quality score below the average and are dominated by non-accredited VHSs based on the results of the analysis. The k-means cluster analysis with 4 optimal clusters gives the result that cluster 2 is the cluster with the best quality value while cluster 4 is the cluster with the lowest quality value. Three analytical concluded that provinces with very good quality scores such as DKI Jakarta, Bali and DI Yogyakarta can be used as pilot provinces for other provinces. Meanwhile, provinces with VHS quality scores below the provincial average as a whole should receive attention in order to further improve the quality of their VHSs
PERBANDINGAN HASIL ANALISIS CLUSTER K-MEANS DAN K-MEDOIDS UNTUK PEMETAAN MUTU SMK Intan Juliana Panjaitan; Tiya Wulandari; Budi Susetyo
Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Vol. 4 No. 1 (2024): Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/bay.v4i1.77

Abstract

The quality of education in a country is one indicator of the country's progress. Indonesia has formulated an assessment of the quality of education using the National Education Standards (NES). To fulfill the SNP, school accreditation is an obligation that must be fulfilled by all intuitions. The results of the accreditation assessment can be grouped into several groups according to their respective categories. To determine the number of clusters formed, an analysis using the K-Means and K-Medoids methods can be performed with 2 clusters formed. Cluster 1 namely the Provinces of Aceh, Banten, West Kalimantan, Central Kalimantan, North Kalimantan, West Nusa Tenggara, Central Nusa Tenggara, West Sulawesi and Central Sulawesi, and Cluster 2 namely the Provinces of Bali, Bengkulu, DI Yogyakarta, DKI Jakarta, Gorontalo, Jambi, Central Java, East Java, South Kalimantan, East Kalimantan, Bangka Belitung, Riau Archipelago, Lampung, Maluku, North Maluku, Papua, West Papua, Riau, South Sulawesi, Central Sulawesi, North Sulawesi, West Sumatra, South Sumatra, North Sumatra
PETA MUTU SATUAN PENDIDIKAN DI INDONESIA (Studi Pilotting Project akreditasi 2020) Karwono, H; Susetyo, Budi
Jurnal Penelitian Kebijakan Pendidikan Vol 14 No 1 (2021)
Publisher : Pusat Standar dan Kebijakan Pendidikan, BSKAP, Kemendikbudristek

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24832/jpkp.v14i1.434

Abstract

The 2020 school accreditation instrument (IASP2020) has changed the paradigm from accreditation as simply fulfilling administrative requirements to performance-based evaluation. IASP2020 focuses on measuring the quality of graduates, the learning process, quality of teachers, and school management. This research aims to examine the roadmap of education quality by type, level and area, and performance quality based on the accreditation components in IASP 2020 and the challenges to attain higher education quality. The data analyzed were the results of the piloting accreditation conducted by Board of National Accreditation for Schools/Madrasas (BAN-S/M) in 2020 on 4817 schools and madrasas. Sample selection was done through quota sampling. The results concluded that the majority of schools were accredited B. Senior High Schools (SMA) had better accreditation rating compared to other levels. In contrast, Elementary Schools/Madrasas (SD/MI) had the fewest A accreditation rating. Quality between provinces vary widely. DKI had the highest percentage of A rating while the lowest is NTT. The teacher quality component had the lowest score compared to the other three components. The factors causing the low quality at SD/MI were the students' low ability to communicate effectively, think critically in problem-solving and the lack of teachers’ initiative to carry out sustainable professional development. The low quality of SMK was due to the lack of graduates who were able to obtain competency certificates from the Professional Certification Institute, the poor management of the production unit/business center/techno park, and the lack of teachers who apply the results of training in the learning process. Instrumen Akreditasi Satuan Pendidikan tahun 2020 (IASP2020) mengubah paradigma penilaian akreditasi dari berbasis pemenuhan administratif menjadi berbasis kinerja. IASP2020 fokus mengukur komponen mutu lulusan, proses pembelajaran, mutu guru, dan manajemen sekolah. Tujuan dari penelitian ini adalah mengkaji peta mutu pendidikan berdasarkan jenis, jenjang dan wilayah serta kinerja komponen mutu dan faktor kendala pencapaian mutu berdasarkan hasil IASP2020. Data yang dianalisis berasal dari hasil piloting yang dilakukan oleh BAN-S/M tahun 2020 terhadap 4817 sekolah dan madrasah. Sekolah sampel pada piloting ini dipilih melalui sampling kuota, yang terwakili di seluruh provinsi, jenjang, dan jenis satuan pendidikan. Hasil analisis menyimpulkan bahwa mayoritas sekolah/ madrasah terakreditasi B. SMA memiliki peringkat akreditasi lebih baik dibandingkan dengan jenjang lainnya, sebaliknya SD/MI memiliki jumlah peringkat akreditasi A terkecil. Mutu antar provinsi sangat bervariasi. Provinsi DKI memiliki jumlah peringkat A terbanyak sedangkan terendah adalah NTT. Komponen mutu guru memiliki skor paling rendah dibandingkan tiga komponen lainnya. Faktor penyebab rendahnya mutu jenjang SD/MI adalah masih rendahnya kemampuan siswa dalam berkomunikasi secara efektif, berpikir kritis dalam pemecahan masalah, dan inisiatif guru melakukan pengembangan profesi berkelanjutan. Rendahnya mutu SMK terletak pada kurangnya lulusan yang memperoleh sertifikat kompetensi dari Lembaga Sertifikat Profesi, pengelolaan unit produksi/business center/technopark belum baik, dan rendahnya guru yang menerapkan hasil pelatihan dalam proses pembelajaran.
Comparing Several Missing Data Estimation Methods in Linear Regression;Real Data Example and A Simulation Study Fitrianto, Anwar; Jia, Jap Ee; Susetyo, Budi; Rahman, La Ode Abdul
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.20548

Abstract

Analysis on incomplete could lead to biased estimation when using standard statistical procedure since it ignores the missing observations. The disadvantage of ignoring missing data is that the researcher might not have enough data to conduct an analysis. The main objective of the study is to compare the performance between listwise deletion (LD), mean substitution (MS) and multiple imputation (MI) method in estimating parameters. The performance will be measured through bias, standard error and 95% confidence interval of interested estimates for handling missing data with 10% missing observations. A complete empirical data set was used and assumed as population data. Ten percent of total observations in the population ere set as missing arbitrarily by generating random numbers from a uniform distribution,  . Then, bias of parameter estimates and confidence interval of parameter estimates are calculated to compare the three methods. A Monte Carlo simulation was carried out to know the properties of missing data and investigated using simulated random numbers. Simulation of 1000 sampled data with 20, 50, and 100 observations and each sample is set to have 10% missing observations. Standard statistical analyses are run for each missing data and get the average of parameter estimates to calculate the bias and standard error of parameter estimates for every missing data method. The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. And, MS method is strongly recommended not to use for handling missing data because it will result in large bias and standard error of parameter estimates.
Penggerombolan provinsi di Indonesia berdasarkan instrumen akreditasi satuan pendidikan jenjang SMK menggunakan K-means dan average linkage Fahriya, Andina; Sembiring, Febryna; Susetyo, Budi
Majalah Ilmiah Matematika dan Statistika Vol. 24 No. 2 (2024): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v24i2.40822

Abstract

Improvement and updates need to be done in order to maintain the existence of a school. Accreditation is one of the references to assess the excellence of a school. There are several components used in the accreditation assessment included in the IASP, namely Graduate Quality, Learning Process, Teacher Quality, and School Management. Additionally, to determine which provinces have low, medium, or high IASP scores, clustering is performed on the IASP scores of those provinces. Cluster analysis is a method used to group research objects based on similarities in their characteristics. In this study, clustering was performed using the K-means and average linkage methods on the average IASP scores of vocational high schools (SMK) in 34 provinces in Indonesia. With the Elbow Criterion approach, four clusters were formed for each method. The results of Dunn Index showed that the average linkage method performed better in clustering compared to the K-Means method. Keywords: IASP, Cluster Analysis, K-Means, Average LinkageMSC2020: 62H30
Comparing Outlier Detection Methods using Boxplot Generalized Extreme Studentized Deviate and Sequential Fences Fitrianto*, Anwar; Wan Muhamad, Wan Zuki Azman; Kriswan, Suliana; Susetyo, Budi
Aceh International Journal of Science and Technology Vol 11, No 1 (2022): April 2022
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.11.1.23809

Abstract

Outliers identification is essential in data analysis since it can make wrong inferential statistics. This study aimed to compare the performance of Boxplot, Generalized Extreme Studentized Deviate (Generalized ESD), and Sequential Fences method in identifying outliers. A published dataset wasused in the study. Based on preliminary outlier identification, the data did not contain outliers. Each outlier detection method'sperformance was evaluated by contaminating the original data with few outliers. The contaminations were conducted by replacing the two smallest and largest observations with outliers. The analysis was conducted using SAS version 9.2 for both original and contaminated data. We found that Sequential Fences have outstanding performance in identifying outliers compared to Boxplot and Generalized ESD.
PENERAPAN ANALISIS REGRESI LOGISTIK ORDINAL MULTILEVEL DENGAN BAYESIAN DALAM MEMODELKAN TINGKAT KESEJAHTERAAN DATA P3KE Hermawati, Neni; Susetyo, Budi; Sadik, Kusman
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v6i1.918

Abstract

The science of statistics is required to continue to develop following the times, because the more the characteristics of the data available in the field are increasingly diverse. The more types of data, the more statistical analysis methods are developed, including hierarchical structured data. P3KE data is new data complementing DTKS which is the basis for the government in distributing social assistance. P3KE data becomes a reference in determining KPM BLT DD. Wanasari is a village that is accustomed to determining KPM BLT DD based on the results of deliberations at the kedusunan level (Musdus). In Wanasari Village, there is often a problem of inconsistency between the KPM candidates from the Musdus and the P3KE data from BKKBN provided through the Cianjur District government. Therefore, it is necessary to analyze the components that have a significant effect on the Welfare Decile of Wanasari Village P3KE data. The data is considered to be hierarchically structured with ordinal response variables. Therefore, multilevel ordinal logistic regression analysis with Bayesian parameter estimation will be used to obtain the best model. Normal (0.10) and Cauchi (0.2.5) priors were compared to find the best model. The results show that the P3KE data of Wanasari Village is hierarchical data because the results of two-level logistic regression analysis are better than one level. The study also concluded that Bayesian parameter estimation is better when using Cauchy prior (0.2.5) both for β coefficient estimation and inter-departmental diversity estimation. The best model obtained is able to explain the diversity between neighborhoods by 1.07 and has an accuracy of 63.23%. Predictor variables that have a significant effect include civil registration equivalents, having money/jewelry/livestock/etc. saved, wall type, cooking fuel, drinking water source, stunting risk, and number of households.
Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Amir, Sulfikar Anak Agung Istri Sri Wiadnyani ASEP SAEFUDDIN Aulia Dwi Oktavia Aunuddin Aunuddin Bagus Sartono Bambang H. Trisasongko Bambang Juanda Brian G. Lees Cici Suhaeni Cut N. Ummu Athiyah DAMAYANTI BUCHORI Darfiana Nur Dewi Jasmina Dewi Jasmina, Dewi Dhea Dewanti Dian Kurniasari Dito, Gerry Alfa Dyah R. Panuju Endah Febrianti Erfiani Erfiani Fadjrian Imran Fahriya, Andina Farit Mochamad Afendi Fitrianto, Anwar H Karwono Hafidz Muksin Hari Wijayanto Herlina Herlina Hermawati, Neni I Made Sumertajaya Inayatul Izzati Diana Yusuf Indahwati Indahwati Indahwati Indahwati, NFN Intan Juliana Panjaitan Iswan Achlan Setiawan Izzati Rahmi HG Jap Ee Jia Jia, Jap Ee Karwono, H Kesuma Millati Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kristuisno Martsuyanto Kapiluka Kriswan, Suliana Kusman Sadik Kusni Rohani Rumahorbo La Ode Abdul Rahman La Ode Abdul Rahman La Ode Abdul Rahman M Nur Aidi M Nur Aidi, M Nur Mahmud A. Raimadoya Muh Nur Fiqri Adham Muhammad Amirullah Yusuf Albasia Muhammad Nur Aidi Muhammad Sayuti Mustofa Usman Nurfadilah, Khalilah Nurfajrin, Tria Ermina Nurul Qomariasih Pannu, Abdullah Pika Silvianti Pika Silvianti Qalbi, Asyifah Qomariasih, Nurul Rachman, Nurul Aulia Rahma Anisa Rahmawat, NFN Rahmawati, nFN Ratnasari, Andika Putri Rifannisa Bahar Rifki Hamdani Rizki, Akbar Robert, Zahira Rahvenia Safitri, Wa Ode Rahmalia Sanusi, Ratna Nur Mustika Satriyo Wibowo Sembiring, Febryna Sri Ningsih Desi Afriany Sulandra, Ardelia Maharani Sulfikar Amir Suliana Kriswan Supriatin, Febriyani Eka Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Sylvia P. Soetantyo Tina Aris Perhati Tiya Wulandari Ulfa Afilia Shofa Utami Dyah Syafitri Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Warsono Wulan Andriyani Pangestu Yasmin Erika Faridhan Zahira Rahvenia Robert Zainal A Koemadji