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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Gaussian IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Zero : Jurnal Sains, Matematika, dan Terapan MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Inferensi International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Seminar Nasional Official Statistics Jurnal Natural Eduvest - Journal of Universal Studies Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Kusman Sadik
Program In Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Generalized Multilevel Linear Model dengan Pendekatan Bayesian untuk Pemodelan Data Pengeluaran Perkapita Rumah Tangga Azka Ubaidillah; Anang Kurnia; Kusman Sadik
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.924 KB) | DOI: 10.34123/jurnalasks.v9i1.91

Abstract

Household per capita expenditure data is one of the important information as an approach to measure the level of prosperity in an area. Such data is needed by the government, both at the central and regional levels in formulating, implementing and evaluating the implementation of development programs. This research is aimed at modeling the household per capita expenditure data which takes into account the specificity of BPS data which has a hierarchical structure, and data distribution pattern which has the right skewed characteristic. The modeling is done by using the three parameters of Log-normal distribution (LN3P) and the three parameters of Log-logistics (LL3P) with a single level (unilevel) and two levels (multilevel) structure. The parameter estimation process is done by Markov Chain Monte Carlo (MCMC) method and Gibbs Sampling algorithm. The results showed that on the unilevel model, the LL3P model is better than the LN3P model. While in multilevel model, LN3P model is better than LL3P model. The results also show that the best model for modeling household per capita expenditure data is the LN3P multilevel model with the smallest Deviance Information Criterion (DIC) value.
PENANGANAN OVERDISPERSI PADA PEMODELAN DATA CACAH DENGAN RESPON NOL BERLEBIH (ZERO-INFLATED) Viarti Eminita; Anang Kurnia; Kusman Sadik
FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Vol 5, No 1 (2019): FIBONACCI: Jurnal Pendidikan Matematika dan Matematika
Publisher : Fakultas Ilmu Pendidikan Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1688.07 KB) | DOI: 10.24853/fbc.5.1.71-80

Abstract

Overdispersi pada data cacah yang disebabkan karena kasus nol berlebih tidak dapat ditangani dengan metode model linier umum biasa seperti Poisson dan Binomial Negatif. Penanganan overdispersi karena nol berlebih dapat dilakukan dengan menggunakan model Zero-Inflated. Zero-Inflated Poisson (ZIP) dan Zero-Inflated Binomial Negatif (ZIBN) telah diyakini performanya dalam menangani masalah ini. Selain menangani masalah tersebut kedua model ini juga dapat memberikan informasi mengenai penyebab nol berlebih pada data respon. Performa ke Empat model tersebut dibandingkan dalam menduga model dari jumlah anak yang tidak sekolah dalam keluarga di Provinsi Jawa Barat pada tahun 2017. Berdasarkan nilai dari ukuran Pearson Chi-Squares, Likelihood Ratio Chi-Square, dan Akaike Information Crieteria (AIC). Pearson Chi-Squares, model ZIP lebih baik dibandingkan ZIBN dan model lainnya, walaupun berbeda sedikit dengan ZIBN.
BISAKAH MEMPEROLEH STATISTIK INDEKS HARGA KONSUMEN TINGKAT PROVINSI DI INDONESIA DENGAN KETELITIAN YANG LEBIH BAIK? Andi Okta Fengki; Khairil Anwar Notodiputro; Kusman Sadik
Seminar Nasional Official Statistics Vol 2019 No 1 (2019): Seminar Nasional Official Statistics 2019
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (660.165 KB) | DOI: 10.34123/semnasoffstat.v2019i1.178

Abstract

Statistik indeks harga konsumen (IHK) atau consumer price index (CPI) juga dibutuhkan pada tingkat provinsi di era desentralisasi saat ini. Ketika IHK ingin diduga pada tingkat provinsi, permasalahan ukuran contoh kecil (small area) muncul karena survei untuk menghasilkan IHK ini di Indonesia dirancang untuk tingkat nasional. Akan tetapi, informasi dari statistik IHK 82 kota dapat membantu untuk menduga IHK provinsi. Metode pendugaan area kecil atau small area estimation (SAE) dapat diterapkan sebagai solusi untuk meningkatkan ketelitian hasil pendugaan langsung. Pada penelitian ini IHK provinsi diduga menggunakan model Fay-Herriot (FH). Hasilnya menunjukan bahwa model FH dapat menghasilkan statistik IHK provinsi dengan ketelitian yang lebih baik dari pendugaan langsung. Hal ini ditunjukan dengan nilai average relative root mean square error (ARRMSE) penduga FH IHK provinsi yang lebih kecil dari penduga langsungnya.
Pengelompokan dan Peramalan Deret Waktu pada Produksi Bawang Merah Tingkat Provinsi di Indonesia Rifqi Aulya Rahman; Farit Mochamad Afendi; Widhiyanti Nugraheni; Kusman Sadik; Akbar Rizki
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (697.411 KB) | DOI: 10.34123/semnasoffstat.v2021i1.910

Abstract

Shallots are strategic vegetable commodity state that can affect the national economy. Shallots production increases every year that in line with domestic household consumption. Every province in Indonesia has a different level of shallot production, both in terms of cycles and harvest amount. Clustering provinces with similar production patterns can help government policies. This research aims to determine cluster time series and to evaluate the shallot production forecast in several provinces in Indonesia. There are three of optimal clusters which have a characteristic pattern in time series and their production. Time series at provincial level and cluster level, then it is modelled based on Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA). The evaluation of cluster level is compared to the provincial level and is concluded that clustering makes forecasting efficiently. This is based on average of Mean Absolute Percentage Error (MAPE) that is smaller that provincial level.
Study on the performance of Robust LASSO in determining important variables data with outliers ROCHYATI ROCHYATI; KUSMAN SADIK; BAGUS SARTONO; EVITA PURNANINGRUM
Jurnal Natural Volume 23 Number 1, February 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v23i1.26279

Abstract

A variable selection method is required to deal with regression models with many variables, and LASSO has been the most widely used methodology.  However, as several authors have noted, LASSO is sensitive to outliers in the data.  For this reason, the Robust-LASSO approach was introduced by applying some weighting schemes for each sample in the data.  This research presented a comparative study of the three weighting schemes in Robust LASSO, namely Huber-LASSO, Tukey-LASSO, and Welsch-LASSO.  The study did a rich simulation containing many scenarios with various characteristics on the covariance structures of the explanatory variable, the types of outliers, the number of outliers, the location of active variables, and the number of variables.  The study then found that Tukey-LASSO outperformed Huber-LASSO and Welsch-LASSO in identifying significant variables.  The Robust LASSO performance generally decreased as the covariances among explanatory variables increased and the data dimension increased.  Exploration of sembung leaf extract data shows that the data is high dimensional data which contains outliers of about 14,28% on the response variable and about 25,71% on the explanatory variables.  Based on the research, the number of variables selected using the Tukey-LASSO method was nine compounds, Huber-LASSO and Welsch-LASSO were eight compounds, and LASSO 13 compounds.  The Tukey-LASSO prediction accuracy is superior to the other three methods.
Mengukur Indeks Kebahagiaan Mahasiswa IPB Menggunakan Analisis Faktor Aulya Permatasari; Khairil Anwar Notodiputro; Kusman Sadik
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (259.167 KB) | DOI: 10.29244/xplore.v2i1.69

Abstract

Undergraduate students of Bogor Agricultural University are spread out in 9 Faculties and 1 School. The difference of faculties and schools illustrate the different characteristics and burdens of student lectures on each faculty and school. This distinction raises various assumptions about the level of student happiness in every faculty and school. Student happiness analysis is measured using loading factor obtained from Factor Analysis. Based on the analysis, found that Faculty of Animal Science is the happiest faculty with happiness index reaching 66.88 and the lowest index of happiness found in the Faculty of Human Ecology with happiness index of 62.39.
Perbandingan Metode Dalil Limit Pusat Transformasi dan Resampling Bootstrap dalam Pembentukan Selang Kepercayaan Yuli Eka Putri; Kusman Sadik; Cici Suhaeni
Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v2i2.108

Abstract

YULI EKA PUTRI. A Comparative Study of Central Limit Theorem, Transformation and Bootstrap Resampling in Determining Confidence Interval. Supervised by KUSMAN SADIK and CICI SUHAENI. The confidence interval is usually established under normality assumption. But, many real-life data does not belong to normal distribution. Many of them are skewed, such as chi-square distribution, generalized extreme value (GEV) or other distribution. For such data, we can use central limit theorem, transformation and bootstrap resampling method to construct confidence intervals. The performance of the methods in constructing the interval can be evaluated using confidence interval accuracy value, interval width, and standard deviation of the interval width. Thus we can determine the best method. The method is determined for having better performance if it has higher accuracy value, smaller interval width, and smaller standard deviation of interval width.This research use both simulated and real-life data. Simulated data is generated from the chi-square distribution, GEV and modified non-normal distribution. The modified non-normal distributed data is a modification of normal distributed data using quadratic and logaritm transformation. So that the data is no longer normally distributed. The results show that transformation method is well used for small sample sizes. Bootstrap resampling dan central limit theorem are better used for large sample sizes.
Perbandingan Metode Koreksi Pencaran pada Data Hasil Alat Pemantau Kadar Glukosa Darah Non-Invasif Siti Raudlah; Mohammad Masjkur; Kusman Sadik; . Erfiani
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v7i3.127

Abstract

Scatter correction is one of the methods in data preprocessing that aim at eliminating the physical properties of the spectrum and reducing the variance between samples. The most commonly methods of scatter correction used are the Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) methods. The MSC method corrects the spectrum by utilizing the results of simple linear regression parameter estimation. The SNV method performs spectral correction with the median and standard deviation. Another alternative method of scatter correction is the Orthogonal Scatter Correction (OSC) applying the principle of orthogonality. The methods used in this research were MSC, SNV, and OSC methods in order to correct the result data of non-invasive blood glucose measuring instrument. The result of this research showed that the time domain spectrum data and intensity had different amount so that the summarized data was needed. Furthermore, this research found that the OSC method with the five series of statistics gained a good correction result compared to the other methods. The OSC method produced a smaller average value of the variance than the other methods.
Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel Gaussian pada Kekuatan Sinyal Seluler Logananta Puja Kusuma; . Indahwati; Kusman Sadik
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.134

Abstract

Cellular signal strength may be affected by its location, so researches concerning signal strength need information about location and analysis method that observe spatial aspect. Spatial Regression analysis evaluates location in modeling relation between explanatory variables and response variable. One of the spatial regression analyses is Geographically Weighted Regression (GWR). This method utilizes location to create weight matrix using certain weighting function. GWR analysis with Gaussian kernel weighting function creates better model than Ordinary Least Square model. The model created using GWR is local model which parameter estimation differs in each observation point. Clustering of observation point is performed to summarize the result of GWR. The number of optimum clusters in clustering based on coefficient is five clusters while the number of optimum clusters in clustering based on p value of t test is four clusters.
Identifikasi Faktor-faktor yang Memengaruhi Hasil Akreditasi SMA di Indonesia Berdasarkan Data ARKAS Muh Nur Fiqri Adham; Budi Susetyo; Kusman Sadik; Satriyo Wibowo
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (540.898 KB) | DOI: 10.29244/xplore.v10i3.837

Abstract

Accreditation is an indicator of the quality of education at the education unit level. One affects the quality of education units is the school budget. School budgets are prepared in order to fulfill 8 national education standards. School budget management uses School Activity Plan and Budget Application (ARKAS) developed by the Ministry of Education, Culture, Research and Technology (Kemendikbudristek). ARKAS is an information system for managing school budget and expenditure planning. The Research is identifies the factors that influence the accreditation of high school (SMA) with accreditation as a response variable and 17 explanatory variables sourced from ARKAS and Dapodik data using ordinal logistic regression analysis. The best model stage is the model formed that has the smallest AIC value and has high model accuracy in determining the best model. The best model stage is the third model stage which is composed of 7 explanatory variables that affect the high school accreditation rating with AIC value of 1886,20 and model accuracy of 65,79%. The variables that affect to results of accreditation include school status, percentage of students eligible PIP, ratio of the number of students per number of teachers, percentage of teachers certified educators, ratio of the number of students per number of study groups, ratio of the number of students per number of computers, and ratio of the number of students per number of toilets
Co-Authors . Erfiani A.Tuti Rumiati Aam Alamudi ACHMAD FAUZAN Adib Roisilmi Abdullah Agus M Soleh Agus M Soleh Agus M. Soleh Agus Mohamad Soleh Agus Muhammad Saleh Ahmad Rifai Nasution Aji Hamim Wigena Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Akmala Firdausi Amalia, Rahmatin Nur Anadra, Rahmi Ananda Shafira Anang Kurnia Andespa, Reyuli Andi Okta Fengki ASEP SAEFUDDIN Astari, Reka Agustia Astari, Reka Agustia Asysta Amalia Pasaribu Aulya Permatasari Azka Ubaidillah Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Budi Susetyo Cici Suhaeni Cici Suhaeni Cici Suhaeni Dewi Zulyani Pomalingo Dhita Elsha Pangestika Dian Handayani Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eriski Isnanda Erwan Setiawan Evita Purnaningrum Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Fradha Intan Arassah Freya, Wa Ode Rona Gede Awantara Gerry Alfa Dito Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Iqbal, Teuku Achmad Jefita Resti Sari Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma Logananta Puja Kusuma Lukmanul Hakim M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Nur Aidi Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Naufalia Alfiryal Nisrina Az-Zahra, Putri Nur Khamidah Nurhidayah Nurhidayah Nusar Hajarisman Panji Lokajaya Arifa Parwati Sofan Purnama Sari Rifqi Aulya Rahman Rita Rahmawati Rizaldi Boer Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sabrina Adnin Kamila Sahamony, Nur Fitriyani Satriyo Wibowo Sentana Putra I Gusti Ngurah Siregar, Jodi jhouranda Siti Aisyah SITI NURADILLA Siti Raudlah Sitti Nurhaliza Soleh, Agus M Sri Kustiara Suhaeni, Cici Sundari, Marta Supriatin, Febriyani Eka Tendi Ferdian Diputra Titin Suhartini Titin Suhartini, Titin Tri Wahyuni Uswatun Hasanah Utami Dyah Syafitri Viarti Eminita Widhiyanti Nugraheni Yenni Angraini Yenni Kurniawati Yuli Eka Putri Zafira Fakhriyah