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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal 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 Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika 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

Departemen Statistika, IPB University

Author-ID : 3379323

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 Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

Published : 65 Documents Claim Missing Document

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Title

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

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

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

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

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

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

Perbandingan ARIMA dan Artificial Neural Networks dalam Peramalan Jumlah Positif Covid-19 Di DKI Jakarta Tri Wahyuni; Indahwati Indahwati; Kusman Sadik
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

DKI Jakarta is the center of the spread of Covid-19. This is indicated by the higher cumulative number of Covid-19 positive in DKI Jakarta compared to other provinces. The high number of cases in DKI Jakarta is a concern for all groups, so it is necessary to do forecasting to predict the number of Covid-19 positive in the next period. Accurate forecasting is needed to get better results. This study compares the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in predicting the number of Covid-19 positive in DKI Jakarta. Forecasting accuracy is calculated using the value of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The results show that the best model for forecasting the number of Covid-19 positive in DKI Jakarta is ARIMA(0,1,1) with drift, with a MAPE value of 15.748, an RMSE of 268.808, and the correlation between the forecast value and the actual value of 0.845. Forecasting using ARIMA(0,1,1) with drift and BP(3,10,1) models produces the best forecast for the long forecasting period of the next six weeks.

Perbandingan Performa Metode Pohon Model Logistik dan Random Forest pada Pengklasifikasian Data Purnama Sari; Kusman Sadik; Mulianto Raharjo
Xplore: Journal of Statistics Vol. 12 No. 1 (2023): Vol. 12 No. 1 (2023)
Publisher : Department of Statistics, IPB

Multicollinearity and missing data are two common problems in big data. Missing data could decrease the prediction accuracy. Logistic model tree (LMT) is used to handle multicollinearity because multicollinearity does not affect the decision tree. Random forest can be used to decrease variance in prediction case. This study aimed to study the comparison of two methods, LMT and random forest, in multicollinearity and missing data in various cases using simulation study and real data as dataset. Evaluation model is based on classification accuracy and AUC measurement. The result stated that random forest had better performance if the multicollinearity level is moderate. LMT with omitted missing data is proven to have better performance for big data and when a high percentage of missing data occurred, and the multicollinearity level is severe. The next step is analysed real data with different sample size. The result stated that random forest have better performance. Omitted missing data have better performance in classification â€œbreast cancerâ€ data which consist 0,3 % missing data.

Kajian Metode Pohon Model Logistik (Logistic Model Tree) dengan Penanganan Ketakseimbangan Data Akmala Firdausi; Aam Alamudi; Kusman Sadik
Xplore: Journal of Statistics Vol. 11 No. 2 (2022):
Publisher : Department of Statistics, IPB

Logistic model tree is a nonparametric modelling method that combines decision tree with linear logistic regression. Logistic model tree handles multicollinearity well, but is not immune to problems that arise due to data imbalance. This study was carried to compare the performance of undersampling, SMOTE, and ROSE in handling imbalanced data when used in tandem with logistic model tree. The data used in the simulation was obtained by generating random numbers following the Bernoulli distribution as the response variable and the Bivariate Normal distribution as the explanatory variables, based on five different imbalance levels. Comparisons done on the AUC value showed that logistic model trees built with methods to handle imbalanced data performed better than logistic model trees built without applying any such method on every level of tested data imbalance in classifying objects. Among those, logistic model trees built with ROSE performed better than logistic model trees built with other methods. On datasets with low level of imbalance, the performance of logistic model trees built with ROSE and undersampling do not significantly differ.

Perbandingan Kinerja Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi Melalui Data Simulasi Ahmad Rifai Nasution; Kusman Sadik; Akbar Rizki
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Poisson regression is a standard method to model count data. Modeling count data frequently causes overdispersion which means that Poisson regression is less precise to model it as Poisson regression has the assumption of equidispersion. Overdispersion can be overcome by using Conway-Maxwell-Poisson (COM-Poisson) and Poisson Tweedie (Poisson-Tw) regression. The best model is determined based on the lowest value of RMSE, absolute bias, variance of parameter estimator, AIC, and BIC. This research uses simulation data. The response variable of simulation data is generated to follow Generalized Poisson distribution with combinations of and The result of simulation study shows that COM-Poisson and Compound Poisson-Tw are the alternatives to model overdispersed count data, but COM-Poisson is better to overcome overdispersion with higher dispersion parameter.

Co-Authors . Erfiani . Indahwati A.Tuti Rumiati Aam Alamudi Abdullah, Adib Roisilmi Achmad Fauzan Agus Mohamad Soleh Ahmad Rifai Nasution 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 Aulya Permatasari Azka Ubaidillah Bagus Sartono Budi Susetyo Cici Suhaeni Cici Suhaeni Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eminita, Viarti EVITA PURNANINGRUM Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah, Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Indahwati Indahwati Intan Arassah, Fradha Iqbal, Teuku Achmad Isnanda, Eriski Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Nisrina Az-Zahra, Putri Nur Khamidah NURADILLA, SITI Nusar Hajarisman Pangestika, Dhita Elsha Parwati Sofan, Parwati Purnama Sari Rifqi Aulya Rahman Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sahamony, Nur Fitriyani Saleh, Agus Muhammad Satriyo Wibowo Siregar, Jodi jhouranda Siti Raudlah Sitti Nurhaliza Soleh, Agus M Suhaeni, Cici 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

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