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All Journal MEDIA PETERNAKAN - Journal of Animal Science and Technology Agromet FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Statistika IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan ComTech: Computer, Mathematics and Engineering Applications Inferensi Jurnal Tanah dan Iklim Jurnal Natural Jurnal Teknik Informatika (JUTIF) Jurnal Natural Xplore: Journal of Statistics STATISTIKA Jurnal Veteriner dan Biomedis PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Statistics and Its Applications
Khairil Anwar Notodiputro
Department Of Statistics, IPB University
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Social Sciences Transportation Veterinary Other

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Kejadian Hujan Asam di Kabupaten Bogor dan Retensi Timbal pada Domba Lokal yang Diberi Ransum Berkadar Timbal Tinggi D Diapari; H. M H Bintoro; J Jachja; K A Notodiputro; M S Saeni
Media Peternakan Vol. 31 No. 3 (2008): Media Peternakan
Publisher : Faculty of Animal Science, Bogor Agricultural University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (155.766 KB)

Abstract

The objectives of this research were: 1) to find out whether acid rain has taken place in Bogor Regency area and its effect on the lead (Pb) content in soil and roughage in the region, 2) to predict the correlation of Pb content in rain water and soil, and the content of Pb in roughage in the Bogor regency area, 3) to study the impact of acid and Pb content in the ration to sheep daily gain and measure Pb content in liver, kidney and meat. The results showed that in Bogor regency area the acid rain occurred during the dry season but not in the wet season. The Pb content in the rainwater and soil were not affected by acid rain. There was no correlation of Pb content between soil and roughage. Acid containing ration decreased daily gain, but not for the ration of high Pb-content. Acid containing ration increased Pb content in the kidney but not in the liver and meat. However, high Pb content in the ration increased the Pb content in the liver and kidney but not in the meat. Key words: acid rain, Pb, Bogor, local sheep
PREDICTION OF DAILY RAINFALL CHARACTERISTICS FROM MONTHLY CLIMATE INDICES(PREDIKSI KARAKTERISTIK CURAH HUJAN HARIAN DARI PARAMETER IKLIM BULANAN) R Boer; K.A. Notodiputro; I Las
Agromet Vol. 21 No. 1 (2007): June 2007
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (468.054 KB) | DOI: 10.29244/j.agromet.21.1.12-20

Abstract

Information on rainfall characteristics such as dry-spell, wet-spell, maximum rainfall and some others are required for agricultural planning. The occurrence of long dry-spell in growing season, in particular during a growing stage sensitive to drought, should be avoided. This information will assist farmer to arrange their planting time and cropping pattern. If information on daily rainfall characteristics could be predicted before planting season is started, better planting arrangement could be developed. Pacific sea surface temperature anomaly, Darwin and Jakarta air pressure difference, Tahiti and Darwin air pressure difference, are climate indices that have been found to be related to Indonesian rainfall variation. Many GCM models have been developed for the prediction of these indices and the predicted indices can be accessed easily from many web-sites. Prediction of the indices for one-year period ahead is given in monthly basis. This study described the development of a weather generator model that used monthly rainfall as inputs for generating daily rainfall data. Relationship between monthly rainfall anomaly and the climate indices is developed. Thus, the likely monthly rainfall anomaly for coming season can be estimated from the indices. This predicted rainfall anomaly is then used to tune the weather generator model for the creation of statistically-based daily weather data for specific sites. The characteristics of daily rainfall such as dry spell, wet spell are generated using Excel spreadsheet that has been furnished with Monte Carlo simulation capability. Results of analysis showed that statistical characteristics of generated rainfall data are similar to the characteristic of observed data. Therefore, the use of predicted monthly rainfall data for coming season as input for the weather data generator model is expected to yield likely daily rainfall data for the coming season.
PENDEKATAN GENERAL LINEAR MIXED MODEL PADA SMALL AREA ESTIMATION Khairil A. Notodiputro; Anang Kurnia
FORUM STATISTIKA DAN KOMPUTASI Vol. 10 No. 2 (2005)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (121.085 KB)

Abstract

Small area estimation is commonly used to describe smaller domain or sub-population. Small area estimation is an important measuring instrument to estimate parameter of smaller domain borrowing strength of population parameter estimate through statistical models with random influence. In this paper we showed the contribution of statistical methods in small area estimation using general linear mixed models.   Keywords :       small area estimation,  general linear mixed model
The Estimation of Price Sensitivity Curves Using Generalized Linear Models Hari Wijayanto; Khairil A. Notodiputro; . Barizi; Jajah K. Wagiono
Forum Pasca Sarjana Vol. 18 No. 1 (1995): Forum Pascasarjana
Publisher : Forum Pasca Sarjana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1398.428 KB)

Abstract

The estimation of price sensitivity curves is usually based on historical data of the product. The estimates obtained, however, are potentially biased especially if the previous condition does not reflect the current market situation. alternativety, the estimation could be based on preference data. This paper introduces the use of Generalized Linear models to estimate the curve based on preference data.
Nested Generalized Linear Model with Ordinal Response for Correlated Data Yekti Widyaningsih; Asep Saefuddin; Khairil A. Notodiputro; Aji H. Wigena
IPTEK The Journal for Technology and Science Vol 23, No 2 (2012)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v23i2.12

Abstract

In this paper, we discuss the generalized linear models with ordinal response for correlated data in nested area. Some basic concepts are described, that is nested spatial, threshold model, and cumulative link function. Due to correlated data used for this modeling, Generalized Estimating Eequation (GEE) is used as model parameters estimation method. Nested is shown by the model building and its application on nested spatially data. In this method, some Working Correlation Matrices (WCM) are able to be specified depend on the nature and type of the data. In this study, 3 types of WCM and 2 types of parameters estimation covariance are used to see the results of parameters estimation from these combinations. As a conclusion, independent WCM is appropriate to the data.
Forecasting Model of Rice Production Using Weighted Rainfall Index in Subang, Karawang, and Indramayu Regency . SUTIKNO; R. BOER; A. BEY; K. ANWAR NOTODIPUTRO; I. LAS
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) No 32 (2010): Desember 2010
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v0n32.2010.%p

Abstract

Various forcasting models of rice production have been developed to support national food security. The forecasting models of national production which use recently have been carried out by the BPS and have not include the climate factors. Whereas, the climate factors influenced the rice’s production. The aim of this research is to develop the harvest area model using independent variables : Weighted Rainfall Index (WRI), SeaSurface Temperature (SST) Nino 3.4, and Dipole Mode Index (DMI). The models which developed was based on BPS models which consist of 3 periods. There are period 1 (January-April), period 2 (May-August), period 3 (September-December).  Furthermore, rice production forecasting is the multiplication of harvest area and yield per ha. Rice production forecasting in one year is sum of the 3 periods. The research location are pantura areas, namely Karawang, Subang, and Indramayu. The result of the research showed that the model performance by WRI for period 2 (May-August) is better than period 1 and period 3. The mean of error for harvest area forecasting for periode 1, 2, and 3 of WRI variable, respectively is 14, 13, and 47%. Based on model validation, harvest area models by independent variable using WRI, SST Nino 3.4, DMI and ratio of harvest area and standard area, relatively have the same performance. One of the reasons is correlation between SST Nino 3.4 and DMI withrainfall is high. Mean of error for rice’s production forecasting of WRI are 13, 15, and 49%, while SST Nino 3.4, DMI, ratio of harvest area and standard area are 29, 12, and 51%. The range of error rice production forecasting at second year are 10-11%.
Prediction of Undergraduate Student’s Study Completion Status Using MissForest Imputation in Random Forest and XGBoost Models Intan Nirmala; Hari Wijayanto; Khairil Anwar Notodiputro
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 1 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i1.7388

Abstract

The number of higher education graduates in Indonesia is calculated based on their completion status. However, many undergraduate students have reached the maximum length of study, but their completion status is unknown. This condition becomes a problem in calculating the actual number of graduates as it is used as an indicator of higher education evaluation and other policy references. Therefore, the unknown completion status of the students who have reached the maximum length of study must be predicted. The research compared the performance of Random Forest and Extreme Gradient Boosting (XGBoost) classification models in predicting the unknown completion status. The research used a dataset containing 13.377 undergraduate students’ profiles from the Higher Education Database (PDDikti), Ministry of Education, Culture, Research, and Technology. The dataset was incomplete, and the proportion of missing data was 20,9% of the total data. Because missing data might lead to prediction bias, the research also used MissForest imputation to overcome the missing data in the classification modelling and compared it to Mean/Mode and Median/Mode imputation. The results show that MissForest outperforms the other two imputations in both classifiers but requires the longest computation time. Furthermore, the XGBoost model with MissForest is significantly superior to the Random Forest model with MissForest. Hence, the best model chosen to predict the completion status is XGBoost with MissForest imputation.
PERKEMBANGAN STATISTIKA DAN PENERAPANNYA DI BERBAGAI BIDANG Khairil A. Notodiputro
Jurnal Natural Vol. 1 No. 2 (2002): Jurnal Natural
Publisher : FMIPA Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jn.v1i2.98

Abstract

Statistic is a meaning of numbers of data analyses that can be used to describe the object or phenomenon faced. For example, the growth of population in a district or area can be analysed or predicted using variabel of birth rate, mortal rate, imigration and emigration rate. The quality of human resources can be reviewed from variabel of life standart index.(mutu standard index). On the other hands, statistica refers to a disipline of collecting, analising and as a skill of summerizing the analysis being mad.
EXTRA TREES METHOD FOR STOCK PRICE FORECASTING WITH ROLLING ORIGIN ACCURACY EVALUATION Dani Al Mahkya; Khairil Anwar Notodiputro; Bagus Sartono
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.36-47

Abstract

Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.
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.
Co-Authors . Barizi . SUTIKNO A. Bey Adlina Khairunnisa Agus Mohamad Soleh Aji H. Wigena Anang Kurnia Annisa Putri Utami ASEP SAEFUDDIN Asfar Aulya Permatasari Ayunda Pratiwi Azlam Nas Bagus Sartono Bayu Suseno Budi Susetyo Bukhari, Ari Shobri Cahyani Oktarina Christiana Anggraeni Putri D Diapari Dani Al Mahkya Dwi Jayanti Erfiani, Erfiani Etis Sunandi FAHREZAL ZUBEDI FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar H. M H Bintoro Hari Wijayanto Hari Wijayanto Hari Wijayanto Herdina Kuswari I Las I Made Sumertajaya I. LAS Indahwati Indahwati, Indahwati Intan Nirmala J Jachja Jajah K. Wagiono Kurnia, Anang Kusman Sadik Laily Nissa Atul Mualifah M S Saeni Muhammad Ilham Abidin Muhammad Nur Aidi Nurfadilah, Khalilah Pardomuan Robinson Sihombing Pratiwi, Windy Ayu R Boer R. BOER Rahardiantoro, Septian Rini Irfani Rizky Zulkarnain Sachnaz Desta Oktarina Septian P Palupi Sri Wahyuni Stephan Chen Suseno Bayu Tri Listianingrum Ulfah Sulistyowati Utami Dyah Syafitri Yekti Widyaningsih