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All Journal Jurnal Ilmu Pertanian Indonesia Jurnal Manajemen dan Organisasi FORUM STATISTIKA DAN KOMPUTASI ComEngApp : Computer Engineering and Applications Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal of Biotechnology Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Jurnal Jamu Indonesia Journal of Enterprise and Development (JED) ARRUS Journal of Mathematics and Applied Science Prosiding Seminar Nasional Official Statistics Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Scientific Journal of Informatics Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications

Farit Mochamad Afendi

Biopharmaca Research Center, Bogor Agricultural University Department Of Statistic, Faculty Of Mathematics And Natural Sciences, Bogor Agricultural University

Author-ID : 3379329

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Analisis Lintas Sifat Morfo-Agronomis dan Fisiologis Jagung (Zea mays L.) Annisa Malik; Farit Mochamad Afendi; Akbar Rizki; . Sutoro
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

Corn (Zea mays L.) is the third most important food commodity after wheat and rice based on the world's staple food, and ranks second after rice based on staple food in Indonesia. High yielding varieties of corn are highly needed to meet food, feed and industrial needs. These varieties can be obtained through plant breeding programs by utilizing the source of genes capable of producing good plant character. Gene sources can be obtained from germplasm or local varieties that exist. Character of plants that can support the productivity of plants can be used as an indicator of the selection process in corn plant breeding. This can be done through characterization of morpho-agronomic and physiological properties of each corn variety, then determine the characters that support the productivity of corn plants directly or indirectly. The direct and indirect effect of a plant's character on crop productivity is identified through path analysis. The results showed that the effective selection criteria for increasing cornâ€™s seed weight directly was the leaf area. While the effective selection criteria for increasing the weight of biomass directly is the age of female flowers out. While the effective selection criteria for increasing cornâ€™s seed weight and biomass indirectly is the plant height through the filling rate of the seeds.

Penerapan Metode Two Step Cluster pada Data Survei Angkatan Kerja Nasional (Sakernas) Maya Deanti; Farit Mochamad Afendi; Aam Alamudi
Xplore: Journal of Statistics Vol. 2 No. 1 (2018): 30 Juni 2018
Publisher : Department of Statistics, IPB

MAYA DEANTI. Implementation of Two Step Cluster Method on National Labor Force Survey Data (Sakernas) 2017 Bogor Regency. Supervised by FARIT MOCHAMAD AFENDI and AAM ALAMUDI. Five labor issues in Indonesia that have not been resolved by 2017 are termination of employment due to digitalization or automation, labor informalization, BPJS, high accident and occupational safety (K3), and outsourcing. In addition, the increasing number of Foreign Workers (TKA) in Indonesia can affect the decrease in local employment opportunities. Therefore, in this study will be carried out clustering to the labor force data to determine the condition of employment in Indonesia, especially Bogor regency. However, this labor force data has considerable observation with mixed data types, namely numerical and categorical. Regular cluster analysis can not be applied directly to the condition of the data, so that to be used in this research is a Two Step Cluster analysis which is a modification of existing cluster analysis. This Two Step Cluster analysis produces 3 clusters, with the characteristics of each cluster that is cluster 1 consisting of resident households or unemployed, cluster 2 consists of self-employed residents, and cluster 3 with the majority of the population working as laborers or employees. This clustering is based on work aspect only because the demography and education aspect of Bogor Regency is quite uniform. Keywords: cluster analysis, cluster, Two Step Cluster, uniform

Analisis pada Data Harga Cabai Merah Keriting Indonesia menggunakan Model ARIMAX Muhammad Ali Umar; Farit Mochamad Afendi; Akbar Rizki; Budi Waryanto
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

The model used to analyze the time series data with one variable is Autoregresive Integrated Moving Average (ARIMA). In some cases, ARIMA model is not good enough in modeling. For instance, the time series data influenced by the outside patterns of observed variable that affect the variable. One way to capture the other patterns is with Autoregressive Integrated Moving Average Exogenous (ARIMAX). The model principle of ARIMAX is by making the other variables as the independent variables in the model used. Calender variation effects are independent variables which are often used in the modeling. In this research, ARIMAX model is applied on the weekly data of red curly chili in the period of Januari 1, 2011 to April 30, 2018. The evaluation result is there are some influential variables such as the peak of rainy season, election campaign, Eid Fitr, Eid al-Adha, and also Imlek. The best ARIMAX model gained is ARIMAX(1,1,2) model with the MAPE value of 5.054 â„….

Dekomposisi Ensemble untuk Peramalan Harga Bawang Merah DKI Jakarta Febie Tri Lestari; Farit Mochamad Afendi; Mohammad Masjkur; Budi Waryanto
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.v8i1.120

Onions are one of the vegetable commodities that are not distributed and included as seasonal crops. Onions are commonly used as cooking spices and traditional medicine. At the time of the religious holidays or non-harvest season, the stock of onions is not able to meet the demand, hence the government has to import them, but that increase the fluctuations of onion prices on the market. Actually, by utilizing the price fluctuation, information about the factors, will be obtained by reviewing the price movement and precise forecasting of the price of onions. Ensamble Empirical Mode Decomposition (EEMD) method can be applied to examine that. EEMD is a decomposition method that can be used to convert a series of time functions from a data signal into several sub-signals resulting from flattening, otherwise known as Intrinsic Mode Function (IMF) and IMF remaining. In this research, this concept applied to data on weekly onion prices in DKI Jakarta from July 2008 to April 2018 as many as 521 data. Based on the results of data processing, as many as 7 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA. In the end, the weekly price forecast for onion in Jakarta from May - July 2018 ranged from Rp34295.67, - to Rp36133.36, - with average forecasting prices for May-July 2018 amounting to Rp34482.39 - Rp 35207.12 and Rp 36024.88 with a MAPE value of 1.85%.

Pemodelan Data Multi-Label dengan Pendekatan Multivariate Generalized Linear Mixed Model (MGLMM) Dairul Fuhron; . Indahwati; Farit Mochamad Afendi
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.v8i1.148

Multi-label data refers to a type of categorical data where an object may has more than one corresponding label or possible values. Multi-label data are commonly found in many fields, one of them is market research of the sweetened condensed milk (SCM) and sweetened condensed creamer (SCC) products. According to product characteristic, market research for the aforementioned product is appropriately conducted on the outlet level. An outlet may use more than one productâ€™s brand in the same time frame. That condition inflict brand choice information to be represented under multi-label data. This research used problem transformation method by tranforming a multi-label variable into several single-label variables. Multivariate Generalized Linear Mixed Modeling or MGLMM was selected under consideration of binary multiple responses and correlated responses presumption. Five responses of SCM and SCC brand choice modeling resulted correct model without overdispersion and the scaled pearson chi square statistic is 0.99. Tests of fixed effects indicate three factor significantly affect SCM and SCC brand choice at the 5% level. They are purchase total, province, and type of business. The variance of the random effect intercept is 1.53Ã—10âˆ’18 or insignificant, hence MGLMM based model was similar compare to separated GLM based model.

Penerapan Regresi Logistik Biner Multilevel terhadap Ketepatan Waktu Lulus Mahasiswa Program Magister Sekolah Pascasarjana IPB Zana Aprillia; Farit Mochamad Afendi; Akbar Rizki
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

The study length of alumnus is one of the study achievement indicator of the university. Study length for Master Program can be divided into two categories which is pass on time (study length â‰¤24 months) and pass not on time (study length >24 months). In the classical regression analysis, each student are assumed to be independent. But in reality, each student are grouped into a different study programs so that the individuals who are in the same study program tend to have a similar characteristics. Multilevel regression is one of the analysis that accomodates the problem. The level used in this study are level 1 (individual student) and level 2 (study programs). The best multilevel regression model obtained is a model with random intercept and the variance is produced from study program is 0.6636. Factors that give an effect to the graduationâ€™s timeliness are age, married status, and the source of the S2 education cost.

Penanganan Pencilan pada Peramalan Data Deret Waktu Menggunakan Metode Pemulusan Holt dan Robust Holt Septanti Kusuma Dwi Arini; Farit Mochamad Afendi; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

The time series data used is time series data following the LLTM (local linear trend model) model with four different error conditions. These conditions are Clean Data (CD), Symmetric Outliers (SO), Asymmetric Outliers (AO) and Fat-tailed data (FT). The time series data contains symmetric and asymmetric outliers that can affect forecasting. The forecasting method used for the trend data pattern is the Holt smoothing method. The forecasting of the data series when it is spinning using the Holt smoothing method is not good enough so that it requires a handler with the smoothing method of Holt robustness. The Holt robustness smoothing method that is carried out on time series simulation data is better used for the condition of scattered data compared to the Holt smoothing method. This is indicated by the value of evaluating the goodness of the method, namely the value of MAD (Mean Absolute Deviation) produced. The smaller MAD value for CD condition training data is the Holt smoothing method, while the data testing method for Holt and robust Holt smoothing is almost comparable. SO's condition for training data and data testing for smaller MAD values is the smoothing method of robust Holt. The condition of AO for training data and data testing for smaller MAD values is the smoothing method of robust Holt. In addition, the MAD value in FT conditions for training data and data testing found almost comparable results between the two methods.

METODE CART UNTUK MENGIDENTIFIKASI FAKTOR-FAKTOR YANG MEMENGARUHI WAKTU PEMBELIAN KENDARAAN KEDUA Eka Setiawaty; Farit Mochamad Afendi; Cici Suhaeni
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Increased competition between personal vehicle dealers make them need strategies to hold their customers and increase their sales. One of the strategies they could apply is prospecting their customers at the right time. We could predict the right time by identifying the relationship between the length of their purchase time and its factors based on the transaction data of Z Company from year 2002 to 2015 using Classification and Regression Trees (CART). Data analysis is separated between groups of customers who made the second purchase maximum of 10 years after the first purchase (group A) and more than 10 years after the first purchase (group B). Group Aâ€™s regression tree produces 8 terminal nodes with MAD value 1.84 years. The independent variables that plays a role are tenor, job, age, and brand. Group Bâ€™s regression tree produces 4 terminal nodes. Authorized service and job come out as independent variables which affect the splitting process. MAD value for Group Bâ€™s regression tree is 0.56 years.

Penerapan Algoritme Genetik Untuk Seleksi Peubah Regresi Logistik Dian Ayuningtyas; Bagus Sartono; Farit Mochamad Afendi
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

In a study, interaction factors are the potential to have important effects on the response variable. But research involving interaction factors often encounters two problems, namely the excessive number of variables and the difficulty of implementing the heredity principle. The alternative solution is to do variable selection using a metaheuristic optimization method, In this study, the logistic regression variable selection was done using a genetic algorithm. The genetic algorithm is modified so that every independent variable has a different probability to be included in the model. That probability is based on the absolute value of the correlation of the independent variable with the response variable. These modifications have a positive effect on the results of variable selection. To choose significant independent variables, 30 repetitions of the genetic algorithm can be performed using the objective function AIC. Of the 30 repetitions, if a variable appears in all formed models, then the variable is an independent variable that has a significant effect on the response variable. The application of this method to Myopia data can show significant variables well.

PENGELOMPOKAN PROVINSI BERDASARKAN CAPAIAN INDIKATOR KESEHATAN LINGKUNGAN DI INDONESIA TAHUN 2020 Maysarah Sabariah Kudadiri; Pika Silvianti; Farit Mochamad Afendi
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Environmental health is part of public health in general. If each province is associated with the achievement of environmental health indicators, the achievements will not be the same. The grouping of provinces will make it easier for the government to determine priorities for environmental health development in Indonesia. The grouping of provinces in this study used cluster analysis. The method used is the k-means because it has the smallest standard deviation ratio compared to other cluster analysis methods. The grouping results obtained are four clusters. The first cluster consists of one province that has the characteristics of high Percentage of Medical Waste (PMW) indicator achievement and the lowest percentage of villages with open defecation stops indicator achievement. The second cluster consists of six provinces that have the highest achievement of the SBS indicator and the lowest achievement of the PMW indicator. The third cluster consists of 20 provinces that have the characteristics of achieving high percentage of public places and facilities that are supervised indicators and the smallest achievement of PMW indicators. The fourth cluster consists of seven provinces that have the characteristics of high achievement of the percentage of drinking water facilities supervised/checked for drinking water quality and the lowest achievement of the PMW indicator.

Co-Authors . Indahwati . Sutoro Aam Alamudi Abd. Rasyid Syamsuri Adeline Vinda Septiani Agus Mohamad Soleh Agus Santoso Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Aki Hirai Anang Kurnia Anggraini Sukmawati Annisa Malik Apino, Ezi Aqmar, Nurzatil Bagus Sartono Budi Susetyo Budi Waryanto Budi Waryanto Budi Waryanto Cici Suhaeni Dairul Fuhron Dalimunthe, Amir Abduljabbar Dian Ayuningtyas Eka Setiawaty Erwandi Erwandi fatimah Fatimah Febie Tri Lestari Fitrianto, Anwar H S, Rahmat Handayani, Vitri Aprilla Handayani, Vitri Aprilla Hari Wijayanto Hari Wijayanto Hasibuan, Rafika Aufa Hasnita Hasnita Herdina Kuswari Heri Retnawati Hiroki Takahashi I Made Sumertajaya Ikhlasul Amalia Rahmi Indahwati Indahwati Indahwati Intan Juliana Panjaitan Isnan Mulia Itasia Dina Sulvianti Izzati, Fatkhul Kensuke Nakamura Khairil Anwar Notodiputro Koesnandy H, Abialam Kusman Sadik Latifah Kosim Darusman M. Rafi Maya Deanti Maysarah Sabariah Kudadiri Md. Altaf-Ul-Amin . Melati Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muchlishah Rosyadah Muhammad Ali Umar Mukhamad Najib Nadhif Nursyahban Nur Hikmah Nur Janah Nur Jannah Nurul Qomariasih Octaviani, Siti Nurfajar Panjaitan, Intan Juliana Pardede, Timbul Pika Silvianti Pika Silvianti Pika Silvianti Puspita, Novi Qomariasih, Nurul Rifqi Aulya Rahman Risnawati, I'lmisukma Rizal Bakri Rossi Azmatul Barro Rosyada, Munaya Nikma Rosyadah, Muchlishah Rudi Heryanto Safitri, Wa Ode Rahmalia Septaningsih, Dewi Anggraini Septanti Kusuma Dwi Arini Shigehiko Kanaya Sulistiyani . Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Usman, Muhammad Syafiuddin Valentika, Nina Widhiyanti Nugraheni Widya Putri Nurmawati Winata, Hilma Mutiara Wisnu Ananta Kusuma Zana Aprillia

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