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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
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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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (789.92 KB) | DOI: 10.29244/xplore.v10i2.237

Abstract

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 A’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 B’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 B’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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (958.838 KB) | DOI: 10.29244/xplore.v9i1.363

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (988.329 KB) | DOI: 10.29244/xplore.v11i3.879

Abstract

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.
Penggerombolan Data Panel Emiten Sektor Pertambangan selama Pandemi Covid-19 Nadhif Nursyahban; Aam Alamudi; Farit Mochamad Afendi
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 (331.787 KB) | DOI: 10.29244/xplore.v12i1.948

Abstract

The Covid-19 pandemic has made people start looking for new income, one of whichis stock investment. Mining Stock recorded the highest sectoral index increase in 2020.The high increase in the mining sector index doesn’t indicate all of the stocks have agood performance. Clustering data of mining stock can help to see which stock has thebest performance. Variables used in clustering are technical factors with details: return,trading volume, transaction frequency, bid volume, and foreign buy. Data in this researchis longitudinal data from March 2020 until January 2022 and the clustering techniqueused is k-means. Clustering on outliers data and non-outliers data is done separately.Definition of outliers is exploratively with biplot analysis. Clustering on outliers dataresults obtained are five clusters and clustering on non-outliers data results obtained aretwo clusters. Best cluster is cluster who obtained ANTM because has highest value inreturn, transaction frequency, and foreign buy.
Deep Learning Approaches for Predicting Intraday Price Movements: An Evaluation of RNN Variants on High-Frequency Stock Data Mochamad Ridwan; Kusman Sadik; Farit Mochamad Afendi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.278

Abstract

This study discusses the comparison of four recurrent neural networks (RNN) models: Simple RNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional RNN (BiRNN), in forecasting minute-level stock price time series data. The performance of these four models is evaluated using the Mean Absolute Percentage Error (MAPE) on a stock dataset from Bank Central Asia (BBCA.JK). The experimental results reveal that the GRU model exhibits the best performance with an average MAPE of 0.0255%, followed by the LSTM model with an average MAPE of 0.0377%. The BiRNN model also demonstrates good performance with an average MAPE of 0.0668%, while the Simple RNN has the highest average MAPE at 0.5118%. This suggests that more complex recurrent architectures like GRU and LSTM have better capabilities in capturing patterns in high-frequency time series data. This study can be expanded by exploring other models such as CNN, conducting tests on diverse datasets, and experimenting with a wider range of hyperparameter variations. Additional variables such as economic indicators, global market data, and social data can also offer a more comprehensive understanding of factors influencing stock prices.
Improving Stroke Detection with Hybrid Sampling and Cascade Generalization Widya Putri Nurmawati; Indahwati Indahwati; Farit Mochamad Afendi
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.19386

Abstract

The prevalence of stroke in Indonesia has increased. One survey in Indonesia that contains information about the health conditions of the Indonesian people is the Indonesian Family Life Survey (IFLS). The proportion of respondents who had a stroke and non-stroke in IFLS5 showed an imbalance with an extreme level of imbalance; hence, this research aims to overcome this problem with SMOTE, SMOTE-Tomek Link, and SMOTE-ENN; then, the balanced dataset is classified using the ensemble and cascade approaches to improve the detection of stroke risk and to identify the important variables. However, the stroke respondents were still challenging to classify after imbalance class handling, presumably because of the large amount of data before and after balancing. The solution is to balance the training data with various percentages. The results showed the best percentage is applied to 5% of the training data, balanced by the SMOTE-ENN, and the ensemble method with the cascade approach increases the sensitivity and balanced accuracy values. Random forest and logistic regression combine models that produce the best performance, with a classification tree as the final model. The important variables obtained from this combination are the addition of probability from random forest, logistic regression, history of hypertension, age, and physical activity.
Pengembangan Syariah Compliant Hotel: Hambatan & Inovasi Octaviani, Siti Nurfajar; Najib, Mukhamad; Afendi, Farit Mochamad
Journal of Enterprise and Development (JED) Vol. 2 No. 2 (2020): Journal of Enterprise and Development (JED)
Publisher : Faculty of Islamic Economics and Business of Universitas Islam Negeri Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/jed.v2i2.2180

Abstract

Hotel Syariah memiliki peranan penting dalam pengembangan pariwisata syariah di Indonesia. Akan tetapi pelaku bisnis di bidang Hotel Syariah mendapatkan beberapa tantangan yang membuatnya sulit berkembang karena meniadakan unsur – unsur nonsyar’i, persepsi masyarakat yang menyamakan dengan hotel konvensional, dan fasilitas yang kurang menarik. Penelitian ini mencoba untuk mengurai tiga aspek (produk, pelayanan, dan pengelolaan) pengembangan Hotel Syariah guna menghadapi tantangan tesebut dengan metode ANP. Dapat dilihat pada aspek produk ruang ibadah menjadi prioritas utama yang harus diperhatikan, pada aspek pelayanan pemisahan layanan untuk tamu laki – laki dan tamu perempuan menjadi prioritas utama yang harus diperhatikan, dan pada aspek pengelolaan manajemen Sumber Daya Manusia menjadi prioritas utama yang harus diperhatikan. sedangkan aspek inovasi yang dianggap dapat menjadi solusi dari tantangan yang ada ialah adanya fasilitas hiburan, Herbal Bar, pusat belanja halal, dan interior yang bernuansa Islami.
Metabolite-Group Selection On Temu Ireng (Curcuma Aeruginosa) Contains Related To Toxicity Activity By Using Group Lasso Regression H S, Rahmat; Afendi, Farit Mochamad; Wijayanto, Hari
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 2 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3626

Abstract

Metabolites are expressed in mass-to-charge ratio (m/z) on mass spectrometry experiments. They can be identified more than once. Some of m/z representing same metabolites can be considered as a group of metabolites. Evaluation of metabolite effects can be considered based on the groups. Group least absolute shrinkage and selection operator (group lasso) regression can be used to evaluate these groups. It shrinks some coefficients of regression exactly to be zero by adding intermediate penalty on ordinary least square (OLS) objective function. The purposes of this study were to estimate groups of metabolite contains of Curcuma aeruginosa (Temu ireng) affecting toxicity activity by using group lasso regression and to compare it to partial least square regression (PLSR). The data used were toxicity activity and metabolite contain, obtained from LC-MS, of temu ireng from three areas in Java. The groups of metabolites which affected toxicity activity, of group lasso regression by using dedicated software of R with gglasso package, were groups of m/z 238.150, 250.165, 262.128, 264.144, 312.275, and 456.183. The estimates of metabolites that affected of group lasso regression and PLSR had similarities. Based on the goodness of fit, group lasso regression was better than PLSR to estimate the affecting groups.
Propensity Score Matching Pada Pemanfaatan Data Hasil Web Scraping Untuk Perbaikan Statistik Resmi Fatimah, Fatimah; Wijayanto, Hari; Afendi, Farit Mochamad
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26568

Abstract

The Central Statistics Agency (BPS) welcomes the challenge of utilizing big data. One of the BPS publications that can be supported using big data is the inflation figure collected from the consumer price survey. One part of the consumer price survey is the HK-4 Survey, which contains house contract rates. So far, the house contract rates produced by BPS have been underestimated or lower than the actual situation. Improvements to house contract rates are carried out by matching BPS data and web scraping of house rental sites using Propensity Score Matching (PSM). The data used in this study includes DKI Jakarta, Bandung, and Semarang from September to October 2023. This study aims to find the best matching model using PSM to improve official statistics (house contract rates) by combining several propensity score value estimation methods and matching algorithms. Furthermore, the results matching the best model will be used to calculate the corrected house contract rates. The study results show that the best matching model generally uses logistic regression propensity score value estimation, the nearest neighbor matching algorithm with returns and uses a 1:1 ratio. The corrected contract rates are far above the official ones (DKI Jakarta corrected 87.27%, Bandung 316.15%, and Semarang 60.04%). Web Scraping allows it to improve official statistics because it is cost and time-saving, enhances the quality of official statistical data, and supports better decision-making in various sectors.
Identification of Significant Proteins Associated with Diabetes Mellitus Using Network Analysis of Protein-Protein Interactions Usman, Muhammad Syafiuddin; Kusuma, Wisnu Ananta; Afendi, Farit Mochamad; Heryanto, Rudi
Computer Engineering and Applications Journal Vol 8 No 1 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.82 KB) | DOI: 10.18495/comengapp.v8i1.283

Abstract

Computation approach to identify significance of proteins related with disease was proposed as one of the solutions from the problem of experimental method application which is generally high cost and time consuming. The case of study was conducted on Diabetes Melitus (DM) type 2 diseases. Identification of significant proteins was conducted by constructing protein-protein interactions network and then analyzing the network topology. Dataset was obtained from Online Mendelian Inheritance in Man (OMIM) and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) which provided protein data related with a disease and Protein-Protein Interaction (PPI), respectively. The results of topology analysis towards Protein-Protein Interaction (PPI) showed that there were 21 significant protein associated with DM where INS as a network center protein and AKTI, TCF7L2, KCNJ11, PPARG, GCG, INSR, IAPP, SOCS3 were proteins that had close relation directly with INS.
Co-Authors . Indahwati . Sutoro Aam Alamudi Abd. Rasyid Syamsuri 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 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 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 Rizal Bakri Rossi Azmatul Barro Rosyada, Munaya Nikma Rosyadah, Muchlishah Rudi Heryanto Safitri, Wa Ode Rahmalia Septaningsih, Dewi Anggraini Septanti Kusuma Dwi Arini Septiani, Adeline Vinda Shigehiko Kanaya Sulistiyani . Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Usman, Muhammad Syafiuddin Widhiyanti Nugraheni Widya Putri Nurmawati Winata, Hilma Mutiara Wisnu Ananta Kusuma Zana Aprillia