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All Journal Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Media Statistika Jurnal Ilmu Dasar Statistika Jurnal Ilmiah Aplikasi Isotop Radiasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktik dalam Bidang Pendidikan dan Ilmu Geografi MAJALAH ILMIAH GLOBE Syntax Literate: Jurnal Ilmiah Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan JURNAL METEOROLOGI DAN GEOFISIKA InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application SRIWIJAYA JOURNAL OF ENVIRONMENT Xplore: Journal of Statistics Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications
Aji Hamim Wigena
Department Of Statistics, IPB University, Jalan Meranti Wing 22 Level 4, Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat 16680
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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Implementasi Metode CHAID (Chi-Squared Automatic Interaction Detection) pada Segmentasi Trend Penjualan Minuman Ringan di Indonesia Via Sulviana; Aji Hamim Wigena; . Indahwati
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 | Full PDF (325.657 KB) | DOI: 10.29244/xplore.v2i2.91

Abstract

Currently some outlet sells their products by looking at sales trends over a period of time to continue developing their business and devising effective marketing strategies. CHAID (Chi-Squared Automatic Interaction Detection) method is one of the efficient non-parametric statistical methods to classify any aspects that can increase the sales of soft drinks. CHAID selects significant variables based on the Chi-Square test between categories of explanatory variables with response categories. The CHAID method is used if the response variable is nominal or ordinal. This research aims to classify characteristics that characterize diversity and determine the target market that is able to maximize profits on the sales trend of various types of soft drinks by using CHAID method. Results from CHAID are tree diagrams that divide categories of response variables by segments from explanatory variables packaged into more easily understood information. CHAID method produces 11 of 20 segments that affect the trend of soft drink sales spread across big cities of Indonesia. There are 4 independent variable from segment that form, there are city, type of outlet, source of buying and payment method which accuracy that form from segmentation are 71.4%.
Aplikasi Structural Equation Modeling-Partial Least Squares dalam Menentukan Faktor yang Mempengaruhi Kinerja Karyawan Amanda Permata Dewi; I Made Sumertajaya; Aji Hamim Wigena
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

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Abstract

Structural Equation Modeling (SEM combines factor and path analysis, so researchers can see the relationship between latent variables and their indicators and the relationship between latent variables. Partial Least Square is a soft modeling approach on SEM that has no assumption of data distribution and minimum number of observations which is often called SEM-PLS. The data used in this study is the performance of 70 constructions company employees. The number of observations is too small and couldn’t fulfill the data normality assumption so the analysis method used is SEM-PLS. This study applies SEM-PLS to identify the factors that influence the performance based on competence data from each of the existing employees. The results of this study indicate that both variables have a significant influence on the performance variables. The model tested in the research is good enough to explain the diversity of the performance variables with the evaluation value of Q2 of 75.24%.
Pemodelan Produksi Ayam Ras di Indonesia Menggunakan Regresi dengan Sisaan Deret Waktu Akhbamah Primadaniyah Febrin; Itasia Dina Sulvianti; Aji Hamim Wigena
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.v8i1.192

Abstract

The production of broiler chicken has fluctuated in recent years and many factors alleged to influence the production. The purpose of this study is modeling a structural equation of forecasting the production of broiler chicken. The study use a dependent variable (Y) that is production of broiler chickens (kilo ton) and five independent variables (X) consist of broiler chicken population (million), national chicken consumption (ton/year), retail price (Rp/kg), real price of corn (Rp), and real price of Kampung chicken (Rp). The variables are time series data with errors does not spread out randomly. Modeling method used and suitable to the conditions is regression with time series errors combined with ARIMA (Autoregressive Integrated Moving Average). The results of the regression analysis showed that only population variable and retail price variable are influencing the production of broiler chicken in Indonesia. Those two independent variables then modeled by a dependent variable using regression with time series errors. The best modeling is regression with time series errors ARIMA(1,1,0) with MAPE (Mean Average Percentage Error) value of 2.4%, RMSE (Root Mean Square Error) value of 39.800, and correlation value 0.980. The results has proved that the production of broiler chicken in Indonesia is influenced by those two variables.
Influencing factors for the human development index in West Java using geographically and temporally weighted regression kernel functions Anis Dyah Rahmawati; Aji Hamim Wigena; Muhammad Nur Aidi
Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktek dalam Bidang Pendidikan dan Ilmu Geografi Vol 28, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um017v28i22023p228-241

Abstract

Human Development Index (HDI) is a competitive index that serves as one of the crucial metrics for evaluating the effectiveness of enhancing the quality of human resources. HDI values from different areas can be compared. This study aims to spatially and temporally explore the HDI data from districts or cities in West Java and examine the factors that influence HDI in each of these districts or cities using the GTWR Great Circle Distance Fixed Kernels model. In this study, we used a combination of cross-sectional data from districts or cities in West Java and time series data with seven annual periods from 2015-2021. The GTWR Great Circle Distance Fixed Kernels model was expected to display coefficient values at each location and time simultaneously, providing more in-depth information and analysis results at each location and time. The analysis results using the GTWR Great Circle Distance Fixed Kernels model show that HDI in West Java carries a positive influence on the location and time. This finding should be of particular concern to the relevant government, particularly the factors presenting a natural effect on HDI based on location and time. The positive influence obtained by an area at a particular time will also have a positive impact on other regions, and if there is a negative influence, it will undoubtedly affect other regions as well. Analysis of the HDI model in West Java using the GTWR Great Circle Distance Fixed Exponential Kernel model also presents better results in comparison to the Global OLS model and the GTWR model without the Great Circle Fixed Exponential Kernel. The final parameter estimator results are displayed in the form of a geographic map to facilitate ease of understanding.
Analisis Kualitas Aplikasi Mobile JKN dan Layanan Care-Center terhadap Kepuasan Peserta JKN-KIS dengan Metode PLS-SEM Made Agung Prebawa Parama Artha; Aji Hamim Wigena; Erfiani Erfiani
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.373 KB) | DOI: 10.36418/syntax-literate.v7i4.6735

Abstract

Sains data (data science) telah menjadi perhatian ahli statistika dunia akhir-akhir ini. Dunia industri, bisnis dan ilmu pengetahuan sangat memerlukan ilmu ini untuk melakukan analisis data maupun prediksi untuk perkembangan bisnis, industri maupun bidang - bidang lain di masa depan. Penelitian ini bertujuan untuk mengetahui pengaruh kualitas aplikasi Mobile JKN dan Layanan Care-Center terhadap kepuasan peserta JKN-KIS dengan menggunakan metode PLS-SEM. Teknik pengumpulan data menggunakan kuisioner dengan jumlah responden sebanyak 1.389 orang. Penelitian ini menggunakan pendekatan Partial Least Square-Strucural Equation Modelling (PLS-SEM) yang dapat memprediksi hubungan kausalitas variabel laten eksogen dengan variabel laten endogen. Hasil yang diperoleh dari penelitian ini yaitu nilai loading factor dari semua indikator sudah memenuhi kriteria melebihi dari angka 0.7 atau sering digunakan batas 0,5 sebagai batasan minimal dari nilai loading factor (Kock, 2020). Inner model yang diperoleh pada data dengan taraf nyata 5%: Kepuasan Peserta = 0.38 Kualitas Aplikasi + 0.31 Kualitas Layanan CareCenter +z. Ini artinya Peubah kualitas aplikasi berpengaruh signifikan pada taraf nyata 5% dengan nilai koefisen sebesar 0.38, sedangkan peubah kualitas layanan CareCenter berpengaruh signifikan dengan nilai koefisien sebesar 0.31. Nilai t-statistik untuk peubah Kualitas aplikasi terhadap Kepuasan Peserta sebesar 11,297 dan peubah Kualitas Layanan Care-Center terhadap Kepuasan Peserta sebesar 9,256. Hal ini menunjukkan nilai t-statistik lebih besar dari t-tabel yang artinya Kualitas Aplikasi Mobile JKN dan Layanan Care-Center berpengaruh terhadap Kepuasan Peserta.
MODEL KLASIFIKASI UNTUK PREDIKSI FASE PERTUMBUHAN PADI DENGAN MACHINE LEARNING BERDASARKAN CITRA SATELIT: (Classification Model for Paddy Growth Phase Prediction with Machine Learning Based on Satellite Imagery) Novian Tamara; Aji Hamim Wigena; Bagus Sartono
Majalah Ilmiah Globe Vol. 23 No. 2 (2021): GLOBE Vol 23 No 2 TAHUN 2021
Publisher : Badan Informasi Geospasial

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Abstract

Padi memegang peranan penting bagi masyarakat Indonesia. Rumah Tangga Usaha Pertanian (RTUP) padi sebesar 64,85% dari RTUP sub sektor tanaman pangan tahun 2018. Data padi yang akurat dapat membantu pemerintah dalam menyusun perencanaan, mengeksekusi program, dan membuat keputusan yang tepat. Tujuan penelitian kami yaitu membangun model klasifikasi untuk prediksi fase pertumbuhan padi sebagai upaya dalam mendukung keakuratan data padi. Pemodelan multi kelas dilakukan dengan teknik machine learning yaitu Random Forest dan Support Vector Machine. Penelitian ini menggunakan citra Landsat-8 dan Sentinel-2 sebagai fitur yang disupervisi oleh data Kerangka Sampling Area (KSA) dari BPS sebagai variabel target. Sebanyak 1239 fitur statistik temporal turunan dari 18 indeks spektral Sentinel-2 dan 15 indeks spektral Landsat-8 diseleksi dengan plot korelasi dan teknik stepwise. Ketidakseimbangan data ditangani dengan teknik sampling SMOTE+TL. Pada klasifikasi tahap 1, performa model dalam memprediksi sawah padi, sawah bukan padi, dan bukan sawah mencapai akurasi 0,95 dan Matthews Correlation Coefficient (MCC) 0,84. Pada klasifikasi tahap 2, performa model dalam memprediksi fase pertumbuhan padi mencapai akurasi 0,87 dan MCC 0,73. Hasil menunjukan kombinasi citra Landsat-8 dan Sentinel-2, seleksi fitur temporal, serta pemilihan metode klasifikasi mampu meningkatkan performa model.
Performance Analysis of Robust Functional Continuum Regression to Handle Outliers Ismah, Ismah; Erfiani, Erfiani; Wigena, Aji Hamim; Sartono, Bagus
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 6, No 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38928

Abstract

Robust functional continuum regression (RFCR) is an innovation as a development of functional continuum regression that can be applied to functional data and is resistant to outliers. The resistance of RFCR depends on the applied weighting function. This study aims to evaluate the RFCR performance to handle outliers. We propose the various weighting functions in this evaluation, i.e., Huber, Hampel, Ramsay, and Tukey (Bisquare), which do not eliminate or give zero weight to observed data identified as outliers. This contribution is essential to determining the appropriate RFCR method without eliminating the outlier data. The result shows that the RFCR performance with the Huber weighting function is better than the others, based on the goodness of fit, consisting of the root means square error of prediction (RMSEP), the correlation between the actual data and the model, and the mean absolute error (MAE).Keywords: Functional data analysis; Huber weighted function; Hampel weighted function; Ramsay weighted function; Tukey (Bisquare) weighted function. AbstrakRegresi kontinum fungsional kekar (RFCR) merupakan inovasi yang merupakan pengembangan dari regresi kontinum fungsional yang dapat diaplikasikan pada data fungsional dan tahan terhadap outlier. Resistansi RFCR bergantung pada fungsi pembobotan. Penelitian ini bertujuan untuk mengevaluasi kinerja RFCR. Kami mengusulkan beberapa fungsi pembobotan dalam evaluasi tersebut, yaitu Huber, Hampel, Ramsay, dan Tukey (Bisquare), dengan tidak menghilangkan atau memberikan bobot nol pada data observasi yang teridentifikasi sebagai outlier. Kontribusi ini penting untuk menentukan metode RFCR yang tepat tanpa menghilangkan data outlier. Hasil menunjukkan bahwa kinerja RFCR dengan fungsi pembobotan Huber lebih baik dibandingkan fungsi pembobotan lain berdasarkan goodness of fit, yang terdiri dari root mean square error of prediksi (RMSEP), korelasi antara data aktual dan model, dan mean kesalahan absolut (MAE).Kata Kunci: Analisis data fungsional; Fungsi berbobot Huber; Fungsi tertimbang Hampel; Fungsi tertimbang Ramsay; Fungsi berbobot Tukey (Bisquare). 2020MSC: 62J99, 62R10
STACKING ENSEMBLE APPROACH IN STATISTICAL DOWNSCALING USING CMIP6-DCPP FOR RAINFALL ESTIMATION IN RIAU Mahkya, Dani Al; Djuraidah, Anik; Wigena, Aji Hamim; Sartono, Bagus
MEDIA STATISTIKA Vol 17, No 1 (2024): 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.17.1.1-12

Abstract

Rainfall modeling and prediction is one of the important things to do. Rainfall has an important relationship and role with various aspects of the environment. One phenomenon that can be associated with rainfall is forest and land fires. Riau is one of the provinces in Indonesia that has a high potential for forest and land fires. This is because Riau has a large area of peatland. One approach that can be used to estimate rainfall is statistical downscaling. The concept of this approach is to form a functional relationship between global and local data. This research uses CMIP6-DCPP output data that will be used to estimate rainfall at 10 observation stations in Riau. The proposed model in this research is Stacking Ensemble with PC Regression and LASSO Regression in the base model and Multiple Linear Regression in the meta model. This research aims to determine the best CMIP6-DCPP model for estimating rainfall in Riau and increasing the accuracy of rainfall estimates using the Stacking Ensemble approach.
PENGGUNAAN SUPPORT VECTOR REGRESSION DALAM PEMODELAN INDEKS SAHAM SYARIAH INDONESIA DENGAN ALGORITME GRID SEARCH Galih Hedy Saputra; Aji Hamim Wigena; Bagus Sartono
Indonesian Journal of Statistics and Applications Vol 3 No 2 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i2.172

Abstract

Indonesia as the largest Muslim population country in the world is a very potential market for sharia stocks. Sharia stocks performance can be seen from the Indonesia Sharia Stock Index (ISSI). Stock index modeling is conducted to determine the factors that affect the stock index or to predict the value of the stock index. Modeling using regression analysis is based on assumptions that do not always match with the characteristics of stock data that fluctuate. Support Vector Regression (SVR) method is a non-parametric approach based on machine learning. The problem often encountered in the analysis using SVR is to determine the optimal parameters to produce the best model. The determination of the optimal parameters can be solved by using the grid search algorithm. The purpose of this research is to make ISSI model using SVR with grid search algorithm with independent variable BI Rate, money supply, and exchange rate (USD / IDR). The best SVR model was obtained using weekly data with a total of 343 periods as well as a linear kernel with parameters ε = 0.03 and C = 2. The evaluation of the best model SVR is RMSE of 2.289 and correlation value of 0.873.
PENENTUAN NILAI AMBANG BATAS SEBARAN PARETO TERAMPAT DENGAN MEASURE OF SURPRISE Yumna Karimah; Aji Hamim Wigena; Agus Mohamad Soleh
Indonesian Journal of Statistics and Applications Vol 3 No 2 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i2.284

Abstract

Extreme rainfall can result in natural disasters such as floods and landslides. These natural disasters will cause damage and losses to the surrounding environment. Prevention of damage from natural disasters can be done by extreme rainfall estimation. Estimates of extreme rainfall are based on Generalized Pareto Distribution (GPD) which requires threshold value information. The threshold value can be determined by two methods, namely Mean Residual Life Plot (MRLP) and Measure of Surprise (MOS). The purpose of this study is to determine and compare the threshold values ​​of MRLP and MOS. The data used are 10-day and monthly rainfall data. The results of this study indicate that the procedure of MOS is shorter and easier than that of MRLP. Based on the cross validation result, the log-likelihood value of MOS is larger than that of MRLP, then MOS is better than MRLP.
Co-Authors . Indahwati . Siswadi Aan Kardiana Abdul Kudus Afnan, Irsyifa Mayzela Agus Mohamad Soleh Akhbamah Primadaniyah Febrin Alwani, Nadira Nisa Amanda Permata Dewi Anang Kurnia Anik Djuraidah Anis Dyah Rahmawati Arina, Faula Arista Marlince Tamonob ASEP SAEFUDDIN Aunuddin Aunuddin Bagus Sartono Budi Suhardjo Budi Susetyo Bunawan Sunarlim Citra, Reza Felix Dani Al Mahkya Dedi Sucahyono Dewi Retno Sari Saputro, Dewi Retno Edvin Aldrian Efriwati Efriwati Embay Rohaeti Erfiani Erfiani Erfiani, Erfiani Erwin Eka Syahputra Makmur Fadjrian Imran Farit Mochamad Afendi Fildzah Hanum Syazwina Galih Hedy Saputra Geri Zanuar Fadli Haikal, Husnul Aris Hanny Komalig Hari Wijayanto Hasibuan, Rafika Aufa Herlina Hanum Herlina Hanum, Herlina Hidayat Pawitan I Made Sumertajaya I Wayan Mangku Indahwati Ismah . Ismah, Ismah Itasia Dina Sulvianti Kusman Sadik Latifah K. Darusman Made Agung Prebawa Parama Artha Maemonah, Maemonah Mia Sri Listiani Ahmad Muhammad Nur Aidi Novian Tamara Nunung Nurjanah Nunung Nuryartono Nurrahman, Fathu Pigitha, Nindi Pingkan Awalia Santoso, Zein Rizky Sinaga, Enny Keristiana Soeranto Human Soleh, Agus M Syafitri, Utami Tiyas Yulita Trinah Wati Urip Haryoko Utami Dyah Syafitri Via Sulviana Woro Estiningtyas Wulan Tri Wahyuni Yonny Koesmaryono Yuan Millafanti Yumna Karimah Yuniarty, Titin Yunus Subagyo Swarinoto _ Aunuddin