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All Journal Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Aplikasi Statistika & Komputasi Statistik Prosiding Seminar Nasional Official Statistics
Ardiansyah, Muhlis
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences

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PENINGKATAN PRESISI DUGAAN BERAT GABAH MELALUI PROSES SELEKSI PEUBAH DALAM PEMBELAJARAN MESIN STATISTIKA Ardiansyah, Muhlis; Notodiputro, Khairil Anwar; Sartono, Bagus
Prosiding Seminar Nasional Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika (VARIANSI) Vol 2 (2020)
Publisher : Program Studi Statistika, FMIPA, Universitas Negeri Makassar

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Abstract

Pandemi COVID-19 berpotensi menyebabkan turunnya tingkat respon beberapa survei yang dilakukan oleh lembaga penyedia statistik resmi di berbagai negara termasuk salah satunya Survei Ubinan oleh Badan Pusat Statistik Indonesia. Pengukuran berat gabah dari Survei Ubinan pada masa pandemi tidak selalu berhasil dilakukan sehingga terjadi nonrespon. Dalam penelitian ini, dicari solusi baru untuk mengatasi masalah nonrespon yaitu dengan menduga data nonrespon berat gabah berdasarkan beberapa peubah yang diperoleh dari hasil wawancara dengan petani setelah kebijakan pembatasan sosial berakhir. Dalam penelitian dicoba berbagai metode seleksi peubah yaitu Stepwise, LASSO, Elastic Net, Adaptive LASSO, dan Relaxed LASSO guna menemukan model terbaik dalam pendugaan berat gabah berdasarkan data Survei Ubinan tahun 2019 di Kalimatan Tengah. Gugus data dibagi menjadi dua, yaitu data latih dan data uji. Pengambilan 75 persen data latih dilakukan secara acak dan diulang sebanyak 100 kali dan setiap kali divalidasi menggunakan data uji. Selanjutnya peubah yang terseleksi digunakan dalam model Quantile Regression Forest (QRF). Hasilnya menunjukkan bahwa Adaptive-QRF memberikan kinerja terbaik dengan nilai RMSE tekecil.  Peubah terpilih yang berpengaruh nyata terhadap berat gabah hasil penyeleksian Adaptive LASSO adalah varietas benih, cara penanaman (monokultur atau tumpangsari), sistem penanaman (jajar legowo atau bukan), terkena serangan hama atau tidak, lokasi penanaman, dan bulan panen.  Kata Kunci: Adaptive LASSO, Produktivitas Padi, Survei Ubinan, Quantile Regression Forest.
Peningkatan Kualitas Statistik Resmi Produktivitas Padi melalui Imputasi Data Non-respons Menggunakan Model Aditif Geospasial Ardiansyah, Muhlis
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v15i2.443

Abstract

This study is motivated by the non-response problem in the Crop Cutting Survey conducted by the BPS-Statistics Indonesia as the official statistics provider. BPS has a vision of providing quality statistical data for advanced Indonesia. Handling non-response is essential to supporting this vision because non-response can potentially cause some sample characteristics to be unrepresented. This study proposed a non-response data imputation technique through statistical modeling. The proposed model was an additive model with the addition of geospatial smoothing functions of thin plate regression splines (TP) and Gaussian process (GP). Selection of the best model based on the smallest MSEP of 1000 iterations. Then we compared the average rice productivity between listwise deletion and imputation techniques through three scenarios of non-response data. The results showed that the model with the addition of the GP smoothing function gave the best performance with the smallest MSEP. The other results showed that the imputation method of non-response data is better than ignoring non-response. BPS can consider the imputation method to improve the quality of official statistics on rice productivity.
Efisiensi Survei KSA melalui Metode Gabungan Antara Model Panel Autoregressive Multinomial Logit dan Citra Satelit Sentinel-2 Ardiansyah, Muhlis; Kusumaningrum, Dian; Irlan, Irlan
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1631

Abstract

Statistics Indonesia provides official statistics on rice harvested area for making government policies on food security. One of the problems encountered in collecting data on rice harvest areas is that it requires a high cost. This study aims to reduce ASF survey costs through a combined method of direct observation on odd months and indirect estimation through modeling on even months. We developed a multinomial logit model for panel data by adding the autoregressive effect and Sentinel-2 spectral indices. The research location was in Seruyan Regency, Kalimantan Tengah, with 144 observation points, and was observed for 37 months from December 2018 to December 2021. The results showed that the ASF observation estimation using the developed model was better than the model without Sentinel-2 satellite imagery and the ordinal pattern method. The average accuracy of the developed model was 84.14 percent. The highest level of sensitivity was to estimate the harvest category, fallow phase, and non-agricultural land. Statistics Indonesia can consider this research to reduce the cost of collecting ASF Survey data.
Co-Authors Bagus Sartono Dian Kusumaningrum Irlan