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All Journal BIMASTER Tensor: Pure and Applied Mathematics Journal
Dea Rizki Darmawanti
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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PREDIKSI REALISASI PENERIMAAN PAJAK BUMI DAN BANGUNAN PROVINSI KALIMANTAN BARAT DENGAN MODEL GREY-MARKOV(1,1) Dea Rizki Darmawanti; Yundari Yundari; Nur’ainul Miftahul Huda
Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 11, No 3 (2022): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v11i3.55449

Abstract

Pajak Bumi dan Bangunan (PBB) merupakan salah satu pajak yang wajib dibayar warga negara Indonesia hanya satu kali dalam setahun. Pengoptimalan penerimaan PBB daerah Provinsi Kalimantan Barat, perlu dilakukan untuk kelancaran pembagunan di daerah Provinsi Kalimantan Barat. Badan Pusat Statistik (BPS) biasanya melakukan proyeksi realisasi penerimaan PBB. Pada data realisasi penerimaan PBB, data yang tersedia terbatas dan jumlahnya tidak terlalu besar. Model prediksi yang digunakan adalah Model Grey-Markov(1,1). Penelitian ini bertujuan mengkaji bentuk Model Grey-Markov(1,1)  dan memprediksi realisasi penerimaan PBB pada tahun 2021. Tahap awal dalam penelitian ini adalah membentuk data penerimaan PBB ke dalam bentuk barisan, tahap kedua menghitung nilai dengan mengakumulasi data penerimaan PBB atau Accumulated Generating Operation (AGO). Selanjutnya menentukan nilai tengah atau rata-rata dari dua data yang berdekatan atau Mean Generating Operation (MGO) dan menentukan nilai parameter Model Grey(1,1). Hasil peramalan Model Grey(1,1)  dimodifikasi dengan rantai markov dengan empat interval keadaan sehingga diperoleh hasil prediksi Model Grey-Markov(1,1). Hasil penelitian ini menunjukkan bahwa, pada tahun 2021 prediksi realisasi penerimaan PBB adalah Rp.447.889.085. Data prediksi ini memiliki nilai akurasi  yaitu 9,67% yaitu berarti model sangat baik.. Kata Kunci: Badan Pusat Statistik, Rantai Markov, Model Grey(1,1)
Forecasting The Production of Crude Palm Oil (CPO) in Indonesia 2022 using the Grey(1.1) Model Nur'ainul Miftahul Huda; Nurfitri Imro'ah; Dea Rizki Darmawanti
Tensor: Pure and Applied Mathematics Journal Vol 3 No 1 (2022): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol3iss1pp1-10

Abstract

Crude Palm Oil (CPO) is a vegetable oil produced from oil palm fruit plants. Palm oil can be used for many things, including for various foods, cosmetics, hygiene products, and can also be used as a source of biofuel or biodiesel. Indonesia is a country with the most CPO production in the world. However, the development of CPO production must be appropriately managed to meet demand from other countries. Therefore, this study aims to predict CPO production in 2022. One of the appropriate statistical methods is the Grey(1.1) model. This model was chosen based on the availability of CPO production data by the Central Statistics Agency, which only presents annual data from 2017 - 2021. So, the number of observations that can be used to predict CPO production in Indonesia is only five observations. The Grey(1.1) model can cover problems in the availability of small amounts of data. There are three main steps in the modelling procedure with Grey(1,1) model, namely forming an Accumulated Generated Operation (AGO) sequence, then forming a Mean Generating Operation (MGO) sequence, and the last step is a prediction with Inverse AGO (1-AGO). This study obtained the 1-AGO sequence on the Grey(1.1) model for CPO production in Indonesia with outstanding accuracy, namely the Mean Absolute Percentage Error (MAPE) value of 0.01%. In addition, a prediction of CPO production in Indonesia for 2022 is made, which is 52590612.99 (an increase of 2339783.668 from 2021).
Co-Authors Nurfitri Imro'ah NUR’AINUL MIFTAHUL HUDA Yundari, Yundari