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All Journal BIMASTER ComTech: Computer, Mathematics and Engineering Applications GERVASI: Jurnal Pengabdian kepada Masyarakat JUDIKA (JURNAL PENDIDIKAN UNSIKA) Jurnal Matematika UNAND Jurnal Pengabdian kepada Masyarakat Nusantara Tensor: Pure and Applied Mathematics Journal Jurnal Abdimas Bina Bangsa Jurnal Eurekamatika
NUR’AINUL MIFTAHUL HUDA
Universitas Tanjungpura
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Tensor: Pure and Applied Mathematics Journal

 1 
Analysis of the Changes in 2019-nCov Before and After the Implementation of the "Required Swab PCR-Test for Entry into West Kalimantan via Air Transport" Policy nurainul Miftahul Huda; Nurfitri Imroah
Tensor: Pure and Applied Mathematics Journal Vol 2 No 1 (2021): 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/tensorvol2iss1pp33-44

Abstract

Implementing policies during the 2019-nCov pandemic are expected to reduce the number of cases added every day. West Kalimantan is one of the provinces that implements a policy of obliging to include negative results on the PCR-test swab every time they use air transportation to West Kalimantan. In this study, we wanted to know whether there were differences in data behavior before and after implementing the policy. These differences can be analyzed simply by looking at the descriptive statistics of the data. Furthermore, in this study, a time series analysis was also carried out, and the data patterns and the suitable models representing the data. Time series analysis is also needed to predict the next 5 days related to the addition of 2019-nCov cases in West Kalimantan. In modeling, modifications have been made by partitioning the data into two data, namely data before the policy is implemented and the rest is data after the policy is implemented. The result shows that the suitable model for before and after the policy is applied is ARIMA (1,0,0) and ARIMA (7,0,0)(1,0,0)7, respectively. This model shows a better performance in translating problems than using the entire data as input in modeling. The smaller MSE value indicates this than using the ARIMA model (1,0,0) for the entire data (without partition). Therefore, in the prediction stage, a model with partitioned data is used. The results showed that there was a decrease in daily cases in the next five days.
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).
Perbandingan Matriks Bobot Invers Jarak dan Bobot Seragam pada Model Gstar (1;1) untuk Data Indeks Harga Konsumen (Studi Kasus: Indeks Harga Konsumen di Kalimantan Barat) Nani Fitria Arini; Nur'ainul Miftahul Huda; Wirda Andani
Tensor: Pure and Applied Mathematics Journal Vol 4 No 1 (2023): 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/tensorvol4iss1pp27-36

Abstract

Kejadian yang berhubungan dengan kejadian-kejadian di masa lalu seringkali dijumpai dalam kehidupan sehari-hari. Perkembangan mengenai analisis deret waktu memunculkan gagasan bahwa beberapa data dari suatu kejadian tidak hanya mempunyai keterkaitan dari kejadian-kejadian pada waktu sebelumnya, tetapi juga mempunyai keterkaitan dengan lokasi disekitarnya. Model Generalized Space Time Autoregressive (GSTAR) digunakan untuk memodelkan data deret waktu yang juga mempunyai keterikatan antar waktu dan lokasi (space time). Salah satu keunikan dari model GSTAR adalah keberadaan matriks bobot. Matriks bobot pada model GSTAR menunjukkan hubungan antar lokasi. Pada penelitian ini, matriks bobot yang digunakan adalah bobot seragam dan invers jarak. Studi kasus yang digunakan yaitu data Indeks Harga Konsumen (IHK) tiga lokasi di Kalimantan Barat yang meliputi Kota Pontianak, Kota Singkawang, dan Kabupaten Sintang pada periode Januari 2020 hingga April 2023. Penelitian ini bertujuan untuk membandingkan kedua matriks bobot invers jarak dan seragam. Selanjutnya memilih model GSTAR terbaik berdasarkan nilai dengan orde model GSTAR dibatasi pada orde (1;1) dengan nilai AIC, RMSE, dan MAPE terbaik. Perkembangan indeks harga konsumen antar kota selain memiliki keterkaitan pada waktu sebelumnya juga memiliki keterkaitan antar lokasi. Langkah-langkah yang dilakukan adalah uji stasioner data, identifikasi orde, estimasi parameter, serta uji diagnostik. Hasil penelitian menunjukan model yang didapat berdasarkan bobot invers jarak dan bobot seragam menggambarkan adanya keterikatan waktu dan lokasi yang ada, hal ini ditunjukkan dengan adanya parameter yang signifikan mempengaruhi lokasi satu dan lokasi lainnya. Model terbaik yang dihasilkan adalah model GSTAR(1;1) dengan bobot seragam, karena memiliki nilai rata-rata RMSE terkecil Sehingga akan memberikan nilai peramalan dengan kesalahan yang lebih kecil dibandingkan dengan model dengan bobot invers jarak
Co-Authors Abang Yogi Pratama Ahmad Fernanda Arini, Nani Fitria Bayu Prihandono Dadan Kusnandar Daniel Happy Putra Dea Rizki Darmawanti Eligia Helvianti Tri Lina P ERNA YULIANTI Evi Novian Evi Noviani Fahiza Syanaya Fansiskus Fran Fransiskus Fran Fransiskus Fran Helmi Helmi Helmi Imro'ah, Nurfitri Laurens Paskhia Dirda Rusanditia Mariatul Kiftiah Meliana Pasaribu Mudinillah, Adam Muhammad Septian Nani Fitria Arini Naomi Nessyana Debataraja Nilamsari Kusumastuti Nurfitri Imro'ah Nurfitri Imro'ah Nurfitri Imroah NURFITRI IMRO’AH Nurfitri Imro’ah Rika Mailanda Sari Devi Asri Shantika Martha Theresia Resi Trydini Umairah, Tarisa Utami, Dewi Setyo Wirda Andani Yudhi Yundari, Yundari Yusi Sania