Article Per Year (5 Year)
p-Index From 2021 - 2026
4.652
P-Index
This Author published in this journals
All Journal BIMASTER Journal of the Indonesian Mathematical Society Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jambura Journal of Mathematics Sarwahita : Jurnal Pengabdian Kepada Masyarakat Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications Insan Cita Jurnal Pengabdian Kepada Masyarakat Jambura Journal of Biomathematics (JJBM) Jurnal Forum Analisis Statistik
Imro'ah, Nurfitri
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia Utami, Dewi Setyo; Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.17487

Abstract

Extreme events in a time series model can be detected when the precise timing of the event, known as the intervention, is known. When the exact timing of an event is unknown, it is referred to as an outlier.  If these factors are neglected, the model's accuracy will be affected. To overcome this situation, it is possible to add the intervention or outlier factor into the time series model. This study proposes the combination of intervention and outlier analysis in time series models, especially ARIMA. It is intended to minimize the residuals and increase the accuracy of the model so that it is suitable for forecasting. Using the data of inflation rate in Indonesia, the conflict between Russia and Ukraine was used as an intervention factor in this case. Pre-intervention data (before February 2022) is used to construct the ARIMA model (1st  model). After that, the modeling process continued by adding the intervention factor to the ARIMA model. The effect caused by the intervention allows an outlier to appear, so the process is continued by adding the outlier factor, called an additive outlier, into the model before (2nd model). The MAPE for the first and second models is 7.96% and 7.57%, respectively. The finding of this research shows that the ARIMA model with intervention and outlier factors, named as the 2nd model, is the best model. This study shows that combining the intervention and outlier factors into ARIMA model can improve the accuracy. The forecasting of the inflation rate in Indonesia for one period ahead in 2023 is in the range of 2.06%.
Looking at GDP from a Statistical Perspective: Spatio-Temporal GSTAR(1;1) Model Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri; Arini, Nani Fitria; Utami, Dewi Setyo; Umairah, Tarisa
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16236

Abstract

The gross domestic product (GDP) is a significant indicator for evaluating the performance of an economy. The GDP of a nation can be used to get a sense of the size and health of that nation's economy. Indonesia is the only nation from Southeast Asia to be represented in the G20. All G20’s countries play vital roles in creating the economic landscape of the region, the world, and everything in between. This research is focused on the increase of the GDP in Indonesia, Malaysia, Singapore, Thailand, and Brunei Darussalam. The spatial influence of GDP can be seen in the growth of each nation's infrastructure and industrial sector, for example. at the regional level, the increase of a country's GDP can also have an effect on the countries that are its neighbors. Using the GSTAR model, the aim of this study is to investigate the spatial and temporal influences on the GDP statistics of five different countries. The GSTAR model is distinguished by the presence of a weight matrix, which is one of its distinguishing features. In addition, the aim of this research is to select the most appropriate weight matrix for the purpose of representing the spatial effect on GDP statistics. Uniform, queen contiguity, and inverse distance weight matrices are the types of weight matrices that are utilized. Calculating each weight matrix, estimating relevant parameters, and performing diagnostic tests are the primary activities involved in this investigation. As a consequence of this, a weight matrix that is uniform in its distribution is the one that performs the best. The spatial and temporal correlations of GDP data may be accurately represented by the GSTAR model when it is equipped with a uniform weight matrix. This model is applied to five different countries.
Pelatihan Infografis Untuk Pegawai PPN Pemangkat Martha, Shantika; Debataraja, Naomi Nessyana; Rizki, Setyo Wira; Imro'ah, Nurfitri; Perdana, Hendra; Kusnandar, Dadan; Satyahadewi, Neva; Tamtama, Ray
Insan Cita : Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2025): Februari 2025-Insan Cita: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32662/insancita.v7i1.2658

Abstract

PPN Pemangkat sebagai sentra perikanan mempunyai beberapa keunggulan, yaitu lokasi strategis, dekat dengan fishing ground dan daerah pemasaran. Dengan berbagai keunggulan tersebut diharapkan dapat meningkatkan kualitas perekonomian masyarakat sekitar. Pentingnya ketersediaan informasi tentang PPN Pemangkat untuk masyarakat dapat menjadi faktor pendukung untuk meningkatkan kualitas perekonomian masyarakat yang terhubung dengan keberadaan PPN Pemangkat seperti nelayan. Infografis sangat diperlukan untuk penyajian data di PPN Pemangkat. Baik itu data tentang kapal, nelayan maupun hasil tangkapan. Infografis dapat menyederhanakan informasi yang rumit, sehingga informasi data lebih dapat dipahami untuk semua kalangan. Untuk itu pelatihan infografis bagi pegawai PPN Pemangkat sangat diperlukan. Hasil dari kegiatan ini yaitu bertambahnya pengetahuan serta kemampuan pegawai PPN Pemangkat dalam mengolah data melalui pembuatan infografis menggunakan excel.
GREY MARKOV (1,1) MODEL FOR FORECASTING THE PERCENTAGE OF THE POPULATION THAT EXPERIENCED HEALTH COMPLAINTS IN INDONESIA Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
Jurnal Matematika UNAND Vol. 12 No. 2 (2023)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.12.2.108-120.2023

Abstract

In mathematics, in addition to the time series model, Autoregressive, Moving Average, or Autoregressive Moving Average, the Grey-Markov (1,1) model can be employed for forecasting. One of the gains of this model is that it may cover a minimum quantity of data, which is beneficial in situations when the amount of data that is available is restricted but is not excessively vast. This model works well with data that does not exhibit a great deal of variability. The Grey model was further developed into the Grey-Markov model by including the idea of a Markov chain into the original model. In this particular investigation, the processes consist of first forming a sequence using a 1-Accumulated Generating Operation (1-AGO), then forming a sequence using an MGO, and finally predicting using an AGO. The procedure that came before it is actually a modeling procedure for the Grey model. In addition, in order to model Grey- Markov(1,1), it is necessary to initially compute the relative inaccuracy of the forecast that came before it. The following step is to partition the outcome of the relative error into numerous states, one for each interval of the relative error. After that, each error is categorized based on a state that has been specified in advance. The state that is defined within the class is used as the basis for making predictions. The percentage of the population in Indonesia that reports having health difficulties on a yearly basis was chosen as the case study for this research because it is relevant to the topic at hand. The data came from the Central Statistics Agency in the United Kingdom. The period covered by the data is from 1996 to 2021. The purpose of this research is to investigate the structure of the Grey-Markov Model (1,1) and provide a forecast regarding the proportion of the general population that will be affected by health issues in the year 2022. According to the findings of this research project, the forecast of the proportion of the population in Indonesia that suffered health complaints in 2022 produced predictive data that was 30.36%, with a very good accuracy value of 2.43%.
AN ANALYSIS OF CLUSTER TIMES SERIES FOR THE NUMBER OF COVID-19 CASES IN WEST JAVA Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
Jurnal Matematika UNAND Vol. 12 No. 3 (2023)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.12.3.203-212.2023

Abstract

The government may be able to develop more effective strategies for dealing with COVID-19 cases if it groups districts and cities according to the features of the number of Covid-19 cases being reported in each district or city. The data can be more easily summarized with the help of cluster analysis, which organizes items into groups according to the degree of similarity between members. Since it is possible to group more than one period together, the generation of clusters based on time series is a more efficient method than clusters that are created for each individual unit. Using a time series cluster hierarchical technique that has complete linkage, the purpose of this study is to categorize the number of instances of Covid-19 that have been found in West Java by district or city. The data that was used comes from monthly reports of Covid-19 instances compiled by West Java districts from 2020 to 2022. The Autocorrelation Function (ACF) distance cluster was utilized in this investigation to determine how closely cluster members are related to one another. According to the findings, there could be as many as seven separate clusters, each including a unique assortment of districts and cities. Cluster 3, which is comprised of three different cities and regencies, including Bandung City, West Bandung Regency, and Sumedang Regency, has an average number of cases that is 66, making it the cluster with the highest number of cases overall. A value of 0.2787590 is obtained for the silhouette coefficient as a result of the established grouping. This value suggests that the structure of the newly created cluster is quite fragile.The government may be able to develop more eective strategies fordealing with COVID-19 cases if it groups districts and cities according to the featuresof the number of Covid-19 cases being reported in each district or city. The data canbe more easily summarized with the help of cluster analysis, which organizes items intogroups according to the degree of similarity between members. Since it is possible togroup more than one period together, the generation of clusters based on time series isa more ecient method than clusters that are created for each individual unit. Using atime series cluster hierarchical technique that has complete linkage, the purpose of thisstudy is to categorize the number of instances of Covid-19 that have been found in WestJava by district or city. The data that was used comes from monthly reports of Covid-19 instances compiled by West Java districts from 2020 to 2022. The AutocorrelationFunction (ACF) distance cluster was utilized in this investigation to determine howclosely cluster members are related to one another. According to the ndings, there couldbe as many as seven separate clusters, each including a unique assortment of districtsand cities. Cluster 3, which is comprised of three dierent cities and regencies, includingBandung City, West Bandung Regency, and Sumedang Regency, has an average numberof cases that is 66, making it the cluster with the highest number of cases overall. Avalue of 0.2787590 is obtained for the silhouette coecient as a result of the establishedgrouping. This value suggests that the structure of the newly created cluster is quitefragile.
Prediksi Jumlah Permintaan Darah UTD PMI Kota Pontianak Menggunakan ARIMA-Kalman Filter Mauditia, Lyra; Imro'ah, Nurfitri; Andani, Wirda
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.85958

Abstract

Ensuring a sufficient supply of blood is a crucial aspect of providing health services. However, the large demand for blood is sometimes difficult to fulfill for one of the work units in the Indonesian Red Cross (PMI), namely the Blood Transfusion Unit. Therefore, blood demand prediction is needed to assist the blod transfuse unit in preparing sufficient blood stock. This study uses the ARIMA-Kalman Filter model to anticipate the quantity of blood demand for Blood Transfusion Unit PMI. The observations modeled in this study are daily observations of the amount of blood demand with the period January 1 to December 26, 2023 as an in-sample of 360 observations and blood demand for the period 27 to 31 December 2023 which amounted to 5 observations as an out-sample used to evaluate the model. The analysis’s findings indicate that the model obtained for predicting the amount of blood demand is the ARIMA (0,0,2) model, then the model parameters are estimated using Kalman Filter. The model used fulfills the diagnostic test and obtained a MAPE value of 15.021% in predicting out-sample data. Thus it can be concluded that the model used is in the very good category and is suitable for prediction. Furthermore, predictions are made for the next three days on the number of blood requests at Blood Transfusion Unit PMI Pontianak City to help health services prepare blood stocks for patients in need.
PENGELOMPOKAN KABUPATEN/KOTA DI KALIMANTAN BARAT BERDASARKAN INDIKATOR KESEJAHTERAAN MENGGUNAKAN K-PROTOTYPE Panawaristia, Brigitha; Martha, Shantika; Imro'ah, Nurfitri
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol. 15 No. 1 (2026): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : Faculty of Mathematics and Natural Sciences Tanjungpura University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kesejahteraan masyarakat merupakan indikator penting dalam menilai keberhasilan pembangunan wilayah. Pengelompokan kabupaten/kota berdasarkan indikator kesejahteraan diperlukan untuk mengidentifikasi wilayah dengan karakteristik yang relatif serupa. Penelitian ini bertujuan mengelompokkan kabupaten/kota di Provinsi Kalimantan Barat menggunakan algoritma K-Prototype, dengan penentuan jumlah klaster optimal berdasarkan Silhouette Coefficient. Data yang digunakan merupakan data sekunder tahun 2024 yang diperoleh dari Badan Pusat Statistik dan portal Satu Data, dengan unit analisis sebanyak 14 kabupaten/kota. Variabel penelitian terdiri atas delapan variabel numerik dan dua variabel kategorik. Variabel numerik distandarisasi menggunakan metode Z-Score, sedangkan seluruh variabel diuji multikolinearitas menggunakan Generalized Variance Inflation Factor (GVIF). Proses klasterisasi menggunakan jarak Squared Euclidean untuk atribut numerik dan Simple Matching untuk atribut kategorik. Hasil analisis menunjukkan bahwa jumlah klaster optimal adalah dua klaster dengan nilai Silhouette Coefficient sebesar 0,40. Klaster pertama terdiri atas Kabupaten Kubu Raya, Kota Pontianak, dan Kota Singkawang, yang menunjukkan indikator kesejahteraan relatif lebih baik dibanding klaster kedua, serta didominasi oleh sektor jasa dan wilayah non-perbatasan. Sebaliknya, klaster kedua didominasi oleh sektor pertanian dengan karakteristik wilayah perdesaan dan sebagian merupakan wilayah perbatasan. Hasil pengelompokan ini menunjukkan bahwa kebijakan pembangunan antar klaster dapat dibedakan, di mana pada klaster pertama diarahkan pada upaya mempertahankan dan mengoptimalkan sektor jasa serta kualitas layanan perkotaan, sedangkan pada klaster kedua diperlukan upaya peningkatan produktivitas sektor pertanian serta pemerataan infrastruktur dan akses layanan dasar, khususnya pada wilayah perbatasan.
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN FAKTOR PENYEBAB STUNTING DENGAN VALIDASI KORELASI COPHENETIC Andini, Syarifah; Perdana, Hendra; Imro'ah, Nurfitri
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol. 15 No. 2 (2026): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : Faculty of Mathematics and Natural Sciences Tanjungpura University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting merupakan gangguan pertumbuhan pada anak balita yang dipicu oleh defisiensi gizi yang tercermin dari panjang atau tinggi badan anak yang tidak sesuai dengan standar usianya. Hingga saat ini, stunting masih menjadi permasalahan kesehatan yang serius di Indonesia dengan karakteristik penyebab yang beragam antarwilayah. Penelitian ini bertujuan untuk mengelompokkan provinsi di Indonesia berdasarkan faktor penyebab stunting serta memvalidasi hasil pengelompokan menggunakan koefisien korelasi Cophenetic. Data yang digunakan merupakan data sekunder yang bersumber dari publikasi Survei Status Gizi Indonesia (SSGI) tahun 2024 dengan sepuluh variabel faktor penyebab stunting, meliputi akses air minum layak, akses sanitasi layak, kelengkapan imunisasi dasar, berat badan lahir rendah (<2500 gram), ASI eksklusif, prevalensi ISPA balita, ANC K4, konsumsi tablet tambah darah (≥90 tablet), bayi segera disusui kurang dari 60 menit setelah lahir, dan keragaman pangan minimal. Metode analisis yang digunakan adalah analisis klaster hierarki dengan metode Ward dan jarak Squared Euclidean. Hasil validasi menggunakan koefisien korelasi Cophenetic menghasilkan nilai sebesar 0,509, yang menunjukkan bahwa dendrogram cukup merepresentasikan jarak antarprovinsi pada data asli. Hasil analisis menghasilkan empat klaster provinsi dengan karakteristik yang berbeda. Klaster 1 merupakan wilayah dengan kondisi paling ideal dalam pencegahan stunting, klaster 2 mencerminkan wilayah dengan kondisi menengah, klaster 3 merupakan wilayah yang tergolong rentan dalam upaya pencegahan stunting, dan klaster 4 menunjukkan wilayah dengan kondisi paling kompleks dan paling rentan terhadap permasalahan stunting.
PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN METODE ARIMAX-EGARCH Felisya, Tasya; Imro'ah, Nurfitri; Perdana, Hendra
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol. 15 No. 2 (2026): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : Faculty of Mathematics and Natural Sciences Tanjungpura University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indeks Harga Saham Gabungan (IHSG) merupakan gabungan beberapa saham di Indonesia yang dapat digunakan untuk mengetahui rata-rata pergerakan saham di Indonesia. Dibandingkan dengan indeks LQ45 dan IDX30 yang hanya mencakup saham tertentu, IHSG memiliki cakupan yang lebih luas dan mampu menggambarkan kondisi pasar saham. Namun, IHSG tidak menyajikan informasi harga saham secara individual. Pergerakan IHSG dipengaruhi oleh berbagai faktor, yaitu faktor internal dan eksternal. Salah satu faktor eksternalnya adalah harga minyak mentah Brent, yang digunakan sebagai salah satu acuan penetapan harga bahan bakar yang berlaku di Indonesia. Fluktuasi harga minyak dapat berdampak pada biaya operasional perusahaan, sehingga memengaruhi pergerakan IHSG. Oleh karena itu, diperlukan model yang mampu mempertimbangkan pengaruh faktor eksternal. Model ARIMAX digunakan untuk mengidentifikasi pengaruh harga minyak mentah Brent terhadap IHSG. Untuk mengatasi masalah varians yang tidak konstan digunakan model GARCH. Namun, karena GARCH belum mampu menangkap efek asimetris pada data, maka digunakan model EGARCH sebagai pengembangan yang lebih sesuai. Analisis volatilitas IHSG penting dilakukan karena dapat membantu investor dalam pengambilan keputusan serta pengelolaan risiko investasi. Penelitian ini bertujuan untuk memodelkan dan meramalkan harga IHSG menggunakan model ARIMAX-EGARCH. Data yang digunakan berupa harga penutupan harian IHSG dengan harga minyak mentah Brent sebagai variabel eksogen pada periode 2 Januari 2020 hingga 29 September 2025. Pemilihan model terbaik dipilih berdasarkan nilai AIC terkecil, dan akurasi peramalan dievaluasi menggunakan nilai MAPE. Hasil analisis menunjukkan bahwa model ARIMAX (5,0,5)-EGARCH (2,2) merupakan model terbaik karena nilai MAPE sebesar 0,77% yang menunjukkan bahwa model ini sangat baik untuk melakukan peramalan.
ANALISIS STATUS NEET DI KAWASAN BARAT INDONESIA MENGGUNAKAN REGRESI LOGISTIK BINER MULTILEVEL Marda; Imro'ah, Nurfitri; Martha, Shantika
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol. 15 No. 2 (2026): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : Faculty of Mathematics and Natural Sciences Tanjungpura University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemuda memegang peran strategis dalam pembangunan nasional, namun masih terdapat pemuda dengan usia 15-24 tahun yang tidak terlibat dalam kegiatan pendidikan, pelatihan dan tidak terserap pasar kerja. Kondisi ini tercermin dari tingginya angka NEET (Not in Education, Employment, or Training) di sejumlah provinsi di Kawasan Barat Indonesia yang melampaui rata-rata nasional sebesar 20,31%. Penelitian ini menggunakan model regresi logistik biner multilevel untuk memodelkan status NEET, mengidentifikasi faktor-faktor individu dan kabupaten/kota yang mempengaruhinya, serta menjelaskan variasi pengaruh faktor individu antar kabupaten/kota. Data yang digunakan adalah data sekunder BPS hasil Survei Angkatan Kerja Nasional Agustus 2024, Publikasi Provinsi Dalam Angka dan Potensi Desa. Hasil analisis menunjukkan bahwa model terbaik adalah model random slope pada variabel pendidikan tertinggi yang ditamatkan. Faktor level individu yang berpengaruh signifikan terhadap status NEET di Kawasan Barat Indonesia terdiri atas jenis kelamin, umur, pendidikan tertinggi yang ditamatkan, status perkawinan, wilayah tempat tinggal, dan keahlian teknologi digital. Pada level kabupaten/kota, Tingkat Kesempatan Kerja (TKK) dan rasio SMA/sederajat per 100 km2 juga berpengaruh signifikan dan menurunkan kecenderungan pemuda berstatus NEET, masing-masing sebesar 11,4% dan 0,4%. Variasi pengaruh pendidikan tertinggi pemuda ditunjukkan oleh nilai varians efek acak slope variabel pendidikan tertinggi yang ditamatkan tidak sama dengan nol sehingga pengaruh pendidikan tidak seragam di seluruh wilayah kabupaten/kota dalam mempengaruhi status NEET. Nilai varians pada tingkat Perguruan Tinggi (0,4108) lebih besar dibandingkan varians pada tingkat SMA/Sederajat (0,1565), dengan kategori referensi adalah SMP ke bawah. Hal ini menunjukkan bahwa pengaruh pendidikan tinggi terhadap status NEET lebih bervariasi antar kabupaten/kota dibandingkan pengaruh pendidikan SMA/Sederajat.
Co-Authors ., Putri Amalia, Disya Recita Andani, Wirda Andini, Syarifah Arini, Nani Fitria Ayyash, Muhammad Yahya Dadan Kusnandar Debataraja, Naomi Nessyana Evy Sulistianingsih Felisya, Tasya Fitriyana, Gita Hendra Perdana Huda, Nur'ainul Miftahul Marda Marda, Marda Margareta, Tiara Marlisa, Herina Martha, Shantika Mauditia, Lyra Neva Satyahadewi Nirwani Novita, Irene Panawaristia, Brigitha Pratiwi, Hesty Rizki, Setyo Wira Tamtama, Ray Umairah, Tarisa Utami, Dewi Setyo Utriweni Mukhaiyar Zuleha, Zuleha