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All Journal BIMASTER CAUCHY: Jurnal Matematika Murni dan Aplikasi 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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IMPLEMENTASI ALGORITMA C5.0 PADA KLASIFIKASI DATA SOSIAL MASYARAKAT (Studi Kasus : Kelayakan Penerimaan BLT di Kelurahan Condong Kota Singkawang) Sari Devi Asri; Helmi Helmi; Nur’ainul Miftahul Huda
Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 12, No 3 (2023): 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.v12i3.66693

Abstract

Algoritma C5.0 merupakan salah satu algoritma pohon keputusan yang dapat memproses data sosial masyarakat menjadi sebuah aturan yang bisa dijadikan masukan dalam pengambilan keputusan. Pada penelitian ini, peneliti menganalisis variabel yang paling menentukan kelayakan untuk Bantuan Langsung Tunai (BLT). Variabel-variabel yang digunakan dalam penelitian ini adalah pendapatan, umur, pendidikan, pekerjaan, kepemilikan rumah, jumlah tanggungan dan keputusan kelayakan. Tujuan penelitian ini adalah mengklasifikasi data sosial masyarakat untuk kelayakan penerimaan bantuan program BLT menggunakan metode Algoritma C5.0 serta mengetahui tingkat akurasi dan laju error hasil klasifikasi metode Algoritma C5.0. Ada beberapa langkah yang dilakukan yaitu menginput data yang diteliti. Selanjutnya pemilihan node akar diawali dengan menghitung nilai entropy. Kemudian proses dilanjutkan dengan mencari nilai gain. Setelah itu mencari nilai gain ratio. Penentuan cabang untuk masing-masing node dengan menghitung nilai gain ratio tertinggi dari variabel bebas yang ada. Penelitian ini menghasilkan decision tree dari kasus yang diangkat menunjukkan bahwa ada beberapa variabel yang mempengaruhi dalam penentuan masyarakat yang mendapatkan BLT.  Setelah dilakukan analisis didapatkan hasil tingkat akurasi sebesar 81,429%. Dengan tingkat akurasi yang tinggi bisa dikembangkan menjadi sebuah aturan yang dapat memberikan prediksi atau masukan dalam membuat keputusan kelayakan penerima bantuan langsung tunai (BLT). Kata kunci: C5.0, bantuan langsung tunai, gain ratio
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
PEMODELAN DATA PRODUKSI KELAPA SAWIT DI KALIMANTAN BARAT MENGGUNAKAN GENERALIZED ADDITIVE MODEL Ahmad Fernanda; Naomi Nessyana Debataraja; Nur’ainul Miftahul Huda
Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 12, No 3 (2023): 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.v12i3.66818

Abstract

Kelapa sawit merupakan salah satu komoditas penting dan strategis peranannya dalam mendorong perekonomian rakyat di Kalimantan Barat. Banyak faktor yang mempengaruhi produksi kelapa sawit seperti luas lahan, produktivitas dan jumlah tenaga kerja pada perkebunan kelapa sawit.  Tujuan penelitian ini untuk menerapkan Generalized Additive Models (GAM) pada data jumlah produksi kelapa sawit di Kalimantan Barat. Penelitian ini menggunakan metode GAM, yang merupakan perluasan dari Generalized linier Models (GLM) dengan menggantikan fungsi linier menjadi fungsi aditif dan mengganti prediktor non-linier dengan suatu fungsi penghalus. Penghalus yang digunakan pada permodelan adalah penghalus spline dan pendugaan parameter model dengan Peneralized Likelihood Maximation. Variabel respon penelitian ini adalah jumlah produksi dan variabel prediktor adalah luas lahan, produktivitas, dan tenaga kerja.Tenaga kerja memiliki pengaruh non-linier terhadap jumlah produksi. Sementara luas lahan dan produktivitas secara linier berpengaruh positif terhadap jumlah  produksi. Nilai koefisien determinasi (R2) yang diperoleh dengan software R sebesar 0,957 yang berarti yang berarti kemampuan model dalam menggambarkan keragaman data sebesar 95,7%.  Kata kunci : Linier, Koefisien Determinasi, Model Aditif.
Aplikasi Algoritma Viterbi dalam Hidden Markov Model untuk Menganalisis Trend Pasar Saham di Bursa Efek (Studi Kasus di PT. Bank Central Asia, Tbk.) Eligia Helvianti Tri Lina P; Yundari Yundari; Nur’ainul Miftahul Huda
Jurnal EurekaMatika Vol 11, No 2 (2023): Jurnal Eurekamatika
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v11i2.63743

Abstract

This study aims to analyze and apply the Viterbi algorithm in the Hidden Markov Model on stock market trend movement data at PT Bank Central Asia, Tbk in 2022. At the initial stage, the closing stock price data of PT Bank Central Asia, Tbk for the next fifteen days is predicted using the ARIMA method. Then, the second stage is classifying closing stock data as a result of predictions. The third stage is to determine the parameters of the Hidden Markov Model. The fourth stage is to determine the Hidden State sequence with the Viterbi Algorithm, and the last stage is to analyze the application of the Viterbi Algorithm to the movement of the stock market trend. The results show that the Viterbi algorithm is used to determine the most optimal Hidden State sequence from a sequence of observed states. The right time for an investor to make a decision to sell shares is when the stock price is rising and the stock market trend is in a bullish position, namely on the March 4th, 10th, 16th, 17th and 21th 2022. Meanwhile, buying shares is when the stock price is down and the stock market trend is in a bearish position, namely on the March 3rd, 9th, 14th, 18th and 23th 2022.Keywords: Hidden Markov Model, Stock Market Trends, Viterbi Algorithm. AbstrakPenelitian ini bertujuan untuk menganalisis dan mengaplikasikan algoritma Viterbi dalam Hidden Markov Model pada data pergerakan trend pasar saham di PT. Bank Central Asia, Tbk tahun 2022. Pada tahap awal dilakukan prediksi data harga saham penutupan PT Bank Central Asia, Tbk selama lima belas hari ke depan menggunakan metode ARIMA. Tahap kedua adalah mengklasifikasi data hasil dari prediksi. Tahapan ketiga adalah menentukan parameter-parameter Hidden Markov Model. Tahapan keempat menentukan barisan Hidden State dengan Algoritma Viterbi, dan tahap terakhir adalah menganalisis pengaplikasian Algoritma Viterbi pada pergerakan trend pasar saham. Hasil dari penelitian ini adalah bahwa algoritma Viterbi digunakan untuk menentukan barisan Hidden State yang paling optimal dari suatu barisan observed state. Waktu yang tepat seorang investor mengambil keputusan untuk menjual saham adalah ketika harga saham dalam keadaan naik dan trend pasar saham dalam posisi bullish yaitu pada tanggal 4, 10, 16, 17, dan 21 Maret 2022. Sedangkan untuk membeli saham adalah ketika harga saham dalam keadaan turun dan trend pasar saham dalam posisi bearish yaitu tanggal 3,9, 14, 18, dan 23 Maret 2022.
PENDEKATAN PEMBELAJARAN BERBASIS PERMAINAN SEBAGAI UPAYA MENINGKATKAN KECERDASAN MATEMATIKA SISWA Mariatul Kiftiah; Nilamsari Kusumastuti; Bayu Prihandono; Yundari Yundari; Helmi Helmi; Evi Noviani; Fransiskus Fran; Yudhi Yudhi; Meliana Pasaribu; Nur’ainul Miftahul Huda
Jurnal Abdimas Bina Bangsa Vol. 5 No. 1 (2024): Jurnal Abdimas Bina Bangsa
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/jabb.v5i1.979

Abstract

The perception of mathematics as a compulsory subject in school is often negative. To address this challenge, a service-learning initiative was implemented at SMAN 1 Sambas, employing a game-based pedagogy. Mathematics seminars and exhibitions were organized by Mathematics Study Program to enhance students' engagement and comprehension of mathematical concepts. The effectiveness of this approach was evaluated through a questionnaire, which revealed a high level of approval among students regarding the relevance, motivation, understanding of mathematical principles, problem-solving abilities, and playing skills in mathematics learning. This approach is expected to change students’ perception of mathematics and improve their learning outcomes
AN ANALYSIS OF CLUSTER TIMES SERIES FOR THE NUMBER OF COVID-19 CASES IN WEST JAVA Nurfitri Imro'ah; Nur'ainul Miftahul Huda
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.
A Two-Stage Kalman Filter and ARIMA Framework for High-Frequency Wind Speed Modeling in Equatorial Regions Nurfitri Imro'ah; Nur'ainul Miftahul Huda; Kartika Sari; Rahmi Hidayati
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.41753

Abstract

High-frequency wind speed data collected via environmental monitoring systems often contain significant stochastic noise that can obscure underlying patterns and degrade the reliability of statistical models. A two-stage modeling framework (integrating a Kalman Filter (KF) for signal purification and Autoregressive Integrated Moving Average (ARIMA) for predictive modeling) was developed and applied to five-minute interval wind speed data in Pontianak, West Kalimantan. The dataset, comprising 3,742 observations recorded from December 11 to 24, 2024, was utilized to evaluate the effectiveness of the KF in enhancing model fitting. The model quality was further assessed using Individual Moving Range (IMR) control charts to monitor residual stability and detect localized anomalies. Results demonstrate that the KF-ARIMA approach significantly improves performance, reducing the Root Mean Square Error (RMSE) from 1.123 m/s to 0.145 m/s, representing an 87.1\% improvement in precision compared to the standalone ARIMA model. The I-MR charts confirmed that the KF-ARIMA residuals remained consistently within the $3\sigma$ control limits, effectively identifying transient variations that standard diagnostic tests might overlook. This integrated framework proves that combining state-space filtering with traditional time-series models provides a robust approach for characterizing high-frequency meteorological data in equatorial regions.
A Hybrid ARIMA-Intervention Modelling for Forest Fire Risk in The Dry Season Nurfitri Imro'ah; Nur'ainul Miftahul Huda; Hesty Pratiwi; Muhammad Yahya Ayyash
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36741

Abstract

This study explores the time-related patterns of forest fires and assesses the impact of measures implemented during the dry season. Special focus is directed towards the effects of these interventions on the frequency and intensity of fires. This study highlights the importance of combining temporal analysis with spatial data to identify high-risk locations and optimize resource allocation for fire prevention. This study develops an ARIMA model to forecast fire risk before intervention. The findings indicate that integrating intervention factors into the ARIMA model will enhance the model's accuracy. The satisfactory MAPE values and the value data plots effectively demonstrate the data patterns. This method establishes a solid basis for predicting and reducing the risk of forest fires in the dry season, thereby enhancing the fire resilience of ecosystems considered at risk. The findings indicate that the onset of the dry season significantly elevates the risk of forest fires, especially in areas near bodies of water.
Combining IoT and Time Series Model for Minute-Level Outlier Detection in Wind Speed Forecasting Nur'ainul Miftahul Huda; Nurfitri Imro'ah; Rahmi Hidayati; Kartika Sari
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35768

Abstract

Renewable energy optimisation and early warning systems require accurate short-term wind speed forecast. Anomalies in environmental data impair forecasting model reliability. This paper presents an integrated approach using IoT-based remote sensing and time series modelling to address the issue. IoT-based anemometer sensors collected wind speed data at one-minute intervals from December 24, 2024, to January 10, 2025. Aggregating the raw data into 5-minute intervals prepared it for the ARIMA model. This model determined temporal patterns and predicted short-term wind speeds. Analyzing residuals between observed and predicted results helped identify wind outliers. This approach is novel because it uses IoT-based continuous sensing and time series modeling for real-time environmental monitoring. Studies showed that a 65-minute frame with 5-minute intervals was best for replicating wind speed dynamics. Six cycles of outlier detection found 87 outliers. The ARIMA model improved predictions by include these outliers as exogenous variables. This emphasizes the importance of fixing time series model anomalies to improve prediction. The augmented ARIMA model with outlier corrections provides minute-level forecasts and reliable anomaly identification for renewable energy optimization and early warning systems. This study shows that new statistical methods and the Internet of Things (IoT) can improve real-time environmental and energy decisions.
ANALYSIS OF FOREST FIRE CASES USING GSTAR(1;1) MODEL WITH SPATIAL ROOK CONTIGUITY WEIGHTS MATRIX IN WEST KALIMANTAN Muhammad Yahya Ayyash; Nur'ainul Miftahul Huda; Nurfitri Imro'ah; Hesty Pratiwi
Jurnal Matematika UNAND Vol. 15 No. 2 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.2.259-273.2026

Abstract

In West Kalimantan, forest and land fires cause damage to ecosystems, the loss of biodiversity, and detrimental repercussions on both health and the local econ- omy. Extreme weather and land clearance for agricultural and plantation purposes are the primary reasons. This study aims to investigate forest fires’ spatial and temporal pat- terns by employing the Generalized Space-Time Autoregressive (GSTAR)(1;1) approach with spatial rook contiguity weights. From January 2020 to March 2024, the data used consisted of the number of monthly forest fires that occurred in the Ketapang, Sanggau, Sintang, Landak, and Sekadau Regencies. According to the findings, the spatial pattern demonstrates strong interactions between regions in which flames in one area affect fires in other locations. The temporal pattern demonstrates that prior fires can impact fires that occur in the subsequent period, depending on the area. The model has an aver- age accuracy level of 13%, which indicates that this model has a reasonable degree of accuracy that can be used for making predictions. This study concluded that a better understanding of the spatial-temporal patterns of forest fires can improve early warning systems and rapid responses to probable future fires.
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 Hesty Pratiwi Hesty Pratiwi Imro'ah, Nurfitri Kartika Sari Kartika Sari Laurens Paskhia Dirda Rusanditia Mariatul Kiftiah Meliana Pasaribu Mudinillah, Adam Muhammad Septian Muhammad Yahya Ayyash Muhammad Yahya Ayyash Nani Fitria Arini Naomi Nessyana Debataraja Nilamsari Kusumastuti Nurfitri Imro'ah Nurfitri Imro'ah Nurfitri Imro'ah Nurfitri Imroah NURFITRI IMRO’AH Nurfitri Imro’ah Rahmi Hidayati Rika Mailanda Sari Devi Asri Shantika Martha Theresia Resi Trydini Umairah, Tarisa Utami, Dewi Setyo Wirda Andani Yudhi Yundari, Yundari Yusi Sania