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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of the Indonesian Mathematical Society 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 Jurnal Statistika dan Aplikasinya Jambura Journal of Biomathematics (JJBM) Jurnal Forum Analisis Statistik

Imro'ah, Nurfitri

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Author-ID : 2123480

Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Peningkatan Keterampilan Analisis Data Bagi Fungsional BPS di Kalimantan Barat Melalui Pelatihan SEM dengan AMOS Martha, Shantika; Andani, Wirda; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Imro'ah, Nurfitri; Satyahadewi, Neva; Tamtama, Ray; Perdana, Hendra; Kusnandar, Dadan
Bahasa Indonesia Vol 22 No 01 (2025): Sarwahita : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/sarwahita.221.9

This Community Service activity is a form of cooperation between Statistics Study Program FMIPA UNTAN and BPS through training activities. The purpose of this PKM is to provide knowledge and insight to BPS functional employees about SEM (Structural Equation Modeling) using AMOS. This activities were carried out on Monday, August 14, 2023 in the Vicon room of the West Kalimantan provincial BPS office with 32 participants attending. The results of this training activity are expected to be applied by BPS functional employees in processing and analyzing data as research needs and work related to statistical data. The level of success in this training was measured through pre-test, post-test and participant satisfaction survey. A wilcoxon test was conducted with α = 0.05 and the result was p-value smaller than 0.01. So that the p-value < α which means rejecting H0 and it can be concluded that the average pretest score < average posttest score. In other words, the post-test results increased significantly, which means that the participants' abilities increased after the training. Based on the participant satisfaction survey, the results showed that all participants (100%) had never used AMOS software before. Overall, participants were satisfied (61.5%) and very satisfied (38.5%) with the training because they could increase their knowledge and the training materials delivered were in accordance with their needs, easy to understand and interesting, could be applied easily, and were delivered in order and systematically. Abstrak Kegiatan Pengabdian Kepada Masyarakat (PKM) ini merupakan salah satu wujud kerjasama Prodi Statistika FMIPA UNTAN dengan BPS melalui kegiatan pelatihan. Adapun tujuan PKM ini yaitu memberikan pengetahuan dan wawasan kepada pegawai fungsional BPS tentang teknik pengolahan dan analisis data SEM (Structural Equation Modelling) dengan menggunakan AMOS. Kegiatan PKM dilaksanakan pada hari Senin, 14 Agustus 2023 di ruang Vicon kantor BPS prov Kalbar dengan jumlah peserta yang hadir 32 orang. Hasil dari kegiatan pelatihan ini diharapkan dapat diterapkan oleh pegawai fungsional BPS dalam mengolah dan menganalisis data sebagai kebutuhan penelitian maupun pekerjaan yang berhubungan dengan data statistika. Tingkat keberhasilan pada pelatihan ini diukur melalui pemberian pretest, posttest dan survey kepuasan peserta. Dilakukan uji beda menggunakan uji wilcoxon dengan α = 0.05 dan didapatkan hasil yaitu berupa p-value lebih kecil dari 0.01. Sehingga p-value < α yang berarti tolak H0 dan dapat disimpulkan rata-rata nilai pretest < rata-rata nilai posttest. Dengan kata lain hasil posttest meningkat secara signifikan yang artinya kemampuan peserta meningkat setelah dilaksanakan pelatihan. Berdasarkan survey kepuasan peserta didapatkan hasil ternyata semua peserta (100%) belum pernah menggunakan software AMOS sebelum pelatihan. Secara keseluruhan peserta merasa puas (61,5%) dan sangat puas (38,5%) mengikuti pelatihan karena dapat menambah pengetahuan serta materi pelatihan yang disampaikan sesuai dengan kebutuhan, mudah dipahami dan menarik, dapat diterapkan dengan mudah, dan disampaikan dengan urut dan sistematis.

Forest Fires in Peatlands Analyzed from Various Perspectives: Spatial, Temporal, and Spatial-Temporal Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri; Ayyash, Muhammad Yahya; Pratiwi, Hesty
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28884

Peatland fires are characterized by the compaction of organic matter below the soil surface. If dry conditions occur, the organic matter can burn, making it difficult to extinguish the fire. This study aims to analyze peatland forest fires with three perspectives, namely temporal, spatial, and spatial-temporal. The data used is the confidence level data of hotspots in forest fires in Kubu Raya Regency, West Kalimantan from January 2014 to December 2023. The methodology used includes collecting fire data from satellite imagery and prepocessing the data. Furthermore, three different data analyzes were carried out, namely temporal, spatial, and spatial-temporal analysis. The results of the study obtained three perspectives, namely from the time period, handling of forest fire cases because they have an impact on the future as seen from the ARIMA model. Regarding spatiality, the distribution of hotspots spread to surrounding areas that were heavily affected by hotspots as seen from the contour map using Kriging interpolation. Finally, regarding spatiality and temporality, forest fire projections show that locations that are close together and have a history of being affected by forest fires have a strong potential for the distribution of forest fire cases as seen from the GSTAR space-time model.

Forecasting Blood Availability in Pontianak City using ARIMA Models to Optimize Inventory Planning at UTD PMI Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Mauditia, Lyra
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24789

It is of utmost importance to control the blood supply in UTD PMI because if there is a requirement for blood, PMI can fulfill the necessary blood needs and keep the ideal blood availability. PMI UTD may encounter a shortfall of blood supply if increases in blood demand are not supported by an increase in the number of donors contributing blood. A forecast of the number of blood requests is essential to estimate the quantity of blood that is necessary and the number of blood donors that are required to be prepared to fulfill the needed blood requests. This study is a quantitative investigation that use the Autoregressive Integrated Moving Average (ARIMA) method in order to provide an accurate prediction regarding the quantity of blood that is required for each blood type in Pontianak City. UTD PMI Pontianak City provided the information that was used in this study. The information that was used included information on the number of blood requests for blood types A, AB, B, and O. Following this, the data was subjected to three iterative steps of Box Jenkins analysis, which included order identification, parameter estimation, and diagnostic testing. The goal was to obtain the most accurate model, which was then utilised to forecast the quantity of blood demand that will occur in the subsequent periods. Furthermore, the findings of this investigation indicate that the ARIMA (2,0,0), ARIMA (3,0,3), ARIMA (1,0,2), and ARIMA (1,0,0) models are the most accurate models for predicting the availability of blood categories A, AB, B, and O. ..UTD Pontianak City is anticipated to be able to manufacture bloodstock consisting of 73 blood bags over the next five days. The bloodstock will include 19 bags of Group A, 6 bags of Group AB, 22 bags of Group B, and 6 bags of Group O specifics. In light of the forecast results, it is envisaged that UTD PMI will be able to maximize inventory planning for blood in Pontianak City to reduce the number of instances in which there are shortages of blood availability.

Modeling the Dynamics of Forest Fires: A Vector Autoregressive Approach Across Three Fire Classifications Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24792

The problem of forest fires is one that, with each passing year, gets more difficult to mitigate. A significant number of people will be affected by this case, particularly in terms of their health. The need for targeted initiatives must be balanced. Look at the forecasts for the number of forest fires expected to occur in the following period. Cases of forest fires reported to the Ministry of Environment and Forestry are categorized into three distinct categories: high, medium, and low. In addition to future estimates, it is reasonable to anticipate that classifications will also affect one another. The vector autoregressive (VAR) model is a statistical tool that may produce future projections based on three categories of forest fires in a specific period. This information can be utilized to make predictions. The aim of the study was to model 3 classifications of forest fire cases using the Vector Autoregressive (VAR) model. The data utilized is a summary of the number of forest fire cases in Pulang Pisau Regency, Central Kalimantan, categorized as low, medium, and high, from January 2013 to March 2024. During this study, the VAR modelling process was broken down into three primary stages: order identification (the findings that were achieved were VAR(4)), parameter estimation, and diagnostic testing (VAR(4) was declared to fulfil the requirements for the diagnostic test). It is possible to generate a predicted value for the subsequent three times based on these stages, which may be considered when calculating the proper amount of effort to put forward. The accuracy of forest fire case modeling utilizing the VAR(4) model is 70.23%. Moreover, the predictive outcomes for each categorization indicate a rise in medium and low-level forest fires compared to previous data, although the contrary is observed for high-level forest fire incidents.

AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL Huda, Nur'ainul Miftahul; Mukhaiyar, Utriweni; Imro'ah, Nurfitri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR model. The first step is to form a GSTAR model without outlier factors. Furthermore, the detection of outliers from the model's residuals. If an outlier is detected, add an outlier factor into the initial model and estimate the parameters so that a new GSTAR model and residuals are obtained from the model. The process is repeated by detecting outliers and adding them to the model until a GSTAR model is obtained with no outliers detected. As a result, outliers are not removed or ignored but add an outlier factor to the GSTAR model. This paper presents case studies about Dengue Hemorrhagic Fever cases in five locations in West Kalimantan Province. These are the subject of the GSTAR model with adding outlier factors. The result of this paper is that using an iterative procedure to detect outliers based on the GSTAR residual model provides better accuracy than the regular GSTAR model (without adding outliers to the model). It can be solved without removing outliers from the data by adding outlier factors to the model. This way, the critical information in the outlier id is not lost, and an accurate ore model is obtained.

CONTROL CHART AS VERIFICATION TOOLS IN TIME SERIES MODEL Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Control charts are generally use in quality control processes, especially in the industrial sector, because they are helpful to increase productivity. However, control charts can also be used in time series analysis. The residuals from the time series model are used as observations in constructing the control chart. Because there is only one variable observed, namely the residual, the control chart used is the Individual Moving Range (IMR). This study analysis the accuracy of the time series model using the IMR control chart in two models, namely the Autoregressive Distributed Lag (ADL) model without outliers and the ADL model with outliers. The results showed that the control chart could be used to measure the accuracy of the time series model. The accuracy of the model can be seen from the statistically controlled residual (in control).

ARIMA MODEL VERIFICATION WITH OUTLIER FACTORS USING CONTROL CHART Umairah, Tarisa; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0579-058

Control charts are often used in quality control processes, especially in the industrial sector because of their significant benefits in increasing industrial production. However, control charts can also be used throughout the field of time series modeling to evaluate measures of accuracy represented by a particular time series model. The application of control charts in this research meets the criteria for evaluating accuracy. However, it is not certain that the time series model will have a high level of accuracy. There are various factors that can influence this phenomenon, one of which is the potential for outliers. Therefore, it is very important to perform time series modeling by adding an outlier factor. The residuals of the time series model obtained are used to create a control chart for model verification. The aim of this research is to evaluate the validity of time series models by looking at the influence of outlier characteristics to improve their accuracy. This research studies the accuracy of a time series model built using Gross Domestic Product (GDP) data in Indonesia. There are two different models, namely the ARIMA model without outlier factors and the ARIMA model with outlier factors which are used for research purposes. Both models were performed using the same data set. The results of this study indicate that the ARIMA model with outlier factors has better accuracy than the ARIMA model without outlier factors. This conclusion can be drawn based on the observation that the residual value is within the predetermined control limits, thus indicating that the process is in a state of statistical control.

APPLICATION OF ADASYN OVERSAMPLING TECHNIQUE ON K-NEAREST NEIGHBOR ALGORITHM Marlisa, Herina; Satyahadewi, Neva; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1829-1838

The K-Nearest Neighbor Algorithm is a commonly used data mining algorithm for classification due to its effectiveness with large datasets and noise. However, class imbalance may impact classification results, where data with unbalanced classes may classify new data based on the majority class and ignore minority class data. The research analyzed whether applying the Adaptive Synthetic (ADASYN) oversampling technique in the K-Nearest Neighbor Algorithm can handle data imbalance problems. The study looks at the resulting accuracy, specificity, and sensitivity values. ADASYN oversamples the minority class data based on the model's difficulty level of data learning using distribution weights. This research uses the Pima Indian Diabetes Dataset from the Kaggle website. The dependent variable was diabetes mellitus status, while the independent variables were number of pregnancies, glucose levels, diastolic blood pressure, insulin levels, Body Mass Index (BMI), and age. The study found that the accuracy, specificity, and sensitivity values were 72.88%, 73.42%, and 71.79%, respectively. Based on the results of the analysis, it can be concluded that using ADASYN in the K-Nearest Neighbor Algorithm to classify diabetes mellitus in Pima Indian women is good enough to address imbalanced data. It is shown that the ADASYN oversampling technique can help the K-Nearest Neighbor Algorithm to classify new data without ignoring the data of the minority class.

Combining IoT and Time Series Model for Minute-Level Outlier Detection in Wind Speed Forecasting Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri; Hidayati, Rahmi; Sari, Kartika
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

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

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.

Control Chart for Correcting the ARIMA Time Series Model of GDP Growth Cases Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Utami, Dewi Setyo; Umairah, Tarisa; Arini, Nani Fitria
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.v8i1.19612

The essential prerequisite for attending the G20 conference is a country's GDP because G20 members can significantly boost the economy and preserve the nation's financial stability. Time series data can be thought of as a country's Gross Domestic Product (GDP) at a particular point in time. In this research, the GDP numbers from five Southeast Asian nations that are attending the G20 fulfilling are used. The total was 47 observations made yearly, which extended from 1975 to 2001. A time series analysis was performed on the data gathered. The correctness of time series models is also evaluated using control charts based on this research. The control chart is constructed using the time series model's residuals as observations. After applying the IMR control chart for verification, the results revealed that the residuals, specifically the models for GDP in Malaysia, Singapore, and Thailand, are out of control. The white noise assumption is fulfilled by the time series model obtained for Brunei and Indonesia's GDP, but the residuals are out of control. Whether controlled residuals are used depends on the accuracy with which the time series model predicts the future. If the amount of residuals is under control, then the time series model produced is accurate and good enough for prediction. After using the IMR control chart to verify the residuals, the results indicate that the residuals, namely the models for GDP in Malaysia, Singapore, and Thailand, are not under control. The assumption of white noise is proved correct by the time series model obtained for the GDP of Brunei Darussalam and Indonesia. With that being said, the residuals are entirely out of control. The model must improve its ability to forecast various future periods. It is a consequence of the unmanageable residuals that the model contains. Even if the best available model has been obtained based on the criteria that have been defined, it is anticipated that the research findings will improve the theories that have previously been developed and raise knowledge regarding the usefulness of testing the time series model. In addition to all of that, it is intended that the research will produce a summary of cases of an increase in GDP from five Southeast Asian countries participating in the G20 conference.

Co-Authors ., Putri Amalia, Disya Recita Andani, Wirda Arini, Nani Fitria Ayyash, Muhammad Yahya Dadan Kusnandar Dadan Kusnandar Debataraja, Naomi Nessyana Debatraja, Naomi Nessyana Evy Sulistianingsih Fitriyana, Gita Hendra Perdana Huda, Nur'ainul Miftahul Huda, Nur’ainul Miftahul Kartika Sari Marda, Marda Marlisa, Herina Martha, Shantika Mauditia, Lyra Neva Satyahadewi Novita, Irene Pratiwi, Hesty Rahmi Hidayati Rizki, Setyo Wira Tamtama, Ray Umairah, Tarisa Utami, Dewi Setyo Utriweni Mukhaiyar Zuleha, Zuleha

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