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All Journal Jurnal Pilar Sains Jurnal Online Mahasiswa (JOM) Bidang Matematika dan Ilmu Pengetahuan Alam Jurnal Sains Matematika dan Statistika Science and Technology Indonesia Sinkron : Jurnal dan Penelitian Teknik Informatika Seminar Nasional Teknologi Informasi Komunikasi dan Industri Indonesian Journal of Chemistry Instructional Development Journal Jurnal Keluarga Berencana Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Journal of Applied Engineering and Technological Science (JAETS) Al-Muqayyad Indonesian Journal of Electrical Engineering and Computer Science Jurnal Statistika dan Aplikasinya Jambura Journal of Mathematics Education Unri Conference Series: Community Engagement Jurnal Ketenagakerjaan J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Indonesian Journal of Advanced Social Works (DARMA) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Arisman Adnan
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Pre-treatment Spectral Data NIRS Menggunakan Support Vector Regression Khairunnissa, Khairunnissa; Adnan, Arisman; Syamsudhuha, Syamsudhuha
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2024: SNTIKI 16
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Potassium content is an important component in oil palm plantation soil. The utilization of Near Infrared Sprctroscopy (NIRS) is an alternative that could replace laboratory test in order to analyze and provide information about measurement of potassium fertilizer content in oil palm soil. . This study aims to examine and evaluate NIRS technology as a faster and proper method in predicting rice moisture content by  Support Vector Regression (SVR) method and determining the best and accurate spectrum correction method to predict rice water content using Standard Normal Variate (SNV) pretreatment Multiplicative Spectral Correction (MSC) and  combination of both. This study used 100 soil samples with a wavelength of 350nm - 2500nm. Data processing using R software®  version 4.4.1. The results showed the prediction of the radial basis kernel SVR method, produced the best correction method in this study, namely Multiplicative Spectral Correction with an R2 value of 0.6025 and an RMSE of 0.0201.
Penguatan Kapasitas Komunitas Statistika Bantar dalam Tata Kelola Data Desa untuk Pembangunan Berkelanjutan Adnan, Arisman; Yolanda, Anne Mudya; Erda, Gustriza; Syamsudhuha, Syamsudhuha; Indra, Zul; Solfitri, Titi; T, Masrina Munawarah
Unri Conference Series: Community Engagement Vol 6 (2024): Seminar Nasional Pemberdayaan Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/unricsce.6.640-645

Abstract

This activity aims to strengthen the capacity of the statistical community in Bantar Village in supporting the transformation and management of data for sustainable development. The program focuses on assisting village officials in effectively managing data at the village level, in line with the Desa Cantik initiative and Indonesia One Data (SDI) program. The goal is to improve data accuracy and the effectiveness of village development planning. As a result, the statistical community, which also includes village officials, has shown increased capabilities in managing sectoral statistics and digitalizing data integrated with the Desa Cantik program. The village officials actively participated in this assistance, supported by the provincial and district BPS, who acted as facilitators. BPS provided training, monitoring, evaluation, and assistance in the preparation of program materials and outputs. One of the key outputs of this program is the creation of an infographic summarizing the statistics and potential of Bantar Village, covering demographic profiles, population density, and key commodities. This infographic serves as a visual communication tool that supports data-driven development planning. The program successfully established a strong foundation for better data management, supporting sustainable village development.
Analysis of Labor Force Participation Rate in Riau Province: A Spatial Autoregressive Approach Adnan, Arisman; Erda, Gustriza; Sirait, Tesa Theresia
Jurnal Ketenagakerjaan Vol 19 No 3 (2024)
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v19i3.345

Abstract

The labor force participation rate (LFPR) is one of the important indicators for measuring the participation of the labor force involved in economic activities. In Riau Province, LFPR has exceeded half the population, resulting in increasingly tight job competition. This research aims to model the factors influencing LFPR in Riau Province in 2021 using the Spatial Autoregressive Model (SAR). Based on the Moran Index, there is positive spatial autocorrelation in LFPR, while based on the Lagrange Multiplier test, the SAR model is appropriate to use because of the lag dependence on the dependent variable. SAR analysis shows that the non-labor force variables (????1), poverty line (????2), productive age population (15-64 years) (????3), and population growth rate (????4) have a significant positive influence on LFPR. In contrast, the type ratio variable gender (????5) has a negative influence. Apart from that, a lag coefficient of 0.4935 was obtained, which means that if the value of the LFPR figure in a region increases by 1 unit, it will increase by 0.4935 times the average LFPR in neighboring areas of the region. This highlights the need for policies aimed at increasing the LFPR to account for regional coordination, as changes in one area's LFPR can influence adjacent regions. Consequently, the Riau Provincial Government should promote collaboration among districts and cities to formulate a cohesive strategy, while each district should design policies that align with their unique local characteristics and the spatial dynamics of surrounding areas.
Robust Method with Cross-Validation in Partial Least Square Regression Sibuea, Nuraini; Syamsudhuha, Syamsudhuha; Adnan, Arisman; Silalahi, Divo Dharma
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.4766

Abstract

Partial Least Squares Regression (PLSR) is a multivariate analysis technique used to handle data with highly correlated predictor variables or when the number of predictor variables exceeds the number of samples. PLSR is not robust to outliers, which can disrupt the stability and accuracy of the model. Cross-validation is an important approach to improve model reliability, particularly in data that contains outliers. This study aims to evaluate the effectiveness of K-fold cross-validation and nested cross-validation in a PLSR model using NIRS data from oil palm plantation soil that contains outliers. The methods used in this study include outlier identification using RBF kernel PCA, followed by the application of K-fold cross-validation and nested cross-validation in the PLSR model. The evaluation is based on the Root Mean Square Error (RMSE) and the Coefficient of Determination (R²). The results show that nested cross-validation performs better than K-fold cross-validation. Nested cross-validation results in lower RMSE and higher R², both with and without outliers. K-fold cross-validation is more susceptible to overfitting, whereas nested cross-validation is more effective in mitigating the impact of outliers and improving model accuracy. The conclusion of this study is that nested cross-validation outperforms K-fold cross-validation in improving prediction accuracy and the stability of the PLSR model, especially in data containing outliers. It is recommended to use nested cross-
Interactive Geographic Visualization and Unsupervised Learning for Optimal Assignment of Preachers to Appropriate Congregations Rahmad Kurniawan; Ibnu Daqiqil ID; Abdul Somad Batubara; Fitra Lestari; Arisman Adnan; Fatayat Fatayat; Ilyas Husti
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5760

Abstract

Riau Province has a population of 6,642,874 and a diverse geography, which poses significant challenges in optimizing Islamic preaching activities. Traditional assignment methods often lead to inefficiencies due to misalignment between the preacher’s expertise and congregational needs, as well as logistical issues. This study integrates K-Means clustering and DBSCAN algorithms with interactive geographic visualization to optimize the assignment of preachers to mosques. We collected 435 data points, including 185 mosques and 250 preachers. K-Means was evaluated using the Elbow Method and Silhouette Score, identifying 10 clusters as optimal with a Silhouette Score of 0.435654. However, K-Means does not handle outliers effectively, as indicated by zero outliers in all configurations. DBSCAN was tested with various epsilon (eps) and minimum sample values. The optimal configuration with eps of 1.5 and 5 minimum samples resulted in 10 clusters with a Silhouette Score of 0.381108 and 60 outliers. DBSCAN effectively manages outliers and varying densities. Although K-Means is advantageous for its simplicity and higher Silhouette Scores, it is unable to handle outliers effectively. DBSCAN provides robust clustering for noisy data. Therefore, it can be concluded that hybridizing unsupervised learning algorithms with geographic visualization can potentially improve the effectiveness of preaching activities in Riau Province and enhance preacher assignment.
Evaluating Statistical Power in t-Test and Welch’s Test Using Monte Carlo Simulation Approach Tengku Irfan Wira Buana; Arisman Adnan
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.7407

Abstract

Statistical hypothesis testing is a key method in inferential statistics for assessing whether group differences are simply due to chance or amount to actual effect. One of the central concepts in hypothesis testing is statistical power. Statistical power is the probability of correctly rejecting the null hypothesis when the alternative hypothesis is true. Low statistical power increases the risk of Type II errors, leading to misleading conclusions. This study explores the key factors influencing statistical power, including sample size, effect size, variance, and significance level. Monte Carlo simulation method was utilized in this study to examine the statistical power associated with the two-sample t-test across various combinations of sample size, effect size (mean difference), and population variance. Simulations were conducted by generating random samples, performing variance tests, and applying either the Student’s t-test or Welch’s t-test based on variance equality. The results confirmed that statistical power increases with larger sample sizes and greater effect sizes, while higher variance and stricter significance levels reduce power. Welch’s t-test was found to be more reliable than the standard t-test in cases of unequal variances, reinforcing its importance in real-world data analysis. These findings show the importance of careful study design in hypothesis testing. Researchers must consider and plan the study so that there is enough power to detect meaningful effects. Future studies should examine different statistical methods of power, and potentially extend the simulation to different non-normal distributions for hypothesis testing.
Dynamic Modeling of Energy Data: World Crude Oil and Coal Prices 2017-2023 (A State-Space Model Analysis of Multivariate Time Series) Russel, Edwin; Wamiliana; Usman, Mustofa; Elfaki, Faiz AM; Adnan, Arisman; Lindrianasari
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1301-1311

Abstract

The analysis of global crude oil and coal prices has attracted considerable research interest, as these prices significantly affect both society and industry, making the topic highly relevant for governments and policy makers. This study examines the correlation between global coal and crude oil prices from 2017 to 2023. It analyzes the behavior of these price series using a unit root test and develops an optimal model for conducting a Granger-causality analysis. To forecast crude oil and coal prices for the next 30 periods, a state-space modeling approach is applied. The unit root test results reveal that these prices are non-stationary, suggesting that any shocks to prices will have persistent effects. The best-fitting model for the association between coal and crude oil prices is a vector autoregressive model of order two (VAR(2)). The Granger-causality results reveal that current crude oil prices are influenced by both their own past values and previous coal prices, and vice versa. Forecasts using the state-space model suggest a modest upward trend for crude oil prices over the next 30 periods, while coal prices are projected to rise more strongly.
Co-Authors ', Firdaus Abdul Somad Batubara Afrianto Daud Agus Ika Putra Agus Kurniawati Ahmad Fadli Ahmad Jamaan Amun Amri Anne Mudya Yolanda Asri Elvita Ayu Agustiani Azhari Setiawan Azra Aulia Dwiputri Bustami Bustami Defrinaldi Absari Deni Rizaldi Devri Maulana Ecelly Indriani Putri Elfaki, Faiz AM Endah Dwi Jayanti Enno Yuniarto Fatayat Feblil Huda Febrianti, Lusi Ferdian Fadly Fikri Marwansyah Firdaus ' Fitra Lestari Goldameir, Noor Ell Gustriza Erda Haposan Sirait Haposan Sirait Harison ' Harison Harison Heru Angrianto Ibnu Daqiqil ID Icha Yulia Ilyas Husti Indra, Zul Irfansyah Irfansyah Isnaini ' Isti Yuliani Iswadi HR Iwantono Iwan Barnawi Jusman, Yessi K Khairat Kesi Marseliani Khairunnisa, Siska Khairunnissa, Khairunnissa Khusnal Marzuqo Lindrianasari Liza Yarmanita Mardiyah Muhgni Maria Erna Mayangsari Mayangsari Mirza Hardian Mohammad Saeri Mustofa Usman Nabilah Fitriyyah Delfira Nike Syelfina Noor Ell Goldameir Nurhayati, Nurhayati Nurqolbi, Leliyana Ody Azis Saputra Okta Bella Syuhada Putri Sion Cahayana Putri Susanti Rahmad Kurniawan Rahmad Ramadhan Laska Rahwana Saputra Raudatul Yusra Ridho Tri Mulya Riko Febrian Rini, Ari Sulistyo Riski Rahmadani Rosma, Iswadi Hasyim Russel, Edwin Rustam Efendi Rustam Efendi S. Siswanto Satibi, Syawal Sehatta Saragih Sibuea, Nuraini Sigit Sugiarto Silalahi, Divo Dharma Sillaturahim Sirait, Tesa Theresia Siska Yuliati Soewignjo Agus Nugroho Sukma Novia Syafitri Sukoco Sukoco Sul Kantri Rahma Wanti Sunarno Syamsu Herman Syamsudhuha Syamsudhuha T, Masrina Munawarah Tengku Irfan Wira Buana Tira Mei Darnis Titi Solfitri Tuti Alawiyah Wamiliana Windy Lasma Sari Purba Windy Maya Sari Wirda Hia Yenita Roza Yennita Y Zul Indra