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All Journal Journal of the Indonesian Mathematical Society Journal of Mathematical and Fundamental Sciences JOIV : International Journal on Informatics Visualization Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Kebijakan Sosial Ekonomi Kelautan dan Perikanan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Utriweni Mukhaiyar
Statistics Research Division, Institut Teknologi Bandung, Jalan Ganesha 10 Bandung, Jawa Barat 40132, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Penerapan Analisa Time Series Terhadap Nilai Matematika di SMAS Alfa Centauri Bandung Nulhakim, Imam; Mukhaiyar, Utriweni
Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya 2016: Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya
Publisher : Universitas Muhammadiyah Surakarta

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

Abstract

Time series merupakan serangkaian pengamatan berdasarkan pada urutan waktu dengan mempelajari pola gerakan nilai-nilai variable pada satu interval waktu. Berdasarkan pengamatan tersebut dapat dilihat suatu model yang dapat digunakan untuk memprediksi kejadian pada periode berikutnya. Hal yang akan dikaji dalam penelitian ini adalah memprediksi secara kuantitatif terjadinya perubahan dan perkembangan kemampuan penguasaan materi pada pelajaran matematika yang ada di SMAS Alfa Centauri Bandung yang dilihat dari pencapian nilai Try Out Ujian Nasional siswa. Metode yang digunakan adalah Autoreressive Integrated Moving Average (ARIMA). Adapun data yang digunakan dalam penelitian ini adalah nilai Try Out siswa dari tahun 2010 sampai dengan ujian nasional pada tahun 2014. Didapatkan Model forecast hasil nilai ujian ke-
On the Interaction between Trichogramma chilonis and Jatiroto Flies with Stem Borer Pests in Sugarcane Plantation Fauzi, Rifky; Mufidah, Zunanik; Maretta, Gres; Edriani, Tiara Shofi; Rizka, Nela; Mukhaiyar, Utriweni; Utami, Riani; Haryani, Sri; Saefudin, Saefudin; Styaningrum, Amalia; Soewono, Edy
Journal of Mathematical and Fundamental Sciences Vol. 56 No. 3 (2024)
Publisher : Directorate for Research and Community Services (LPPM) ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.math.fund.sci.2024.56.3.3

Abstract

Pests in sugarcane plantations are a major cause of damage, which could lead to severe damage to the crop, reduction of sugar quality, and significant economic loss. One of the major pests known in sugarcane plantations is stem borer (Chilo sacchriphagus), which attacks the canes. Two primary parasitoids, Trichogramma chilonis, which predates the stem borer eggs, and the Jatiroto fly (Diatraeophaga striatalis), which predates the stem borer larvae, are discussed here. This paper presents a time-dependent ten-dimensional dynamical mathematical model consisting of four-stage stem borer compartments, three-stage Trichogramma compartments, and three-stage Jatiroto compartments. Simulations are presented to describe the phenomenon of Trichogramma predation, Jatiroto predation, and simultaneous predation of both predators. It is shown that the release rate of each predator and a combined release of two predators can significantly reduce the infestation levels to a tolerable level for sugarcane production. The oscillatory dynamics of the stem borers and the Jatiroto flies affected the release timing strategy based on the level of infestation in the field. The results are expected to help us better understand the predator-prey phenomenon in the field and improve the forecasting of infestations in the field.
BEEF PRICE FORECASTING BASED ON TEMPORAL, SPATIAL AND SPACE-TIME PARAMETER INDICES Fatimah, Syifa Nurul; Zainnuddin, Ahmad Fuad; Mardiana, Novi; Mukhaiyar, Utriweni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1805-1824

Abstract

Beef is among the most sought-after commodities in Indonesia, resulting in significant price fluctuations, particularly during religious holidays. These price variations affect inflation and necessitate adjustments in government policies concerning beef distribution and imports. Therefore, it is essential to analyze and predict beef prices using empirical data from regions with the highest beef production and consumption levels. This study aims to examine beef price data through the lenses of temporal, spatial, and space-time dependencies within Java. The methodologies employed in this research include ARIMA, Semivariogram, Kriging, and GSTAR models applied to weekly beef price data from Java. ARIMA is used to analyze and forecast time series data based on past values and past forecast errors. The Semivariogram measures spatial dependence by quantifying how price similarities change with distance. Kriging is a geostatistical interpolation method that predicts price values at unobserved locations based on spatial correlation. GSTAR extends ARIMA by incorporating spatial and temporal dependencies to model interactions across different locations over time. The data used in this study consists of weekly beef price records from major markets across Java, obtained from National Food Agency of Indonesia, from August 2022 to May 2024. The findings of this study reveal that beef price fluctuations in Java are primarily influenced by temporal factors, particularly major religious holidays, rather than by location or a combination of location and time. However, there are spatial variations in beef prices across different observation locations. The best predictive model for forecasting beef prices is the ARIMA model. These results provide valuable insights into the patterns of beef prices based on temporal, spatial, and space-time parameters, offering a robust framework for understanding and anticipating price dynamics in the region.
Cluster analysis of Sumatra Island earthquake distribution Anggraini, Dian; Indratno, Sapto Wahyu; Mukhaiyar, Utriweni; Puspito, Antonius Nanang T.
Desimal: Jurnal Matematika Vol. 7 No. 3 (2024): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v7i3.25108

Abstract

Sumatra Island is highly vulnerable to earthquakes due to multiple seismic sources, including megathrusts, faults, and volcanic activities spanning from Aceh to Lampung. The International Seismological Centre (ISC) has recorded 9,414 earthquakes in Sumatra with magnitudes ranging from 4.0 to 9.1 since 1907. Insufficient preparedness in responding to sudden earthquakes challenges local and central governments in managing impacts. To address this, a risk classification of earthquake-prone areas was conducted using cluster analysis. The "K-means cluster" method identified five earthquake clusters in Sumatra. Cluster 4 has the most events (3,787) but with generally lower magnitudes, resulting in minimal damage. Cluster 2, however, is more concerning due to shallow earthquakes from subduction zones, faults, and volcanoes. This clustering analysis provides critical information for government planning in earthquake risk mitigation and preparedness.
The Value at Risk Analysis using Heavy-Tailed Distribution on the Insurance Claims Data Mukhaiyar, Utriweni; Dianpermatasari, Aprilia; Dzakiya, Azizah; Widyani, Sasqia Bunga; Syam, Husnul Khatimah
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.v8i4.25053

Abstract

The insurance has often been involved to minimize financial losses. As the product providers, the insurance companies must effectively manage risks to prevent errors in risk measurement. The amount of risk or loss experienced by the policyholder refers to the claim amount. The Value at Risk (VaR) is commonly used to measure risk. The VaR is calculated from the probability function, which can be obtained by evaluating the distribution of claims data. Most claim frequencies are small, but occasionally, huge claims appear. Therefore, the appropriate distribution would be characterized by a heavy-tailed. Thus, this research aims to model and evaluate insurance claims data using exponential, Weibull, Pareto, and lognormal distributions to assess financial risk through VaR. The insurance claims data were collected from a single insurance company and include 1,326 claims. This research specifically examines variables such as gender, diabetic status, smoking status, the number of claims, and the level of confidence. The data were analysed using descriptive statistics, Maximum Likelihood Estimation for parameter estimation, and Goodness of Fit tests to determine the best-fitting distribution, along with VaR calculations based on the results. The suitability of the distribution model is assessed through the VaR and is analysed based on the appropriate distribution of insurance claims data. It is obtained that the Weibull and lognormal distributions appropriately model insurance claims data. The highest VaR is observed in the claim data for female non-diabetic smokers, with a level of confidence of 99.5%. The lowest VaR is obtained from the claim data for male diabetic non-smokers, with a level of confidence of 90%. This approach enhances the prediction of large potential losses for specific demographic groups, aiding more informed decision-making in premium pricing and risk management. The integration of heavy-tailed distributions in risk assessment, with a particular focus on demographic specificity, constitutes a substantial and novel contribution to this research.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (781.726 KB) | DOI: 10.30598/barekengvol16iss3pp975-984

Abstract

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.
COMPARISON OF SURVIVAL ANALYSIS USING ACCELERATED FAILURE TIME MODEL AND COX MODEL FOR RECIDIVIST CASE Arfan, Nuraziza; Irfanullah, Asrul; Hamidi, Muhammad Rozzaq; Mukhaiyar, Utriweni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp629-642

Abstract

Recidivists, or ex-prisoners who commit crimes after serving a prior sentence, pose a critical challenge to the criminal justice system. This study examines social and economic factors that may reduce the likelihood of recidivists being re-arrested. Using survival analysis, the probability that a recidivist could survive in society without being re-arrested could be estimated. The purpose of this work is to compare the AFT and Cox models to determine which provides a better fit to identify factors affecting the likelihood of re-arrest within one year after release and to statistically assess the impact of these factors. This study utilizes a stratified Cox model to address variables that violate the proportional hazards (PH) assumption. The analysis is limited to four types of AFT models: Weibull, log-normal, log-logistic, and exponential. Results show that the stratified Cox model provides the best fit, based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). This demonstrates the Cox model's robustness in analyzing survival data, accurately approximating the distribution of survival times without restrictive assumptions, unlike AFT models. The study reveals that recidivists who received financial aid upon release have a lower risk of re-arrest compared to those who did not, and each additional prior theft arrest increased the risk of re-arrest by 1.09193 times.
Premiums of Deposit Insurance with Maximum Limit Under the Black-Scholes Model Puspita, Dila; Delima, Anggun Citra; Mukhaiyar, Utriweni; Eka, Maharani; Azis, Rheznandya Arkaputra
Journal of the Indonesian Mathematical Society Vol. 31 No. 2 (2025): JUNE
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.v31i2.1792

Abstract

Deposit insurance is an important mechanism in protecting bank customers from the risk of bankruptcy and providing a sense of security for their savings. Under deposit insurance, customers will still receive a refund of their funds up to a certain limit determined by the deposit insurance agency. This research aims to construct a formula to calculate the premium of deposit insurance with a given upper claim limit. To the best of the authors' knowledge, this article is the first study that gives a formula for deposit insurance premiums with a coverage limit. First, we present a theorem that shows the equivalence between the claim of deposit insurance with coverage limit with the payoff of two put options. Secondly, under the assumption that the asset follows Geometric Brownian Motion, we determine the fair price of the premium of deposit insurance. The main research findings indicate that the sum of two Black-Scholes options with different strike prices can be used to determine the premium value of deposit insurance while considering the applied coverage limits. Finally, we simulate some sensitivity analysis to gain a deeper understanding on the impact of several important variables on the magnitude of premium. Based on sensitivity analysis, it is found that the premium value is inversely proportional to interest rates and directly proportional to asset price volatility.
Attributes Classification for Elaborating the Information of Digital and Imaging Mapping Mukhaiyar, Riki; Mukhaiyar, Utriweni
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.2353

Abstract

The rapid development of information has made it possible for everyone to obtain the latest information, complete and accurate, in real time, anytime, anywhere, all over the world. Any information is fine catching up by updated with the latest news and even detailed information on local conditions. With the same analogy, detailed information regarding land utilization, land containing, landscape provision, earth surface contour, etc., are required to inform and elaborate any appropriate decision needed. The Geographic information systems (GIS) is a recent technology commonly used by research in earth science to facilitate many layered detail information by one way to get up-to-date, detailed information. In this research, the GIS utilizes several types of imaging data such remote sensing images and digitize images. As the name suggests, this system captures detailed geographic information about a location or region. By inputting classified images of remote sensing results into a GIS database at regular intervals (adjusted as necessary, such as every year, every two years, every three years, etc.), the number of information sources that can be obtained increases. There are several reasons for that. First, remote sensing images are images that cover the entire surface of the Earth. Next, remote sensing images are images that contain information about the state of the earth's surface. Third, a variety of information can be obtained by performing appropriate image processing. Furthermore, this research could be elaborated by implementing an artificial intelligent algorithm to create a robust outcome.
Pemetaan Spasial Hasil Tangkapan Tuna Untuk Mendorong Program Pemerintah Tepat Sasaran (Studi Kasus: Maluku Utara) Mukhaiyar, Utriweni; Sari, Febrina Puspa; Suherlan, Bagas Caesar; Eldhyawati, Silmie Yashifa
Jurnal Kebijakan Sosial Ekonomi Kelautan dan Perikanan Vol 14, No 2 (2024): Desmeber 2024
Publisher : Balai Besar Riset Sosial Ekonomi Kelautan dan Perikanan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/jksekp.v14i2.13523

Abstract

Data produksi tuna Tahun 2019, 2020, dan 2021 di Provinsi Maluku Utara (Malut) dapat dipetakan secara spasial sehingga terlihat bahwa penambahan jumlah kapal dengan jenis dan ukuran tertentu akan menyebabkan peningkatan atau penurunan hasil produksi tuna. Jenis dan ukuran kapal merupakan variabel penjelas dengan kecenderungan memiliki pengaruh besar terhadap hasil produksi tuna berdasarkan nilai R-square dan CP Mallow’s. Selain jenis dan ukuran kapal, variabel penjelas lainnya adalah alat tangkap dan lokasi pendaratan ikan. Hasil pemetaan spasial menggunakan pendekatan Geographically Weighted Regression (GWR) ini menunjukkan pola yang berbeda setiap tahunnya. Kecuali, pada beberapa kabupaten seperti Halmahera Barat, Kepulauan Sula, dan Pulau Morotai yang cukup konsisten menunjukkan bahwa peningkatan produksi tuna terjadi jika ada penambahan kapal penangkap ikan ukuran di bawah 10 GT. Daerah-daerah ini merupakan produsen tuna tertinggi di Prov Malut. Penambahan produksi tuna tertinggi mencapai 450 ribu ton pada Tahun 2019 di Kepulauan Sula, 350 ribu ton pada 2020 di Pulau Morotai, dan 400 ribu ton pada 2021 di Halmahera Barat. Potensi tuna yang melimpah ini harus dijaga dan didukung dengan tata kelola yang baik oleh Pemerintah. Hasil analisis spasial tentang potensi produksi tuna ini dapat menjadi rekomendasi dalam menentukan daerah tujuan bantuan kapal perikanan beserta alat tangkapnya sehingga bantuan tersebut lebih efektif dan tepat sasaran. Daerah tujuan bantuan alat tangkap dan kapal perikanan paling direkomendasikan adalah daerah yang produksi tunanya paling melimpah, dengan catatan memperhatikan kebijakan Penangkapan Ikan Terukur. Title: Spatial Mapping of Tuna Catch Productionsto Encourage Targeted Government Aid (Case Study: Maluku Utara) una production data for 2019, 2020, and 2021 in North Maluku (Malut) Province can be mapped spatially so that it will be showed that the increase of the number of a certain fishing vessel type and size will lead to the increase or decrease of tuna.Fishing vessel type and size which are as the explanatory variables tend to have more influence on tuna production based on the R-square and CP Mallow’s. Apart from vessel type and size, other explanatory variables are fishing gear and fish landing sites. The results of spatial mapping using the Geographically Weighted Regression (GWR) approach show a different pattern each year. However, several regencies such as Halmahera Barat, Sula Islands, and Morotai Island are the potential areas if the number of operations of fishing vessels under 10 GT size is added. Additional tuna production could reach 450 thousand tons in 2019 in the Sula Islands, 350,000 tons in 2020 on Morotai Island, and 400 thousand tons in 2021 on Halmahera Barat. The abundant tuna potential of Malut Province has to be maintained and supported by a good governance by the Government. This spatial mapping information about tuna production can be used as a recommendation in determining the government aid target areas of fishing vessel and its fishing gear so that will lead to be more effective and targeted. The recommended target area of fishing gear and vessels are the area with the most abundant tuna production. The abundance of tuna production take the measured fishing policy into account.
Co-Authors Azis, Rheznandya Arkaputra DELIMA, ANGGUN CITRA Dian Anggraini Dianpermatasari, Aprilia Dzakiya, Azizah Edriani, Tiara Shofi Edy Soewono Eka, Maharani Eldhyawati, Silmie Yashifa Fatimah, Syifa Nurul Gres Maretta, Gres Hamidi, Muhammad Rozzaq Huda, Nur'ainul Miftahul Imro'ah, Nurfitri Indratno, Sapto Wahyu Irfanullah, Asrul Mufidah, Zunanik Mukhaiyar, Riki Nadwah, Hamidah Qurrotun Novi Mardiana Nulhakim, Imam Nuraziza Arfan Puspita, Dila Puspito, Antonius Nanang T. Rifky Fauzi Rizka, Nela Saefudin Saefudin Sari, Febrina Puspa Sri Haryani Styaningrum, Amalia Suherlan, Bagas Caesar Syam, Husnul Khatimah Utami, Riani Widyani, Sasqia Bunga Zainnuddin, Ahmad Fuad