cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nur Inayah
Contact Email
inprime.journal@uinjkt.ac.id
Phone
+6285280159917
Journal Mail Official
inprime.journal@uinjkt.ac.id
Editorial Address
Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah Jl. Ir H. Juanda No.95, Cemp. Putih, Kec. Ciputat, Kota Tangerang Selatan, Banten 15412
Location
Kota tangerang selatan,
Banten
INDONESIA
InPrime: Indonesian Journal Of Pure And Applied Mathematics
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 26865335     EISSN : 27162478     DOI : 10.15408/inprime
Core Subject : Science, Education,
Computer Science & IT Mathematics
InPrime: Indonesian Journal of Pure and Applied Mathematics is a peer-reviewed journal and published on-line two times a year in the areas of mathematics, computer science/informatics, and statistics. The journal stresses mathematics articles devoted to unsolved problems and open questions arising in chemistry, physics, biology, engineering, behavioral science, and all applied sciences. All articles will be reviewed by experts before accepted for publication. Each author is solely responsible for the content of published articles. This scope of the Journal covers, but not limited to the following fields: Applied probability and statistics, Stochastic process, Actuarial, Differential equations with applications, Numerical analysis and computation, Financial mathematics, Mathematical physics, Graph theory, Coding theory, Information theory, Operation research, Machine learning and artificial intelligence.
Arjuna Subject : Matematika - Matematika Terapan
Articles 7 Documents
 1 
Search results for , issue "Vol 2, No 1 (2020)" : 7 Documents clear
Classification of Tuberculosis and Pneumonia in Human Lung Based on Chest X-Ray Image using Convolutional Neural Network Muhaza Liebenlito; Yanne Irene; Abdul Hamid
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1192.116 KB) | DOI: 10.15408/inprime.v2i1.14545

Abstract

AbstractIn this paper, we use chest x-ray images of Tuberculosis and Pneumonia to diagnose the patient using a convolutional neural network model. We use 4273 images of pneumonia, 1989 images of normal, and 394 images of tuberculosis. The data are divided into 80% as the training set and 20% as the testing set. We do the preprocessing steps to all of our images data, such as resize, converting RGB to grayscale, and Gaussian normalization. On the training dataset, the sampling technique used is undersampling and oversampling to balance each class. The best model was chosen based on the Area under Curve value i.e. the area under the curve of Receiver Operating Characteristics. This method shows that the best model obtains when trains the training dataset using oversampling. The Area under Curve value is 0.99 for tuberculosis and 0.98 for pneumonia. Therefore, this best model succeeds to identify 86% true for tuberculosis and 96% true for pneumonia.Keywords: chest X-ray images; tuberculosis; pneumonia; convolutional neural network.                                                                AbstrakPada penelitian ini memanfaatkan data citra chest x-ray penderita penyakit tuberculosis dan pneumonia. Model convolutional neural network digunakan untuk membantu mendiagnosis kedua penyakit ini. Data yang digunakan masing-masing sudah dilabeli sebanyak 4273 citra pneumonia, 1989 citra normal dan 394 citra tuberculosis. Data tersebut dibagi menjadi 80% himpunan data latih dan 20% data uji. Himpunan data tersebut telah melalui 3 tahap prepocessing yaitu resize citra, merubah citra RGB menjadi grayscale dan standarisasi gausian pada citra. Pada data latih dilakukan teknik sampling berupa undersampling dan oversampling data untuk menyeimbangkan data latih antar kelas. Model terbaik dipilih berdasarkan nilai Area under Curve yaitu luas daerah di bawah kurva Receiver Operating Chracteristics. Hasil menunjukkan bahwa model terbaik dihasilkan ketika dilatih menggunakan data latih hasil oversampling dengan nilai Area under Curve kelas tuberculosis sebesar 0,99 dan nilai Area under Curve kelas pneumonia sebesar 0,98. Oleh karena itu, model terbaik ini mampu mengindentifikasi sebanyak 86% penyakit tuberculosis dan 96% penyakit pneumonia.Kata Kunci: citra chest X-ray; penyakit infeksi paru; pengolahan citra digital Convolutional Neural Network.
Application of Mathematical Models Two Predators and Infected Prey by Pesticide Control in Nilaparvata Lugens Spreading in Bantul Regency Irham Taufiq; Denik Agustito
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (754.888 KB) | DOI: 10.15408/inprime.v2i1.14887

Abstract

AbstractIn this paper, we develop a mathematical model to analyze interactions between planthopper pests as prey and menochilus sexmaculatus and mirid ladybug as two predators where prey is controlled by pesticides. The interaction between predator and prey is modeled using the Holling type II response function. The predator and prey growth are modeled using a logistic function. From this model, we obtain eight equilibrium points. The three of these equilibrium points are analyzed using linearization and locally asymptotically stable. We simulate this model using data to predict the dynamics of planthopper population and its predators. Simulation result shows that all of these populations will survive because they are influenced by pesticide control and predation rates.Keywords: control of pest; predator-prey model; the Holling type II; the logistic function.                                                                                     AbstrakPada penelitian ini, kami membangun model matematika untuk menganalisis interaksi antara hama wereng sebagai mangsa (prey) dan menochilus sexmaculatus dan mirid ladybug sebagai dua pemangsa (predator) dimana mangsa dikontrol oleh pestisida. Interaksi antara predator dan prey dimodelkan menggunakan fungsi respon Holling tipe II sedangkan pertumbuhan predator dan prey dimodelkan menggunakan fungsi logistik. Dari model tersebut diperoleh delapan titik ekuilibrium. Tiga titik ekuilibrium dari titik-titik equilibrium tersebut dianalisis menggunakan metode linierisasi dan bersifat stabil asimtotik lokal. Kemudian model ini diaplikasikan pada data.  Untuk memudahkan interpretasi antara mangsa dan dua pemangsa dilakukan simulasi numerik untuk memprediksikan dinamika populasi wereng dan predatornya. Hasil simulasi menunjukkan bahwa semua populasi tersebut akan bertahan hidup karena dipengaruhi oleh kontrol pestisida dan tingkat pemangsaan.Kata Kunci: kontrol pestisida; model predator-prey; Holling tipe II; fungsi logistik.
Traffic Model Based Predictive Control: A Piecewise-Affine using METANET M. Wakhid Musthofa
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1116.552 KB) | DOI: 10.15408/inprime.v2i1.14332

Abstract

AbstractTraffic congestion on the freeway is a serious problem for modern society. Dynamic traffic management is a good alternative solution to improve efficiency on congestion problems. This article aims to analyze parts of freeway traffic network by using METANET model which is part of macroscopic traffic flow model that describes a set of parameters such as mean speed, traffic flow, and density of a traffic system. The piecewise-affine (PWA) approximation on METANET model is used to design traffic predictive controls and test them on a traffic model structure. This approach guarantees more intensive calculation for METANET traffic flow model in nonlinear form in the context of model predictive control (MPC). Some equations in the METANET model will be approximated by PWA function. With PWA-MPC approximation as direct calculation, equation of PWA model can be transformed into mixed-integer linear programming (MILP). Furthermore, to see the control of the model with MPC control, numerical simulations will be carried out on mean speed, traffic density, traffic flow, queue length, and MPC control. We use time 0 – 2.5 hours. Simulation result shows that the density of traffic, traffic flow, and queue length decreased in this time period, while the mean speed increased.Keywords: traffic control; model predictive control; piecewise-affine model; METANET; mixed-integer linear programming (MILP). AbstrakKemacetan lalu lintas di jalan bebas hambatan merupakan masalah yang sangat serius bagi masyarakat modern. Pengelolaan lalu lintas yang dinamis merupakan solusi alternatif yang baik untuk meningkatkan efisiensi pada masalah kemacetan. Artikel ini bertujuan untuk menganalisis bagian jaringan pada jalan bebas  hambatan dengan mengkaji model METANET yang termasuk bagian dari model arus lalu lintas secara makroskopik yang menggambarkan kumpulan parameter seperti kecepatan rata-rata, arus lalu lintas, dan kepadatan. Pendekatan piecewise-affine (PWA) pada model METANET digunakan untuk mendesain kendali prediktif lalu lintas dan mengujinya pada suatu struktur model lalu lintas. Pendekatan ini menjamin penghitungan yang lebih intensif untuk model arus lalu lintas METANET yang berbentuk nonlinear dalam konteks kendali model prediktif (model predictive control/MPC). Beberapa persamaan pada model METANET akan didekati oleh fungsi PWA. Dengan pendekatan PWA-MPC sebagai perhitungan secara langsung, persamaan model PWA dapat diubah menjadi program linear bilangan bulat campuran (mixed- integer linear programming/MILP). Selanjutnya untuk melihat keterkendalian model dengan kendali MPC, simulasi numerik akan dilakukan terhadap kecepatan rata-rata, kepadatan lalu lintas, arus lalu lintas, panjang antrian, serta  kendali MPC. Waktu yang digunakan pada simulasi adalah 0 – 2.5 jam. Hasil simulasi menunjukkan bahwa kepadatan lalu lintas, arus lalu lintas, panjang antrian mengalami penurunan dalam kurun waktu tersebut, sedangkan kecepatan rata-rata mengalami peningkatan.Kata Kunci: endali lalu lintas; model lalu lintas berbasis kendali prediktif; pendekatan model piecewise-affine; METANET; program linear bilangan bulat campuran.
Regency grouping in East Java based on Variable Type of Agriculture uses Hybrid Hierarchical Clustering Via Mutual Cluster Method Sulthan Fikri Mu'afa; Nurissaidah Ulinnuha
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3131.917 KB) | DOI: 10.15408/inprime.v2i1.14167

Abstract

AbstractEast Java Province is one of the provinces that has the largest agricultural resources in Indonesia. The Government of East Java needs to produce superior commodities in each region. This study aims to group districts in East Java Province based on variable types of agriculture with the hybrid hierarchical clustering via mutual cluster method that combines the merging of bottom-up clustering advantages and top-down clustering advantages. Mutual cluster is a grouping with the largest distance between small groups of the shortest distance for each point outside the group. In this research, the calculation uses Euclidean distance. The data used in this study are from the East Java Central Statistics Agency (BPS) in 2017. The division calculation is obtained by finding the minimum  (standard deviation of intra cluster) value and the maximum  (standard deviation of inter clusters) value and using the analysis of variance calculation. The grouping results obtained were nine groups with  value of 725.934,  value of 1.475.978 and  value of 7,908.Keywords: agriculture; Hybrid Hierarchical Clustering; mutual cluster; Euclidean distance; analysis of variance. AbstrakProvinsi Jawa Timur merupakan salah satu provinsi yang memiliki sumber daya pertanian terbesar di Indonesia. Pemerintah Jawa Timur perlu mengembangkan komoditi unggulan di tiap daerah di Jawa Timur. Penelitian ini bertujuan untuk mengelompokkan kabupaten di Provinsi Jawa Timur berdasarkan variabel jenis pertanian dengan metode hybrid hierarchical clustering via mutual cluster yaitu menggabungkan kelebihan bottom-up clustering dan kelebihan top-down clustering. Mutual cluster yakni pengelompokkan dengan jarak terbesar antara bagian dalam kelompok yang kecil dari jarak yang terpendek kepada tiap titik di luar kelompok. Dalam penelitian ini, perhitungan jarak menggunakan jarak Euclidean. Data yang digunakan dalam penelitian ini dari Badan Pusat Statistik Jawa Timur tahun 2017. Perhitungan pembagian didapat dengan mencari nilai (simpangan baku dalam klaster) yang minimal dan nilai  (simpangan baku antar klaster) yang maksimal, serta digunakan perhitungan analyze of varians. Hasil pengelompokkan yang diperoleh didapatkan sebanyak sembilan kelompok dengan nilai  sebesar 725.934, nilai sebesar 1.475.978 dan nilai  sebesar 7,908.Kata Kunci: pertanian; Hybrid Hierarchical Clustering; mutual cluster; jarak Euclid; analisis variansi.
Aggregate Risk Model and Risk Measure-Based Risk Allocation Khreshna Syuhada
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2675.61 KB) | DOI: 10.15408/inprime.v2i1.14494

Abstract

AbstractIn actuarial modeling, aggregate risk is known as more attractive rather than individual risk. It has, however, usual difficulty in finding (the exact form of) joint probability distribution. This paper considers aggregate risk model and employ translated gamma approximation to handle such distribution function formulation. In addition, we deal with the problem of risk allocation in such model. We compute in particular risk allocation based on risk measure forecasts of Value-at-Risk (VaR) and its extensions: improved VaR and Tail VaR. Risk allocation shows the contribution of each individual risk to the aggregate. It has a constraint that the risk measure of aggregate risk is equal to the aggregate of risk measure of individual risk.Keywords: allocation methods; tail-value-at-risk; translated gamma approximation. AbstrakRisiko agregat merupakan kajian yang lebih menarik dalam pemodelan aktuaria, dibandingkan dengan risiko individu. Namun fungsi distribusi risiko agregat sulit ditentukan bentuk eksaknya. Artikel ini membahas mengenai model risiko agregat dan menggunakan metode aproksimasi Translasi Gamma untuk menentukan fungsi distribusi risiko agregat. Berdasarkan fungsi distribusi tersebut, dapat diprediksi alokasi risiko agregat. Metode alokasi risiko agregat diterapkan pada ukuran risiko Value-at-Risk (VaR) dan pengembangannya: improved VaR dan Tail-VaR. Alokasi risiko menyatakan nilai kontribusi setiap risiko individu terhadap ukuran risiko agregat. Jumlahan atau agregat dari setiap alokasi risiko individu sama dengan ukuran risiko agregat.Kata kunci: aproksimasi Translasi Gamma; alokasi risiko; Tail-Value-at-Risk.
Table of Integration Model for Motor Vehicle Sharia Insurance Rini Cahyandari; Asep Solih Awalluddin; Dara Selvi Mariani; Sukono Sukono; Puspa Liza Ghazali
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3070.751 KB) | DOI: 10.15408/inprime.v2i1.14811

Abstract

AbstractMotor vehicle insurance is one of the general insurance types that provide coverage for loss, damages, and disappearance of a motor vehicle due to risks experienced by the covered object. The product illustration of motor vehicle insurance is generally presented in a condensed form, containing few pages and limited information. Based on a product illustration of motor vehicle sharia insurance issued by PT. Asuransi Tri Pakarta (TRIPA) treated as a case study, an alternative version of product illustration in form of a table of integration model was designed to not only provided general information but also vehicle prices, values of premiums, tabarru, ujrah, investment, insurance costs, and surplus (if any). The partitions also performed on the additional protection offered by TRIPA so that the benefits that would be received by the insured would be greater. The generated table of integration model presents richer information regarding the insurance products, better scheme, and transparency in the management of total premiums.Keywords: general insurance; product illustration; management of total premiums; integration model; benefits. AbstrakAsuransi kendaraan bermotor merupakan salah satu jenis asuransi umum yang memberikan jaminan terhadap kerugian, kerusakan dan kehilangan kendaraan bermotor akibat terjadinya risiko yang menimpa obyek pertanggungan. Umumnya ilustrasi produk asuransi kendaraan bermotor disajikan dalam bentuk ringkasan yang beragam dalam beberapa halaman dan hanya menjelaskan informasi secara umum saja. Mengambil studi kasus berupa ilustrasi produk asuransi syariah kendaraan bermotor dari PT. Asuransi Tri Pakarta (TRIPA), dirancang bentuk alternatif penyajian berupa tabel model integrasi yang memberikan informasi tidak hanya harga kendaraan dan besaran premi, tetapi juga informasi tentang tabarru, ujrah, investasi, biaya asuransi, surplus (jika ada). Selanjutnya, dilakukan partisi terhadap perlindungan tambahan sehingga manfaat yang diperoleh tertanggung lebih luas. Melalui tabel model integrasi ini, informasi yang tertulis tentang produk asuransi lebih lengkap dan skema pengelolaan premi total asuransi syariah kendaraan bermotor juga lebih jelas.Kata Kunci: asuransi umum; ilustrasi produk; pengelolaan premi total; model integrase; manfaat.
Estimating the Cost of Car Warranty in Indonesia using the Gertsbakh-Kordonsky Method Anggis Sagitarisman; Aceng Komarudin Mutaqin
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 2, No 1 (2020)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (660.57 KB) | DOI: 10.15408/inprime.v2i1.14556

Abstract

AbstractCar manufacturers in Indonesia need to determine reasonable warranty costs that do not burden companies or consumers. Several statistical approaches have been developed to analyze warranty costs. One of them is the Gertsbakh-Kordonsky method which reduces the two-dimensional warranty problem to one dimensional. In this research, we apply the Gertsbakh-Kordonsky method to estimate the warranty cost for car type A in XYZ company. The one-dimensional data will be tested using the Kolmogorov-Smirnov to determine its distribution and the parameter of distribution will be estimated using the maximum likelihood method. There are three approaches to estimate the parameter of the distribution. The difference between these three approaches is in the calculation of mileage for units that do not claim within the warranty period. In the application, we use claim data for the car type A. The data exploration indicates the failure of car type A is mostly due to the age of the vehicle. The Kolmogorov-Smirnov shows that the most appropriate distribution for the claim data is the three-parameter Weibull. Meanwhile, the estimated using the Gertsbakh-Kordonsky method shows that the warranty costs for car type A are around 3.54% from the selling price of this car unit without warranty i.e. around Rp. 4,248,000 per unit.Keywords: warranty costs; the Gertsbakh-Kordonsky method; maximum likelihood estimation; Kolmogorov-Smirnov test.                                   AbstrakPerusahaan produsen mobil di Indonesia perlu menentukan biaya garansi yang bersifat wajar tidak memberatkan perusahaan maupun konsumen. Beberapa pendekatan statistik telah dikembangkan untuk menganalisis biaya garansi. Salah satunya adalah metode Gertsbakh-Kordonsky yang mereduksi masalah garansi dua dimensi menjadi satu dimensi. Pada penelitian ini, metode Gertsbakh-Kordonsky akan digunakan untuk mengestimasi biaya garansi untuk mobil tipe A pada perusahaan XYZ. Data satu dimensi hasil reduksi diuji kecocokan distribusinya menggunakan uji kecocokan Kolmogorov-Smirnov dan taksiran parameter distribusinya menggunakan metode penaksir kemungkinan maksimum. Ada tiga pendekatan yang digunakan untuk menaksir parameter distribusi. Perbedaan dari ketiga pendekatan tersebut terletak pada perhitungan jarak tempuh untuk unit yang tidak melakukan klaim dalam periode garansi. Sebagai bahan aplikasi, kami menggunakan data klaim unit mobil tipe A. Hasil eksplorasi data menunjukkan bahwa kegagalan mobil tipe A lebih banyak disebabkan karena faktor usia kendaraan. Hasil uji kecocokan distribusi untuk data hasil reduksi menunjukkan bahwa distribusi yang cocok adalah distribusi Weibull 3-parameter. Sementara itu, hasil perhitungan taksiran biaya garansi menunjukan bahwa taksiran biaya garansi untuk unit mobil tipe A sekitar 3,54% dari harga jual unit mobil tipe A tanpa garansi, atau sekitar Rp. 4.248.000,- per unit.Kata Kunci: biaya garansi; metode Gertsbakh-Kordonsky; penaksiran kemungkinan maksimum; uji Kolmogorov-Smirnov.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2020 2020


Reset
Filter By Issues
All Issue Vol 7, No 2 (2025) Vol. 7 No. 2 (2025) Vol 7, No 1 (2025) Vol. 7 No. 1 (2025) Vol 6, No 2 (2024) Vol. 6 No. 2 (2024) Vol. 6 No. 1 (2024) Vol 6, No 1 (2024) Vol 5, No 2 (2023) Vol. 5 No. 2 (2023) Vol. 5 No. 1 (2023) Vol 5, No 1 (2023) Vol. 4 No. 2 (2022) Vol 4, No 2 (2022) Vol 4, No 1 (2022) Vol. 4 No. 1 (2022) Vol. 3 No. 2 (2021) Vol 3, No 2 (2021) Vol 3, No 1 (2021) Vol. 3 No. 1 (2021) Vol 2, No 2 (2020) Vol 2, No 1 (2020) Vol 1, No 2 (2019) Vol 1, No 1 (2019) More Issue