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All Journal Statistika Jurnal Infinity Jurnal Pengajaran Matematika dan Ilmu Pengetahuan Alam EurekaMatika (Jurnal Online Matematika S1) Kubik Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Jurnal Penelitian Sains Jurnal Pendidikan (Teori dan Praktik) Jurnal Cendekia : Jurnal Pendidikan Matematika Majalah Ilmiah Matematika dan Statistika (MIMS) KUBIK: Jurnal Publikasi Ilmiah Matematika ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat These proceedings represent the work of researchers participating in The International Conference on Elementary Education (ICEE) which is being hosted by the Elementary Education Study Programme School of Postgraduate Studies, Universitas Pendidikan Jurnal Eurekamatika JP (Jurnal Pendidikan) : Teori dan Praktik Indonesian Journal of Applied Mathematics JIPI (Jurnal IPA dan Pembelajaran IPA) JMT (Jurnal Matematika dan Terapan) Jurnal Infinity
Dewi Rachmatin
Universtitas Pendidikan Indonesia
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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ANALISIS REGRESI LINEAR PIECEWISE DUA SEGMEN DENGAN MENGGUNAKAN METODE KUADRAT TERKECIL Titia Ningsih; Nar Herrhyanto; Dewi Rachmatin
Jurnal EurekaMatika Vol 7, No 2 (2019): Jurnal EurekaMatika
Publisher : Mathematics Program Study, Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v7i2.22136

Abstract

ABSTRAK. Analisis regresi linear adalah teknik statistika untuk membentuk model dalam menentukan hubungan sebab akibat antara dua variabel atau lebih, yaitu variabel bebas dan variabel terikat. Analisis ini dapat dilakukan pada semua data atau membagi nilai variabel bebas menjadi beberapa bagian, kemudian menerapkan analisis regresi pada setiap bagian (segmen) sehingga akan dipelajari lebih dari satu model dalam satu studi kasus. Pembagian nilai variabel x menjadi beberapa segmen ini kemudian disebut analisis regresi linear piecewise dan nilai x yang menjadi titik pembagian segmen disebut breakpoint.  Metode yang digunakan untuk memperkirakan nilai regresi linear dan parameter breakpoint adalah metode kuadrat terkecil. Kasus yang akan diteliti dalam penerapan regresi linear piecewise ini adalah hubungan inflasi dengan jumlah uang beredar di Indonesia pada 2014-2017.Kata kunci: piecewise, metode kuadrat terkecil, inflasi, jumlah uang beredar.               LINEAR REGRESSION ANALYSIS OF PIECEWISE TWO SEGMENTS USING ORDINARY LEAST SQUARE METHOD ABSTRACT. Linear regression analysis is a statistical technique to form a model in determining the causal relationship between two or more variables, namely the independent variable and dependent variable. This analysis can be done on all data or divide the value of free variable into several parts then apply regression analysis on each part (segment) therefore more than one model in one case study is studied. The division of the value of the variable x into some of these segments is then called the linear piecewise regression analysis and the value of x which becomes the point of division of the segment is called breakpoint. The method used to estimate  linear regression and breakpoint parameter values is the least squares method. The case to be investigated for the application of linear piecewise regression is the inflationary relationship with the money supply in Indonesia at 2014-2017.Keyword : piecewise, ordinary least squares method, inflation, money supply
PROGRAM APLIKASI PENGELOMPOKAN OBJEK DENGAN METODE SELF ORGANIZING MAP MENGGUNAKAN BAHASA R Siti Kania; Dewi Rachmatin; Jarnawi Afgani Dahlan
Jurnal EurekaMatika Vol 7, No 2 (2019): Jurnal EurekaMatika
Publisher : Mathematics Program Study, Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (252.183 KB) | DOI: 10.17509/jem.v7i2.22132

Abstract

ABSTRAKAnalisis klaster merupakan salah satu teknik analisis statistika multivariat yang bertujuan untuk mengalokasikan sekelompok objek pada suatu kelompok-kelompok yang saling bebas, yang disebut sebagai klaster, sehingga objek-objek di dalam satu kelompok homogen, sedangkan objek-objek di dalam kelompok yang berbeda heterogen. Pada penelitian ini, proses analisis klaster dilakukan dengan menggunakan metode Self Organizing Map. Self Organizing Map merupakan salah satu metode dalam jaringan syaraf tiruan yang menggunakan pembelajaran tak terawasi. Akan tetapi proses pengelompokan dengan metode Self Organizing Map memerlukan waktu yang cukup lama serta dapat terjadi kesalahan dalam perhitungannya apabila dilakukan secara manual, sehingga pada penelitian ini dibuat program aplikasi untuk proses pengelompokan objek dengan metode Self Organizing Map menggunakan bahasa pemrograman R. Output dari program aplikasi tersebut berupa proses clustering yang terdiri dari hasil perhitungan tiap iterasi dan hasil pengelompokan objek yang termuat dalam lembar kerja ‘Console’ pada software R. Setelah program aplikasi selesai dibuat, kemudian diaplikasikan pada data Indeks Pembangunan Manusia Provinsi Aceh tahun 2013. Dari program aplikasi tersebut dengan menentukan terlebih dahulu klaster yang akan dibentuk yaitu 4 klaster diperoleh jumlah anggota klaster ke 1,2,3, dan 4 secara berturut-turut adalah 10, 2, 1, dan 11.Kata kunci : Analisis Klaster, Self-Organizing Map, R  OBJECTGROUPING APPLICATION PROGRAM WITH SELF ORGANIZING MAP METHOD USING R ABSTRACTCluster analysis is one of multivariate analysis technique that is purposed to alocate a group of object of independent groups, the so-called cluster, so as to each object in a same group is homogeneous, whilst each object in different group is heterogeneous. In this research, the process of cluster analysis is conducted by employing Self Organizing Map method. Self Organizing Map is one of method in artificial neural network that use unsupervised learning. However, the process of grouping of Self Organizing Map took a long time and it can generate mistakes in its calculation if it is conducted manually, for that concern this research provides a program application for the grouping process with Self Organizing Map using R programming language. Output of the application program  is a clustering process that consist of calculation result for each iteration and object grouping result provided in Console worksheet in R software. After the application program is completely created, finally it is applied to data of Human Development Index of Aceh province in 2013. From the application program, by first determining the cluster to be formed ie 4 clusters obtained by the number of cluster members to 1,2,3 and 4 respectively are 10, 2, 1, and 11.Key Words: Cluster Analysis, Self-Organizing Map, R-language
Estimasi Harga Tanah Dengan Menggunakan Metode Universal Kriging (Studi Kasus Pada Kecamatan Taman Sari, Kota Pangkalpinang, Kepulauan Bangkabelitung) Desya Salwa Ramadhianti; Dewi Rachmatin; Husty Serviana Husain
Jurnal EurekaMatika Vol 8, No 2 (2020): Jurnal Eurekamatika
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.872 KB) | DOI: 10.17509/jem.v8i2.30741

Abstract

Pada penelitian ini dilakukan pengestimasian harga tanah dengan metode Universal Kriging. Metode Kriging memanfaatkan nilai spasial pada lokasi tersampel dan variogram untuk memprediksi nilai pada lokasi lain yang belum dan/atau tidak tersampel dimana nilai prediksi tersebut tergantung pada kedekatannya terhadap lokasi tersampel. Proses penghitungan estimasi dilakukan dengan bantuan software R menggunakan package sp dan gstat. Berdasarkan hasil penelitian, diperoleh informasi harga tanah yang tidak tersampel di sekitar lokasi yang sudah dilakukan survey lapangan yaitu sebanyak 186 titik hasil estimasi dengan harga tanah tertinggi yaitu sebesar Rp 2.251.604,1 /m² pada lokasi absis X 632564 meter dan ordinat Y 9764654 meter dengan variansi error sebesar 9,82E+17 dan nilai harga tanah terendah sebesar Rp 500.615,6 /m² pada lokasi absis X 623854 meter dan ordinat Y 9766992 meter dengan variansi error sebesar 1,20E+18.
PERAMALAN DATA RUNTUN WAKTU MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Yukeu S Febriani; Fitriani Agustina; Dewi Rachmatin
Jurnal EurekaMatika Vol 7, No 2 (2019): Jurnal EurekaMatika
Publisher : Mathematics Program Study, Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v7i2.22134

Abstract

ABSTRAK. PT. Asuransi Jiwasraya merupakan salah satu perusahaan asuransi jiwa yang ada di Indonesia. Pada setiap tahunnya, PT. Asuransi Jiwasraya mempunyai target premi. Keberadaan target premi ini, bertujuan untuk mencapai visi dan misi dari PT. Asuransi Jiwasraya. Karena PT. Asuransi Jiwasraya merupakan perusahaan milik negara, maka setiap tahunnya perusahaan harus memberikan kontribusi kepada negara. Oleh karena itu, PT Asuransi Jiwasraya harus meramalkan pendapatan premi pada setiap tahunnya. Pada artikel ini, akan dibahas mengenai peramalan jumlah premi PT Asuransi Jiwasraya dengan tujuan untuk memprediksi jumlah premi periode selanjutnya berdasarkan data jumlah premi menggunakan metode Adaptive Neuro Fuzzy Inference System (ANFIS). Metode ANFIS merupakan metode yang mengkombinasikan konsep Neural Network dan Fuzzy Logic. Ramalan yang tepat berguna untuk membuat kebijakan yang tepat agar tercapainya visi dan misi dari PT Asuransi Jiwasraya. Berdasarkan hasil analisis diketahui bahwa nilai MAPE untuk metode ANFIS sebesar 0.858820136%. Dengan demikian dapat dikatakan bahwa metode ANFIS sudah cukup baik digunakan untuk meramalkan jumlah premi PT. Asuransi Jiwasraya Cabang Bandung Timur.Kata kunci : Jumlah premi, ANFIS, MAPE               FORECASTING TIME DATA FORECASTING USING THE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM METHOD ABSTRACT. PT. Asuransi Jiwasraya is one of the life insurance companies in Indonesia. In each year, PT. Asuransi Jiwasraya has a premi target. The premi target is aimed to achieve the vision and mission of PT. Asuransi Jiwasraya. Since PT Asuransi Jiwasraya is a state-owned company, every year the company must contribute to the state. Therefore, PT Asuransi Jiwasraya must predict the premi income every year. This article will discuss about forecasting the amount of premi PT Asuransi Jiwasraya done to predict the amount of premi next period based on premi data amount using Adaptive Neuro Fuzzy Inference System (ANFIS) method. ANFIS method is a method that combines the concept of Neural Network and Fuzzy Logic. Precise prophecy is useful to make the right policy in order to achieve the vision and mission of PT Asuransi Jiwasraya. From the results that have been done obtained MAPE value of 0.858820136%. Thus it can be said that the ANFIS method is good enough to be used to predict the amount of premium PT. Asuransi Jiwasraya, East Bandung Branch.Keywords: Total Premi, ANFIS, MAPE
DESAIN GAME ONLINE MATEMATIKA MENGGUNAKAN HTML DAN FLASH DALAM PERKULIAHAN MULTIMEDIA PENDIDIKAN MATEMATIKA BERBANTUAN E-LEARNING Eyus Sudihartinih; Dewi Rachmatin
Jurnal Pendidikan (Teori dan Praktik) Vol 5 No 1 (2020): Volume 5, Nomor 1, April 2020
Publisher : Fakultas Ilmu Pendidikan Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (539.915 KB) | DOI: 10.26740/jp.v5n1.p%p

Abstract

This study aims to obtain a description of online mathematical game design using HTML and Flash in e-learning-assisted mathematics education multimedia lectures. This research is a pre-experimental type of one-shot case study. The research participants were a class of students consisting of 34 people (5 men and 29 women), 5th- semester students in multimedia mathematics education courses in the mathematics education department at one of the universities in Indonesia. The research instrument was a questionnaire through Google forms and interviews. In this study, triangulation was carried out using documentation, interviews, and theory. Based on the results of the study note that HTML and Flash can be used in the design of mathematical games even though students have not previously learned both but the results are good. The author suggests that students be able to try the math game at school during research or teaching practice. In addition, the authors hope the game can be published on the internet so that it can be used by many students.
Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung Salsabila Ayu Pratiwi; Dewi Rachmatin; Rini Marwati
KUBIK Vol 7, No 1 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v7i1.18156

Abstract

This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%.
Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung Salsabila Ayu Pratiwi; Dewi Rachmatin; Rini Marwati
KUBIK Vol 7, No 1 (2022): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v7i1.18156

Abstract

This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%.
Peramalan Jumlah Penderita DBD di Provinsi Jawa Barat dengan Metode Hybrid Sarimax-Ann Indriany Rahayu; Rini Marwati; Dewi Rachmatin
JMT : Jurnal Matematika dan Terapan Vol 4 No 2 (2022): JMT (Jurnal Matematika dan Terapan)
Publisher : Program Studi Matematika Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jmt.4.2.2

Abstract

Indonesia is one of the tropical countries in the world, therefore Indonesia has two seasons, namely the dry season and the rainy season. Because it has two seasons, it can cause tropical diseases that is growing very fast is Dengue Hemorrhagic Fever (DHF). DHF is time series data tha can be collected annually and has a seasonal cycle. Because it is time series data, it can be forecasted using SARIMAX method, but SARIMAX is only able to solve linear problems and to overcone non-linear prolblems it can be solved using the ANN Backpropagation method. Therefore, in this study using the Hybrid SARIMAX-ANN method. The data in this study contained the dependent variable and the independent variable. The dependent variable is DHF data, while the independent variable is air humidity, air temperature, and rainfall data. The result obtained in this study, namely the factor that greatly affects DHF is air humidity. Forecasting result form Januari 2021 to June 2021 are 1.081, 960, 1.132, 1.103, 2.467, and 1.605. the it produces a MAPE value of 16,33% which means a good level of accuracy.
Peramalan dan dekomposisi untuk mata uang kripto dengan model facebook prophet Rahman, Dany; Rachmatin, Dewi; Marwati, Rini
Majalah Ilmiah Matematika dan Statistika Vol. 24 No. 1 (2024): Majalah Ilmiah Matematika dan Statistika
Publisher : Jurusan Matematika FMIPA Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/mims.v24i1.39159

Abstract

Cryptocurrencies are becoming one of the hottest topics in Indonesia's society. One of those issues concerns investors who incur financial losses as a result of investing in crypto. The facebook Prophet model, one of the forecast models, can offer a solution to this problem. The Prophet model is built using four function. These variables are trend, seasonality, holidays, and additional regressions. The Prophet model benefits from a number of advantages, one of which is its ability to generate decomposition graphs. The decomposition may give analysts more insight into the data they are analyzing. The Prophet model is used to forecast and decompose the price of a cryptocurrenciy called Solana in this study. A multiplicative model with linear function as trend function, weekly seasonality, and daily seasonality as seasonality function is the best model for Solana price forecasting and decomposition. Additionally, hyperparameters in the model are tuned so the model won’t suffer underfitting or overfitting indications. The fitted Prophet model is good at forecasting as a result of the evaluation process. As a result of the forecast and decomposition, the forecasted value and the decomposition graph of the Solana exchange rate for one hour later show that the price of Solana will remain constant. Keywords: Cryptocurrency, time series, forecasting, decomposition, facebook prophetMSC2020: 62M10
Penyelesaian Multi Depot Vehicle Routing Problem with Time Windows Menggunakan Particle Swarm Optimization Algorithm Azzahra, Khairunnisa Aulia; Novianingsih, Khusnul; Rachmatin, Dewi
Jurnal EurekaMatika Vol 12, No 1 (2024): Jurnal EurekaMatika
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v12i1.69199

Abstract

AbstractThis research addresses the Multi Depot Vehicle Routing Problem with Time Windows (MDVRPTW), the problem of determining vehicle routes from several depots to multiple customers while considering time window constraints for each route. The goal of solving MDVRPTW is to obtain optimal routes with the shortest total travel time without exceeding their respective time windows. The Particle Swarm Optimization (PSO) algorithm is used to solve MDVRPTW, adapted from the social behavior of a flock of birds in search of food. The algorithm operates through initialization, evaluation, route construction, and route updates to achieve optimality. The research was tested on a case study involving raw material pickup for a company with 2 storage depots and 169 agents. The implementation of PSO successfully generated an average travel time of 7.83 hours for each route, indicating adherence to time windows and fulfillment of vehicle capacity.Keywords:Multi Depot Vehicle Routing Problem, Particle Swarm Optimization, Route, Time WindowsAbstrakPenelitian ini membahas Multi Depot Vehicle Routing Problem with Time Windows (MDVRPTW), masalah penentuan rute kendaraan dari sejumlah depot ke beberapa pelanggan dengan mempertimbangkan batasan time windows dalam setiap rutenya. Tujuan penyelesaian MDVRPTW adalah mendapatkan rute optimal dengan total travel time terkecil dan tidak melebihi time windows-nya. Algoritma Particle Swarm Optimization (PSO) digunakan untuk menyelesaikan MDVRPTW. Cara kerja PSO diadaptasi dari perilaku sosial dari sekawanan burung dalam mencari makan. Algoritma ini bekerja dengan cara melakukan inisialisasi, mengevaluasi, mengonstruksi rute, dan memperbaharui rute hingga optimal. Penelitian diuji pada studi kasus pengambilan bahan baku suatu perusahaan dengan 2 depot penyimpanan dan 169 agen. Implementasi PSO berhasil membentuk rata-rata travel time setiap rute adalah 7,83 jam yang artinya time windows tidak dilanggar dan kapasitas kendaraan terpenuhi.
Co-Authors Anisa Bella Fathia, Anisa Bella Atep Sujana Azzahra, Khairunnisa Aulia Budhiprameswari, Tania Dianda Desya Salwa Ramadhianti Dian Usdiyana Diva, Destiara Rahma Dwi Haryanto Elah Nurlaelah Entit puspita Eyus Sudihartinih Febrian Andi Hidayat Fitriani agustina Fophin, Theodore Dwivaland Gita Alfa, Alodya Ann Gita Novita Hafizha, Zahra Ridha Hakim, Rakhmat Nurul Herhyanto, Nar Husty Serviana Husain Ilyas, Isti Agustia Indra Hardiyana, Indra Indriany Rahayu Jarnawi Afgani Dahlan Jarnawi Afgani Dahlan Khusnul Novianingsih Lukman Lukman Muftiyah, Salma Nar Herrhyanto Pebriani, Dwi Putri Rahman, Dany Ramadany, Lisa Dewi Rini Marwati Sabila, Salma Salsabila Ayu Pratiwi Sari, Asyifa Anggun Sekar Wilujeng Sihombing, Rika Elizabet Siti Kania Sujana*, Atep Tia Purniati Titia Ningsih Yukeu S Febriani Yunus, Sitti Rahma