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Jurnal Statistika Universitas Muhammadiyah Semarang
Published by Universitas Muhammadiyah Semarang
ISSN : 23383216     EISSN : 25281070     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Focus and Scope a. Statistika Teori, Statistika Komputasi, Statistika terapan b. Matematika Teori dan Aplikasi c. Design of Experiment
Arjuna Subject : Matematika - Pemodelan dan Simulasi
Articles 200 Documents
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MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA DURASI PROSES KELAHIRAN DENGAN TIES Triastuti Wuryandari; Danardono Danardono; Gunardi Gunardi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.47-55

Abstract

Survival data are usually found in the fields of health, insurance, epidemiology, demography, etc. Survival data is characterized by a response in the form of time, one example is the duration of the birth process. The duration of the birth process is thought to be influenced by several factors, including the baby's weight, baby's height, mother's age, gestational age, gender and the method used to birth process. One of the regression models for survival data is the Cox regression proportional hazard model. Parameter estimation in the Cox regression is based on partial likelihood. If two or more individuals have the same survival value, it is called ties. If there are ties, then the partial likelihood will have problems in determining the risk set, so it is necessary to modify the partial likelihood. Methods that can be used to overcome ties are the Breslow, Efron and Exact methods. This method is a modification of parameter estimation using maximum partial likelihood. Parameter estimation results are obtained by maximizing the partial likelihood function using Newton Raphson iteration. The case study in this paper is data on the duration of the birth process. The best model for the duration of the birth process with ties is the Exact method because it has the smallest AIC value
KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019) Amanah Saeroni; Memi Nor Hayati; Rito Goejantoro
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.88-94

Abstract

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analysis and comparing the accuracy of the K-NN method classification and Fisher discriminant analysis on the insurance customer premium payment status. The data used is the insurance customer data of PT. Prudential Life Samarinda in 2019 with current premium payment status or non-current premium payment status and four independent variables are age, duration of premium payment, income and premium payment amount. The results of the comparative measurement of accuracy from the two analyzes show that the K-NN method has a higher level of accuracy than Fisher discriminant analysis for the classification of insurance customers premium payment status. The results of misclassification using the APER (Apparent Error Rate) in K-NN method is 15% while in Fisher discriminant analysis is 30%.
PEMILIHAN MODEL REGRESI SPASIAL PADA TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA TENGAH Nurika Nidyashofa; Moh. Yamin Darsyah
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 1 (2020): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.1.2020.%p

Abstract

Unemployment is a condition where someone who is included in the labor force wants to get a job but has not yet got it. For most people, losing a job is a condition where a person's standard of living has decreased. In Central Java the Open Unemployment Rate is high as seen from the influence of the number of labor force, the last education completed and the Human Development Index (HDI). Regression is an analysis used to measure the presence or absence of a correlation or relationship between variables. A model that can explain the relationship between an area and its surroundings is a spatial model. In spatial regression there are six models, namely SAR, SEM, SDM, SDEM, SAC and SAC-Mixed. This study will compare the results of the spatial regression analysis model using a Queen Contiguity weight. The best model obtained is SAC Mixed by looking at the smallest AIC value, namely 61,111
Pendugaan Kemiskinan Menggunakan Small area Estimation dengan Pendekatan Emperical Best Linear Unbiased Prediction (EBLUP) Dini Gartina; Laelatul Khikmah
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.159-165

Abstract

Kemiskinan merupakan permasalahan yang berkaitan dengan berbagai aspek kehidupan manusia. Selama ini kemiskinan diduga menggunakan data Susenas yang diukur melalui pendekatan pengeluaran perkapita. Faktanya, objek yang disurvei pada Susenas ini hanyalah rumah tangga yang melakukan kegiatan ekonomi, sehingga memungkinkan jumlah sampel tidak mewakili karakteristik dari keseluruhan populasi. Jika data tersebut digunakan untuk menduga kemiskinan akan menghasilkan pendugaan yang bias dan varians yang besar karena jumlah sampel kecil kurang representatif untuk mewakili data. Upaya yang dapat dilakukan untuk menduga pada area kecil dengan menambah sampel, namun hal ini membutuhkan biaya yang banyak sehingga untuk mengatasi masalah tersebut yaitu dengan mengoptimalkan data yang tersedia dengan menggunakan small area estimation (SAE). Salah satu pendekatan yang dapat digunakan pada pendugaan area kecil yaitu dengan menggunakan pendekatan Emperical Best Linear Unbiased Prediction (EBLUP). Pada penelitian ini keakuratan dari penduga EBLUP dapat dievaluasi dengan Mean Square Error (MSE). Hasil penelitiannya penduga Emperical Best Linear Unbiased Prediction (EBLUP) lebih baik dibandingkan dengan pendugaan langsung. MSE penduga langsung lebih besar daripada MSE penduga tidak langsung. Nilai rata-rata MSE penduga langsung sebesar 0.005729 dan rata-rata MSE penduga EBLUP sebesar 0.002873.
ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE DETERMINISTIK DAN STOKASTIK Yuciana Wilandari; Gunardi Gunardi; Adhitya Ronnie Effendie
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.56-63

Abstract

The estimated of claims reserve has a very important in insurance companies, because it is the company's liability to policyholders in the future and can also result in the bankruptcy of the insurance company. In general, there are two methods for calculating claims reserves are the deterministic method (Chain Ladder and Bornhuetter Ferguson) and the stochastic method (Benktander Hovinen and Cape Cod). This article compares the two methods and determines the best method. Using the claim payments data that have been paid by an insurance company in Indonesia, the best method is the Benktander Hovinen method.
Optimasi Laju Pengerjaan Material AISI P20 Proses Electrical Discharge Machining (EDM) Sinking Menggunakan Metode Taguchi Farizi Rachman; Dhika Aditya Purnomo; Tri Andi Setiawan; Ridhani Anita Fajardini
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 2 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.8.2.2020.95-102

Abstract

Electrical Discharge Machining atau EDM merupakan proses permesinan non-konvensional. Sistem kerja EDM dengan memanfaatkan energi panas dari proses loncatan bunga api listrik (spark) akibat adanya perbedaan muatan di antara elektroda dan benda kerja yang terisi cairan dielektrik. EDM sinking secara digunakan untuk memproduksi mold dan dies. Salah satu material yang digunakan dalam industri pembuatan mold adalah AISI P20. Pada proses pembuatan mold menggunakanEDM sinking perlu memperhatikan laju pengerjaan material yang memiliki karakteristik kualitas semakin besar nilainya maka semakin baik. Nilai laju pengerjaan material yang besar dapat meminimalkan biaya produksi. Metode Taguchi digunakan dalam penelitian ini untuk menghasilkan nilai optimal laju pengerjaan material. Desain eksperimen menggunakan orthogonal array L18 untuk mengkombinasikan empat variabel proses yaitu jenis elektroda, gap voltage, on time, dan off time. Berdasarkan hasil analisa setting parameter untuk laju pengerjaan material yang optimal menggunakan tembaga, gap voltage 40 volt, on time 150 μs, dan off time 20 μs.
PENCARIAN KERNEL TERBAIK SUPPORT VECTOR REGRESSION PADA KASUS DATA KEMISKINAN DI INDONESIA DENGAN USER INTERFACE (GUI) MATLAB Muhammad Ghazali; Ramdani Purnamasari
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika
Publisher : Department Statistics, Faculty Mathematics and Natural Science, Universitas Muhammadiyah S

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.1-8

Abstract

Poverty is a topic that is often discussed in various scientific study forums. Facts on the ground show that increasing development has not been able to reduce the increasing number of poor people. Statistical studies on poverty data need to be carried out to assist the government in mapping policy-making patterns. One of the variables in mapping poverty data patterns is the Poverty Depth Index. The poverty depth index is a measure of the average gap in the distribution of each population's expenditure on the poverty line. Many factors affect the poverty depth index, especially from health, human resources and economic indicators. Therefore, a statistical modeling is needed to analyze the factors that affect the poverty depth index in Indonesia. The poverty data used in this study were sourced from the 2019 SUSENAS data in the form of data with individual observations of all provinces in Indonesia. Several previous studies using the Support Vector Regression (SVR) method to estimate the Poverty Depth Index as a response variable with several variables from health and economic indicators showed a very good level of model accuracy. However, SVR is constrained by choosing the right kernel to find the optimum prediction accuracy. So it is necessary to create a user interface that automatically selects the best type of kernel to facilitate the modeling process. The user interface will also help users to use the SVR even if they do not know the programming language. This study aims to: (1) produce a statistical analysis that makes it easier to map the pattern of factors that influence poverty in Indonesia, (2) produce a user interface that makes it easier for users to analyze poverty data in Indonesia. The conclusion obtained from this study is that the most accurate estimation is to use a degree 1 Gaussian kernel (RBF) SVR model while using the Polynomial kernel is not enough to provide a good estimate.
PEMODELAN PERSEPSI PEMBELAJARAN ONLINE MENGGUNAKAN LATENT DIRICHLET ALLOCATION Jerhi Wahyu Fernanda
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.79-85

Abstract

Latent Dirichlet Allocation (LDA) merupakan metode untuk pemodelan topik adalah yang didasarkan kepada konsep probabilitas untuk mencari kemiripan suatu dokumen dan mengelompokkan dokumen-dokumen menjadi beberapa topik atau kelompok.   Metode ini masuk dalam unsupervised learning karena tidak ada label atau target pada data yang dianalisis. Penelitian ini bertujuan untuk mengelompokkan persepsi tentang pembelajaran online ke dalam beberapa topik menggunakan metode LDA. Data penelitian ini adalah data primer yang dikumpulkan melalui formulir online. Hasil analisis menunjukkan bahwa pemodelan LDA menggunakan 6 topik memiliki coherence score paling besar. Hasil visualisasi data text menggunakan wordcloud didapatkan kata tidak memiliki frekuensi kemunculan terbesar. Penentuan jumlah topik yang optimal berdasarkan coherence score, didapatkan pemodelan LDA dengan 6 topik adalah yang paling optimal. secara garis besar terdapat beberapa kata yang saling beririsan dengan topik yang lain. Hasil pemodelan memberikan gambaran bahwa persepsi/pandangan mahasiswa terdapat pembelajaran online terkait pemahaman materi yang diberikan dosen, sinyal atau jaringan internet, kuota, dan tugas. Pada kata-kata terkait pemahaman materi, mahasiswa memberikan pandangan bahwa mereka tidak dapat memahami dengan baik materi yang diberikan oleh dosen.
PERAMALAN DENGAN METODE SARIMA PADA DATA INFLASI DAN IDENTIFIKASI TIPE OUTLIER (Studi Kasus: Data Inflasi Indonesia Tahun 2008-2014) Iin Fadliani; Ika Purnamasari; Wasono Wasono
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.109-116

Abstract

Inflation is defined as rising prices of goods in general and continuously. The effect of inflation on the economy can cause the currency to decline, resulting in the country's economic power becoming weak. Time series data is data arranged in order of time or data collected over time. Changes in the inflation rate tend to make inflation data unstable and affect the forecasting process in the time series data. The method used in this study is the seasonal autoregressive integrated moving (SARIMA) method to predict the time series in one or two periods ahead. This study also used outlier identifiers on models that still have outlier tendencies in residuals. The forecasting results of the SARIMA method become inaccurate when residual data contains outliers. The presence of outlier data in residual data results in residuals is not a normal distribution. The method used obtained the best model results, namely the SARIMA model (0,1,1) (0,1,1)12 with inflation forecast value for January to May 2015 is in the range of 5-6 %. On SARIMA models (0,1,1) (1,1,1)12 and SARIMA models (1,1,0) (2,1,0)12 outliers are detected in residual are Additive Outlier (AO) and Temporary Change (TC) type.
DETERMINAN PENGANGGURAN TERDIDIK DI PROVINSI NUSA TENGGARA TIMUR (NTT) TAHUN 2018 MENGGUNAKAN REGRESI LOGISTIK BINER Maria Valentina Makung; Ristanto Hadi; Yohana Rosaripatria; Siskarossa Ika Oktora
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 2 (2021): Jurnal Statistika
Publisher : Department Statistics, Faculty Mathematics and Natural Science, Universitas Muhammadiyah S

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.2.2021.64-78

Abstract

Pengangguran telah menjadi masalah serius yang harus diselesaikan di NTT. Tingkat pengangguran di NTT telah meningkat dari tahun ke tahun. Ironisnya, tingkat pengangguran ini didominasi oleh pengangguran terdidik. Berdasarkan data dari BPS, tingkat pengangguran di NTT tahun 2018 cenderung menurun, tetapi pengangguran yang berpendidikan meningkat. Dengan menggunakan data Sakernas Agustus 2018, penelitian ini bertujuan untuk melihat faktor-faktor yang mempengaruhi pengangguran terdidik di NTT yang dilihat berdasarkan karakteristik individualnya. Karakteristik individu yang digunakan adalah jenis kelamin, usia, status rumah tangga, status perkawinan, pelatihan kerja, pengalaman kerja, dan daerah tempat tinggal. Metode analisis yang digunakan dalam penelitian ini adalah analisis deskriptif dan regresi logistik biner. Hasil analisis deskriptif menunjukkan karakteristik tenaga kerja terdidik di NTT tahun 2018 mayoritas memiliki usia 15-24 tahun, jenis kelamin perempuan, tidak berstatus kepala rumah tangga, tidak pernah menikah, tidak pernah berpartisipasi dalam pelatihan kerja, memiliki pengalaman kerja dan tinggal di wilayah perkotaan. Hasil inferensia menunjukkan bahwa variabel yang mempengaruhi pengangguran berpendidikan adalah variabel umur, status rumah tangga, status perkawinan, pengalaman kerja dan daerah tempat tinggal. Sementara jenis kelamin dan pelatihan kerja tidak memengaruhi tenaga kerja terdidik untuk menjadi pengangguran. Hasil penelitian ini sangat penting bagi pemerintah NTT untuk mengambil kebijakan yang tepat untuk menyelesaikan pengangguran terdidik.

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