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Suliadi
Prodi Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Islam Bandung, Indonesia
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Diagram Kendali Triple Homogeneously Weighted Moving Average dan Penerapannya pada Proses Plating Aksesori Mobil PT. XYZ Fairuza Amanda Belia; Suliadi Suliadi
Bandung Conference Series: Statistics Vol. 3 No. 2 (2023): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v3i2.8892

Abstract

Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are of 10.691 dan of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled. Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are of 10.691 dan of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled.
Diagram Kendali Nonparamterik Composite Exponentially Weighted Moving Average (CEWMA) Sign dalam Proses Plating Aksesori Mobil PT. XYZ Firda Balqisa Ahmad; Suliadi
Bandung Conference Series: Statistics Vol. 3 No. 2 (2023): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v3i2.9054

Abstract

Abstract. Control charts are one of the tools in Statistical Process Control (SPC) that are graphically used to control the production process. The first control chart to exist was the Shewhart control chart, but the control chart is not sensitive in detecting small shifts. So the alternative is the EWMA control chart which is able to detect small shifts, but there is still a problem because this diagram has the assumption that the observation process is normally distributed. Then a Nonparametric EWMA Sign control chart is proposed which can detect small shifts when the data conditions are not normally distributed. Furthermore, the combination of Shewhart and EWMA statistics has been done which aims to improve the accuracy of the diagram. Combining two EWMA statistics has also been done but assuming the distribution of quality characteristics is normal. Raza et al., 2022 proposed the CEWMA Sign Nonparametric control diagram method by combining two EWMA statistics to increase the sensitivity of the diagram. This control chart is more flexible to use because the parameter value λ in both EWMA statistics can be different ( and ). In this thesis we apply the CEWMA Sign Nonparametric control chart to the data of the car accessory plating process at PT. XYZ which is a final or finishing process that has objectives including to beautify the appearance and protect a metal from interaction with the environment. Abstrak. Diagram kendali merupakan salah satu alat dalam Statistical Process Control (SPC) yang secara grafis digunakan untuk mengontrol proses produksi. Diagram kendali yang pertama kali ada yaitu diagram kendali Shewhart, tetapi diagram kendali tersebut tidak sensitif dalam mendeteksi pergeseran yang kecil. Sehingga alternatifnya yaitu diagram kendali EWMA yang mampu mendeteksi pergeseran yang kecil, tetapi masih ada masalah karena diagram ini memiliki asumsi bahwa proses pengamatan berdistribusi normal. Kemudian diusulkanlah diagram kendali Nonparametrik EWMA Sign yang dapat mendeteksi pergeseran kecil saat kondisi data tidak berdistribusi normal. Selanjutnya penggabungan statistik Shewhart dan EWMA pernah dilakukan yang bertujuan untuk meningkatkan akurasi diagram. Penggabungan dua statistik EWMA juga pernah dilakukan tetapi dengan asumsi distribusi karakteristik kualitasnya normal. Raza et al., 2022 mengajukan metode diagram kendali Nonparametrik CEWMA Sign dengan menggabungkan dua statistik EWMA untuk meningkatkan sensitivitas diagram. Diagram kendali ini lebih fleksibel untuk digunakan karena nilai parameter pada kedua statistik EWMA dapat berbeda ( dan ). Dalam skripsi ini kami menerapkan diagram kendali Nonparametrik CEWMA Sign pada data proses plating aksesori mobil pada PT. XYZ yang merupakan proses akhir atau finishing yang memiliki tujuan diantaranya adalah untuk memperindah tampilan dan melindungi suatu logam dari interaksi dengan lingkungan.
Monte Carlo Based Sampling Distribution of Annual Rate of Exceedance for Earthquake Insurance Darwis, Sutawanir; Hajarisman, Nusar; Suliadi; Widodo, Achmad; Diahsty Marasabessy, Munira; Arya Ramadhan, Iqbal
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.3173

Abstract

ABSTRACT Seismic hazard expressed in terms of annual rate of exceedance and is used to calculate the earthquake insurance premium. Annual rate of exceedance is a complicated function of magnitudes, distances from site to earthquake sources and attenuation. Due to its complexity, determination of exact sampling distribution of earthquake insurance premium is not an easy task. This research proposes Monte Carlo simulation approach to determine the sampling distribution of earthquake insurance premium. Annual rate of exceedance was simulated first and then the insurance premium calculated based on simulation of annual rate of exceedance. The simulation involves quantifying synthetic catalogue similar to historical catalogue. Its simulation is conducted in order to construct annual rate of exceedance as an indicator of earthquake risk used in earthquake insurance. The simulation of 25 iteration and sample size of 100 shows that the sampling distribution of insurance premium is skewed to the right. The idea of using Monte Carlo simulation to study the sampling distribution of annual rate of exceedance is the originality of the study and the study contributes to the methodology of earthquake insurance.
Diagram Kendali Wilcoxon Triple Exponential Weighted Moving Average dengan IMFIR dalam Pengendalian Kualitas Produksi Celana PT. XYZ 10060119138, Irsan Arisandi; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.10448

Abstract

Abstract. Control diagrams are Statistical Process Control which are used to control the production process. In the case of control charts, there are parametric and non-parametric control charts. One of the SPCs is the EWMA control chart which is capable of detecting small shifts, assuming the data is normally distributed. Then the Wilcoxon EWMA nonparametric control diagram was proposed which can detect small shifts with data that is not normally distributed, then W EWMA was developed into Wilcoxon TEWMA so that it can detect smaller shifts by carrying out the shifting process three times. Control charts have a common problem, namely that shifts at the beginning of time are not quickly detected. To overcome this weakness, Fast Initial Response was implemented to increase sensitivity in the initial period, Letshedi et al. (2021) proposed this feature by developing FIR, becoming IMFIR which is able to produce better out of control performance than the previous feature. In this research, we apply the W TEWMA control chart with IMFIR to the waist circumference data of PT cargo pants production. XYZ. The results of the nonparametric Shewhart control diagram in phase I showed that there were no observation points that were outside the control limits. Next, in phase II, using the W TEWMA IMFIR control diagram with the values λ=0.5, L=2.937, f=0.5 and a=0.3, the W TEWMA control diagram with IMFIR is statistically uncontrolled. Abstrak. Diagram kendali merupakan Statistic Process Control yang digunakan untuk mengontrol proses produksi. Pada kasus diagram kendali terdapat diagram kendali parametrik dan nonparametrik, Salah satu SPC yaitu diagram kendali EWMA yang mampu mendeteksi pergeseran yang kecil, dengan asumsi data berdistribusi normal. Kemudian diusulkanlah diagram kendali nonparametrik Wilcoxon EWMA yang dapat mendeteksi pergeseran kecil dengan data yang tidak berdistribusi normal, lalu W EWMA dikembangkan menjadi Wilcoxon TEWMA sehingga dapat mendeteksi pergeseran lebih kecil dengan melakukan proses pergeseran sebanyak tiga kali. Diagram kendali memiliki permasalahan umum yaitu pergeseran pada awal waktu tidak cepat terdeteksi. Untuk mengatasi kelemahan tersebut, diterapkanlah Fast Initial Response untuk meningkatkan kepekaan pada periode awal, Letshedi et al. (2021) mengusulkan fitur ini dengan mengembangkan FIR, menjadi IMFIR yang mampu menghasilkan kinerja out of control lebih baik dari fitur yang sebelumnya. Dalam penelitian ini menerapkan diagram kendali W TEWMA dengan IMFIR pada data panjang lingkar pinggang produksi celana cargo PT. XYZ. Hasil diagram kendali Shewhart nonparametrik pada fase I, diperoleh bahwa tidak ada titik pengamatan yang berada diluar batas kendali. Selanjutnya pada fase II, menggunakan diagram kendali W TEWMA IMFIR dengan nilai , , dan diperoleh diagram kendali W TEWMA dengan IMFIR tidak terkendali secara statistik.
Regresi Ridge Menggunakan Metode Dorugade untuk Memodelkan Kemisikinan di Jawa Barat 10060119083, Dhea Hely Yulianti; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.10673

Abstract

Abstract. Linear regression analysis is a statistical method for forming a relationship model between a dependent variable and one or more independent variables. There are several assumptions that must be met in multiple linear regression, one of which is multicollinearity. One method to overcome multicollinearity problems is to use the ridge regression method. Dorugade (2016) developed a ridge parameter estimator for regression models based on the average MSE value which resulted in the Dorugade parameter estimator being able to overcome multicollinearity problems. This research applies that method to model the level of poverty in districts/cities in West Java in 2022 with the dependent variable poverty level and the independent variables gross regional domestic product, open unemployment, number of labor force, human development index, and average years of schooling. It was found that there was a multicollinearity problem and ridge regression using the Dorugade method was used. Obtained bias constant , the ridge equation model and the equation model . It is obtained that (number of labor force) is significan with negative effect on the poverty level. Abstrak. Analisis regresi linier adalah suatu metode statistika untuk membentuk model hubungan antara variabel dependent dengan satu atau lebih variabel independent. Ada beberapa asumsi yang harus terpenuhi dalam regresi linier berganda salah satunya adalah multikolinearitas. Salah satu metode untuk mengatasi masalah multikolinieritas adalah menggunakan metode regresi ridge. Dorugade (2016) mengembangkan estimator parameter Ridge pada model regresi berdasarkan nilai rata-rata MSE yang menghasilkan estimator parameter Dorugade mampu mengatasi masalah multikolinieritas. Skripsi ini menerapkan metode tersebut untuk memodelkan tingkat kemiskinan di Kabupaten/Kota di Jawa Barat Tahun 2022 dengan variabel dependent tingkat kemiskinan dan variabel independent produk domestik regional bruto, pengangguran terbuka, jumlah angkatan kerja, indeks pembangunan manusia, dan rata-rata lama sekolah. Pada pemeriksaan asumsi, diperoleh ada masalah multikolinieritas sehingga digunakan regresi ridge dengan metode dorugade. Diperoleh tetapan bias . Sehingga model persamaan ridge dan model persamaan . Diperoleh (jumlah angkatan kerja) yang signifikan. Dimana jumlah angkatan kerja berpengaruh negatif terhadap tingkat kemiskinan.
Pengaruh Pertumbuhan Ekonomi, Kemiskinan, Indeks Pembangunan Manusia (IPM) dan Pengangguran terhadap Ketimpangan Pendapatan Kabupaten/Kota di Pulau Kalimantan Tahun 2022 10060116099, Laras Sintya Noorachmadan; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.10784

Abstract

Abstract. Income inequality is still a problem in Indonesian economic development. Particularly Borneo Island still faces this problem among regions. This study aims to analyze the effect of several factors such as Economic Growth Rate (, Poverty Level (, Human Development Index (HDI) (, and Open Unemployment Rate ( to income inequality, Gini Ratio ( of Regency/City in Borneo Island. Multiple linear regression analysis was used to understand the complex relationship between those factors and the Gini index to see the influence of existing factors on income inequality. The result showed that economic growth rate and unemployment have a negative effect on income inequality of districts/cities in Borneo Island in 2022, at the same time HDI and poverty have a positive effect. The regression model obtained was dengan 30,1%. Abstrak. Di Indonesia, terdapat ketimpangan pendapatan yang masih menjadi masalah dalam pembangunan ekonomi. Pulau Kalimantan khususnya masih menghadapi ketidakmerataan pendapatan antarwilayah. Penelitian ini bertujuan untuk menganalisis Faktor-faktor seperti Laju Pertumbuhan Ekonomi (, Tingkat Kemiskinan (, Indeks Pembangunan Manusia (IPM) (, dan Tingkat Pengangguran Terbuka ( terhadap ketimpangan pendapatan, Gini Ratio (Kabupaten/Kota di Pulau Kalimantan. Analisis regresi linier berganda digunakan untuk memahami hubungan kompleks antara faktor-faktor dengan indeks gini untuk melihat pengaruh dari faktor-faktor yang ada terhadap ketimpangan pendapatan. Hasil penelitian menunjukan bahwa laju pertumbuhan ekonomi dan pengangguran berpengaruh negatif terhadap ketimpangan pendapatan Kabupaten/Kota di Pulau Kalimantan, pada saat yang sama IPM dan kemiskinan berpengaruh positif terhadap ketimpangan pendapatan Kabupaten/Kota di Pulau Kalimantan tahun 2022. Model regresi yang diperoleh adalah dengan 30,1%.
Pemodelan Jumlah Kematian Ibu dan Bayi di Kota Bandung pada Tahun 2021 dengan Menggunakan Regresi Bivariate Poisson Inverse Gaussian 10060119110, Azka Aulia Ariazetra; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.10791

Abstract

Abstract. Poisson Regression is one of the regression models that involves count data with the assumption of equidispersion, meaning that the mean and variance have the same value. However, in practice, Poisson regression methods often encounter the problem of overdispersion, where the variance is greater than the mean. To address overdispersion, the mixed Poisson distribution is employed. Bivariate Poisson Inverse Gaussian regression is suitable for modeling paired count data that experiences overdispersion. The data used in this thesis consists of the number of maternal and infant deaths. Maternal and infant deaths are two interconnected aspects because they relate to the nutritional and health status of the infant during the mother's pregnancy. Therefore, both variables can be modeled using a bivariate distribution. With five predictor variables: the percentage of K1 visits during pregnancy (X1), the percentage of K4 visits by pregnant women (X2), the percentage of pregnant women receiving Fe+ tablets (X3), the percentage of obstetric complication management (X4), and the percentage of mothers and infants receiving Vitamin A (X5). This study utilized data from the health profile of Bandung City in 2021. In this thesis, I will apply Bivariate Poisson Inverse Gaussian regression to the data on maternal and infant deaths in Bandung City in 2021. Abstrak. Regresi Poisson merupakan salah satu dari regresi yang dapat memodelkan data cacahan dengan memiliki asumsi equidispersion yaitu nilai rata-rata dan varians memiliki nilai yang sama. Namun dalam prakteknya metode regresi poisson sering terjadi masalah overdispersi yaitu nilai varians lebih besar daripada rata-rata. Untuk mengatasi data overdispersi yaitu dengan menggunakan distribusi mixed poisson. Regresi Bivariate Poisson Inverse Gaussian cocok untuk pemodelan data cacahan berpasangan dengan mengalami overdispersi. Data yang digunakan dalam skripsi ini adalah data jumlah kematian ibu dan kematian bayi. Kematian ibu dan kematian bayi termasuk dua hal yang saling berhubungan karena dari status gizi dan kesehatan bayi pada masa kandungan ibu. Oleh karena itu kedua variabel tersebut dapat dimodelkan menggunakan distribusi bivariate. Dengan terdapat 5 variabel prediktor yaitu persentase kunjungan K1 pada ibu hamil (X1), persentase kunjungan K4 ibu hamil (X2), persentase ibu hamil mendapatkan tablet Fe+ (X3), persentase penanganan komplikasi kebidanan (X4), dan persentase ibu dan bayi mendapatkan Vitamin A (X5). Penelitian ini mengambil data dari profil kesehatan Kota Bandung pada tahun 2021. Pada skripsi ini saya akan menerapkan regresi Bivariate Poisson Inverse Gaussian pada data kematian ibu dan kematian bayi di Kota Bandung pada tahun 2021.
Kekuatan Hubungan Rata-Rata Lama Sekolah dan Indeks Keparahan Kemiskinan dari Kabupaten/Kota di Indonesia Tahun 2022 Menggunakan Metode Taba (T) 10060119007, Khalda Farah Kamila; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.11036

Abstract

Abstract. Correlation analysis is a technique used to determine the strength of relationship between two quantitative variables (numerical values). A common correlation used for numerical data is Pearson's Product Moment correlation. It is suitable for data from bivariate normal distribution without outlier. Tabatabai, et al (2021) proposed a new correlation method that is robust to outliers, namely Taba (T), TabWil (TW), and TabWil rank (TWR). Taba (T) and Tabwill (TW) correlations measure the linear association between two continuous or ordinal variables, while the TabWil rank (TWR) measures the monotonic association. The new correlation method is very competitive compared to Pearson and Spearman, and Quadrant, Median, and Minimum Coavariance Determinant (MCD) measures. Spearman, Quadrant, Median, and MCD correlations have limitations in dealing with outliers and normality deviations in the data. This study applied Taba correlation (T) to masure the strength of the relationship between the average years of schooling variable and the poverty severity index in districts / cities in Indonesia in 2022, since there were outliers on the data. It was found that the strength of the relationship between both variables was negative and very low, with a value of rTaba(X,Y) = -0.2584861. Abstrak. Analisis korelasi adalah teknik yang digunakan untuk menentukan kekuatan hubungan antara dua variabel kuantitatif (nilai numerik). Korelasi yang umum digunakan untuk data numerik adalah korelasi Product Moment Pearson. Korelasi ini cocok untuk data yang berdistribusi normal bivariat tanpa pencilan. Tabatabai, dkk (2021) mengusulkan metode korelasi baru yang robust terhadap pencilan, yaitu Taba (T), TabWil (TW), dan TabWil rank (TWR). Korelasi Taba (T) dan TabWil (TW) mengukur hubungan linier antara dua variabel kontinu atau ordinal, sedangkan peringkat TabWil (TWR) mengukur hubungan monotonik. Metode korelasi baru ini sangat kompetitif dibandingkan dengan Pearson dan Spearman, serta ukuran Kuadran, Median, dan Minimum Coavariance Determinant (MCD). Korelasi Spearman, Kuadran, Median, dan MCD memiliki keterbatasan dalam menangani pencilan dan penyimpangan normalitas pada data. Penelitian ini menggunakan korelasi Taba (T) untuk mengukur kekuatan hubungan antara variabel rata-rata lama sekolah dengan indeks keparahan kemiskinan di kabupaten/kota di Indonesia pada tahun 2022, karena terdapat pencilan pada data. Ditemukan bahwa kekuatan hubungan antara kedua variabel tersebut adalah negatif dan sangat rendah, dengan nilai rTaba(X,Y) = -0,2584861.
Diagram Kendali Nonparametrik Sintetis Exponentially Weighted Moving Average dalam Memantau Rata-Rata Proses dan Penerapannya pada Data Pengukuran Core 4st PT. ABCD 10060119005, Shelly Tri Yuliyanti; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.11049

Abstract

Abstract. Control chart is a tools in statistical process control (SPC) used to monitor whether the the production process is stable or not. Control chrat was first developed by Shewhart. However, the Swehart type chart is not sensitive in detecting small shifts and the alternative is the EWMA control chart which is capable of detecting small shifts, but this chart assumming the observation process comes from normal distribution. The altervative is nonparametric EWMA Sign control chart that is appropriate for non normal data. To increase its power, Haq et al. (2018) proposed a synthetic chart that combines EWMA chart and conforming run length (CRL) sub-chart, called SynEWMA chart. This chart has better performance compared to control charts in general. This research applied the SynEWMA nonparametric control diagram to the core 4st data. Core 4st is one of the components in the motor starter dynamo and starter coil whose function is to strengthen the magnetic field and act as a fastener for the motor frame mount. In the phase I, it used non-parametric Shewhart control chart, it was obtained the mean process is µ0 = 485150. Using the SynEWMA control chart in phase II and L=10, a value of k = 1,4679, the control limits obtained were as follows: UCL= 0.8380 and LCL=0.7854. There were several points outside of the control limits or nonconforming. From these points, by comparing them with the L value, it can be concluded that there were out of control processes in observations 2, 3, 4, 5, 18, and 19. Abstrak. Diagram kendali merupakan suatu alat dalam pengendalian proses statistik (SPC) yang digunakan untuk memantau apakah suatu proses produksi stabil atau tidak. Diagram kendali pertama kali dikembangkan oleh Shewhart. Namun diagram kendali tipe Swehart kurang sensitif dalam mendeteksi pergeseran kecil dan alternatifnya adalah peta kendali EWMA yang mampu mendeteksi pergeseran kecil, namun diagram kendali ini mengasumsikan proses pengamatan berasal dari distribusi normal. Alternatifnya adalah diagram kendali Tanda EWMA nonparametrik yang sesuai untuk data non normal. Untuk meningkatkan kekuatannya, Haq et al. (2018) mengusulkan diagram kendali sintetis yang menggabungkan diagram kendali EWMA dan sub-grafik run length (CRL) yang sesuai, yang disebut diagram kendali SynEWMA. Grafik ini mempunyai kinerja yang lebih baik dibandingkan dengan diagram kendali pada umumnya. Penelitian ini menerapkan diagram kendali nonparametrik SynEWMA pada data core 4st. Core 4st merupakan salah satu komponen pada dinamo starter motor dan koil starter yang fungsinya untuk memperkuat medan magnet dan berfungsi sebagai pengikat dudukan rangka motor. Pada tahap I menggunakan diagram kendali Shewhart non parametrik diperoleh rata-rata proses µ = 4,8515. Dengan menggunakan diagram kendali SynEWMA tahap II dan L=10 diperoleh nilai k = 1,4679 batas kendali diperoleh sebagai berikut: BKA=0.8380 dan BKB=0.7854. Terdapat beberapa titik di luar batas kendali atau tidak sesuai. Dari poin-poin tersebut, jika dibandingkan dengan nilai L maka dapat disimpulkan bahwa terjadi proses diluar kendali pada pengamatan ke-2, 3, 4, 5, 18, dan 19.
Hubungan antara Pergerakan Beberapa Indeks Harga Saham di Indonesia Menggunakan Metode Statistik Gerber 10060119010, Resty Utami Aprillia; Suliadi
Bandung Conference Series: Statistics Vol. 4 No. 1 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i1.11506

Abstract

Abstract. Correlation analysis is a statistical analysis used to measure the strength reslationship between two variables. For the non-parametric one, Kendall τ is suitable when there are outliers or at least ordinal/ranking scale data or for non normal data. The Gerber statistic is similar to Kendall τ, introduced by Gerber, et al., (2021) which is suitable for looking at the strength of relationships of variables that move quickly over time such as hours or days as in stock data. This statistic measures the correlation between two variables as the ratio of the difference between the number of concordant and discordant pairs. The Gerber statistic has robust properties that are not sensitive large movements (outlier) and departure from normality. It also ignores the small movement, since it can be only a noise (s). Hence, it is useful to measure the strength of relationship of rapid and extreme movement of two variables. This study applied the Gerber statistics to stock price index return data to measure the strength of the relationship between the movements of several stock price index returns, namely the Composite Stock Price Index (CSPI), LQ45 Index, IDX30 Index, Kompas100 Index, BISNIS-27 Index, Investor33 Index, and MNC36 Index for the period 2018-2022. It is found that the strength of the relationship between the movements of several stock price index returns is generally positive with a significant strength of the relationship. Abstrak. Analisis korelasi adalah analisis statistik yang digunakan untuk mengukur kekuatan hubungan antara dua variabel. Untuk non-parametrik, Kendall τ cocok digunakan ketika terdapat outlier atau minimal data berskala ordinal/ranking atau data tidak berdistrubusi normal. Statistik Gerber mirip dengan Kendall τ, diperkenalkan oleh Gerber, dkk., (2021) yang cocok untuk melihat kekuatan hubungan variabel yang bergerak cepat dari waktu ke waktu seperti jam atau hari seperti pada data saham. Statistik ini mengukur korelasi antara dua variabel sebagai rasio perbedaan antara jumlah pasangan konkordandan diskordan. Statistik Gerber memiliki sifat kuat yang tidak sensitif terhadap pergerakan besar (outlier) dan penyimpangan dari normalitas. Statistik ini juga mengabaikan pergerakan kecil, karena hal tersebut hanya berupa noise. Oleh karena itu, statistik ini berguna untuk mengukur kekuatan hubungan pergerakan cepat dan ekstrem dari dua variabel.Penelitian ini mengaplikasikan Statistik Gerber pada data return indeks harga saham untuk mengukur kekuatan hubungan antara pergerakan beberapa indeks harga saham, yaitu Indeks Harga Saham Gabungan (IHSG), Indeks LQ45, Indeks IDX30, Indeks Kompas100, Indeks BISNIS-27, Indeks Investor33, dan Indeks MNC36 periode 2018-2022. Hasil penelitian menunjukkan bahwa kekuatan hubungan antara pergerakan beberapa return indeks harga saham secara umum adalah positif dengan kekuatan hubungan yang signifikan.
Co-Authors 10060116099, Laras Sintya Noorachmadan 10060119005, Shelly Tri Yuliyanti 10060119007, Khalda Farah Kamila 10060119010, Resty Utami Aprillia 10060119083, Dhea Hely Yulianti 10060119110, Azka Aulia Ariazetra 10060119131, Alvina Damayanti Fauzia 10060119138, Irsan Arisandi 10060120068, Nabilah Disya Arviani Achmad Widodo Achmad Widodo Achmad Widodo Ahmad Arif Nurrahman Aisha Kusuma Putri Anneke Iswani A. Arya Ramadhan, Iqbal Aulia Athhar Zakiyyan Aulia, Githa Bambang Saiful Ma’arif Bella Novia Syabandina Dea Yustia Diningrum Deri Dzikria Khofiyandi Dewi Shofi Mulyati Diahsty Marasabessy, Munira Fairuza Amanda Belia Fathin Husna Fadhilah Fatiha nurfauzan, Arsyi Fikroh, Seviyenti Firda Balqisa Ahmad Firman Aji Utama Fithri Amalia Rahma Fitriana Novitasari Ghea Putri Amalia P Herdian Syah Hudallah Indah Lestari Laila Budianti Nadya Yuliyani Nailil Mala Candra Nirwan Nusar Hajarisman Parihat, Parihat Rahmania Marwah Darmawan Rejeki Wulan Islamiyati Riani Shifa Rahmadani Ridho Febriansyah Tambunan Riyani Desriawati Saquila Beninurhadi Putri Selma Yulistiani Shafira Nur Fauziah Shania Wilda Fitris Silmi Nur Husnayaini Siti Nurhayati Sutawanir Darwis Sutawanir Darwis Tasyah Syarmilati Thania Indra Mutiara Trianto Syahbannu Prayoga Tridayanti Cahya Meiland Tridayanti Cahya Meiland Umar Yusuf Weni Nuryati Wilda Riani Hafsah Daulay