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All Journal Statistika MediaTor: Jurnal Komunikasi Jurnal Riset Statistika Pattimura Proceeding : Conference of Science and Technology Bandung Conference Series: Statistics
Suliadi Suliadi
Statistika, Universitas Islam Bandung
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Journal : Statistika

 1 
Kombinasi Regresi Tak Bias Ridge dengan Regresi Komponen Utama untuk Mengatasi Masalah Multikolinieritas Fitriana Novitasari; Suliadi Suliadi; Anneke Iswani A.
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 17, No 1 (2017)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v17i1.2713

Abstract

Metode kuadrat terkecil (MKT) merupakan salah satu metode penaksir parameter regresi. Dalam metode MKT terdapat salah satu asumsi yang harus dipenuhi yaitu tidak adanya multikolinieritas. Multikolinieritas merupakan hubungan linier antara sesama variabel bebas () yang akan berakibat pada ragam penduga koefisisen regresi menjadi besar sehingga menyebabkan selang kepercayaan untuk parameter regresi cenderung akan lebih lebar. Salah satu metode yang dapat digunakan untuk mengatasi multikolinieritas adalah regresi MCRR, dimana metode MCRR ini merupakan kombinasi antara regresi tak bias ridge (URR) dan regresi komponen utama. Makalah ini akan membahas penanganan multikolinieritas dengan menggunakan MCRR, sebagai bahan aplikasi akan digunakan data tingkat produksi Crude Palm Oil (CPO).
Deteksi Pencilan pada Model ARIMA dengan Bayesian Information Criterion (BIC) Termodifikasi Selma Yulistiani; Suliadi Suliadi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4740

Abstract

Time series data may be affected by special events or circumstances such as promotions, natural disasters, etc. These events can lead to inconsistent observations in the series called outliers. Because outliers can make invalid conclusions, it is important to carry out procedures in detecting outlier effects. In outlier detection there is one type of outlier, namely additive outlier (AO). The process of detecting additive outliers in the ARIMA model can be said as a model selection problem, where the candidate model assumes additive outliers at a certain time. In the selection of models there are criteria that must be considered in order to produce the best model. The good criteria for models selection  can use the Bayesian Information Criterion (BIC) derived by Schwarz (1978). Galeano and Pena (2011) proposed a modified Bayesian Information Criterion for model selection and detect potential outliers. The modified Bayesian Information Criterion for outlier detection will be applied to the data OutStanding Loan PT.Pegadaian Cimahi year 2013-2017. So that the best model is obtained that the model with adding 2 potential outliers with the ARIMA model (1.0,0), that outliers at observations 48, and 58 because it has a minimum BICUP value of 1064.95650.
PENDUGAAN SELANG KEPECERCAYAAN KOEFISIEN DETERMINASI Suliadi Suliadi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.904

Abstract

Dalam analisis regresi, koefisien determinasi merupakan suatu ukuran yang sangat penting. Koefisien ini mengukurkualitas model, yang menunjukkan seberapa jauh model yang kita peroleh dapat menerangkan data. Ada dua macamkoefisien determinasi, yaitu R2 dan 2adj R atau R2 terkoreksi. Permasalahannya adalah bahwa sebaran atau distribusi R2 atau2adj R tidak diketahui, sehingga tidak dapat ditentukan apakah R2 atau 2adj R kita berbeda atau tidak berbeda dengan nol.Tulisan ini membahas penentuan koefisien determinasi dengan menggunakan pendekatan boostrap, dan melakukansimulasi dengan data bangkitan dengan beberapa ukuran contoh (n) untuk beberapa nilai ragam sisaan.
Pengendalian Mutu Produk dengan Metode Pre-Control Suliadi Suliadi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 2, No 2 (2002)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v2i2.502

Abstract

Makalah ini membahas suatu metode dalam pengendalian mutu statistik, yang didasarkanpada batas-batas spesifikasi, yaitu Pre-Control. Metode ini sangat mudah untuk diterapkan karenahanya berpatokan pada batas-batas spesifikasi. Metode ini tidak ditujukan untuk memperbaikiproses, akan tetapi ditujukan agar proses memproduksi barang dalam batas-batas spesifikasi yangdiinginkan.
Statistical Process Control Vibrasi Bearing untuk Identifikasi Degradasi Riyani Desriawati; Sutawanir Darwis; Nusar Hajarisman; Suliadi Suliadi; Achmad Widodo
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 20, No 1 (2020)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v20i1.5298

Abstract

Statistical Process Control (SPC) is usually applied to  the production process of goods, with the aim of detecting the quality of a production item that is within or beyond the specified specifications. In this study, SPC was applied to the bearing vibration signal to detect the first observable defect on a machine that functions as part of a prognostic tool for maintenance decision making. The detection of damage and prognostic are two important aspects in machine maintenance based on current conditions or better known as Condition (data) Based Maintenance (CBM). This paper discusses the shewhart average level chart and adaptive shewhart average level chart to detect the first observable defect. The  shewhart chart is built with two assumptions, i.e. that the data must vary randomly around an established mean and follows a normal distribution. However, the adaptive Shewhart  chart there is no need for normal assumption. The exploration of our data shows that the assumption of normality is not fulfilled, so that the Shewhart average level chart is not implemented. The adaptive Shewhart  chart shows that the warning line for bearing 1 amounted to 5.547 and 3.631, for bearing 2 amounted to 5.491 and 3.635, for bearing 3 amounted to 5.762 and 3, 573, for bearing 4 of 5.604 and 33.615. The action line for bearing 1 is 6.026 and 3.152, for bearing 2 is 5.955 and 3.171, for bearing 3 is 6.309 and 3.026, for bearing 4 is 6.101 and 3.118. The first observable defect was t = 81 for bearing 1,  t = 146 for bearing 2,  t = 40 for bearing 3 and  t = 61 for bearing 4.  The adaptive Shewart chart can be used as a toll to estimate the initiation of transition state from normal to degenerate.
Peluang Kejadian Fuzzy (Fuzzy Event) Suliadi Suliadi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 1 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i1.811

Abstract

Tulisan ini membahas bagaimana peluang suatu kejadian (event), dimana kejadian tersebut merupakan kejadian fuzzy. Peluang kejadian fuzzy diperoleh melalui nilai harapan dari fungsikeanggotaan kejadian (himpunan) fuzzy yang bersangkutan.
Uji Kehomogenan Ragam Robust dengan SAS IML Suliadi Suliadi
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 2, No 1 (2002)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v2i1.494

Abstract

Makalah ini membahas penggunaan SAS IML untuk menguji hipotesis apakah ragam daribeberapa kelompok sampel homogen atau tidak. Makro SAS IML yang dibuat dapat digunakanuntuk Uji Bartlett's, dan dua uji yang bersifat robust yaitu Uji c2 dan Uji Jackknife.
Co-Authors Achmad Widodo Achmad Widodo Anneke Iswani A. Bambang Saiful Ma’arif Fithri Amalia Rahma Fitriana Novitasari Nusar Hajarisman Parihat, Parihat Riani Shifa Rahmadani Ridho Febriansyah Tambunan Riyani Desriawati Selma Yulistiani Seviyenti Fikroh Siti Nurhayati Sutawanir Darwis Sutawanir Darwis Umar Yusuf Weni Nuryati