Pengolahan Data Penelitian Lengkap (3 bulan)

Course Content

Smart PLS

Landasan kuantitatif dan pengenalan Smart PLS

10:00
MATERI, DATA, DAN SOFTWARE CRACK

00:00
Bab 2 – Konsep Partial Least Square : Materi

10:00
Bab 3 – Mempersiapkan data menginput data [Materi]

10:00
Bab 4 – Membuat model PLS-SEM [Materi]

05:00
Bab 5 – Evaluasi PLS-SEM Algorithm [Materi]

10:00
[Praktik] Bab 5 – Evaluasi PLS-SEM Algorithm

10:00
Bab 6 – Evaluasi PLS-SEM Bootstrapping [Materi]

10:00
[Praktik] Bab 6 – Evaluasi PLS-SEM Bootstrapping

10:00
Bab 7 – Menguji variable intervening [Materi]

10:00
[Praktik] Bab 7 – Menguji variable intervening

08:00
Bab 8 – Menguji variable moderating [Materi]

02:00
[Praktik] Bab 8 – Menguji variable moderating

07:00
Bab 9 – Importance-Performance Map Analysis [Materi]

02:00
[Praktik] Bab 9 – Importance-Performance Map Analysis

08:00
Bab 10 – Higher order construct [Materi]

02:00
[Praktik] Bab 10 – Higher order construct

10:00
[Praktik] Bab 11 – Multigroup Analysis

09:00
Bab 12 – Membuat laporan PLS-SEM [Materi]

00:00
[Praktik] Bab 12 – Membuat laporan PLS-SEM

11:00

AMOS

Bab 1 – Introduction to Structural Equation Modeling (SEM)

10:00
The Advantages of SEM Compared to OLS

10:00
Converting Regression Models into AMOS Graphic

10:00
The Concept of Latent Construct in Research

10:00
The Minimum Sample Size Required in using SEM

10:00
The Variable Terms in SEM using AMOS

10:00
Bab 2 – The Models Involved in Structural Equation Modeling [Introduction]

10:00
The Role of Theory in Structural Equation Modeling

10:00
The Measurement Model for a Construct

10:00
The Structural Model in AMOS Graphic and Causal Relationship

10:00
The Structural Model: Modeling the Mediator

10:00
Bab 3 – Validating the Measurement Model using CFA [Introduction]

10:00
Unddimensionalitas

10:00
Convergen Validity

10:00
Discriminant Validity Fornell & Larcker criterion

10:00
Discriminant Validity HTMT ratio

10:00
Reliability

10:00
Bab 4 – The Measurement Model

00:00
Evaluating the Fitness of a Model: The Measurement and Structural Model

10:00
The Steps Involved in Validatinga the Measurement Model

10:00
Validating the Pooled Measurement Model

10:00
Assessing the Validity and Reliability for a Pooled Measurement Model

09:00
[Praktik] Model Drawing Using Amos

25:00
[Praktik] Model Drawing Using Amos for Model Fit Index

30:00
[Praktik] Model Drawing Using Amos Model Struktural

25:00
[Praktik] Model Drawing Using Amos direct and indirect efffect

25:00
[Praktik] Confirmatory Factor Analisys with second order variable

25:00

Eviews

Bab 1 – Pengenalan Software Eviews

00:00
Manajemen Data dan Jenis-jenis Data dengan Eviews

05:00
Statistik Deskriptif

10:00
Uji Regresi Berganda

10:00
[Praktik] Manajemen Data

15:00
[Praktik] Analysis Regression

15:00
Bab 2 – Mengolah Data Panel

00:00
Praktik dan Pertanyaan tentang Program AI (Chat GPT dan SpeedWrite)

10:00
Analysis Regression Data Panel

10:00
Bab 3 – Mengolah Data Time Series dengan AR, MA, ARMA, ARIMA

00:00
Jenis Analisis Time Series

15:00
Uji Stasioner

15:00
Mengolah Data Autoregressive dan ARIMA + Praktik

15:00
Uji Normalitas

15:00
Uji Multikolinearitas

17:00
Uji Heteroskedastisitas

10:00
Uji Autokorelasi

10:00

Nvivo – Kualitatif

Bab 1 – Pengantar Penelitian Kualitatif

10:00
Perbedaan Penelitian Kualitatif dan Kuantitatif

10:00
Fungsi NVivo dalam Analisis Data Kualitatif

10:00
Posisi Peneliti dalam Penelitian Kualitatif

10:00
Input Data pada NVivo

10:00
Bab 2 – Koding

10:00
Prinsip melakukan Koding

13:00
Tahap-tahap melakukan Koding

15:00
Latihan Koding pada NVivo

15:00
Tips Melakukan Koding

15:00
Proses Generalisasi Data

15:00
Bab 3 – Nodes dan Cases

05:00
Perbedaan antara Nodes dan Cases

15:00
Melakukan Autocade berdasarkan Nama Informan

15:00
Membuat Case Classification pada NVivo

15:00
Eksplorasi Data menggunakan Fitur Text Search dan Word Tree

15:00
Melakukan Koding Tahap 2 dan Tahap 3

15:00
Bab 4 – Matrix Coding, Concept Map, dan Project Map

10:00
Melakukan Matrix Coding

15:00
Interpretasi atas Hasil Matrix Coding

15:00
Menggunakan Fitur Concept Map

15:00
Menggunakan Fitur Project Map

15:00
Memperbaiki Cara Koding

15:00
Bab 5 – Penyajian Data dan Visualisasi Data NVivo

17:00
Membuat Judul BAB dan Sub BAB

17:00
Menarasikan Hasil Interpretasi

15:00
Alternatif Penyajian Visualisasi Data NVivo

15:00

Matlab

Bab 1 – Introduction

00:00
Basic MATLAB programming

11:00
Mathematics and matrix operations

30:00
MATLAB scripts and functions

20:00
Plotting and data visualization

08:30
Bab 2 – Data science

06:00
Data Mining

03:00
Diagram Venn (data analysis,data mining, machine learning)

03:00
Machine Learning

04:00
Data Analysis : Metode Simple Additive Weighting (SAW)

04:00
Algoritma Metode Simple Additive Weighting (SAW)

03:00
Praktik Metode Simple Additive Weighting (SAW)

28:00
Machine Learning : K-Nearest Neighbor (KNN)

04:00
Algoritma K-Nearest Neighbor (KNN)

04:00
Praktik K-Nearest Neighbor (KNN)

04:00
Data Analysis : Jaringan Syaraf Tiruan (JST)

04:00
Algoritma Jaringan Syaraf Tiruan (JST)

03:00
Praktik Jaringan Syaraf Tiruan (JST)

07:00
BAB 3 – Citra Digital

15:00
Akuisisi Citra Digital

01:00
Pengolahan Citra (Image Processing)

03:00
Pengenalan Pola (Pattern Recognition)

06:00
Computer Vision

15:00
Aplikasi Pengolahan Citra : Segmentasi Citra Medis

05:00
Aplikasi Pengenalan Pola : Klasifikasi Kualitas Biji Kopi

02:00
Deep Learning

03:00
Aplikasi Deep Learning : Klasifikasi Jenis Jambu Biji

00:00
Transfer Learning

03:00
Aplikasi Transfer Learning : Jenis Penyakit Daun Kopi

02:00
Praktik Citra Digital

15:00
Praktik Analisis Citra : Histogram

05:00
Praktik Konversi Citra Grayscale menjadi Biner

05:00
Bab 4 – Video Processing

15:00
Penerapan Video Processing : Frame Extraction

08:00
Praktik Frame Extraction

05:00
Penerapan Video Processing : Background Substraction

14:00
Praktik Background Substraction

12:00
Penerapan Video Processing : Skin Detection

02:00
Praktik Skin Detection

06:00
Penerapan Video Processing : Lane Detection

03:00
Praktik Lane Detection

06:00
Audio Processing

05:00
Penerapan Audio Processing : Deteksi Kebisingan Motor

04:00
Praktik Deteksi Kebisingan Motor

05:00
Penerapan Audio Processing : Klasifikasi Suara Anak Tuna Wicara

02:00
Praktik Klasifikasi Suara Anak Tuna Wicara

07:00
Bab 5 – Persamaan GUIDE dan App Designer

05:00
Perbedaan GUIDE dan App Designer

20:00
GUIDE

04:00
Praktik GUIDE : Grafik Gerak Parabola

25:00
Praktik GUIDE Export to MATLAB-file

05:00
Praktik GUIDE Migrate to App Designer

03:00
Praktik App Designer: Konversi Suhu

15:00
Praktik GUIDE : Metode SAW dan Import Export Data

06:00

What Will You Learn?

Course Content

Smart PLS

Landasan kuantitatif dan pengenalan Smart PLS

MATERI, DATA, DAN SOFTWARE CRACK

Bab 2 – Konsep Partial Least Square : Materi

Bab 3 – Mempersiapkan data menginput data [Materi]

Bab 4 – Membuat model PLS-SEM [Materi]

Bab 5 – Evaluasi PLS-SEM Algorithm [Materi]

[Praktik] Bab 5 – Evaluasi PLS-SEM Algorithm

Bab 6 – Evaluasi PLS-SEM Bootstrapping [Materi]

[Praktik] Bab 6 – Evaluasi PLS-SEM Bootstrapping

Bab 7 – Menguji variable intervening [Materi]

[Praktik] Bab 7 – Menguji variable intervening

Bab 8 – Menguji variable moderating [Materi]

[Praktik] Bab 8 – Menguji variable moderating

Bab 9 – Importance-Performance Map Analysis [Materi]

[Praktik] Bab 9 – Importance-Performance Map Analysis

Bab 10 – Higher order construct [Materi]

[Praktik] Bab 10 – Higher order construct

[Praktik] Bab 11 – Multigroup Analysis

Bab 12 – Membuat laporan PLS-SEM [Materi]

[Praktik] Bab 12 – Membuat laporan PLS-SEM

AMOS

Bab 1 – Introduction to Structural Equation Modeling (SEM)

The Advantages of SEM Compared to OLS

Converting Regression Models into AMOS Graphic

The Concept of Latent Construct in Research

The Minimum Sample Size Required in using SEM

The Variable Terms in SEM using AMOS

Bab 2 – The Models Involved in Structural Equation Modeling [Introduction]

The Role of Theory in Structural Equation Modeling

The Measurement Model for a Construct

The Structural Model in AMOS Graphic and Causal Relationship

The Structural Model: Modeling the Mediator

Bab 3 – Validating the Measurement Model using CFA [Introduction]

Unddimensionalitas

Convergen Validity

Discriminant Validity Fornell & Larcker criterion

Discriminant Validity HTMT ratio

Reliability

Bab 4 – The Measurement Model

Evaluating the Fitness of a Model: The Measurement and Structural Model

The Steps Involved in Validatinga the Measurement Model

Validating the Pooled Measurement Model

Assessing the Validity and Reliability for a Pooled Measurement Model

[Praktik] Model Drawing Using Amos

[Praktik] Model Drawing Using Amos for Model Fit Index

[Praktik] Model Drawing Using Amos Model Struktural

[Praktik] Model Drawing Using Amos direct and indirect efffect

[Praktik] Confirmatory Factor Analisys with second order variable

Eviews

Bab 1 – Pengenalan Software Eviews

Manajemen Data dan Jenis-jenis Data dengan Eviews

Statistik Deskriptif

Uji Regresi Berganda

[Praktik] Manajemen Data

[Praktik] Analysis Regression

Bab 2 – Mengolah Data Panel

Praktik dan Pertanyaan tentang Program AI (Chat GPT dan SpeedWrite)

Analysis Regression Data Panel

Bab 3 – Mengolah Data Time Series dengan AR, MA, ARMA, ARIMA

Jenis Analisis Time Series

Uji Stasioner

Mengolah Data Autoregressive dan ARIMA + Praktik

Uji Normalitas

Uji Multikolinearitas

Uji Heteroskedastisitas

Uji Autokorelasi

Nvivo – Kualitatif

Bab 1 – Pengantar Penelitian Kualitatif

Perbedaan Penelitian Kualitatif dan Kuantitatif

Fungsi NVivo dalam Analisis Data Kualitatif

Posisi Peneliti dalam Penelitian Kualitatif

Input Data pada NVivo

Bab 2 – Koding

Prinsip melakukan Koding

Tahap-tahap melakukan Koding

Latihan Koding pada NVivo

Tips Melakukan Koding