====================================

“Analisis Pengaruh Penggunaan Media Sosial terhadap Dampak Psikologis Gen Z Menggunakan PLS-SEM”

====================================

1. LOAD LIBRARY

library(plspm)

## Warning: package 'plspm' was built under R version 4.5.3

library(readr)

## Warning: package 'readr' was built under R version 4.5.3

library(dplyr)

## Warning: package 'dplyr' was built under R version 4.5.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(psych)

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:plspm':
## 
##     alpha, rescale, unidim

2. IMPORT DATASET

data <- read_csv("social_media_addiction.csv")

## Rows: 20 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (4): Gender, Platform, Addiction_Level, Emotional_State_Post_Usage
## dbl (11): User_ID, Age, Daily_Usage_Time_min, Posts_Per_Day, Likes_Received_...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

3. MENAMPILKAN DATA

Melihat 6 Data Pertama

head(data)

## # A tibble: 6 × 15
##   User_ID   Age Gender     Platform  Daily_Usage_Time_min Posts_Per_Day
##     <dbl> <dbl> <chr>      <chr>                    <dbl>         <dbl>
## 1    1001    23 Female     Instagram                  145             3
## 2    1002    19 Male       TikTok                     210             1
## 3    1003    28 Female     LinkedIn                    45             0
## 4    1004    35 Male       Facebook                    90             2
## 5    1005    21 Non-binary Twitter                    180            15
## 6    1006    25 Male       YouTube                    240             0
## # ℹ 9 more variables: Likes_Received_Daily <dbl>,
## #   Comments_Received_Daily <dbl>, Messages_Sent_Daily <dbl>,
## #   Scroll_Rate_ppm <dbl>, Addiction_Level <chr>,
## #   Emotional_State_Post_Usage <chr>, Productivity_Loss_Score <dbl>,
## #   Mental_Health_Index <dbl>, FOMO_Score <dbl>

Mengecek Struktur Data

str(data)

## spc_tbl_ [20 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ User_ID                   : num [1:20] 1001 1002 1003 1004 1005 ...
##  $ Age                       : num [1:20] 23 19 28 35 21 25 31 22 29 40 ...
##  $ Gender                    : chr [1:20] "Female" "Male" "Female" "Male" ...
##  $ Platform                  : chr [1:20] "Instagram" "TikTok" "LinkedIn" "Facebook" ...
##  $ Daily_Usage_Time_min      : num [1:20] 145 210 45 90 180 240 60 165 30 50 ...
##  $ Posts_Per_Day             : num [1:20] 3 1 0 2 15 0 1 5 0 1 ...
##  $ Likes_Received_Daily      : num [1:20] 120 45 5 30 200 10 50 90 2 15 ...
##  $ Comments_Received_Daily   : num [1:20] 25 5 2 10 60 1 8 12 0 3 ...
##  $ Messages_Sent_Daily       : num [1:20] 45 10 15 20 50 0 30 35 5 10 ...
##  $ Scroll_Rate_ppm           : num [1:20] 40 85 10 25 55 10 20 70 15 10 ...
##  $ Addiction_Level           : chr [1:20] "High" "Severe" "Low" "Moderate" ...
##  $ Emotional_State_Post_Usage: chr [1:20] "Anxious" "Depressed" "Motivated" "Neutral" ...
##  $ Productivity_Loss_Score   : num [1:20] 7 9 2 4 8 9 3 6 1 2 ...
##  $ Mental_Health_Index       : num [1:20] 45 30 80 65 40 35 75 50 85 70 ...
##  $ FOMO_Score                : num [1:20] 8 9 3 5 7 4 4 8 2 3 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   User_ID = col_double(),
##   ..   Age = col_double(),
##   ..   Gender = col_character(),
##   ..   Platform = col_character(),
##   ..   Daily_Usage_Time_min = col_double(),
##   ..   Posts_Per_Day = col_double(),
##   ..   Likes_Received_Daily = col_double(),
##   ..   Comments_Received_Daily = col_double(),
##   ..   Messages_Sent_Daily = col_double(),
##   ..   Scroll_Rate_ppm = col_double(),
##   ..   Addiction_Level = col_character(),
##   ..   Emotional_State_Post_Usage = col_character(),
##   ..   Productivity_Loss_Score = col_double(),
##   ..   Mental_Health_Index = col_double(),
##   ..   FOMO_Score = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

Mengecek Nama Variabel

names(data)

##  [1] "User_ID"                    "Age"                       
##  [3] "Gender"                     "Platform"                  
##  [5] "Daily_Usage_Time_min"       "Posts_Per_Day"             
##  [7] "Likes_Received_Daily"       "Comments_Received_Daily"   
##  [9] "Messages_Sent_Daily"        "Scroll_Rate_ppm"           
## [11] "Addiction_Level"            "Emotional_State_Post_Usage"
## [13] "Productivity_Loss_Score"    "Mental_Health_Index"       
## [15] "FOMO_Score"

Ringkasan Statistik

summary(data)

##     User_ID          Age           Gender            Platform        
##  Min.   :1001   Min.   :18.00   Length:20          Length:20         
##  1st Qu.:1006   1st Qu.:21.75   Class :character   Class :character  
##  Median :1010   Median :25.50   Mode  :character   Mode  :character  
##  Mean   :1010   Mean   :26.50                                        
##  3rd Qu.:1015   3rd Qu.:30.25                                        
##  Max.   :1020   Max.   :40.00                                        
##  Daily_Usage_Time_min Posts_Per_Day   Likes_Received_Daily
##  Min.   : 20.0        Min.   : 0.00   Min.   :  1.00      
##  1st Qu.: 57.5        1st Qu.: 0.00   1st Qu.: 13.75      
##  Median :137.5        Median : 1.50   Median : 37.50      
##  Mean   :132.5        Mean   : 3.35   Mean   : 54.90      
##  3rd Qu.:182.5        3rd Qu.: 4.25   3rd Qu.: 82.50      
##  Max.   :300.0        Max.   :15.00   Max.   :200.00      
##  Comments_Received_Daily Messages_Sent_Daily Scroll_Rate_ppm Addiction_Level   
##  Min.   : 0.00           Min.   :  0.00      Min.   : 5.0    Length:20         
##  1st Qu.: 2.00           1st Qu.:  8.75      1st Qu.:10.0    Class :character  
##  Median : 9.00           Median : 22.50      Median :27.5    Mode  :character  
##  Mean   :14.00           Mean   : 45.00      Mean   :34.5                      
##  3rd Qu.:21.25           3rd Qu.: 41.25      3rd Qu.:50.0                      
##  Max.   :60.00           Max.   :300.00      Max.   :95.0                      
##  Emotional_State_Post_Usage Productivity_Loss_Score Mental_Health_Index
##  Length:20                  Min.   : 1.00           Min.   :25.00      
##  Class :character           1st Qu.: 2.75           1st Qu.:40.00      
##  Mode  :character           Median : 5.00           Median :57.50      
##                             Mean   : 5.25           Mean   :56.75      
##                             3rd Qu.: 8.00           3rd Qu.:71.25      
##                             Max.   :10.00           Max.   :90.00      
##    FOMO_Score   
##  Min.   : 2.00  
##  1st Qu.: 3.75  
##  Median : 5.50  
##  Mean   : 5.70  
##  3rd Qu.: 8.00  
##  Max.   :10.00

4. MEMILIH VARIABEL UNTUK SEM

sem_data <- data %>%
  select(
    Daily_Usage_Time_min,
    Posts_Per_Day,
    Likes_Received_Daily,
    Comments_Received_Daily,
    Productivity_Loss_Score,
    Mental_Health_Index
  )

5. STATISTIK DESKRIPTIF

describe(sem_data)

##                         vars  n   mean    sd median trimmed   mad min max range
## Daily_Usage_Time_min       1 20 132.50 77.11  137.5  128.75 85.25  20 300   280
## Posts_Per_Day              2 20   3.35  4.44    1.5    2.50  2.22   0  15    15
## Likes_Received_Daily       3 20  54.90 54.96   37.5   46.56 48.18   1 200   199
## Comments_Received_Daily    4 20  14.00 16.16    9.0   10.94 10.38   0  60    60
## Productivity_Loss_Score    5 20   5.25  2.88    5.0    5.25  4.45   1  10     9
## Mental_Health_Index        6 20  56.75 19.62   57.5   56.56 25.95  25  90    65
##                         skew kurtosis    se
## Daily_Usage_Time_min    0.23    -0.91 17.24
## Posts_Per_Day           1.34     0.52  0.99
## Likes_Received_Daily    1.05     0.23 12.29
## Comments_Received_Daily 1.40     1.18  3.61
## Productivity_Loss_Score 0.03    -1.47  0.64
## Mental_Health_Index     0.07    -1.38  4.39

6. VISUALISASI DATA

Histogram Daily Usage Time

hist(
  sem_data$Daily_Usage_Time_min,
  main = "Histogram Daily Usage Time",
  xlab = "Daily Usage Time (Minutes)",
  col = "lightblue"
)

## Boxplot Productivity Loss Score

boxplot(
  sem_data$Productivity_Loss_Score,
  main = "Boxplot Productivity Loss Score",
  col = "lightgreen"
)

# 7. MEMBANGUN INNER MODEL

inner_model <- matrix(c(
  0, 0, 0,
  1, 0, 0,
  1, 1, 0
),
nrow = 3,
byrow = TRUE)

colnames(inner_model) <- rownames(inner_model) <- c("SMU", "SIP", "PI")

8. MEMBANGUN OUTER MODEL

outer_model <- list(

c("Daily_Usage_Time_min", "Posts_Per_Day"),

c("Likes_Received_Daily", "Comments_Received_Daily"),

c("Productivity_Loss_Score", "Mental_Health_Index")

)

9. MENENTUKAN MODE PENGUKURAN

modes <- c("A", "A", "A")

10. MENJALANKAN ANALISIS PLS-SEM

pls_model <- plspm(
  sem_data,
  inner_model,
  outer_model,
  modes = modes
)

## Warning: Setting row names on a tibble is deprecated.

11. RINGKASAN HASIL MODEL

summary(pls_model)

## PARTIAL LEAST SQUARES PATH MODELING (PLS-PM) 
## 
## ---------------------------------------------------------- 
## MODEL SPECIFICATION 
## 1   Number of Cases      20 
## 2   Latent Variables     3 
## 3   Manifest Variables   6 
## 4   Scale of Data        Standardized Data 
## 5   Non-Metric PLS       FALSE 
## 6   Weighting Scheme     centroid 
## 7   Tolerance Crit       1e-06 
## 8   Max Num Iters        100 
## 9   Convergence Iters    4 
## 10  Bootstrapping        FALSE 
## 11  Bootstrap samples    NULL 
## 
## ---------------------------------------------------------- 
## BLOCKS DEFINITION 
##     Block         Type   Size   Mode
## 1     SMU    Exogenous      2      A
## 2     SIP   Endogenous      2      A
## 3      PI   Endogenous      2      A
## 
## ---------------------------------------------------------- 
## BLOCKS UNIDIMENSIONALITY 
##      Mode  MVs  C.alpha  DG.rho  eig.1st  eig.2nd
## SMU     A    2    0.499   0.800     1.33   0.6676
## SIP     A    2    0.968   0.984     1.94   0.0628
## PI      A    2    0.000   0.000     1.98   0.0190
## 
## ---------------------------------------------------------- 
## OUTER MODEL 
##                              weight  loading  communality  redundancy
## SMU                                                                  
##   1 Daily_Usage_Time_min      0.608    0.813        0.661       0.000
##   1 Posts_Per_Day             0.618    0.820        0.672       0.000
## SIP                                                                  
##   2 Likes_Received_Daily      0.502    0.984        0.968       0.585
##   2 Comments_Received_Daily   0.514    0.985        0.969       0.586
## PI                                                                   
##   3 Productivity_Loss_Score  -0.490    0.995        0.990       0.834
##   3 Mental_Health_Index       0.515   -0.995        0.991       0.835
## 
## ---------------------------------------------------------- 
## CROSSLOADINGS 
##                                 SMU     SIP      PI
## SMU                                                
##   1 Daily_Usage_Time_min      0.813   0.370   0.961
##   1 Posts_Per_Day             0.820   0.895   0.458
## SIP                                                
##   2 Likes_Received_Daily      0.747   0.984   0.479
##   2 Comments_Received_Daily   0.783   0.985   0.471
## PI                                                 
##   3 Productivity_Loss_Score   0.850   0.459   0.995
##   3 Mental_Health_Index      -0.874  -0.502  -0.995
## 
## ---------------------------------------------------------- 
## INNER MODEL 
## $SIP
##             Estimate   Std. Error    t value   Pr(>|t|)
## Intercept   9.62e-17        0.148   6.49e-16   1.00e+00
## SMU         7.77e-01        0.148   5.24e+00   5.49e-05
## 
## $PI
##              Estimate   Std. Error     t value   Pr(>|t|)
## Intercept    9.53e-17       0.0963    9.90e-16   1.00e+00
## SMU          1.24e+00       0.1531    8.10e+00   3.06e-07
## SIP         -4.82e-01       0.1531   -3.15e+00   5.89e-03
## 
## ---------------------------------------------------------- 
## CORRELATIONS BETWEEN LVs 
##        SMU    SIP     PI
## SMU  1.000  0.777  0.866
## SIP  0.777  1.000  0.483
## PI   0.866  0.483  1.000
## 
## ---------------------------------------------------------- 
## SUMMARY INNER MODEL 
##            Type     R2  Block_Communality  Mean_Redundancy    AVE
## SMU   Exogenous  0.000              0.666            0.000  0.666
## SIP  Endogenous  0.604              0.969            0.585  0.969
## PI   Endogenous  0.842              0.991            0.834  0.991
## 
## ---------------------------------------------------------- 
## GOODNESS-OF-FIT 
## [1]  0.7956
## 
## ---------------------------------------------------------- 
## TOTAL EFFECTS 
##    relationships  direct  indirect   total
## 1     SMU -> SIP   0.777     0.000   0.777
## 2      SMU -> PI   1.241    -0.374   0.866
## 3      SIP -> PI  -0.482     0.000  -0.482

12. OUTER MODEL

pls_model$outer_model

##                      name block     weight    loading communality redundancy
## 1    Daily_Usage_Time_min   SMU  0.6076327  0.8129142   0.6608295  0.0000000
## 2           Posts_Per_Day   SMU  0.6175039  0.8195038   0.6715864  0.0000000
## 3    Likes_Received_Daily   SIP  0.5023403  0.9838068   0.9678758  0.5849943
## 4 Comments_Received_Daily   SIP  0.5137452  0.9845234   0.9692863  0.5858468
## 5 Productivity_Loss_Score    PI -0.4897566  0.9950039   0.9900328  0.8339840
## 6     Mental_Health_Index    PI  0.5150165 -0.9954831   0.9909866  0.8347874

13. PATH COEFFICIENT

pls_model$path_coefs

##           SMU        SIP PI
## SMU 0.0000000  0.0000000  0
## SIP 0.7774384  0.0000000  0
## PI  1.2408106 -0.4816153  0

14. GOODNESS OF FIT

pls_model$gof

## [1] 0.7956399

15. VISUALISASI MODEL SEM

plot(pls_model)

Modul 5 SEM

Yanaka Sofia Pardede

2026-05-24

====================================

“Analisis Pengaruh Penggunaan Media Sosial terhadap Dampak Psikologis Gen Z Menggunakan PLS-SEM”

====================================

1. LOAD LIBRARY

2. IMPORT DATASET

3. MENAMPILKAN DATA

Melihat 6 Data Pertama

Mengecek Struktur Data

Mengecek Nama Variabel

Ringkasan Statistik

4. MEMILIH VARIABEL UNTUK SEM

5. STATISTIK DESKRIPTIF

6. VISUALISASI DATA

Histogram Daily Usage Time

8. MEMBANGUN OUTER MODEL

9. MENENTUKAN MODE PENGUKURAN

10. MENJALANKAN ANALISIS PLS-SEM

11. RINGKASAN HASIL MODEL

12. OUTER MODEL

13. PATH COEFFICIENT

14. GOODNESS OF FIT

15. VISUALISASI MODEL SEM