LAPORAN FINAL PROJECT VISUALISASI DATA EXPLORATORI
Kelompok 6
2025-06-15
Group Team
- Elena Miska 5052241022
- Alycia Zada 5052241034
- Inessa Regina Angelica Munda 5052241037
Latar Belakang
Dataset Title: AI Content Impact in Global Industries
Dataset ini menyajikan beberapa faktor yang diduga mempengaruhi prospek kerja manusia dalam industri skala global. Dilatarbelakangi dengan perasaan gelisah manusia terkait perkembangan AI yang semakin pesat, bahkan berpotensi untuk menggantikan peran manusia dalam kehidupan sosial, merupakan alasan kami tertarik untuk menganalisa seluas mungkin sudut pandang bahkan potensi baru yang bisa diusahakan manusia terkait perkembangan AI.
1. Loading the Dataset
We start by loading the dataset and viewing its structure.
dataai <- read.csv("C:/Users/User/Downloads/Global_AI_Content_Impact_Dataset.csv")
head(dataai)## Country Year Industry AI.Adoption.Rate....
## 1 South Korea 2022 Media 44.29
## 2 China 2025 Legal 34.75
## 3 USA 2022 Automotive 81.06
## 4 France 2021 Legal 85.24
## 5 France 2021 Gaming 78.95
## 6 USA 2021 Retail 66.95
## AI.Generated.Content.Volume..TBs.per.year. Job.Loss.Due.to.AI....
## 1 33.09 16.77
## 2 66.74 46.89
## 3 96.13 10.66
## 4 93.76 27.70
## 5 45.62 17.45
## 6 47.72 0.86
## Revenue.Increase.Due.to.AI.... Human.AI.Collaboration.Rate....
## 1 46.12 74.79
## 2 52.46 26.17
## 3 45.60 39.66
## 4 78.24 29.45
## 5 1.05 21.70
## 6 27.58 64.42
## Top.AI.Tools.Used Regulation.Status Consumer.Trust.in.AI....
## 1 Bard Strict 40.77
## 2 DALL-E Strict 35.67
## 3 Stable Diffusion Moderate 54.47
## 4 Claude Moderate 51.84
## 5 Midjourney Strict 41.77
## 6 ChatGPT Lenient 68.14
## Market.Share.of.AI.Companies....
## 1 18.73
## 2 35.02
## 3 22.76
## 4 1.93
## 5 21.41
## 6 8.09glimpse(dataai)## Rows: 200
## Columns: 12
## $ Country <chr> "South Korea", "China", "US…
## $ Year <int> 2022, 2025, 2022, 2021, 202…
## $ Industry <chr> "Media", "Legal", "Automoti…
## $ AI.Adoption.Rate.... <dbl> 44.29, 34.75, 81.06, 85.24,…
## $ AI.Generated.Content.Volume..TBs.per.year. <dbl> 33.09, 66.74, 96.13, 93.76,…
## $ Job.Loss.Due.to.AI.... <dbl> 16.77, 46.89, 10.66, 27.70,…
## $ Revenue.Increase.Due.to.AI.... <dbl> 46.12, 52.46, 45.60, 78.24,…
## $ Human.AI.Collaboration.Rate.... <dbl> 74.79, 26.17, 39.66, 29.45,…
## $ Top.AI.Tools.Used <chr> "Bard", "DALL-E", "Stable D…
## $ Regulation.Status <chr> "Strict", "Strict", "Modera…
## $ Consumer.Trust.in.AI.... <dbl> 40.77, 35.67, 54.47, 51.84,…
## $ Market.Share.of.AI.Companies.... <dbl> 18.73, 35.02, 22.76, 1.93, …2. Pre - Processing
2.1 🔍 Missing Values
We identify which columns contain missing values and how many.
dataai %>%
summarise(across(everything(), ~ sum(is.na(.))))## Country Year Industry AI.Adoption.Rate....
## 1 0 0 0 0
## AI.Generated.Content.Volume..TBs.per.year. Job.Loss.Due.to.AI....
## 1 0 0
## Revenue.Increase.Due.to.AI.... Human.AI.Collaboration.Rate....
## 1 0 0
## Top.AI.Tools.Used Regulation.Status Consumer.Trust.in.AI....
## 1 0 0 0
## Market.Share.of.AI.Companies....
## 1 02.2 🧩 Duplicate Rows
We check if the dataset has any duplicate rows that may affect analysis. and there is no any duplicate, so good!
dataai %>%
duplicated() %>%
sum()## [1] 02.3 🔢 Unique Values Count
Count unique values in each column (to identify categorical variables).
sapply(dataai, function(x) length(unique(x)))## Country
## 10
## Year
## 6
## Industry
## 10
## AI.Adoption.Rate....
## 199
## AI.Generated.Content.Volume..TBs.per.year.
## 199
## Job.Loss.Due.to.AI....
## 196
## Revenue.Increase.Due.to.AI....
## 197
## Human.AI.Collaboration.Rate....
## 199
## Top.AI.Tools.Used
## 7
## Regulation.Status
## 3
## Consumer.Trust.in.AI....
## 195
## Market.Share.of.AI.Companies....
## 1952.4 🗂️ List Unique Entries
View all unique values in each column.
lapply(dataai, unique)## $Country
## [1] "South Korea" "China" "USA" "France" "Australia"
## [6] "UK" "Canada" "India" "Japan" "Germany"
##
## $Year
## [1] 2022 2025 2021 2023 2020 2024
##
## $Industry
## [1] "Media" "Legal" "Automotive" "Gaming"
## [5] "Retail" "Education" "Healthcare" "Marketing"
## [9] "Manufacturing" "Finance"
##
## $AI.Adoption.Rate....
## [1] 44.29 34.75 81.06 85.24 78.95 66.95 68.23 91.27 17.02 25.50 11.94 39.04
## [13] 71.00 25.62 49.71 67.94 43.85 64.87 62.98 62.94 94.76 44.10 12.86 49.97
## [25] 88.41 79.59 26.12 87.95 42.53 32.74 67.15 28.48 44.97 71.57 16.79 74.81
## [37] 77.52 27.61 44.61 81.18 41.28 48.54 62.64 57.35 26.63 25.98 20.47 83.40
## [49] 61.49 89.96 33.23 30.94 91.41 93.00 93.16 26.27 88.75 50.27 76.22 28.27
## [61] 41.32 38.42 20.25 68.39 68.21 44.99 50.34 45.27 66.93 78.21 83.20 12.97
## [73] 70.70 78.60 43.55 82.82 75.42 13.90 82.72 63.04 72.88 26.57 80.83 73.22
## [85] 45.30 89.74 55.30 87.73 71.90 88.38 67.18 28.60 46.72 35.91 27.95 33.20
## [97] 38.21 43.40 67.97 92.96 18.91 70.59 83.74 52.43 32.00 43.12 27.02 29.49
## [109] 57.15 35.83 27.50 49.72 79.64 53.76 91.67 27.53 67.08 77.96 74.66 82.25
## [121] 85.09 82.41 17.05 14.31 73.89 56.81 83.85 43.22 52.86 46.79 57.44 11.99
## [133] 70.04 64.43 20.23 33.91 93.49 45.37 86.58 19.03 34.28 67.49 76.35 46.44
## [145] 78.57 73.11 64.03 21.02 14.76 15.77 20.14 15.20 69.67 70.48 80.75 31.96
## [157] 82.54 93.72 21.63 23.27 63.03 54.67 60.69 44.17 34.27 35.22 20.95 72.59
## [169] 68.24 55.19 18.43 88.16 29.45 11.14 46.62 43.32 59.85 88.23 87.79 21.57
## [181] 45.09 50.97 52.18 40.80 77.33 80.04 39.48 30.18 36.52 48.39 39.94 78.43
## [193] 81.86 29.61 89.44 70.11 65.77 45.35 10.53
##
## $AI.Generated.Content.Volume..TBs.per.year.
## [1] 33.09 66.74 96.13 93.76 45.62 47.72 6.14 33.87 87.77 18.74 91.59 78.44
## [13] 77.53 86.47 50.19 96.81 52.54 49.04 24.98 7.49 17.58 87.85 5.08 5.59
## [25] 5.34 20.37 73.78 12.55 44.38 25.34 65.71 34.49 43.93 72.40 8.50 54.67
## [37] 3.40 14.49 26.16 3.56 22.14 89.58 61.24 94.06 95.37 52.90 1.61 66.33
## [49] 17.53 80.30 75.51 82.97 67.49 42.70 50.91 38.13 30.56 17.89 75.93 3.98
## [61] 1.52 40.78 99.06 34.74 68.40 7.70 50.70 82.47 6.59 33.34 4.85 76.22
## [73] 94.67 19.64 14.89 75.53 24.03 51.20 17.97 82.49 1.55 38.87 67.55 94.47
## [85] 42.81 15.64 67.76 44.74 21.66 12.43 7.92 49.95 85.74 2.25 56.66 71.37
## [97] 4.31 1.04 75.92 38.18 44.86 57.40 64.57 19.68 90.69 44.26 42.47 8.68
## [109] 33.14 92.02 94.62 23.67 73.60 87.25 94.77 90.28 95.29 80.64 69.51 26.14
## [121] 6.81 40.12 77.29 52.86 35.59 16.63 56.52 7.00 52.80 10.91 80.92 74.71
## [133] 28.00 66.60 41.41 68.16 86.40 16.96 48.59 20.96 46.60 76.54 35.37 78.99
## [145] 7.24 98.77 5.21 20.50 58.35 23.95 3.70 30.75 47.75 19.97 60.93 49.43
## [157] 71.06 27.55 36.58 38.75 26.18 64.10 56.38 82.35 4.19 84.15 26.53 51.37
## [169] 36.33 41.06 7.44 14.83 92.91 28.47 72.37 18.93 61.00 20.18 40.86 77.01
## [181] 11.80 16.05 5.84 42.29 51.28 36.49 63.35 97.54 4.20 57.31 57.79 84.82
## [193] 35.53 59.56 52.98 28.26 49.83 20.49 20.97
##
## $Job.Loss.Due.to.AI....
## [1] 16.77 46.89 10.66 27.70 17.45 0.86 6.20 41.67 4.59 15.07 26.28 31.32
## [13] 17.41 47.64 14.09 8.84 32.07 36.31 27.51 22.40 14.48 39.96 18.50 22.61
## [25] 36.07 39.06 21.51 12.61 8.99 9.41 8.92 18.44 32.93 18.28 28.43 22.30
## [37] 14.58 35.67 49.34 33.94 10.33 34.88 37.25 37.36 49.58 16.06 2.43 24.27
## [49] 48.25 25.11 37.08 6.67 12.92 36.57 21.79 31.35 6.51 43.30 3.19 34.42
## [61] 23.06 3.81 1.41 9.32 18.15 1.04 12.07 3.23 6.54 40.84 33.17 37.59
## [73] 1.29 13.87 22.34 28.82 49.71 23.56 6.68 47.77 16.33 42.39 26.42 3.03
## [85] 10.14 3.21 42.89 37.69 23.70 40.55 40.02 15.77 48.37 15.67 49.33 14.77
## [97] 36.85 9.64 21.57 14.64 40.53 23.88 33.55 37.90 0.09 17.26 25.44 13.89
## [109] 29.64 29.11 2.21 44.52 12.32 4.08 49.65 29.08 26.79 21.76 8.89 30.99
## [121] 34.67 11.75 46.37 14.59 47.25 15.98 22.88 43.20 17.30 15.36 25.91 21.71
## [133] 29.79 38.39 49.16 17.15 20.47 47.42 24.08 31.81 26.52 3.82 49.10 17.23
## [145] 38.22 41.36 5.47 39.61 7.51 20.68 45.44 27.81 33.75 32.20 28.25 44.65
## [157] 48.33 42.05 44.22 31.41 38.29 29.34 42.79 28.58 20.44 5.89 40.30 22.77
## [169] 29.26 2.75 2.86 43.11 22.03 22.13 41.04 17.85 32.52 27.79 46.78 36.80
## [181] 21.03 23.60 29.72 25.56 18.39 25.53 1.20 46.08 26.41 28.46 24.68 48.47
## [193] 27.62 39.94 33.21 23.64
##
## $Revenue.Increase.Due.to.AI....
## [1] 46.12 52.46 45.60 78.24 1.05 27.58 53.13 56.26 52.45 40.81 73.93 28.93
## [13] 12.56 71.10 12.41 51.06 73.68 38.16 27.51 71.69 71.63 33.44 20.65 17.55
## [25] 13.78 44.06 22.63 3.82 49.69 33.42 19.32 23.50 18.56 49.83 65.47 52.34
## [37] 67.90 18.49 58.53 68.33 6.67 66.31 43.72 17.20 1.75 11.74 31.04 34.95
## [49] 20.74 74.04 63.58 60.61 71.61 55.65 35.31 3.37 60.23 30.02 10.63 6.12
## [61] 17.23 58.05 6.61 68.00 57.89 52.91 0.58 57.15 0.85 76.76 22.58 75.91
## [73] 44.70 8.47 41.09 1.44 42.27 0.57 50.08 50.72 76.86 27.70 51.87 28.86
## [85] 24.96 3.80 11.15 78.50 28.02 52.51 52.54 32.20 75.69 71.07 38.03 0.93
## [97] 66.25 8.52 33.23 17.60 25.17 49.28 69.35 9.78 40.67 26.12 8.51 4.19
## [109] 73.89 65.36 56.52 66.78 54.75 31.27 26.79 13.91 15.70 41.93 6.35 76.88
## [121] 50.96 17.78 70.85 36.05 68.91 69.17 59.43 51.15 57.82 49.09 47.80 5.14
## [133] 23.17 19.26 38.34 59.20 26.27 40.66 2.49 47.63 68.89 49.79 0.73 64.98
## [145] 47.61 67.17 53.59 79.55 76.52 43.32 52.01 16.62 55.74 15.07 14.67 56.09
## [157] 31.86 61.25 67.88 57.91 27.12 28.55 49.74 23.40 73.08 2.28 3.84 24.34
## [169] 51.90 12.96 26.45 14.73 50.61 31.66 61.74 77.48 10.99 13.61 0.14 62.71
## [181] 60.81 62.27 23.16 76.74 55.14 14.76 71.01 17.95 44.31 4.72 7.11 46.92
## [193] 12.14 57.86 79.44 50.50 34.27
##
## $Human.AI.Collaboration.Rate....
## [1] 74.79 26.17 39.66 29.45 21.70 64.42 53.16 59.93 64.72 64.86 57.55 77.95
## [13] 31.57 36.22 40.18 23.04 74.08 82.41 53.78 63.54 67.44 72.72 75.67 34.97
## [25] 36.24 83.18 65.41 25.44 47.11 73.55 40.95 35.14 24.81 32.73 23.38 84.80
## [37] 66.23 42.88 75.29 76.84 40.15 60.28 20.21 20.37 72.35 47.03 52.49 47.21
## [49] 47.72 51.67 77.34 56.01 54.73 51.00 78.29 65.36 46.53 20.76 61.88 49.89
## [61] 36.29 81.05 80.08 35.05 61.89 80.25 68.07 57.03 71.52 87.80 82.54 27.28
## [73] 78.66 64.18 37.81 40.39 54.72 65.40 24.06 81.71 55.78 77.03 74.39 57.06
## [85] 78.13 33.44 48.72 39.34 26.70 59.75 50.30 22.45 38.20 68.44 33.05 53.70
## [97] 81.74 24.88 42.07 20.43 68.19 82.93 55.02 64.79 67.03 32.98 38.60 68.60
## [109] 46.40 81.93 82.68 60.77 73.26 82.48 32.23 25.29 52.29 59.71 56.36 58.99
## [121] 57.00 43.18 33.19 79.54 76.71 60.15 51.21 66.73 51.23 45.95 37.15 82.92
## [133] 66.59 58.46 88.29 24.65 32.10 83.71 80.62 84.48 33.97 61.67 53.15 70.78
## [145] 52.91 43.84 87.65 23.62 53.09 36.03 36.55 37.02 31.27 74.99 64.12 61.33
## [157] 79.12 39.83 75.36 53.00 63.90 24.78 39.50 74.11 24.04 65.49 47.14 72.92
## [169] 40.04 49.04 54.31 47.53 68.45 78.20 35.21 33.94 28.32 85.27 70.58 37.65
## [181] 39.09 68.18 42.56 21.16 84.93 69.01 20.82 49.67 21.92 61.69 38.14 33.28
## [193] 63.48 57.21 30.60 58.71 43.73 41.73 45.67
##
## $Top.AI.Tools.Used
## [1] "Bard" "DALL-E" "Stable Diffusion" "Claude"
## [5] "Midjourney" "ChatGPT" "Synthesia"
##
## $Regulation.Status
## [1] "Strict" "Moderate" "Lenient"
##
## $Consumer.Trust.in.AI....
## [1] 40.77 35.67 54.47 51.84 41.77 68.14 71.60 46.98 60.55 51.07 65.79 39.63
## [13] 56.93 63.66 57.05 33.16 56.79 78.14 55.93 44.26 71.96 76.74 38.35 78.71
## [25] 58.85 45.04 44.56 34.37 55.15 70.72 48.10 57.31 54.38 42.97 59.92 42.06
## [37] 31.46 30.12 32.96 41.73 61.31 30.52 79.68 88.69 53.62 55.83 35.80 47.81
## [49] 87.90 78.51 48.51 85.10 53.96 47.34 65.04 54.57 70.19 32.28 65.57 54.77
## [61] 70.43 80.19 80.65 88.43 32.02 37.36 42.57 43.34 71.22 76.14 64.00 64.86
## [73] 59.15 86.44 36.48 74.85 68.69 77.96 33.36 57.09 76.47 33.56 30.17 66.28
## [85] 42.99 58.79 78.54 40.80 54.27 73.15 51.58 83.61 85.92 74.86 87.06 49.00
## [97] 39.31 87.28 46.50 76.16 68.44 89.88 70.63 63.92 60.99 84.37 53.68 60.67
## [109] 38.02 36.75 89.07 49.54 51.34 38.75 53.86 69.00 83.38 33.48 61.28 81.88
## [121] 83.17 76.46 34.40 54.41 43.37 60.89 79.04 33.05 49.17 41.71 41.08 45.13
## [133] 58.54 76.05 70.17 74.96 60.05 78.85 57.88 35.56 70.39 68.32 34.52 31.54
## [145] 37.89 44.82 81.84 60.71 81.94 61.68 68.67 48.13 83.72 69.59 33.51 73.23
## [157] 57.27 72.79 83.66 75.60 30.81 81.95 87.03 48.02 82.45 76.92 45.16 50.12
## [169] 73.34 59.28 73.82 81.93 46.02 69.97 60.03 33.61 50.81 60.09 43.60 77.31
## [181] 85.01 48.27 42.94 89.76 84.90 66.25 46.49 64.49 31.33 31.70 77.21 78.74
## [193] 81.58 47.42 58.52
##
## $Market.Share.of.AI.Companies....
## [1] 18.73 35.02 22.76 1.93 21.41 8.09 15.26 28.51 22.70 39.97 29.30 22.79
## [13] 38.36 29.92 5.76 7.60 25.07 20.54 30.55 41.99 34.65 40.98 5.25 27.60
## [25] 35.86 14.03 3.46 31.99 46.58 19.93 22.15 14.27 46.37 28.45 16.40 11.70
## [37] 41.11 42.23 43.24 39.04 33.26 24.81 47.08 7.53 48.53 42.56 24.29 47.40
## [49] 29.32 17.66 4.66 38.38 29.35 21.16 9.04 38.02 30.79 26.84 6.82 4.40
## [61] 37.79 25.28 5.50 34.83 41.30 48.91 28.87 39.35 5.41 49.04 35.30 35.40
## [73] 3.86 13.48 17.29 11.26 38.59 5.46 38.60 12.10 16.73 21.66 48.56 10.51
## [85] 32.23 45.09 32.72 37.87 48.93 8.66 29.90 33.06 33.33 49.08 20.68 16.85
## [97] 11.84 48.39 20.88 9.47 12.68 4.47 46.78 47.98 22.31 10.43 14.69 43.36
## [109] 37.77 20.93 12.56 20.52 20.17 1.18 9.06 46.63 39.55 29.08 27.13 25.64
## [121] 24.11 45.60 2.64 27.19 20.22 21.52 33.40 4.09 32.61 43.77 5.19 8.39
## [133] 6.24 1.63 37.53 45.17 47.48 42.29 19.08 35.63 47.38 45.49 34.39 39.19
## [145] 31.14 34.26 13.25 22.64 48.02 43.92 14.00 9.18 40.32 32.54 21.86 49.28
## [157] 9.67 22.23 13.50 6.21 32.81 3.26 2.49 39.95 22.55 44.86 27.59 15.34
## [169] 46.08 20.74 17.82 47.32 20.08 13.22 30.75 12.22 18.87 20.61 31.02 14.06
## [181] 12.95 24.33 44.80 36.32 29.93 44.40 37.29 45.04 11.61 28.90 44.35 31.73
## [193] 14.62 43.11 33.372.5 📊 Summary Statistics
View summary (min, mean, median, max, etc.) for numerical columns.
summary(dataai$AI.Adoption.Rate....)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.53 33.22 53.31 54.27 76.22 94.76summary(dataai$AI.Generated.Content.Volume..TBs.per.year.)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.04 20.32 44.32 46.07 71.62 99.06summary(dataai$Job.Loss.Due.to.AI....)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.09 14.99 25.73 25.79 37.42 49.71summary(dataai$Revenue.Increase.Due.to.AI....)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.14 17.91 42.10 39.72 58.70 79.55summary(dataai$Human.AI.Collaboration.Rate....)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.21 37.77 54.52 54.10 69.40 88.29summary(dataai$Consumer.Trust.in.AI....)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.12 44.76 59.22 59.43 74.89 89.88summary(dataai$Market.Share.of.AI.Companies....)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.18 14.05 27.39 26.57 38.43 49.282.6 🔍 Outlier Detection
2.6.1 Boxplot Visualization
Create boxplots for each numeric variable to visually detect outliers.
boxplot_outlier <- function(data, kolom, warna = "#A7E4EB") {
ggplot(data, aes(y = .data[[kolom]])) +
geom_boxplot(outlier.colour = warna, fill = "#E9B5EF", color = "#006994") +
labs(
title = paste("Boxplot Outlier:", kolom),
y = kolom
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", size = 16),
axis.title.y = element_text(face = "bold"),
axis.text = element_text(size = 12)
)
}
boxplot_outlier(dataai,"AI.Adoption.Rate....")
boxplot_outlier(dataai,"AI.Generated.Content.Volume..TBs.per.year.")
boxplot_outlier(dataai,"Job.Loss.Due.to.AI....")
boxplot_outlier(dataai,"Revenue.Increase.Due.to.AI....")
boxplot_outlier(dataai,"Human.AI.Collaboration.Rate....")
boxplot_outlier(dataai,"Consumer.Trust.in.AI....")
boxplot_outlier(dataai,"Market.Share.of.AI.Companies....")
2.6.2 Outlier Detection using IQR Method
Compute outliers programmatically using Interquartile Range (IQR).
IQR_outliers <- function(data, column_name) {
Q1 <- quantile(data[[column_name]], 0.25, na.rm = TRUE)
Q3 <- quantile(data[[column_name]], 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
lower <- Q1 - 1.5 * IQR
upper <- Q3 + 1.5 * IQR
outliers <- data %>%
filter((.data[[column_name]] < lower) | (.data[[column_name]] > upper))
cat("Jumlah outlier di kolom", column_name, ":", nrow(outliers), "\n")
return(outliers)
}
IQR_outliers(dataai,"AI.Adoption.Rate....")## Jumlah outlier di kolom AI.Adoption.Rate.... : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)IQR_outliers(dataai,"AI.Generated.Content.Volume..TBs.per.year.")## Jumlah outlier di kolom AI.Generated.Content.Volume..TBs.per.year. : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)IQR_outliers(dataai,"Job.Loss.Due.to.AI....")## Jumlah outlier di kolom Job.Loss.Due.to.AI.... : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)IQR_outliers(dataai,"Revenue.Increase.Due.to.AI....")## Jumlah outlier di kolom Revenue.Increase.Due.to.AI.... : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)IQR_outliers(dataai,"Human.AI.Collaboration.Rate....")## Jumlah outlier di kolom Human.AI.Collaboration.Rate.... : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)IQR_outliers(dataai,"Consumer.Trust.in.AI....")## Jumlah outlier di kolom Consumer.Trust.in.AI.... : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)IQR_outliers(dataai,"Market.Share.of.AI.Companies....")## Jumlah outlier di kolom Market.Share.of.AI.Companies.... : 0## [1] Country
## [2] Year
## [3] Industry
## [4] AI.Adoption.Rate....
## [5] AI.Generated.Content.Volume..TBs.per.year.
## [6] Job.Loss.Due.to.AI....
## [7] Revenue.Increase.Due.to.AI....
## [8] Human.AI.Collaboration.Rate....
## [9] Top.AI.Tools.Used
## [10] Regulation.Status
## [11] Consumer.Trust.in.AI....
## [12] Market.Share.of.AI.Companies....
## <0 rows> (or 0-length row.names)3. Rumusan Masalah
3.1 Dampak Penggunaan AI Dalam Lingkup Sosial
3.1.1 Tren Rata-rata Job Loss dan Human-AI Collaboration per Tahun
Untuk tahap awal, kami membandingkan variabel “Human-AI Collaboration” dan “Job Loss due to AI” untuk melihat apakah keberadaan AI mempengaruhi kehidupan sosial secara global.
Untuk mengetahui perkembangan dampak AI dari waktu ke waktu, kami menghitung rata-rata tahunan dari dua variabel utama:
Job.Loss.Due.to.AI....: menunjukkan persentase rata-rata pekerja yang kehilangan pekerjaan akibat AI.Human.AI.Collaboration.Rate....: menunjukkan rata-rata tingkat kolaborasi antara manusia dan AI.
Dengan menghitung rata-rata tiap tahun, kami memperoleh representasi tren global tanpa harus melihat detail per industri atau negara.
Langkah 1: meringkas data menjadi rata-rata tahunan untuk 2 variabel
data_ringkas <- dataai %>%
group_by(Year) %>%
summarise(
job_loss = mean(Job.Loss.Due.to.AI...., na.rm = TRUE),
collab = mean(Human.AI.Collaboration.Rate...., na.rm = TRUE)
) %>%
pivot_longer(cols = c(job_loss, collab), names_to = "Kategori", values_to = "Persentase")Gambar 1. Tren Rata-rata Job Loss dan Human-AI Collaboration per Tahun
ggplot(data_ringkas, aes(x = Year, y = Persentase, color = Kategori)) +
geom_line(size = 1.3) +
geom_point(size = 2.5) +
scale_color_manual(
values = c("job_loss" = "#00FFFF", "collab" = "#FF00FF"),
labels = c("Job Loss due to AI", "Human-AI Collaboration")
) +
labs(
title = "Tren Dampak AI terhadap Pekerjaan Manusia (per Tahun)",
x = "Tahun",
y = "Rata-rata (%)",
color = "Kategori"
) +
theme(
plot.background = element_rect(fill = "black"),
panel.background = element_rect(fill = "black"),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "#2A2A40"),
axis.text.x = element_text(angle = 45, hjust = 1, colour = "#EAF2FF", face = "bold", size = 12),
axis.text.y = element_text(colour = "#EAF2FF", face = "bold", size = 12),
axis.title.x = element_text(colour = "#FFE59D", face = "bold", size = 13),
axis.title.y = element_text(colour = "#FFE59D", face = "bold", size = 13),
plot.title = element_text(colour = "#FFE59D", face = "bold", size = 17),
plot.subtitle = element_text(colour = "#FFE59D", face = "bold"),
legend.position = "top",
legend.background = element_rect(fill = "#FBF9E4"),
legend.text = element_text(colour = "#567C8D", face = "bold", size = 11),
legend.title = element_text(colour = "#567C8D", face = "bold", size = 12)
)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Selanjutnya muncul pertanyaan “Apakah peran AI dalam kehidupan manusia ini sifatnya protagonis atau justru antagonis?”, bila merujuk pada line chart diatas, tidak bisa disimpulkan secara mentah mengenai peran AI yang antagonis maupun protagonis dikarenakan dalam lingkup global selama 6 tahun terakhir terdapat banyak sekali masalah lain yang lebih prioritas, contohnya covid-19. Namun, jika dianalisa lebih teliti kita bisa menyimpulkan bahwa tidak semua manusia memiliki kemampuan untuk memanfaatkan atau mengkolaborasikan AI. Sehingga, peran AI bisa menjadi protagonis bila manusia secara mayoritas dapat menerapkan Human-AI Collaboration sehingga menekan persentase Job Loss. Sebaliknya, jika manusia tidak dapat menerapkan Human-AI Collaboration, maka persentase Job Loss berpotensi meningkat karena besarnya peluang AI menggantikan peran manusia.
3.1.2 Perbandingan AI Adoption, Job Loss, dan Human-AI Collaboration per Industri
Perkembangan AI merupakan dampak dari kemajuan teknologi yang tidak bisa kita tolak sebagai negara berkembang. Oleh karena itu, penting untuk menyeimbangkan AI Adoption yang digunakan di berbagai sektor industri global dengan Human-AI Collaboration.
Langkah 1: Ringkas data per industri
industry_summary_raw <- dataai %>%
group_by(Industry) %>%
summarise(
AI_Adoption = mean(AI.Adoption.Rate...., na.rm = TRUE),
Job_Loss = mean(Job.Loss.Due.to.AI...., na.rm = TRUE),
Human_AI_Collab = mean(Human.AI.Collaboration.Rate...., na.rm = TRUE)
)Langkah 2: Simpan urutan industri berdasarkan Job Loss tertinggi
ordered_industries <- industry_summary_raw %>%
arrange(desc(Job_Loss)) %>%
pull(Industry)Langkah 3: Ubah ke long format
industry_summary <- industry_summary_raw %>%
pivot_longer(cols = c(AI_Adoption, Job_Loss, Human_AI_Collab),
names_to = "Kategori", values_to = "Persentase")Langkah 4: Ganti label kategori agar lebih rapi
industry_summary$Kategori <- factor(
industry_summary$Kategori,
levels = c("AI_Adoption", "Job_Loss", "Human_AI_Collab"),
labels = c("AI Adoption", "Job Loss", "Human‑AI Collaboration")
)Langkah 4: Atur urutan industri berdasarkan job loss tertinggi
industry_summary$Industry <- factor(industry_summary$Industry,
levels = ordered_industries)Gambar 2. Perbandingan AI Adoption, Job Loss, dan Human-AI Collaboration per Industri
ggplot(industry_summary,
aes(x = Industry, y = Persentase, fill = Kategori)) +
geom_col(position = position_dodge(width = 0.75), width = 0.65) +
geom_text(aes(label = round(Persentase, 1)),
position = position_dodge(width = 0.75),
vjust = -0.4, size = 4, colour = "#EAF2FF", fontface = "bold") +
scale_fill_manual(
values = c(
"AI Adoption" = "#7F00FF",
"Job Loss" = "#FF00FF",
"Human‑AI Collaboration" = "#00FFFF"
)
) +
labs(
title = "Dampak AI terhadap Tenaga Kerja di Berbagai Industri",
subtitle = "Perbandingan AI Adoption, Job Loss, dan Human‑AI Collaboration",
x = "Industri (Urut Job Loss Tertinggi)",
y = "Rata‑rata (%)",
fill = "Kategori"
) +
theme_minimal(base_size = 13) +
theme(
plot.background = element_rect(fill = "black", color = NA),
panel.background = element_rect(fill = "black", color = NA),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "#2A2A40"),
axis.text.x = element_text(angle = 45, hjust = 1, colour = "#EAF2FF", face = "bold", size = 12),
axis.text.y = element_text(colour = "#EAF2FF", face = "bold", size = 12),
axis.title.x = element_text(colour = "#FFE59D", face = "bold", size = 13),
axis.title.y = element_text(colour = "#FFE59D", face = "bold", size = 13),
plot.title = element_text(colour = "#FFE59D", face = "bold", size = 17),
plot.subtitle = element_text(colour = "#FFE59D", face = "bold", size = 14),
legend.position = "top",
legend.background = element_rect(fill = "#000000", color = NA),
legend.text = element_text(colour = "#EAF2FF", face = "bold", size = 11),
legend.title = element_text(colour = "#FFE59D", face = "bold", size = 12)
)
Tampak persentase seberapa besar suatu industri menggunakan AI dan menerapkan Human-AI Collaboration guna menekan persentase Job Loss. Namun, dapat diketahui bahwa tidak semua sektor industri yang mengadopsi AI memiliki penerapan Human-AI Collaboration yang tinggi, sehingga persentase Job Loss masih dalam rentang yang tinggi, contohnya industri legal (hukum) dan gaming. Bukan berarti mustahil untuk menyeimbangkan AI Adoption dan Human-AI Collaboration, karena sektor industri marketing diketahui mampu menyeimbangkan keduanya dan menekan angka persentase Job Loss hingga 19,6%.
Kesimpulan
Kesimpulan untuk rumusan masalah pertama Dari 2 buah visualisasi diatas, kami menarik kesimpulan bahwa AI Content secara langsung berdampak pada kehidupan sosial dan diperlukan adanya Human-AI Collaboration yang tinggi agar peran manusia tidak bisa digantikan oleh AI.
3.2 Apakah Industri yang Cerdas Memilih & Mengadopsi AI Memiliki Peningkatan Pendapatan yang Signifikan?
🔍 Visualisasi Pengantar: Tren Adopsi AI Global
Sebelum menganalisa lebih dalam mengenai pengaruh adopsi AI terhadap pendapatan industri, kami terlebih dahulu meninjau bagaimana tren adopsi AI berkembang dari tahun ke tahun secara global. Hal ini bertujuan untuk memperkuat konteks bahwa adopsi AI merupakan fenomena yang semakin luas dan penting untuk dianalisis lebih lanjut.
ai_trend <- dataai %>%
group_by(Year) %>%
summarise(Avg_AI_Adoption = mean(AI.Adoption.Rate...., na.rm = TRUE))Gambar 3. Line plot tren AI Adoption
ggplot(ai_trend, aes(x = Year, y = Avg_AI_Adoption)) +
geom_line(colour = "#55B9E2", size = 1.5) +
geom_point(colour = "#BABDF6", size = 3) +
labs(
title = "Tren Adopsi AI dari Tahun ke Tahun",
x = "Tahun",
y = "Rata-rata AI Adoption (%)"
) +
theme_minimal()
Berdasarkan grafik tersebut, tampak bahwa tingkat adopsi AI cenderung meningkat dari tahun ke tahun, yang mencerminkan dorongan global untuk mengintegrasikan AI dalam berbagai proses industri. Ini menjadi pengantar menuju pertanyaan inti berikutnya: apakah adopsi AI tersebut memang membawa peningkatan manfaat nyata seperti kenaikan pendapatan industri?
Selanjutnya kami menganalisa lebih dalam mengenai adopsi AI pada setiap sektor industri dan perubahan apa saja yang diberikan AI dalam setiap sektor industri tersebut.
3.2.1 Top 5 Industri dengan AI Adoption Tertinggi
Dari 10 sektor industri, kami menyortir 5 industri dengan adopsi AI tertinggi untuk mendapatkan analisa dampak AI yang signifikan pada sektor industri global. Hasilnya, sama seperti pada rumusan masalah pertama diketahui bahwa industri gaming menjadi sektor industri dengan adopsi AI tertinggi.
Langkah 1: Hitung top 5 industri berdasarkan rata-rata adopsi AI
top5_avg <- dataai %>%
group_by(Industry) %>%
summarise(avg_adopt = mean(AI.Adoption.Rate...., na.rm = TRUE)) %>%
arrange(desc(avg_adopt)) %>%
slice_head(n = 5) %>%
mutate(
Industry = factor(Industry, levels = Industry), # 🔥 bikin pie chart urut
lbl = paste0(Industry, "\n", round(avg_adopt, 1), "%")
)Gambar 4. Top 5 Industri dengan AI Adoption Tertinggi
ggplot(top5_avg, aes(x = "", y = avg_adopt, fill = Industry)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
geom_text(
aes(label = lbl),
position = position_stack(vjust = 0.5, reverse = TRUE),
colour = "white",
fontface = "bold",
size = 4
) +
scale_fill_manual(values = c(
"#7F00FF", # indigo
"#FF00FF", # blue violet
"#00CED1", # slate blue
"#9370DB", # medium purple
"#FF69B4" # hot pink
)) +
labs(title = "Top 5 Industri – Rata-rata AI Adoption") +
theme_void() +
theme(
legend.position = "none",
plot.title = element_text(face = "bold", size = 14)
)
3.2.2 Jumlah Penggunaan AI Tools di 5 Industri dengan AI Adoption Tertinggi
Langkah 1: Urutan industri top 5
industry_order <- c("Gaming", "Education", "Manufacturing", "Legal", "Finance")Langkah 2: Palet warna custom untuk AI tools
warna_custom <- c(
"#94ADE4", "#55B9E2", "#CAB3F4", "#E4DFE6", "#BABDF6",
"#5B91BF", "#A7E4EB", "#B2CFEC", "#E1E0F7", "#E9B5EF"
)Langkah 3: JUMLAH PENGGUNAAN AI TOOLS
tools_count <- dataai %>%
filter(Industry %in% industry_order & !is.na(Top.AI.Tools.Used)) %>%
separate_rows(Top.AI.Tools.Used, sep = ",\\s*") %>%
group_by(Industry, Top.AI.Tools.Used) %>%
summarise(Jumlah = n(), .groups = 'drop') %>%
mutate(
Industry = factor(Industry, levels = industry_order),
Tool_reorder = reorder_within(Top.AI.Tools.Used, Jumlah, Industry),
Tool_clean = Top.AI.Tools.Used
)Langkah 4: Palet warna fix (berdasarkan nama asli tools)
warna_tools <- setNames(rep(warna_custom, length.out = length(unique(tools_count$Tool_clean))),
unique(tools_count$Tool_clean))Gambar 5. Jumlah Penggunaan AI Tools di 5 Industri dengan AI Adoption Tertinggi
plot_jumlah <- ggplot(tools_count, aes(x = Tool_reorder, y = Jumlah, fill = Tool_clean)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = Jumlah), hjust = -0.2, color = "white", fontface = "bold", size = 4) +
coord_flip() +
facet_wrap(~Industry, scales = "free_y") +
scale_x_reordered() +
scale_fill_manual(values = warna_tools) +
labs(
title = "Jumlah Penggunaan AI Tools di 5 Industri dengan AI Adoption Tertinggi",
x = "AI Tools",
y = "Jumlah Penggunaan"
) +
theme_minimal(base_size = 16) +
theme(
plot.background = element_rect(fill = "black", color = NA),
panel.background = element_rect(fill = "black", color = NA),
panel.grid.major = element_line(color = "#2A2A2A"),
panel.grid.minor = element_blank(),
axis.text.x = element_text(face = "bold", color = "white", size = 14),
axis.text.y = element_text(face = "bold", color = "white", size = 14),
axis.title.x = element_text(face = "bold", color = "white", size = 15),
axis.title.y = element_text(face = "bold", color = "white", size = 15),
strip.text = element_text(face = "bold", color = "white", size = 14),
strip.background = element_rect(fill = "black"),
plot.title = element_text(face = "bold", color = "white", size = 18, hjust = 0.5)
)
plot_jumlah
Kemudian, kami tertarik untuk menganalisa lebih lanjut mengenai Content AI yang menjadi variabel utama dalam dataset ini. Content AI ini berbentuk semua output dari semua AI Tools yang digunakan pada setiap sektor industri. Seperti yang kita ketahui, setiap sektor industri berkecimpung dalam spealist yang berbeda sehingga kebutuhannya pun berbeda-beda. Pada sektor industri Gaming dan Finance diketahui AI Tools MidJourney paling banyak digunakan. Sementara pada sektor industri Manufacture diketahui AI Tools Midjourney dan ChatGPT paling banyak digunakan. Di sektor industri Education, diidentifikasi bahwa AI Tools ChatGPT dan Stable Diffussion memiliki frekuensi AI Adoption yang sama. Pada sektor industri hukum/legal menggunakan AI Tools berupa Dall-E paling banyak.
Analisis ini memicu rasa ingin tahu kami terkait bentuk nyata dari output AI Tools yang dominan digunakan di masing-masing sektor industri.
Di industri Gaming, AI Tool MidJourney dimanfaatkan untuk pembuatan visual kreatif seperti desain karakter, senjata, dan lingkungan dalam game. Salah satu contoh penerapannya ada pada game Ebon Blade. Sementara itu, di sektor Manufacturing, ChatGPT digunakan untuk membantu menjawab pertanyaan teknis serta menyusun SOP, sedangkan MidJourney digunakan untuk riset visual terkait branding edukatif, seperti dalam platform Augmentir.
Pada sektor Finance, MidJourney mendukung pembuatan ilustrasi visual seperti ikon, grafik presentasi, dan pitch deck investor, serta edukasi finansial visual—contohnya digunakan dalam Notion AI+. Di sektor Legal, DALL-E menghasilkan ilustrasi TKP dan elemen grafis yang formal namun menarik, seperti yang diterapkan pada aplikasi hukum berbasis web CoCounsel.
Terakhir, pada sektor Education, kombinasi ChatGPT dan Stable Diffusion digunakan untuk menjawab pertanyaan sesuai level pengguna serta menciptakan gambar ilustratif untuk media pembelajaran, seperti yang diterapkan pada Duolingo Max.
3.2.3 Rata-rata Peningkatan Revenue karena AI Tools
Sebagai analisa lanjutan, kami ingin tahu apakah setelah menggunakan AI terdapat perubahan atau kenaikan pendapatan dalam setiap sektor perusahaan tersebut. Hal ini akan menjadi pembanding kenaikan pendapatan dari hasil output di setiap sektor industri yang orisinal buatan manusia dan buatan AI.
revenue_per_tool <- dataai %>%
filter(Industry %in% industry_order & !is.na(Top.AI.Tools.Used)) %>%
separate_rows(Top.AI.Tools.Used, sep = ",\\s*") %>%
group_by(Industry, Top.AI.Tools.Used) %>%
summarise(
avg_revenue_increase = mean(Revenue.Increase.Due.to.AI...., na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
Industry = factor(Industry, levels = industry_order),
Tool_reorder = reorder_within(Top.AI.Tools.Used, avg_revenue_increase, Industry),
Tool_clean = Top.AI.Tools.Used
)Gambar 6. Rata-rata Peningkatan Revenue karena AI Tools
plot_revenue <- ggplot(revenue_per_tool, aes(x = Tool_reorder, y = avg_revenue_increase, fill = Tool_clean)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = paste0(round(avg_revenue_increase, 1), "%")),
hjust = -0.2, size = 3.5, fontface = "bold", color = "white") +
coord_flip() +
facet_wrap(~Industry, scales = "free_y") +
scale_x_reordered() +
scale_fill_manual(values = warna_tools) +
labs(
title = "Rata-rata Peningkatan Revenue karena AI Tools",
x = "AI Tools",
y = "Rata-rata Peningkatan Revenue (%)"
) +
theme_minimal(base_size = 13) +
theme(
plot.background = element_rect(fill = "black", color = NA),
panel.background = element_rect(fill = "black", color = NA),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "#2A2A40"),
strip.background = element_rect(fill = "black"),
strip.text = element_text(face = "bold", color = "white", size = 12),
axis.title = element_text(face = "bold", color = "white", size = 12),
axis.text = element_text(face = "bold", color = "white", size = 11),
plot.title = element_text(face = "bold", color = "white", size = 15)
)
plot_revenue
Seperti yang tertera pada grafik, terdapat kenaikan yang signifikan pada beberapa AI Tools utama di setiap sektor industri. Data ini yang kemudian menjadi titik temu kami mengenai alasan mengapa manusia butuh untuk menerapkan Human-AI Collaboration secara nyata dan menyeluruh. Pada grafik ini, kami berpendapat bahwa AI Adoption benar berpotensi menggantikan peran manusia dalam lingkup industri global, terutama pada top 5 sektor industri diatas.
3.3 Segmen Pasar dengan Penggunaan AI Adoption Tertinggi
Sebagai analisa tambahan, kami ingin tahu apakah AI Adoption ini hanya digunakan dalam pekerjaan di dalam sektor industri tersebut atau bahkan berinteraksi dalam kehidupan nyata secara sosial? #### Segmen pasar mana yang paling “terjamah” AI?
segment_industry <- dataai %>%
mutate(
Market.Segment = case_when(
Industry %in% c("Legal", "Manufacturing") ~ "Business to Business",
Industry %in% c("Education", "Gaming") ~ "Business to Consumer",
Industry == "Finance" ~ "Business to Consumer",
TRUE ~ "Other"
)
) %>%
filter(Market.Segment != "Other") %>%
group_by(Industry, Market.Segment) %>%
summarise(avg_adoption = mean(AI.Adoption.Rate...., na.rm = TRUE), .groups = "drop") %>%
arrange(desc(avg_adoption))Gambar 7. Segmen Pasar dengan Penggunaan AI Adoption Tertinggi
ggplot(segment_industry, aes(x = reorder(Industry, avg_adoption), y = avg_adoption, fill = Market.Segment)) +
geom_col(width = 0.6) +
geom_text(aes(label = paste0(round(avg_adoption, 1), "%")),
vjust = -0.4, fontface = "bold", size = 3.5, color = "white") +
scale_fill_manual(values = c(
"Business to Business" = "#FF00FF",
"Business to Consumer" = "#00FFFF"
)) +
labs(
title = "Rata-rata AI Adoption per Industri Berdasarkan Segmen Pasar",
x = "Industri",
y = "Rata-rata AI Adoption (%)",
fill = "Segmen Pasar"
) +
theme_minimal(base_size = 12) +
theme(
plot.background = element_rect(fill = "black"),
panel.background = element_rect(fill = "black"),
axis.text = element_text(face = "bold", color = "white"),
axis.title = element_text(face = "bold", color = "white"),
plot.title = element_text(face = "bold", size = 15, color = "white"),
legend.text = element_text(color = "white"),
legend.title = element_text(face = "bold", color = "white")
)
🧠 What We Got (Besides Eye Strain)
Dari analisa kami mengenai dataset ini, kami menemukan bahwa AI Adoption tidak bisa ditolak perkembangannya oleh manusia karena merupakan satu dari sekian banyak dampak perkembangan teknologi. AI Adoption yang tidak dilengkapi dengan Human-AI Collaboration nyata secara data berpotensi untuk menggantikan peran manusia dalam pekerjaan maupun lingkup sosial. Sehingga, solusi yang kami tawarkan dan harus dipertimbangkan adalah penguatan atau bahkan perubahan dalam pola pikir dan kemampuan manusia untuk meningkatkan integritas keberadaan manusia, sehingga hadirnya AI justru akan mendukung dan bukan memperalat keberadaan manusia secara pekerjaan maupun lingkup sosial dalam skala global.