{"id":947,"date":"2025-06-25T04:38:19","date_gmt":"2025-06-25T04:38:19","guid":{"rendered":"https:\/\/specialskill.id\/article\/?p=947"},"modified":"2026-03-18T05:23:35","modified_gmt":"2026-03-18T05:23:35","slug":"exploratory-data-analysis-eda","status":"publish","type":"post","link":"https:\/\/specialskill.id\/article\/2025\/06\/25\/exploratory-data-analysis-eda\/","title":{"rendered":"Apa Itu Exploratory Data Analysis? Yuk Kenalan Lebih Dalam"},"content":{"rendered":"\n

Kamu pernah denger istilah Exploratory Data Analysis<\/em> atau EDA, tapi sebenernya masih bingung apa itu Exploratory Data Analysis?<\/em>Santai, aku bakal jelasin pakai bahasa yang lebih relate. Jadi gini, EDA<\/a> itu ibaratnya kamu stalk <\/em>instagram gebetan baru. Kamu liat fotonya, scroll-scroll <\/em>dulu, cari tahu hobi dia, sampai pantengin highlight. <\/em>Kalau udah puas, sampai deh kamu ke tahap narik kesimpulan: worth it <\/em>ngga buat diajak serius?<\/p>\n\n\n\n

Nah, EDA tuh versi keren buat stalking data. Kamu bakal jalanain proses awal buat explorasi <\/em>dan kenalan sama dataset, sebelum lanjut ke tahap analisis data yang lebih serius. Intinya Exploratory Data Analysis <\/em>tuh tahap kamu jadi detektif buat data. 
Kamu bakal lihat tuh pattern <\/em>yang menarik, nemuin hal-hal aneh, sampai bisa dapet intuisi tetntang apa yang sebenernya lagi diceritain sama data tersebut. Tanpa EDA, analisis data kamu bisa-bisa salah alamat, alias nyasar.<\/p>\n\n\n\n

Fungsi EDA: Kenapa Sih Kita Harus “Jalan-Jalan” Dulu di Data?<\/strong><\/h2>\n\n\n\n

Jadi, buat apa sebenernya kamu jelajahin data pakai EDA? Apa cuma buat buang-buang waktu? Eits, salah besar. Fungsi utama EDA tuh buat kasih kamu pemahaman lebih dalam dan rasa terhadap data yang lagi di pegang.<\/p>\n\n\n\n

Bayangin aja, kamu dikasih data penjualan 6 bulan toko online. Daripada  langsung kasih prediksi, EDA bakal ngajakin kamu buat nanya: “Produk mana sih yang paling laku keras?”, “Waduh, penjualan lagi turun drastis nih pas bulan apa ya?”, atau “Jangan-jangan ada data penjualan yang aneh, nominalnya gak masuk akal?”<\/p>\n\n\n\n

Fungsi EDA bakal bantuin kamu nemuin pertanyaan-pertanyaan dasar kaya gini. Proses ini juga bantu kamu ngerti karakter data, jadi nggak cuma sebatas angka-aka aja, tapi ujungnya kamu bisa rangkai jadi cerita yang ada maknanya. Alhasil, keputusan lanjutan yang kamu ambil bakal berbasis fakta yang tajam.<\/p>\n\n\n\n

Cara Bikin Pertanyaan Analisis <\/strong><\/h3>\n\n\n\n

Sekarang kamu mulai punya gambaran apa itu Exploratory Data Analysis kan ?<\/em> Supaya proses EDA efektif, kamu butuh pertanyaan jitu. Jangan tanya \u201c Bisnis ku kedepannya bagaimana?\u201d terlalu umum. Coba kamu spesifikkan kaya: “Apakah customer dari usia 20-30 tahun memang paling banyak borong?”, atau \u201cSeberpengaruh apa weekend <\/em>terhadap jumlah order daripada weekdays?\u201d.<\/em><\/p>\n\n\n\n

Pertanyaan yang spesisifik bakal nuntun proses eksplorasi kamu ke arah yang tepat. Kamu jadi tahu harus ngapain ke depannya, dan data mana aja yang perlu di highlight.  Karena segala sesuatunya sudah terukur.<\/p>\n\n\n\n

Eksplorasi Parameter Statistik Data<\/strong><\/h3>\n\n\n\n

Nah, saat kamu ngejalanin EDA, ada beberapa “teman baru” statistik yang wajib dikenalin. Mereka ini yang bakal bantu kamu buat deskripsiin data secara singkat dan padat. <\/p>\n\n\n\n

Pertama, ada keluarga <\/strong>Tendensi Sentral<\/em><\/strong> <\/strong>yang terdiri dari Mean, Median,<\/em><\/strong> <\/strong>dan Modu<\/em><\/strong>s<\/em>. Mereka ini nunjukin “nilai tengah” atau nilai yang paling mewakili data kamu. Terus, ada juga keluarga <\/strong>Dispersi<\/em><\/strong> <\/strong>kayak Range<\/em><\/strong> dan <\/strong>Standard Deviation.<\/em><\/strong> Mereka ini ngasih tau seberapa “nyebar” atau variatif data kamu. Data yang seragam sama sekali beda ceritanya sama data yang nilainya simpang siur. <\/p>\n\n\n\n

Kamu juga cek keluarga distribusi data <\/em> (Distribution Shape)<\/em><\/strong> . Ini penting banget buat ngerti sifat dasar data. Caranya kamu bisa lihat pakai Histogram <\/em><\/strong>atau Boxplot. <\/em><\/strong><\/p>\n\n\n\n

Distribusi data itu ada yang berbentuk normal (simetris kayak lonceng), skewed <\/em><\/strong>(miring ke kiri atau kanan), atau bimodal (<\/em><\/strong>punya dua puncak). Nah, bentuk distribusi ini ngaruh banget ke metode analisis yang bakalkamu pilih nantinya. Misalnya, data yang miring biasanya butuh treatment <\/em>khusus sebelum dimasukkan ke model statistika.. <\/p>\n\n\n\n

Terakhir, jangan lupa cek Outlier,<\/em><\/strong> si penyimpang yang nilai nya jauh banget dari lainnya. Bisa jadi dia salah input, atau justru bintang yang lagi sembunyi!<\/p>\n\n\n\n

Cara Mengelompokkan Data Biar Gampang Dibaca<\/strong><\/h3>\n\n\n\n

Kalau udah mulai paham apa itu Exploratory Data Analysis, <\/em>kamu juga harus paham cara mengelompokkan data biar gampang dibaca. Bayangin aja, kamu punya data 2000 baris data penjualan. Pasti kamu ngerasa ribet kan kalau harus pantengin satu per-satu. Makannya, kamu perlu mengelompokkan datanya.<\/p>\n\n\n\n

Misalnya kamu mau ngelompokin data usia customer dari angka 15-25 tahun, maka kamu bisa pakai Binning. <\/em><\/strong> <\/strong>Kalau pingin ngeringkas data bisa pakai Pivot Tables, <\/em><\/strong>misalnya baut ringkas total penjualan tiap bulan dan kategori produk.<\/p>\n\n\n\n

Selain itu ada juga Teknik Clustering<\/em><\/strong> <\/strong> buat nemuin grup-grup alami di data kamu, , misalnya ngelompokin customer berdasarkan perilaku belanjanya. Dengan begini, data yang awalnya berantakan jadi tertata dan polanya langsung keliatan.<\/p>\n\n\n\n

Jadi habis ini aku harap kamu mulai paham apa itu Exploratory Data Analysis. <\/em>EDA adalah dasar di dunia data science. <\/em>Proses ini juga jadi jembatan buat gabungin data mentah dengan insight <\/em>berharga. Jadi kamu jangan sampai skip EDA ya! Makin dalem kamu kenal data, makin keren juga hasil analisisnya.
<\/p>\n","protected":false},"excerpt":{"rendered":"

Kamu pernah denger istilah Exploratory Data Analysis atau EDA, tapi sebenernya masih bingung apa itu Exploratory Data Analysis?Santai, aku bakal jelasin pakai bahasa yang lebih relate. Jadi gini, EDA itu ibaratnya kamu stalk instagram gebetan baru. Kamu liat fotonya, scroll-scroll dulu, cari tahu hobi dia, sampai pantengin highlight. Kalau udah puas, sampai deh kamu ke tahap narik kesimpulan: worth it ngga buat diajak serius? Nah, EDA tuh versi keren buat stalking data. Kamu bakal jalanain proses awal buat explorasi dan kenalan sama dataset, sebelum lanjut ke tahap analisis data yang lebih serius. Intinya Exploratory Data Analysis tuh tahap kamu jadi detektif buat data. Kamu bakal lihat tuh pattern yang menarik, nemuin hal-hal aneh, sampai bisa dapet intuisi tetntang apa yang sebenernya lagi diceritain sama data tersebut. Tanpa EDA, analisis data kamu bisa-bisa salah alamat, alias nyasar. Fungsi EDA: Kenapa Sih Kita Harus “Jalan-Jalan” Dulu di Data? Jadi, buat apa sebenernya kamu jelajahin data pakai EDA? Apa cuma buat buang-buang waktu? Eits, salah besar. Fungsi utama EDA tuh buat kasih kamu pemahaman lebih dalam dan rasa terhadap data yang lagi di pegang. Bayangin aja, kamu dikasih data penjualan 6 bulan toko online. Daripada  langsung kasih prediksi, EDA bakal ngajakin kamu buat nanya: “Produk mana sih yang paling laku keras?”, “Waduh, penjualan lagi turun drastis nih pas bulan apa ya?”, atau “Jangan-jangan ada data penjualan yang aneh, nominalnya gak masuk akal?” Fungsi EDA bakal bantuin kamu nemuin pertanyaan-pertanyaan dasar kaya gini. Proses ini juga bantu kamu ngerti karakter data, jadi nggak cuma sebatas angka-aka aja, tapi ujungnya kamu bisa rangkai jadi cerita yang ada maknanya. Alhasil, keputusan lanjutan yang kamu ambil bakal berbasis fakta yang tajam. Cara Bikin Pertanyaan Analisis Sekarang kamu mulai punya gambaran apa itu Exploratory Data Analysis kan ? Supaya proses EDA efektif, kamu butuh pertanyaan jitu. Jangan tanya \u201c Bisnis ku kedepannya bagaimana?\u201d terlalu umum. Coba kamu spesifikkan kaya: “Apakah customer dari usia 20-30 tahun memang paling banyak borong?”, atau \u201cSeberpengaruh apa weekend terhadap jumlah order daripada weekdays?\u201d. Pertanyaan yang spesisifik bakal nuntun proses eksplorasi kamu ke arah yang tepat. Kamu jadi tahu harus ngapain ke depannya, dan data mana aja yang perlu di highlight.  Karena segala sesuatunya sudah terukur. Eksplorasi Parameter Statistik Data Nah, saat kamu ngejalanin EDA, ada beberapa “teman baru” statistik yang wajib dikenalin. Mereka ini yang bakal bantu kamu buat deskripsiin data secara singkat dan padat.  Pertama, ada keluarga Tendensi Sentral yang terdiri dari Mean, Median, dan Modus. Mereka ini nunjukin “nilai tengah” atau nilai yang paling mewakili data kamu. Terus, ada juga keluarga Dispersi kayak Range dan Standard Deviation. Mereka ini ngasih tau seberapa “nyebar” atau variatif data kamu. Data yang seragam sama sekali beda ceritanya sama data yang nilainya simpang siur.  Kamu juga cek keluarga distribusi data  (Distribution Shape) . Ini penting banget buat ngerti sifat dasar data. Caranya kamu bisa lihat pakai Histogram atau Boxplot.  Distribusi data itu ada yang berbentuk normal (simetris kayak lonceng), skewed (miring ke kiri atau kanan), atau bimodal (punya dua puncak). Nah, bentuk distribusi ini ngaruh banget ke metode analisis yang bakalkamu pilih nantinya. Misalnya, data yang miring biasanya butuh treatment khusus sebelum dimasukkan ke model statistika..  Terakhir, jangan lupa cek Outlier, si penyimpang yang nilai nya jauh banget dari lainnya. Bisa jadi dia salah input, atau justru bintang yang lagi sembunyi! Cara Mengelompokkan Data Biar Gampang Dibaca Kalau udah mulai paham apa itu Exploratory Data Analysis, kamu juga harus paham cara mengelompokkan data biar gampang dibaca. Bayangin aja, kamu punya data 2000 baris data penjualan. Pasti kamu ngerasa ribet kan kalau harus pantengin satu per-satu. Makannya, kamu perlu mengelompokkan datanya. Misalnya kamu mau ngelompokin data usia customer dari angka 15-25 tahun, maka kamu bisa pakai Binning.  Kalau pingin ngeringkas data bisa pakai Pivot Tables, misalnya baut ringkas total penjualan tiap bulan dan kategori produk. Selain itu ada juga Teknik Clustering  buat nemuin grup-grup alami di data kamu, , misalnya ngelompokin customer berdasarkan perilaku belanjanya. Dengan begini, data yang awalnya berantakan jadi tertata dan polanya langsung keliatan. Jadi habis ini aku harap kamu mulai paham apa itu Exploratory Data Analysis. EDA adalah dasar di dunia data science. Proses ini juga jadi jembatan buat gabungin data mentah dengan insight berharga. Jadi kamu jangan sampai skip EDA ya! Makin dalem kamu kenal data, makin keren juga hasil analisisnya.<\/p>\n","protected":false},"author":1,"featured_media":1168,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[9],"tags":[104,105],"class_list":["post-947","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analyst","tag-apa-itu-exploratory-data-analysis","tag-pengertian-eda"],"yoast_head":"\nApa Itu Exploratory Data Analysis? 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