How to make applying for jobs less painful | The Way We Work, a TED series

Applying for jobs online

Translator: Leslie Gauthier Reviewer: Hafizh Alfarisi

Melamar kerja secara daring

is one of the worst digital experiences of our time.

adalah salah satu pengalaman digital paling meresahkan.

And applying for jobs in person really isn't much better.

Melamar secara langsung pun tak jauh berbeda.

[The Way We Work]

[Cara Kita Bekerja]

(Musik)

Hiring as we know it is broken on many fronts.

Ada banyak cela di berbagai sisi perekrutan.

It's a terrible experience for people.

Rekrutmen pun jadi pengalaman buruk.

About 75 percent of people

Sekitar 75 persen orang yang melamar kerja dengan beragam metode setahun belakangan,

who applied to jobs using various methods in the past year

said they never heard anything back from the employer.

mengaku tak pernah mendengar kabar lagi dari pemberi kerja.

And at the company level it's not much better.

Perusahaan pun tak jauh berbeda.

46 percent of people get fired or quit

Sekitar 46% pekerja dipecat atau berhenti dalam tahun pertama bekerja.

within the first year of starting their jobs.

It's pretty mind-blowing.

Cukup mengejutkan dan buruk bagi perekonomian.

It's also bad for the economy.

For the first time in history,

Pertama kalinya dalam sejarah,

we have more open jobs than we have unemployed people,

jumlah lowongan lebih banyak daripada pengangguran.

and to me that screams that we have a problem.

Artinya, kita punya masalah.

I believe that at the crux of all of this is a single piece of paper: the résumé.

Aku yakin semua ini berawal dari selembar kertas: resume.

A résumé definitely has some useful pieces in it:

Resume berisi hal-hal penting.

what roles people have had, computer skills,

Pengalaman kerja pelamar, kemahiran komputer, bahasa yang dikuasai.

what languages they speak,

but what it misses is what they have the potential to do

Namun, resume tak membahas potensi yang belum diperdalam oleh pelamar.

that they might not have had the opportunity to do in the past.

Dengan perekonomian yang cepat berubah sehingga pekerjaan jadi serba daring,

And with such a quickly changing economy where jobs are coming online

that might require skills that nobody has,

keahlian yang sungguh baru mungkin diperlukan.

if we only look at what someone has done in the past,

Jika kita cuma melihat keahlian yang sudah-sudah,

we're not going to be able to match people to the jobs of the future.

akan sulit menyerasikan orang ke pekerjaan masa depan.

So this is where I think technology can be really helpful.

Menurutku, di sinilah teknologi berguna.

You've probably seen that algorithms have gotten pretty good

Kau mungkin tahu kalau algoritma makin pandai mengategorikan pilihan orang.

at matching people to things,

but what if we could use that same technology

Tapi, apa jadinya bila kita gunakan teknologi ini,

to actually help us find jobs that we're really well-suited for?

untuk membantu menemukan pekerjaan yang sesuai?

But I know what you're thinking.

Aku tahu apa pendapatmu.

Algorithms picking your next job sounds a little bit scary,

Algoritma yang mencarikan pekerjaanmu memang terdengar seram.

but there is one thing that has been shown

Namun, ada satu hal yang terbukti

to be really predictive of someone's future success in a job,

bisa memperkirakan keberhasilan pekerjaan seseorang kelak.

and that's what's called a multimeasure test.

Itulah yang disebut tes multi-ukur.

Multimeasure tests really aren't anything new,

Tes multi-ukur bukanlah hal baru,

tapi umumnya sangat mahal dan harus didampingi seorang doktor

but they used to be really expensive

and required a PhD sitting across from you

and answering lots of questions and writing reports.

serta perlu menjawab banyak pertanyaan dan menulis laporan.

Multimeasure tests are a way

Tes multi-ukur adalah cara memahami sifat bawaan seseorang,

to understand someone's inherent traits --

your memory, your attentiveness.

ingatanmu, ketelitianmu.

What if we could take multimeasure tests

Apa jadinya bila kita gunakan tes ini dan membuatnya terukur serta mudah diakses

and make them scalable and accessible,

and provide data to employers about really what the traits are

selagi memberi data kepada pemberi kerja tentang sifat macam apa

of someone who can make them a good fit for a job?

yang menunjukkan kecocokan seseorang dengan pekerjaannya?

This all sounds abstract.

Memang terdengar abstrak.

Let's try one of the games together.

Mari kita coba satu permainan.

You're about to see a flashing circle,

Kau akan lihat sekelebat lingkaran.

Tugasmu ialah bertepuk tangan saat lingkarannya berwarna merah

and your job is going to be to clap when the circle is red

and do nothing when it's green.

dan diam saat warnanya hijau.

[Ready?]

[Siap?]

[Begin!]

[Mulai!]

[Green circle]

[Lingkaran Hijau]

[Green circle]

[Lingkaran Hijau]

[Red circle]

[Lingkaran Merah]

[Green circle]

[Lingkaran Hijau]

[Red circle]

[Lingkaran Merah]

Maybe you're the type of person

Mungkin kau tipe orang yang lekas bertepuk saat lingkaran merah muncul.

who claps the millisecond after a red circle appears.

Or maybe you're the type of person

Atau mungkin kau tipe yang menunggu sebentar agar sepenuhnya yakin.

who takes just a little bit longer to be 100 percent sure.

Atau mungkin kau tak sengaja bertepuk saat warnanya hijau.

Or maybe you clap on green even though you're not supposed to.

Bagian menariknya adalah ini bukan tes standar

The cool thing here is that this isn't like a standardized test

yang menentukan seseorang dapat dipekerjakan atau tidak.

where some people are employable and some people aren't.

Instead it's about understanding the fit between your characteristics

Tes ini membantu menyesuaikan karaktermu dengan pekerjaan mana yang cocok denganmu.

and what would make you good a certain job.

Jika kau telat bertepuk pada warna merah dan tak pernah bertepuk pada warna hijau,

We found that if you clap late on red and you never clap on the green,

you might be high in attentiveness and high in restraint.

kau mungkin punya tingkat ketelitian dan kontrol diri yang tinggi.

People in that quadrant tend to be great students, great test-takers,

Orang-orang di kuadran itu merupakan murid dan peserta ujian yang mahir,

great at project management or accounting.

cakap dalam manajemen proyek atau akuntansi.

But if you clap immediately on red and sometimes clap on green,

Jika kau lekas bertepuk saat warna merah dan kadang saat hijau,

that might mean that you're more impulsive and creative,

kau mungkin orang yang impulsif serta kreatif

and we've found that top-performing salespeople often embody these traits.

dan kami melihat para sales terbaik mempunyai sifat ini.

The way we actually use this in hiring

Kami bisa menerapkan tes ini di perekrutan

is we have top performers in a role go through neuroscience exercises

ialah karena pekerja terbaik di bidangnya menjalani uji ilmu saraf serupa.

like this one.

Then we develop an algorithm

Lalu, kami membuat algoritma yang memahami keunikan dari para pekerja terbaik ini.

that understands what makes those top performers unique.

And then when people apply to the job,

Saat ada yang melamar suatu pekerjaan,

we're able to surface the candidates who might be best suited for that job.

kami bisa memperkirakan kandidat yang paling sesuai untuk pekerjaan itu.

So you might be thinking there's a danger in this.

Kau mungkin menduga tes ini berisiko.

The work world today is not the most diverse

Dunia kerja saat ini tak terlalu beragam.

and if we're building algorithms based on current top performers,

Jika kita membangun algoritma berdasarkan pekerja terbaik saja,

how do we make sure

bagaimana memastikan kalau kita justru tak memperkeruh bias yang telah ada?

that we're not just perpetuating the biases that already exist?

For example, if we were building an algorithm based on top performing CEOs

Contohnya, jika algoritma dibuat berdasarkan CEO dengan kinerja terbaik

and use the S&P 500 as a training set,

dan menggunakan indeks perusahaan besar sebagai alat uji,

you would actually find

kemungkinannya kau cenderung merekrut

that you're more likely to hire a white man named John than any woman.

pria kulit putih bernama John daripada wanita.

And that's the reality of who's in those roles right now.

Itulah kenyataan pimpinan perusahaan yang ada saat ini.

But technology actually poses a really interesting opportunity.

Namun, teknologi telah membuka kesempatan yang sangat menarik.

We can create algorithms that are more equitable

Kita bisa membuat algoritma yang lebih adil dan merata

and more fair than human beings have ever been.

daripada yang diukur manusia.

Every algorithm that we put into production has been pretested

Tiap algoritma yang kami ciptakan sudah dites sebelumnya

to ensure that it doesn't favor any gender or ethnicity.

agar hasilnya tak condong ke gender atau etnik tertentu.

And if there's any population that's being overfavored,

Jika ada populasi tertentu yang lebih sering terpilih,

we can actually alter the algorithm until that's no longer true.

kita bisa mengubah algoritmanya hingga hal itu tak terjadi lagi.

When we focus on the inherent characteristics

Jika kita berfokus pada karakteristik asli

that can make somebody a good fit for a job,

yang mencocokkan seseorang ke suatu pekerjaan,

we can transcend racism, classism, sexism, ageism --

kita bisa mengesampingkan pembedaan ras, kelas sosial, gender, umur,

even good schoolism.

dan bahkan asal sekolahnya.

Our best technology and algorithms shouldn't just be used

Teknologi dan algoritma terbaik kita jangan hanya digunakan

for helping us find our next movie binge or new favorite Justin Bieber song.

untuk mencari rekomendasi film atau lagu Justin Bieber terbaru.

Imagine if we could harness the power of technology

Bayangkan jika kita bisa memanfaatkan kekuatan teknologi

to get real guidance on what we should be doing

sebagai panduan atas apa yang harusnya kita lakukan

based on who we are at a deeper level.

sesuai karakter kita secara mendalam.