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The first Address Quality benchmark result was 49.1%, as documented in How I Benchmark Address Quality. Lower than the odds of a coin toss. LOL. But the benchmark does more than give me a number. It keeps the result for every address: the expected location, API response, generated candidates, and their confidence. That report gives me something to inspect when the number looks bad. I went through the report expecting to find problems in the engine, but the first mistake I found was in the test data. ...

In the previous post, I wrote about how Address Quality interprets an address. It extracts evidence, builds possible candidates, then ranks them based on how well they match the available evidence. While building it, I tested the engine with addresses like this: Jl. Wastukencana No. 2, Babakan Ciamis, Sumur Bandung, Kota Bandung, Jawa Barat It works. This is just a happy flow. Another person could input the address in an unexpected or completely different way. They may abbreviate something, skip part of the administrative hierarchy, put the road name first, or simply write the same address differently from how I would write it. ...

In the previous post, I asked why address forms still make you select information you have already typed. My proposed solution was to move that effort into an API. You provide the address, the API interprets it, then you confirm the result. So I built Address Quality, an experiment for turning free-text Indonesian addresses into structured administrative data. The first alpha version tries to answer two questions: Which parts of the address can it recognize? Which possible location matches those parts best? That sounds simple. But something that sounds simple isn’t always easy to solve. ...

One question keeps coming up while I build Address Quality: Why not just use Google Maps API? If I have a messy address, Google Maps API can turn it into a structured result. It also knows roads, buildings, businesses, and landmarks that my administrative data doesn’t. It is the top of mind when we are trying to solve an address problem. Take this place name: Paskal Hyper Square On its own, that doesn’t tell me much about province, city, district, or subdistrict. Yet, Google can still make sense of it. ...

I’ve noticed something common about address forms in Indonesia. You type your address. Then you have to fill in other fields such as kelurahan(subdistrict), kecamatan(district), city, and even province. No matter how complete your address is. For example, you have entered this address: Sudirman Central Business District(SCBD), Jl. Jend. Sudirman kav 52-53 No.LOT 6-8, RT.5/RW.1, Senayan, Kec. Kebayoran. Baru, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta 12190 Complete and well structured. But then you have to select again: ...

This is the first post in a series about building Address Quality, an API that validates Indonesian addresses. I want to document it honestly: the failures, the measurements, and the changes in thinking. It is not a “nailed it” story yet, because the work is just getting started. This post covers the why and the how. It ends with the first benchmark: 49% accuracy on my own test set. I will explain later why that number turned out to be useful. ...

There are various challenge in managing multiple golang microservice. One of them is develop the service with right golang version. I’ve work on a legacy service that set with go1.16. But the thing is I have go1.23. Without using same version, it is painful just to start in local. I have to run go mod vendor, met incompatibilities, go get, go mod vendor, broken there broken here.. Waste of time! ...

Fellas, imagine you’ve built a powerful API. It perform well at first. No sign of sluggish, your users is happy as the app feels smooth. The business is thriving, your user base grows. Things start slowing down. Loading after click button take longer, and sometimes, the server crashes returning error. Looking at the server metric, it’s your API that doesn’t run as fast as your first deployment. The problem, you notice that the API goes slow only during high concurrent requests. While at single request it does fine. Now, how will you replicate this condition? ...

Port conflict usually happen whenever a service try to run and listen to a specific port while another server currently utilizing it. For those who manage multiple service repository, this could be quite common. The simplest way is to search and terminate the binary. But what if the binary stalled or we have no idea where did we ran it. No worry, with a simple command, we can figure it out in less than a minute! ...

Golang provide a way to know our code efficiency by benchmark our code. By benchmarking our code, we able to see how fast our code can run and even gain more insight. An efficient code displayed from the resource required to complete it execution. The faster it can run, the better. The lower resource required, the better. This concept also applied for codes that run with serverless platform such as AWS Lambda or Google Cloud Function. You are charged based on the number of requests for your functions and the duration it takes to execute. ...