Pola Keyword SEO Spam Judi Online di Indonesia

Hasil pengamatan menunjukkan bahwa changeset SEO spam judi online di OpenStreetMap memiliki pola yang relatif konsisten. Brand umumnya menggunakan akhiran “88”, sedangkan komentar changeset berisi kumpulan kata kunci promosi yang diulang untuk kepentingan optimasi mesin pencari (SEO), bukan untuk mendeskripsikan perubahan data OpenStreetMap.

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Observations indicate that SEO spam changesets related to online gambling in OpenStreetMap follow a relatively consistent pattern. Brand names commonly use the “88” suffix, while changeset comments consist of collections of promotional keywords repeated for search engine optimization (SEO) purposes rather than describing the actual OpenStreetMap data edits.

Keywords are ordered by descending frequency

Very High / Dominant Frequency

–88 (brand suffix)
slot
gacor
judi
casino
terpercaya
terbaik

High Frequency

login
daftar
link alternatif
RTP
maxwin
jackpot
bonus
deposit
withdraw
mahjong
zeus
Pragmatic Play
PG Soft
togel
toto
–77 (suffix)
–99 (suffix)
–138 (suffix)
–168 (suffix)
–188 (suffix)
–303 (suffix)
–777 (suffix)

Moderate Frequency

pola
cashback
free spin
scatter
wild
multiplier
cuan
menang
sabung ayam
SV388
poker
domino
QQ
Habanero
Joker
Spadegaming
Microgaming
Playtech

Unique Study: Trojan Horse Spam

Pada teknik ini, komentar changeset tampak sepenuhnya normal dan tidak mengandung kata kunci yang berkaitan dengan perjudian. Sebaliknya, konten spam disembunyikan di dalam tag objek OSM, sehingga deteksi yang hanya mengandalkan kata kunci pada komentar changeset menjadi kurang efektif.

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In this technique, the changeset comment appears completely legitimate and contains no gambling-related keywords. Instead, the spam is concealed within the OSM object’s tags, making keyword-based changeset detection less effective.

Contoh / Example:

… See full entry

If you use the amazing better-osm-org browser extension/userscript to review changesets, you are probably familiar with its default red, green, and yellow diff highlighting.

While the default colors are great, sometimes you want a custom color scheme that feels more comfortable for your eyes during long validation sessions. For instance, changing the added tags to a cool cyan, keeping deleted tags red, and making modified tags a soft cream/yellow.

Since the script dynamically manages light/dark themes, the easiest way to override these colors globally without messing up the extension’s code is by using custom CSS via the Stylus extension.

How to Apply It:

1. Install the Stylus extension (Chrome/Firefox/Edge).
2. Go to openstreetmap.org.
3. Click the Stylus extension icon and select ”(+) Write style for: openstreetmap.org”.
4. Paste the CSS code below into the editor, give it a name (e.g., Better OSM Custom Colors), and hit Save.

The CSS Code:

/* 1. MODIFIED TAGS -> SOFT CREAM / YELLOW */
    tr.quick-look-modified-tag th,
    tr.quick-look-modified-tag td {
        background: rgba(255, 235, 150, 0.7) !important;
        color: #000000 !important;
    }

/* 2. DELETED TAGS -> RED */
    tr.quick-look-deleted-tag th,
    tr.quick-look-deleted-tag td {
        background: rgba(238, 51, 9, 0.6) !important;
        color: #000000 !important;
    }

/* 3. NEW TAGS -> CYAN */
    tr.quick-look-new-tag th,
    tr.quick-look-new-tag td,
    tr.quick-look-added-tag th,
    tr.quick-look-added-tag td {
        background: rgba(0, 200, 220, 0.6) !important;
        color: #000000 !important;
    }

Alright, after successfully updating the boundaries in Bali, now, I’m shifting my focus further east across the Lombok Strait. Next stop: Lombok Island.

For those who don’t know, Lombok is a gem in the Lesser Sunda Islands, famous for the Mandalika International Circuit and its world-class surf breaks. It’s a new major tourist hub that definitely deserves some love on the map. I’ve already spent some time mapping the natural=* and landuse=* in the smaller islands.

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Baiklah, setelah berhasil mengupdate batas wilayah di Bali, kini saya alihkan fokus saya ke arah timur melintasi Selat Lombok. Selanjutnya: Pulau Lombok.

Bagi yang belum tahu, Lombok adalah permata di Kepulauan Sunda Kecil, terkenal dengan Sirkuit Internasional Mandalika dan ombak selancar kelas dunia. Wilayah ini bisa jadi pusat wisata baru yang layak mendapat perhatian di peta. Sebelumnya, saya telah memetakan natural=* dan landuse=* di pulau-pulau kecilnya.

If you’ve been surveying the OpenStreetMap (OSM) map notes in Indonesia, you’ve likely encountered a wave of “spam” or irrelevant notes. These aren’t usually malicious; rather, they are often misplaced private memos from third-party mobile applications that use OSM tiles.

Users often mistake the “Add a Note” feature for a personal notepad or a private “Save Location” tool. Some of these notes are even remnants of data synced from apps nearly a decade ago.

Why is this happening? Many apps integrate OSM. Users see a “Notes” button and assume it’s a private diary for their travels.

This list of spam keywords is not exhaustive. I will continue to update this diary entry as new patterns emerge from the Indonesian mapping community.

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Jika Anda melihat catatan peta OpenStreetMap (OSM) di Indonesia, kemungkinan besar Anda akan menemukan “spam” atau catatan yang tidak relevan. Memo tersebut sering kali merupakan memo pribadi yang salah tempat dari aplikasi seluler pihak ketiga yang menggunakan tile OSM.

Pengguna sering salah mengira fitur “Tambahkan Catatan” sebagai catatan pribadi atau fitur “Simpan Lokasi” pribadi. Beberapa catatan ini bahkan merupakan sisa data yang disinkronkan dari aplikasi hampir satu dekade lalu.

Mengapa ini terjadi? Banyak aplikasi mengintegrasikan OSM. Pengguna melihat tombol “Catatan” dan menganggapnya sebagai buku harian pribadi untuk perjalanan mereka.

Daftar kata kunci spam ini tidak lengkap. Saya akan terus memperbarui catatan harian ini seiring munculnya pola-pola baru dari komunitas pemetaan Indonesia.

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… See full entry

Saya sedang memulai proyek untuk menambahkan tag name:id pada objek place=* di wilayah Jepang, Korea Selatan, dan Korea Utara. Tujuannya adalah mempermudah pengguna Indonesia dalam mencari dan mengenali nama tempat di Asia Timur sesuai dengan ejaan dan istilah administratif yang lazim (seperti Prefektur dan Provinsi).

Untuk saat ini, penyuntingan hanya dilakukan pada level Node. Objek berupa Relation (seperti batas administrasi) belum termasuk dalam cakupan kerja saya saat ini karena batasan workflow teknis.

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Localizing place names (name:id) in Japan and the Koreas

I am starting a project to add the name:id tag to place=* objects in the Japan, South Korea, and North Korea regions. The purpose is to assist Indonesian users in searching for and recognizing place names in East Asia according to common spellings and administrative terms (such as Prefecture and Province).

For now, editing is only being performed at the Node level. Relation objects (such as administrative boundaries) are not yet included in my current scope of work due to technical workflow limitations.

A few days ago, I asked the community about converting general GIS polygons into OSM multipolygon relations. I’ve searched online but haven’t found a workflow that fits my needs. Specifically, I am looking for a way to handle three different levels of administrative boundaries where adjacent areas share a single boundary line connected via a relation.

My question on the OSM forum is still awaiting a solution: Link

However, someone from my local community mentioned that what I’m looking for is “topology.” While that is a broad GIS term, they clarified that TopoJSON is a specific format designed for this. There are many converters available to turn GeoJSON into TopoJSON.

Interestingly, I found that someone opened a ticket for a TopoJSON converter in JOSM back in 2020, but it hasn’t received a response yet: Link

I’m planning to update and expand the administrative boundaries for Bali in OSM. I’ve already prepared the multipolygons for admin_level 5, 6, and 7 using single shared ways for efficiency. By leveraging Google Sheets, I’ve also compiled a comprehensive list of Wikidata, Wikipedia links, and multilingual names to better serve Bali’s international profile.

However, the conflation process is proving to be a challenge. The existing data is quite a “nightmare” to clean up; many roads and waterways are currently shared with administrative relations, and landuse or natural features are glued to the boundaries. Time to start untangling!

Memetakan batas administrasi di Indonesia bisa jadi cukup rumit, terutama saat menghadapi nama wilayah yang serupa. Berikut adalah alur kerja (workflow) sederhana saya dalam menyiapkan data tersebut:

1. Sumber Data

Pertama, unduh data spasial resmi dari Peta Rupa Bumi oleh Badan Informasi Geospasial (BIG) atau mencari di Satu Data Indonesia. Data ini berfungsi sebagai sumber geometri utama.

2. Ekstraksi Titik Lokasi (Place Nodes)

Karena data sumber berbentuk poligon, saya menggunakan QGIS untuk mengekstrak titik tengah (centroid). Titik-titik ini penting untuk membuat tag place=* yang mewakili pusat dari tiap wilayah administrasi.

3. Pentingnya Kode Kemendagri

Poligon tersebut mencakup kode referensi Kemendagri. Kode ini sangat vital untuk:

• Konflasi: Memastikan data cocok dengan set data lainnya.

• Identifikasi: Banyak desa (admin_level 7 atau 8) memiliki nama yang sama. Kode ini membantu membedakannya dalam satu Kabupaten atau Provinsi.

4. Pengayaan Metadata

Menggunakan alat spreadsheet dan teknik konflasi, saya mencocokkan data untuk menambahkan:

• Kode pos

• Tag wikidata dan wikipedia.

• Nama dalam berbagai bahasa (name:en, dsb).

5. Pengolahan Geometri

Sesuai dengan praktik terbaik (best practices) di OSM, saya mengubah poligon menjadi garis terpisah (polylines).

• Hal ini memungkinkan wilayah yang bertetangga untuk berbagi satu garis batas yang sama melalui relasi multipolygon.

• Setelah dikonversi, saya mengekspor hasilnya dalam format .geojson.

6. Pengetagan Akhir (Final Tagging)

Terakhir, saya menggunakan titik lokasi (place nodes) yang telah diekstrak sebelumnya untuk menyalin dan menempelkan tag yang relevan ke dalam relasi multipolygon baru di editor OSM.

Mapping administrative boundaries in Indonesia can tricky especially when dealing with overlapping names. Here is my simplified workflow for preparing this data:

1. Data Sourcing

First, download the official spatial data from Peta Rupa Bumi by Badan Informasi Geospasial or searching it in Satu Data Indonesia. This serves as the primary geometry source.

2. Extracting Place Nodes

Since the source data is in polygon format, I use QGIS to extract the centroids (points). These points are essential for creating the place=* tags that represent the center of each administrative area.

3. The Importance of Kemendagri Codes

The polygons include Kemendagri reference codes. These are vital for:

• Conflation: Ensuring data matches across different sets.

• Identification: Many villages (admin_level 7 or 8) share the same name. The code helps distinguish them within a Regency or Province.

4. Enriching Metadata

Using spreadsheet tools and conflation techniques, I cross-reference the data to add:

• Postal codes

• Wikidata and Wikipedia tags.

• Multilingual names (name:en, etc.).

5. Geometry Processing

To follow OSM best practices, I convert the polygons into independent ways (polylines).

• This allows adjacent areas to share a single boundary line via a multipolygon relation.

• Once converted, I export the result as a .geojson file.

6. Final Tagging

Finally, I use the previously extracted place nodes to quickly copy and paste the relevant tags into the new multipolygon relations in my OSM editor.