Machine Tool Setup Operator
Rp159.857.308 (IDR)/yr
Rp76.854,48 (IDR)/hr
Rp2.557.717 (IDR)/yr
The average machine tool setup operator gross salary in Lampung, Indonesia is Rp159.857.308 or an equivalent hourly rate of Rp76.854. This is 23% lower (-Rp48.503.756) than the average machine tool setup operator salary in Indonesia. In addition, they earn an average bonus of Rp2.557.717. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Lampung, Indonesia. An entry level machine tool setup operator (1-3 years of experience) earns an average salary of Rp120.564.807. On the other end, a senior level machine tool setup operator (8+ years of experience) earns an average salary of Rp181.382.962.
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Rp181.871.890 (IDR)
14 %
Based on our compensation data, the estimated salary potential for Machine Tool Setup Operator will increase 14 % over 5 years.
This chart displays the highest level of education for:
Machine Tool Setup Operator, the majority at 100% with high schools.
Typical Field of Study: Machine Tool Technology/Machinist Training
See how education can impact your salaryThe cost of living in Lampung, Indonesia is 2% less than the average cost of living in Indonesia. Cost of living is calculated based on accumulating the cost of food, transportation, health services, rent, utilities, taxes, and miscellaneous.
View Cost of Living PageLampung (English: ; Indonesian: [ˈlampʊŋ]), officially the Province of Lampung (Indonesian: Provinsi Lampung; Indonesian pronunciation: [proˌfinsi ˈlampʊŋ]), is a province of Indonesia. It is located on the southern tip of the island of Sumatra. It has a short border with the province of Bengkulu to the northwest, and a longer border with the province of South Sumatra to the north, as well as a maritime border with the provinces of Banten and Jakarta to the east. It is the home of the Lampung pe
Sourced from WikipediaERI's compensation data are based on salary surveys conducted and researched by ERI. Cost of labor data in the Assessor Series are based on actual housing sales data from commercially available sources, plus rental rates, gasoline prices, consumables, medical care premium costs, property taxes, effective income tax rates, etc.
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