The statistic depicts the total population of South Sumatra, Indonesia in 2000 and 2010 with estimates up to 2030. In 2030, it was forecasted that the number of inhabitants in South Sumatra would amount to around **** million.

Indonesia BPS Projection: Population: Sumatra data was reported at 68,500.000 Person th in 2035. This records an increase from the previous number of 68,026.600 Person th for 2034. Indonesia BPS Projection: Population: Sumatra data is updated yearly, averaging 48,401.250 Person th from Dec 1980 (Median) to 2035, with 56 observations. The data reached an all-time high of 68,500.000 Person th in 2035 and a record low of 28,120.800 Person th in 1980. Indonesia BPS Projection: Population: Sumatra data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GAA002: Population Projection: by Province: Central Bureau of Statistics.

Indonesia BPS Projection: Population: Sumatra: West Sumatra data was reported at 6,130.400 Person th in 2035. This records an increase from the previous number of 6,102.000 Person th for 2034. Indonesia BPS Projection: Population: Sumatra: West Sumatra data is updated yearly, averaging 4,730.450 Person th from Dec 1980 (Median) to 2035, with 56 observations. The data reached an all-time high of 6,130.400 Person th in 2035 and a record low of 3,419.500 Person th in 1980. Indonesia BPS Projection: Population: Sumatra: West Sumatra data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GAA002: Population Projection: by Province: Central Bureau of Statistics.

Indonesia BPS Projection: Population: Sumatra: South Sumatra data was reported at 9,610.700 Person th in 2035. This records an increase from the previous number of 9,563.800 Person th for 2034. Indonesia BPS Projection: Population: Sumatra: South Sumatra data is updated yearly, averaging 8,318.650 Person th from Dec 2000 (Median) to 2035, with 36 observations. The data reached an all-time high of 9,610.700 Person th in 2035 and a record low of 6,767.645 Person th in 2005. Indonesia BPS Projection: Population: Sumatra: South Sumatra data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GAA002: Population Projection: by Province: Central Bureau of Statistics.

2.80 (births per woman) in 2012. Source : 1971, 1980, 1990 , 2000 Population Census,1985 Intercensal Population Census, and 1991, 1994 Indonesian Demographic and Health Survey

Abstract

The 1991 Indonesia Demographic and Health Survey (IDHS) is a nationally representative survey of ever-married women age 15-49. It was conducted between May and July 1991. The survey was designed to provide information on levels and trends of fertility, infant and child mortality, family planning and maternal and child health. The IDHS was carried out as collaboration between the Central Bureau of Statistics, the National Family Planning Coordinating Board, and the Ministry of Health. The IDHS is follow-on to the National Indonesia Contraceptive Prevalence Survey conducted in 1987.

The DHS program has four general objectives: - To provide participating countries with data and analysis useful for informed policy choices; - To expand the international population and health database; - To advance survey methodology; and - To help develop in participating countries the technical skills and resources necessary to conduct demographic and health surveys.

In 1987 the National Indonesia Contraceptive Prevalence Survey (NICPS) was conducted in 20 of the 27 provinces in Indonesia, as part of Phase I of the DHS program. This survey did not include questions related to health since the Central Bureau of Statistics (CBS) had collected that information in the 1987 National Socioeconomic Household Survey (SUSENAS). The 1991 Indonesia Demographic and Health Survey (IDHS) was conducted in all 27 provinces of Indonesia as part of Phase II of the DHS program. The IDHS received financial assistance from several sources.

The 1991 IDHS was specifically designed to meet the following objectives: - To provide data concerning fertility, family planning, and maternal and child health that can be used by program managers, policymakers, and researchers to evaluate and improve existing programs; - To measure changes in fertility and contraceptive prevalence rates and at the same time study factors which affect the change, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and the availability of contraception; - To measure the development and achievements of programs related to health policy, particularly those concerning the maternal and child health development program implemented through public health clinics in Indonesia.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data [ssd]

Sampling procedure

Indonesia is divided into 27 provinces. For the implementation of its family planning program, the National Family Planning Coordinating Board (BKKBN) has divided these provinces into three regions as follows:

• Java-Bali: Jakarta, West Java, Central Java, Yogyakarta, East Java, and Bali
• Outer Java-Bali I: Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, West Kalimantan, South Kalimantan, North Sulawesi, South Sulawesi, and West Nusa Tenggara
• Outer Java-Bali II: Riau, Jambi, Bengkulu, East Nusa Tenggara, East Timor, Central Kalimantan, East Kalimantan, Central Sulawesi, Southeast Sulawesi, Maluku, and Irian Jaya.

The 1990 Population Census of Indonesia shows that Java-Bali contains about 62 percent of the national population, while Outer Java-Bali I contains 27 percent and Outer Java-Bali II contains 11 percent. The sample for the Indonesia DHS survey was designed to produce reliable estimates of contraceptive prevalence and several other major survey variables for each of the 27 provinces and for urban and rural areas of the three regions.

In order to accomplish this goal, approximately 1500 to 2000 households were selected in each of the provinces in Java-Bali, 1000 households in each of the ten provinces in Outer Java-Bali I, and 500 households in each of the 11 provinces in Outer Java-Bali II for a total of 28,000 households. With an average of 0.8 eligible women (ever-married women age 15-49) per selected household, the 28,000 households were expected to yield approximately 23,000 individual interviews.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The DHS model "A" questionnaire and manuals were modified to meet the requirements of measuring family planning and health program attainment, and were translated into Bahasa Indonesia.

Cleaning operations

The first stage of data editing was done by the field editors who checked the completed questionnaires for completeness and accuracy. Field supervisors also checked the questionnaires. They were then sent to the central office in Jakarta where they were edited again and open-ended questions were coded. The data were processed using 11 microcomputers and ISSA (Integrated System for Survey Analysis).

Data entry and editing were initiated almost immediately after the beginning of fieldwork. Simple range and skip errors were corrected at the data entry stage. Secondary machine editing of the data was initiated as soon as sufficient questionnaires had been entered. The objective of the secondary editing was to detect and correct, if possible, inconsistencies in the data. All of the data were entered and edited by September 1991. A brief report containing preliminary survey results was published in November 1991.

Response rate

Of 28,141 households sampled, 27,109 were eligible to be interviewed (excluding those that were absent, vacant, or destroyed), and of these, 26,858 or 99 percent of eligible households were successfully interviewed. In the interviewed households, 23,470 eligible women were found and complete interviews were obtained with 98 percent of these women.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the IDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate analytically.

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the IDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results. Sampling error is usually measured in terms of standard error of a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which one can reasonably be assured that, apart from non-sampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the IDHS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

Abstract

The Indonesia Demographic and Health Survey (IDHS), which is part of the Demographic and Health Surveys (DHS) Project, is one of prominent national surveys in the field of population, family planning, and health. The survey is not only important nationally for planning and evaluating population, family planning, and health developments, but is also important internationally since IDHS has been designed so uniquely that it can be compared with similar surveys in other developing countries.

The 1997 Indonesia Demographic and Health Survey (IDHS) is a follow-on project to the 1987 National Indonesia Contraceptive Prevalence Survey (NICPS), the 1991 IDHS, and the 1994 IDHS. The 1997 IDHS was expanded from the 1994 survey to include a module on family welfare; however, unlike the 1994 survey, the 1997 survey no longer investigated the availability of family planning and health services. The 1997 IDHS also included as part of the household schedule a household expenditure module that provided a means of identifying the household's economic status.

The 1997 IDHS was specifically designed to meet the following objectives: - Provide data concerning fertility, family planning, maternal and child health, maternal mortality, and awareness of AIDS that can be used by program managers, policymakers, and researchers to evaluate and improve existing programs - Provide data about availability of family planning and health services, thereby offering an opportunity for linking women's fertility, family planning, and child care behavior with the availability of services - Provide household expenditure data that which can be used to identify the household's economic status - Provide data that can be used to analyze trends over time by examining many of the same fertility, mortality, and health issues that were addressed in the earlier surveys (1987 NICPS, 1991 IDHS and 1994 IDHS) - Measure changes in fertility and contraceptive prevalence rates and at the same time study factors that affect the changes, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and the availability of contraception - Measure the development and achievements of programs related to health policy, particularly those concerning the maternal and child health development program implemented through public health clinics in Indonesia - Provide indicators for classifying families according to their welfare status.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

Indonesia is divided into 27 provinces. For the implementation of its family planning program, the National Family Planning Coordinating Board (NFPCB) has divided these provinces into three regions as follows:

• Java-Bali: DKI Jakarta, West Java, Central Java, DI Yogyakarta, East Java, and Bali
• Outer Java-Bali I: Dista Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, West Nusa Tenggara, West Kalimantan, South Kalimantan, North Sulawesi, and South Sulawesi
• Outer Java-Ball II: Riau, Jambi, Bengkulu, East Nusa Tenggara, East Timor, Central Kalimantan, East Kalimantan, Central Sulawesi, Southeast Sulawesi, Maluku, and Irian Jaya

The 1990 Population Census of Indonesia shows that Java-Bali accounts for 62 percent of the national population, Outer Java-Bali I accounts for 27 percent, and Outer Java-Bali II accounts for 11 percent. The sample for the 1997 IDHS was designed to produce reliable estimates of fertility, contraceptive prevalence and other important variables for each of the provinces and urban and rural areas of the three regions.

In order to meet this objective, between 1,650 and 2,050 households were selected in each of the provinces in Java-Bali, 1,250 to 1,500 households in the ten provinces in Outer Java-Bali I, and 1,000 to 1,250 households in each of the provinces in Outer Java-Bali II, for a total of 35,500 households. With an average of O.8 ever-married women 15-49 per household, the sample was expected to yield approximately 28,000 women eligible for the individual interview.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1997 IDHS used three questionnaires: the household questionnaire, the questionnaire on family welfare, and the individual questionnaire for ever-married women 15-49 years old. The general household and individual questionnaires were based on the DHS Model "A" Questionnaire, which is designed for use in countries with high contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to provide detailed information specific to Indonesia. The questionnaires were developed mainly in English and were translated into Indonesian. One deviation from the standard DHS practice is the exclusion of the anthropometric measurement of young children and their mothers. A separate survey carried out by MOH provides this information.

The household questionnaire includes an expenditure schedule adapted from the core Susenas questionnaire model. Susenas is a national household survey carried out annually by CBS to collect data on various demographic and socioeconomic indicators of the population. The family welfare questionnaire was aimed at collecting indicators developed by the NFPCB to classify families according to their welfare status. Families were identified from the list of household members in the household questionnaire. The expenditure module and the family welfare questionnaire were developed in Indonesian.

Cleaning operations

The first stage of data editing was carried out by the field editors who checked the completed questionnaires for thoroughness and accuracy. Field supervisors then further examined the questionnaires. In many instances, the teams sent the questionnaires to CBS through the regency/municipality statistics offices. In these cases, no checking was done by the PSO. In other cases, Technical Coordinators are responsible for reviewing the completeness of the forms. At CBS, the questionnaires underwent another round of editing, primarily for completeness and coding of responses to open-ended questions. The data were processed using microcomputers and the DHS computer program, ISSA (Integrated System for Survey Analysis). Data entry and office editing were initiated immediately after fieldwork began. Simple range and skip errors were corrected at the data entry stage. Data processing was completed by February 1998, and the preliminary report of the survey was published in April 1998.

Response rate

A total of 35,362 households were selected for the survey, of which 34,656 were found. Of the encountered households, 34,255 (99 percent) were successfully interviewed. In these households, 29,317 eligible women were identified, and complete interviews were obtained from 28,810 women, or 98 percent of all eligible women. The generally high response rates for both household and individual interviews were due mainly to the strict enforcement of the rule to revisit the originally selected household if no one was at home initially. No substitution for the originally selected households was allowed. Interviewers were instructed to make at least three visits in an effort to contact the household or eligible woman.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (I) non-sampling errors and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 1997 IDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1997 IDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1997 IDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1997 IDHS is the ISSA Sampling Error Module. This module

Demographics, population, housing, income, education, schools, and geography for ZIP Code 59083 (Sumatra, MT). Interactive charts load automatically as you scroll for improved performance.

What are the top vacation rentals in Koto Tinggi? How many vacation rentals have private pools in Koto Tinggi? Which vacation homes in Koto Tinggi are best for families? How many Rentbyowner vacation rentals are available in Koto Tinggi?

Indonesia BPS Projection: Population: Sumatra: Jambi data was reported at 4,322.900 Person th in 2035. This records an increase from the previous number of 4,289.900 Person th for 2034. Indonesia BPS Projection: Population: Sumatra: Jambi data is updated yearly, averaging 2,765.250 Person th from Dec 1980 (Median) to 2035, with 56 observations. The data reached an all-time high of 4,322.900 Person th in 2035 and a record low of 1,451.400 Person th in 1980. Indonesia BPS Projection: Population: Sumatra: Jambi data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GAA002: Population Projection: by Province: Central Bureau of Statistics.

Abstract

The 1994 Indonesia Demographic and Health Survey (IDHS) is a follow-on project to the 1987 National Indonesia Contraceptive Prevalence Survey (NICPS) and to the 1991 IDHS. The 1994 IDHS was significantly expanded from prior surveys to include two new modules in the women's questionnaire, namely maternal mortality and awareness of AIDS. The survey also investigated the availability of family planning and health services, which provides an opportunity for linking women's fertility, family planning and child health care with the availability of services. The 1994 IDHS also included a household expenditure module, which provides a means of identifying the household's economic status.

The 1994 IDHS was specifically designed to meet the following objectives: - Provide data concerning fertility, family planning, maternal and child health, maternal mortality and awareness of AIDS that can be used by program managers, policymakers, and researchers to evaluate and improve existing programs; - Provide data about availability of family planning and health services, thereby offering an opportunity for linking women's fertility, family planning and child-care behavior with the availability of services; - Provide data on household expenditures, which can be used to identify the household's economic status; - Provide data that can be used to analyze trends over time by examining many of the same fertility, mortality and health issues that were addressed in the earlier surveys (1987 NICPS and 1991 IDHS); - Measure changes in fertility and contraceptive prevalence rates and at the same time study factors that affect the changes, such as marriage patterns, urban/rural residence, education, breastfeeding habits, and the availability of contraception; - Measure the development and achievements of programs related to health policy, particularly those concerning the maternal and child health development program implemented through public health clinics in Indonesia.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

Indonesia is divided into 27 provinces. For the implementation of its family planning program, the National Family Planning Coordinating Board (BKKBN) has divided these provinces into three regions as follows:

• Java-Bali: DKI Jakarta, West Java, Central Java, DI Yogyakarta, East Java, and Bali
• Outer Java-Bali I: Dista Aceh, North Sumatra, West Sumatra, South Sumatra, Lampung, West Nusa Tenggara, West Kalimantan, South Kalimantan, North Sulawesi, and South Sulawesi
• Outer Java-Bali II: Riau, Jambi, Bengkulu, East Nusa Tenggara, East Timer, Central Kalimantan, East Kalimantan, Central Sulawesi, Southeast Sulawesi, Maluku, and Irian Jaya

The 1990 Population Census of Indonesia shows that Java-Bali accounts for 62 percent of the national population, Outer Java-Bali I accounts for 27 percent, and Outer Java-Bali II accounts for 11 percent. The sample for the 1994 IDHS was designed to produce reliable estimates of fertility, contraceptive prevalence and other important variables for each of the provinces and for urban and rural areas of the three regions.

In order to meet this objective, between 1,650 and 2,050 households were selected in each of the provinces in Java-Bali, 1,250 to 1,500 households in the ten provinces in Outer Java-Bali I, and 1,000 to 1,250 households in each of the provinces in Outer Java-Bali II, for a total of 35,500 households. With an average of 0.8 ever-married women 15-49 per household, the sample was expected to yield approximately 28,000 women eligible for the individual interview.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 1994 IDHS used four questionnaires--three at the household level and one at the community level. The three questionnaires administered at the household level are the household questionnaire, an individual questionnaire for women, and the household expenditure questionnaire. The household and individual questionnaires were based on the DHS Model "A" Questionnaire, which is designed for use in countries with high contraceptive prevalence. A deviation from the standard DHS practice is the exclusion of the anthropometric measurement of young children and their mothers. Topics covered in the 1994 IDHS that were not included in the 1991 IDHS are knowledge of AIDS and maternal mortality. Additions and modifications to the model questionnaire were made in order to provide detailed information specific to Indonesia. Except for the household expenditure module, the questionnaires were developed mainly in English and were translated into Babasa Indonesia. The household expenditure schedule was adapted from the core Susenas questionnaire model. Susenas is a national household survey carried out annually by BPS to collect data on various demographic and socioeconomic indicators of the population.

Cleaning operations

The first stage of data editing was carried out by the field editors who checked the completed questionnaires for thoroughness and accuracy. Field supervisors then further examined the questionnaires. In many instances, the teams sent the questionnaires to CBS through the regency/municipality statistics offices. In these cases, no checking was done by the PSO. At CBS, the questionnaires underwent another round of editing, primarily for completeness and coding of responses to open-ended questions.

The data were processed using 16 microcomputers and the DHS computer program, ISSA (Integrated System for Survey Analysis). Data entry and office editing were initiated immediately after fieldwork began. Simple range and skip errors were corrected at the data entry stage. Data processing was completed by November 1994, and the preliminary report of the survey was published in January 1995.

Response rate

A total of 35,510 households were selected for the survey, of which 34,060 were found. Of the encountered households, 33,738 (99.1 percent) were successfully interviewed. In these households, 28,800 eligible women were identified and complete interviews were obtained from 28,168 women, or 97.8 percent of all eligible women. Generally high response rates for both household and individual interviews were due mainly to the strict enforcement of the role to revisit the originally selected household if no one was at home initially. No substitution for the originally selected households was allowed. Interviewers were instructed to make at least three visits in an effort to contact the household or eligible woman.

Note: See summarized response rates by place of residence in Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during implementation of the 1994 IDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 1994 IDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 1994 IDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1994 IDHS is the ISSA Sampling Error Module. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jacknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the report which is presented in this documentation.

In 1800, the population of Indonesia was estimated to be approximately 16 million. The population of the island nation would grow steadily over the course of the 19th century, as the Dutch colonial administration launched several initiatives to modernize the region. After reaching 38 million people in 1900, the population of Indonesia would continue to grow until the 1940’s, when the Japanese occupation of the country would see between four to ten million Indonesians moved away from the island nation to be made to work on Japanese military projects, and in combination with wartime famine, this would result in the death or displacement of up to four million Indonesians by the end of the Japanese occupation in 1945. Despite this, Indonesia's population continued to grow throughout these years.

Following the Second World War, Indonesia claimed its independence from the Netherlands, and achieved this in 1949. In the second half of the 20thcentury, the population would continue to grow exponentially in size through the remainder of the 20th century, although the growth rate would slow somewhat in the 1980s, the result of a decline in fertility rate throughout the country which some studies suggest may be attributed to improved access to birth control and improved mass education. In 2020, Indonesia is estimated to have just over 273.5 million people living within its borders, making it the fourth most populous country in the world (behind the U.S. and above Pakistan).

In 2024, the monthly minimum wage in South Sumatra was around **** million Indonesian rupiah. This indicates an increase after being relatively stable from 2021 to 2022.

This statistic shows the median age of the population in Indonesia from 1950 to 2100. The median age is the age that divides a population into two numerically equal groups; that is, half the people are younger than this age and half are older. It is a single index that summarizes the age distribution of a population. In 2020, the median age of the Indonesian population was 29.1 years. Life in Indonesia The Republic of Indonesia is a sovereign state archipelago in Southeast Asia. Indonesia is made up of more than 17,000 islands, with the biggest three being Java, Sumatra and Borneo. In 2010, Indonesia reported a total population of around 238 million people, and it is estimated that this figure will increase to around 255 million inhabitants by 2015. The biggest cities in Indonesia are its capital Jakarta, Surabaya, and Bandung. Jakarta alone is home to more than 9.6 million inhabitants. Currently, there are more than 7 billion people in the world and Asia is the continent with the largest population. More than 4 billion people lived in Asia in mid-2014. Indonesia is the second most populous country in Asia, behind China and the fourth most populous nation in the world. As a result of an improving economy and better health and living conditions, life expectancy in Indonesia is steadily increasing - between 2002 and 2012, it increased by almost 3 years . Due of a decreasing fertility rate, Indonesian parents are able to more easily provide for their families and the population is still increasing and living longer. The average age of the population in Indonesia is estimated to be around 28.4 years in 2015.

Demographics, population, housing, income, education, schools, and geography for ZIP Code 32335 (Sumatra, FL). Interactive charts load automatically as you scroll for improved performance.

In 2024, the monthly minimum wage in North Sumatra was around **** million Indonesian rupiah. Although the wage remained stable at *** million rupiah during 2020 and 2021, it has generally been on an upward trend.

In the 200 years since the Sumatran rhinoceros was first scientifically described (Fisher 1814), the range of the species has contracted from a broad region in Southeast Asia to three areas on the island of Sumatra and one in Kalimantan, Indonesia. Assessing population and spatial distribution of this very rare species is challenging because of their elusiveness and very low population number. Using an occupancy model with spatial dependency, we assessed the fraction of the total landscape occupied by Sumatran rhinos over a 30,345-km2 survey area and the effects of covariates in the areas where they are known to occur. In the Leuser Landscape (surveyed in 2007), the model averaging result of conditional occupancy estimate was ψ^(SE[ψ^])=0.151(0.109) or 2,371.47 km2, and the model averaging result of replicated level detection probability p^(SE[p^])=0.252(0.267); in Way Kambas National Park—2008: ψ^(SE[ψ^])=0.468(0.165) or 634.18 km2, and p^(SE[p^])=0.138(0.571); and in Bukit Barisan Selatan National Park—2010: ψ^(SE[ψ^])=0.322(0.049) or 819.67 km2, and p^(SE[p^])=0.365(0.42). In the Leuser Landscape, rhino occurrence was positively associated with primary dry land forest and rivers, and negatively associated with the presence of a road. In Way Kambas, occurrence was negatively associated with the presence of a road. In Bukit Barisan Selatan, occurrence was negatively associated with presence of primary dryland forest and rivers. Using the probabilities of site occupancy, we developed spatially explicit maps that can be used to outline intensive protection zones for in-situ conservation efforts, and provide a detailed assessment of conserving Sumatran rhinos in the wild. We summarize our core recommendation in four points: consolidate small population, strong protection, determine the percentage of breeding females, and recognize the cost of doing nothing. To reduce the probability of poaching, here we present only the randomized location of site level occupancy in our result while retaining the overall estimation of occupancy for a given area.

In 2022, there were ******* Buddhists in Jakarta, making it the province with the largest Buddhist population in Indonesia. It was followed by North Sumatra, where the Buddhist population reached nearly *******.

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