In 2023, over ** percent of Indonesians declared themselves to be Muslim, followed by *** percent who were Christians. Indonesia has the largest Islamic population in the world and for this reason is often recognized as a Muslim nation. However, Indonesia is not a Muslim nation according to its constitution. The archipelago is a multifaith country and officially recognizes six religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism. Not all provinces in Indonesia are Muslim majority The spread of Islam in Indonesia began on the west side of the archipelago, where the main maritime trade routes were located. Until today, most of the Indonesian Muslim population are residing in Western and Central Indonesia, while the majority religion of several provinces in Eastern Indonesia, such as East Nusa Tenggara and Bali, is Christian and Hindu, respectively. Discrimination towards other beliefs in Indonesia The Indonesian constitution provides for freedom of religion. However, the Government Restrictions Index Score on religion in Indonesia is relatively high. Indonesians who practice unrecognized religions, including Indonesia’s indigenous or traditional belief systems, such as animism, dynamism, and totemism, face legal restrictions and discrimination. Indonesian law requires its citizens to put one of the recognized religions on their national identity cards, with some exceptions for indigenous religions. Although legally citizens may leave the section blank, atheism or agnosticism is considered uncommon in Indonesia.

According to the population census data in 2010, 54.14 percent of the population in East Nusa Tenggara, Indonesia were Catholics. East Nusa Tenggara is the province with the least Muslim population in Indonesia. Indonesia has the largest Islamic population in the world. However, Indonesia is a multi-faith country that recognizes six official religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

As of 2021, approximately 98.6 percent of the population in Aceh, Indonesia were Muslims. Despite being the largest Muslim-majority country, Indonesia is a multi-faith country by the constitution and officially recognizes six religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

In 2023, approximately 244.4 million people in Indonesia identified as Muslims. Indonesia has the largest Islamic population in the world. However, it is a multi-faith country and officially recognizes six religions: Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

In 2022, there were approximately 48 million Muslims in West Java, making it the province with the largest Muslim population in Indonesia. It was followed by East Java, where the Muslim population reached around 40 million.

Recent issues on politics have been dominant in Indonesia that people are divided and become more intolerant of each other. Indonesia has the biggest Muslim population in the world and the role of Islam in Indonesian politics is significant. The current Indonesian government claim that moderate Muslims are loyal to the present political system while the opposing rivals who are often labelled’intolerant and radical Muslims’ by Indonesian mass media often disagree with the central interpretation of democracy in Indonesia. Studies on contributing factors and discourse strategies used in news and articles in secular and Islamic mass media which play a vital role in the construction of Muslim and Islamic identities in Indonesia are, therefore, recommended.

The 2010 census recorded that there were approximately 104.5 million Muslims in urban areas in Indonesia. Meanwhile, the Muslim population in rural areas was lower, at around 102.65 million. Indonesia conducts its census every ten years. Detailed demographic breakdowns by religion from the 2020 census are not yet publicly available.

Pew Research Center surveyed 13,122 adults across six countries in Asia about religious identity, beliefs, and practices, using nationally representative methods. Interviews were conducted face-to-face in Cambodia, Indonesia, Sri Lanka, and Thailand. They were conducted on mobile phones in Malaysia and Singapore. Local interviewers administered the survey from June to September 2022, in eight languages.

This survey is part of the Pew-Templeton Global Religious Futures project, a broader effort by Pew Research Center to study religious change and its impact on societies around the world. The Center previously has conducted religion-focused surveys across sub-Saharan Africa; the Middle East-North Africa region and many countries with large Muslim populations; Latin America; Israel; Central and Eastern Europe; Western Europe; India; and the United States.

This survey includes three countries in which Buddhists make up a majority of the population (Cambodia, Sri Lanka, and Thailand); two countries with Muslim majorities (Malaysia and Indonesia); and one country that is religiously diverse, with no single group forming a majority (Singapore). We also are surveying five additional countries and territories in Asia, to be covered in a future report.

Pew Research Center has produced a supplemental syntax file containing SPSS code to generate common analytic variables in the survey's corresponding report and toplines. The ARDA has provided this syntax in a copyable PDF document as an additional download.

Abstract

A Population Census encompasses the entire process of collecting, processing, compiling, and publishing demographic, economic and social data pertaining to all residents in a country or a region at a specified time. A Population Census in Indonesia is usually referred to as population enumeration, that is, the collecting of data/information for all residents who live in the territorial area of Indonesia. Data collected, among others, are: name, age, sex, education, religion, citizenship, occupation, and birth place. The result is the total number of residents with their characteristics, which are very useful in planning, monitoring, and assessing the development programs. The 2010PC will provide information on the actual conditions of the population, their housing conditions, education and details of the labor force at the lowest administrative level.

Since Indonesia's independence, five population censuses have been conducted, that is, in 1961, 1971, 1980, 1990, and 2000. The 2010 Population Census (SP2010) will be the sixth population census. SP2010 is an enormous activity that consists of interrelated stages of activities beginning with planning, preparation, data collection, data processing and dissemination, and data analysis. Activities for SP2010 began in 2007 and should be completed by 2013. The series of SP2010 activities began with the collection of village potential data (PODES), followed by the mapping of administrative areas and census blocks, a census test and other activities related to the preparation of SP2010. A pilot census was conducted in 2009 to test the quality, validity, and ease of use of all instruments, procedures and systems that will be used in SP2010. The peak of activity for SP2010 will be in May 2010 when the building and household registration will be implemented and the household and complete enumeration of all residents will take place.

Geographic coverage

Throughout Indonesia, including: * Number of provinces: 33 provinces * Number of districts / municipalities: 497 regencies / municipalities * Number of districts: 6651 district * Number of villages / urban: 77126 villages / wards.

Analysis unit

Individual or population

Universe

The Population Census (SP 2010 / PHC 2010) covers all Indonesian (WNI) as well as foreign (WNA) population who reside in the territorial area of Indonesia, regardless of whether the person has permanent residence or not; those who do not have a permanent residence include homeless, refugees, crews of Indonesian ships, those living in inaccessible areas, and those living on boats or houseboats. Members of the diplomatic corps of other countries and their families will not be covered in SP2010 even though they reside in the territorial area of Indonesia. On the other hand, members of the diplomatic corps of Indonesia and their families who reside abroad will be covered in SP 2010.

Kind of data

Census/enumeration data

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2010 Population Census questionnaire consists of 4 sections. The sections are i) Identification, ii) Individual Characteristics, iii) Mortality, and iv) Housing Unit Characteristics.

In 2021, around 86.8 percent of the population in Bali were Hindus. Indonesia has the largest Islamic population in the world and therefore the largest Muslim nation. However, Indonesia is not a Muslim nation by constitution. The archipelago has six official religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

Muslim Ingredients Market Outlook

The global Muslim ingredients market size is projected to grow from USD 1.9 billion in 2023 to USD 3.5 billion by 2032, at a compound annual growth rate (CAGR) of 7.2%. The burgeoning Muslim population, coupled with increasing awareness and demand for halal-certified products, is a key growth factor driving this market.

The rising Muslim population globally is one of the primary drivers of the Muslim ingredients market. As of 2023, Muslims constitute about 24% of the worldÂ’s population, and this demographic is expected to grow significantly over the next decade. This growth is predominantly seen in regions such as the Asia-Pacific, Middle East, and Africa. The increasing Muslim population naturally leads to a higher demand for halal products, including food, beverages, pharmaceuticals, and cosmetics. A deeper understanding and adherence to religious dietary laws have further solidified the importance of incorporating halal ingredients in daily consumption.

The surge in disposable income among Muslim consumers has also played a pivotal role in the market's expansion. Improved economic conditions in countries with significant Muslim populations, such as Indonesia, Malaysia, Saudi Arabia, and UAE, have led to increased spending on premium halal-certified products. This trend is particularly evident in the food and beverage sector, where consumers are willing to pay a premium for assurance of quality and compliance with Islamic dietary laws. Consequently, manufacturers are increasingly investing in halal certification and aligning their products with Islamic principles to tap into this lucrative market.

Technological advancements and innovation in the processing and certification of halal products have further bolstered market growth. The introduction of blockchain technology for halal certification ensures transparency, traceability, and authenticity, thus gaining consumer trust. Additionally, advancements in food science have enabled the development of new halal-friendly ingredients, expanding the range of available products. Companies investing in research and development are better positioned to cater to the evolving preferences of Muslim consumers and gain a competitive edge in the market.

Halal Food plays a pivotal role in the Muslim ingredients market, as it aligns with the religious and cultural practices of Muslim consumers. The demand for halal food is not only limited to Muslim-majority countries but is also gaining traction in regions with growing Muslim populations, such as Europe and North America. This trend is driven by the increasing awareness of halal food as a symbol of quality and ethical production, appealing to both Muslim and non-Muslim consumers. The assurance of halal certification provides consumers with confidence that the food products adhere to strict Islamic dietary laws, which is crucial for maintaining religious observance. As a result, food manufacturers are increasingly investing in halal certification to cater to this expanding market segment and capitalize on the growing demand for halal food products globally.

Regionally, the Asia-Pacific holds the largest market share, driven by countries like Indonesia and Malaysia, where the majority of the population adheres to Islamic dietary laws. North America and Europe are also witnessing increased demand for halal products, fueled by the growing Muslim immigrant population and rising awareness among non-Muslim consumers about the benefits of halal-certified products. The Middle East and Africa region, with its predominantly Muslim population, remains a critical market, contributing significantly to the global revenue.

Product Type Analysis

The product type segment of the Muslim ingredients market comprises halal meat, halal dairy products, halal beverages, halal confectionery, halal nutraceuticals, and others. Halal meat holds a significant share in this segment due to the stringent religious guidelines governing meat consumption in Islam. The demand for halal meat is particularly high in regions with large Muslim populations, such as the Middle East, North Africa, and Southeast Asia. Companies are increasingly focusing on ensuring that their meat products are certified halal to cater to this growing demand. The advent of online meat delivery services has further propelled the growth of this segment.

Halal dairy products, including milk, cheese, and yogurt, are another crucial segment that has see

In 2024, there were 59 active state universities with religious affiliation in Indonesia. In the same year, there were nearly than 850 active private universities with religious affiliation in the country.

The file contains dataset on the study on the relations betwen social identities and relational wellbeing among the general population in Indonesia

This study explores the nature and conceptualisation of mental health and well-being among Indonesians living in an urban environment. Little is known about the nature of mental health and well-being in the everyday living context in developing countries. In Indonesia, as one of the most populous countries and the largest Muslim population in the world, the incidence of mental health problems has increased immensely in the last decade. However, there is a very limited number of studies that incorporate relevant cultural contexts into the understanding of mental health and well-being in Indonesia. This study aims to elucidate the relationship of specific psychosocial factors, as protective and risk factors, to mental health and well-being in the everyday urban living contexts experienced by a growing middle class in Indonesia in the perspective of Keyes' model of mental well-being. The data for this study were collected through semi-structured interviews and were analysed using Giorgi’s descriptive phenomenological approach.

Average Monthly Expenditure per Capita: Urban: Parties and Ceremonies: Religious/ Traditional Ceremony data was reported at 3,433.000 IDR in 2018. This records an increase from the previous number of 3,299.000 IDR for 2017. Average Monthly Expenditure per Capita: Urban: Parties and Ceremonies: Religious/ Traditional Ceremony data is updated yearly, averaging 1,329.500 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 3,433.000 IDR in 2018 and a record low of 648.000 IDR in 2003. Average Monthly Expenditure per Capita: Urban: Parties and Ceremonies: Religious/ Traditional Ceremony data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC002: Average Monthly Expenditure per Capita: Urban.

IndQNER

IndQNER is a Named Entity Recognition (NER) benchmark dataset that was created by manually annotating 8 chapters in the Indonesian translation of the Quran. The annotation was performed using a web-based text annotation tool, Tagtog, and the BIO (Beginning-Inside-Outside) tagging format. The dataset contains:

3117 sentences

62027 tokens

2475 named entities

18 named entity categories

Named Entity Classes

The named entity classes were initially defined by analyzing the existing Quran concepts ontology. The initial classes were updated based on the information acquired during the annotation process. Finally, there are 20 classes, as follows:

Allah

Allah's Throne

Artifact

Astronomical body

Event

False deity

Holy book

Language

Angel

Person

Messenger

Prophet

Sentient

Afterlife location

Geographical location

Color

Religion

Food

Fruit

The book of Allah

Annotation Stage

There were eight annotators who contributed to the annotation process. They were informatics engineering students at the State Islamic University Syarif Hidayatullah Jakarta.

Anggita Maharani Gumay Putri

Muhammad Destamal Junas

Naufaldi Hafidhigbal

Nur Kholis Azzam Ubaidillah

Puspitasari

Septiany Nur Anggita

Wilda Nurjannah

William Santoso

Verification Stage

We found many named entity and class candidates during the annotation stage. To verify the candidates, we consulted Quran and Tafseer (content) experts who are lecturers at Quran and Tafseer Department at the State Islamic University Syarif Hidayatullah Jakarta.

Dr. Eva Nugraha, M.Ag.

Dr. Jauhar Azizy, MA

Dr. Lilik Ummi Kultsum, MA

Evaluation

We evaluated the annotation quality of IndQNER by performing experiments in two settings: supervised learning (BiLSTM+CRF) and transfer learning (IndoBERT fine-tuning).

Supervised Learning Setting

The implementation of BiLSTM and CRF utilized IndoBERT to provide word embeddings. All experiments used a batch size of 16. These are the results:

Maximum sequence length Number of e-poch Precision Recall F1 score

256 10 0.94 0.92 0.93

256 20 0.99 0.97 0.98

256 40 0.96 0.96 0.96

256 100 0.97 0.96 0.96

512 10 0.92 0.92 0.92

512 20 0.96 0.95 0.96

512 40 0.97 0.95 0.96

512 100 0.97 0.95 0.96

Transfer Learning Setting

We performed several experiments with different parameters in IndoBERT fine-tuning. All experiments used a learning rate of 2e-5 and a batch size of 16. These are the results:

Maximum sequence length Number of e-poch Precision Recall F1 score

256 10 0.67 0.65 0.65

256 20 0.60 0.59 0.59

256 40 0.75 0.72 0.71

256 100 0.73 0.68 0.68

512 10 0.72 0.62 0.64

512 20 0.62 0.57 0.58

512 40 0.72 0.66 0.67

512 100 0.68 0.68 0.67

This dataset is also part of the NusaCrowd project which aims to collect Natural Language Processing (NLP) datasets for Indonesian and its local languages.

How to Cite

@InProceedings{10.1007/978-3-031-35320-8_12,author="Gusmita, Ria Hariand Firmansyah, Asep Fajarand Moussallem, Diegoand Ngonga Ngomo, Axel-Cyrille",editor="M{\'e}tais, Elisabethand Meziane, Faridand Sugumaran, Vijayanand Manning, Warrenand Reiff-Marganiec, Stephan",title="IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran",booktitle="Natural Language Processing and Information Systems",year="2023",publisher="Springer Nature Switzerland",address="Cham",pages="170--185",abstract="Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world's largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research's domain range.",isbn="978-3-031-35320-8"}

Contact

If you have any questions or feedback, feel free to contact us at ria.hari.gusmita@uni-paderborn.de or ria.gusmita@uinjkt.ac.id

Indonesia Average Monthly Expenditure per Capita: Rural: Parties and Ceremonies: Religious/ Traditional Ceremony data was reported at 2,712.000 IDR in 2018. This records a decrease from the previous number of 3,299.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Rural: Parties and Ceremonies: Religious/ Traditional Ceremony data is updated yearly, averaging 1,147.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 3,299.000 IDR in 2017 and a record low of 339.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Rural: Parties and Ceremonies: Religious/ Traditional Ceremony data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC003: Average Monthly Expenditure per Capita: Rural.

Indonesia Average Monthly Expenditure per Capita: Rural: Parties and Ceremonies: Religious and Birthday data was reported at 583.000 IDR in 2018. This records a decrease from the previous number of 854.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Rural: Parties and Ceremonies: Religious and Birthday data is updated yearly, averaging 452.000 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 1,511.000 IDR in 2013 and a record low of 316.000 IDR in 2009. Indonesia Average Monthly Expenditure per Capita: Rural: Parties and Ceremonies: Religious and Birthday data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC003: Average Monthly Expenditure per Capita: Rural.

Abstract

The 1993 Indonesia Family Life Survey (IFLS) provides data at the individual and family level on fertility, health, education, migration, and employment. Extensive community and facility data accompany the household data. The survey was a collaborative effort of Lembaga Demografi of the University of Indonesia and RAND, with support from the National Institute of Child Health and Human Development, USAID, Ford Foundation, and the World Health Organization. In Indonesia, the 1993 IFLS is also referred to as SAKERTI 93 (Survai Aspek Kehidupan Rumah Tangga Indonesia). The IFLS covers a sample of 7,224 households spread across 13 provinces on the islands of Java, Sumatra, Bali, West Nusa Tenggara, Kalimantan, and Sulawesi. Together these provinces encompass approximately 83 percent of the Indonesian population and much of its heterogeneity. The survey brings an interdisciplinary perspective to four broad topic areas:

• Fertility, family planning, and contraception • Infant and child health and survival • Education, migration and employment • The social, economic, and health status of adults, young and old

Additionally, extensive community and facility data accompany the household data. Village leaders and heads of the village women's group provided information in each of the 321 enumeration areas from which households were drawn, and data were collected from 6,385 schools and health facilities serving community residents.

Geographic coverage

National

Analysis unit

Households

Universe

Household Survey data were collected for household members through direct interviews (for adults) and proxy interviews (for children, infants and temporarily absent household members). The IFLS-1 conducted detailed interviews with the following household members:

• The household head and their spouse
• Two randomly selected children of the head and spouse aged 0 to 14 (interviewed by proxy)
• An individual age 50 and above and their spouse, randomly selected from remaining members
• For a randomly selected 25 percent of the households, an individual age 15 to 49 and their spouse, randomly selected from remaining members.

The Community and Facility Survey collected data from a variety of respondents including: the village leader and his staff and the leader of the village women's group; Ministry of Health clinics and subclinics; private practices of doctors, midwives, nurses, and paramedics; community-based health posts and contraceptive distribution centers; public, private, and religious elementary schools; public, private, and religious junior high schools; public, private, and religious senior high schools. Unlike many other surveys, the sample frame for the survey of facilities was drawn from the list of facilities used by household survey respondents in the area.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. HOUSEHOLD SELECTION

(a) SAMPLING

The IFLS sampling scheme stratified on provinces, then randomly sampled within provinces. Provinces were selected to maximize representation of the population, capture the cultural and socioeconomic diversity of Indonesia, and be cost effective given the size and terrain of the country. The far eastern provinces of East Nusa Tenggara, East Timor, Maluku and Irian Jaya were readily excluded due to the high costs of preparing for and conducting fieldwork in these more remote provinces. Aceh, Sumatra's most northern province, was deleted out of concern for the area's political violence and the potential risk to interviewers. Finally, due to their relatively higher survey costs, we omitted three provinces on each of the major islands of Sumatra (Riau, Jambi, and Bengkulu), Kalimantan (West, Central, East), and Sulawesi (North, Central, Southeast). The resulting sample consists of 13 of Indonesia's 27 provinces: four on Sumatra (North Sumatra, West Sumatra, South Sumatra, and Lampung), all five of the Javanese provinces (DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java), and four provinces covering the remaining major island groups (Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi). The resulting sample represents 83 percent of the Indonesian population. (see Figure 1.1 of the Overview and Field Report in External Documents). Table 2.1 of the same document shows the distribution of Indonesia's population across the 27 provinces, highlighting the 13 provinces included in the IFLS sample.

The IFLS randomly selected enumeration areas (EAs) within each of the 13 provinces. The EAs were chosen from a nationally representative sample frame used in the 1993 SUSENAS, a socioeconomic survey of about 60,000 households. The SUSENAS frame, designed by the Indonesian Central Bureau of Statistics (BPS), is based on the 1990 census. The IFLS was based on the SUSENAS sample because the BPS had recently listed and mapped each of the SUSENAS EAs (saving us time and money) and because supplementary EA-level information from the resulting 1993 SUSENAS sample could be matched to the IFLS-1 sample areas. Table 2.1 summarizes the distribution of the approximately 9,000 SUSENAS EAs included in the 13 provinces covered by the IFLS. The SUSENAS EAs each contain some 200 to 300 hundred households, although only a smaller area of about 60 to 70 households was listed by the BPS for purposes of the annual survey. Using the SUSENAS frame, the IFLS randomly selected 321 enumeration areas in the 13 provinces, over-sampling urban EAs and EAs in smaller provinces to facilitate urban rural and Javanese-non-Javanese comparisons. A straight proportional sample would likely be dominated by Javanese, who comprise more than 50 percent of the population. A total of 7,730 households were sampled to obtain a final sample size goal of 7,000 completed households. Table 2.1 shows the sampling rates that applied to each province and the resulting distribution of EAs in total, and separately by urban and rural status. Within a selected EA, households were randomly selected by field teams based upon the 1993 SUSENAS listings obtained from regional offices of the BPS. A household was defined as a group of people whose members reside in the same dwelling and share food from the same cooking pot (the standard BPS definition). Twenty households were selected from each urban EA, while thirty households were selected from each rural EA. This strategy minimizes expensive travel between rural EAs and reduces intra-cluster correlation across urban households, which tend to be more similar to one another than do rural households. Table 2.2 (Overview and Field Report) shows the resulting sample of IFLS households by province, separately by completion status.

(b) SELECTION OF RESPONDENTS WITHIN HOUSEHOLDS For each household selected, a representative member provided household-level demographic and economic information. In addition, several household members were randomly selected and asked to provide detailed individual information.

1. THE COMMUNITY SURVEY SAMPLING PROCEDURE

(a) SAMPLING

The goal of the CFS was to collect information about the communities of respondents to the household questionnaire. The information was solicited in two ways. First, the village leader of each community was interviewed about a variety of aspects of village life (the content of this questionnaire is described in the next section). Information from the village leader was supplemented by interviewing the head of the village women's group, who was asked questions regarding the availability of health facilities and schools in the area, as well as more general questions about family health in the community. In addition to the information on community characteristics provided by the two representatives of the village leadership, we visited a sample of schools and health facilities, in which we conducted detailed interviews regarding the institution's activities. A priori we wanted data on the major sources of outpatient health care, public and private, and on elementary, junior secondary, and senior secondary schools. We defined eight strata of facilities/institutions from which we wanted data. Different types of health providers make up five of the strata, while schools account for the other three. The five strata of health care providers are: government health centers and subcenters (puskesmas, puskesmas pembantu); private doctors and clinics (praktek umum/klinik); the private practices of midwives, nurses, and paramedics (perawats, bidans, paramedis, mantri); traditional practitioners (dukun, sinshe, tabib, orang pintar); and community health posts (posyandu, PPKBD).The three strata of schools are elementary, junior secondary, and senior secondary. Private, public, religious, vocational, and general schools are all eligible as long as they provide schooling at one of the three levels. Our protocol for selecting specific schools and health facilities for detailed interview reflects our desire that selected facilities represent the facilities available to members of the communities from which household survey respondents were drawn. For that reason, we were hesitant to select facilities based solely either on information from the village leader or on proximity to the village center. The option we selected instead was to sample schools and health care providers from lists provided by respondents to the household survey. For each enumeration area lists of facilities in each of the eight strata were constructed by compiling information provided by the household regarding the names and locations of facilities the household respondent either knew about or used. To generate lists of relevant health and family planning facilities, the CFS drew on two pieces of information from the household survey. The IFLS

The 2010 census recorded that there were approximately 20 million Muslims aged five to nine years in Indonesia, making it the largest age group among the country's Muslim population. Indonesia conducts its census every ten years. Detailed demographic breakdowns by religion from the 2020 census are not yet publicly available.