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.

As of 2021, approximately **** 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 *** religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

In 2023, approximately ***** 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.

Dataset

This dataset was created by Fajar Khaswara

Contents

In 2021, around **** 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 *** official religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

About this dataset

Context

This list ranks the 7 cities in the Christian County, MO by Indonesian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indonesian Population: This column displays the rank of city in the Christian County, MO by their Indonesian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indonesian Population: The Indonesian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indonesian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Christian County Indonesian Population: This tells us how much of the entire Christian County, MO Indonesian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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

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.

According to the population census data in 2010, ***** percent of Buddhist population in Indonesia resided in Jakarta, making it the province where the largest Buddhist population in Indonesia lived. 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.

About this dataset

Context

This list ranks the 4 cities in the Christian County, KY by Indonesian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indonesian Population: This column displays the rank of city in the Christian County, KY by their Indonesian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indonesian Population: The Indonesian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indonesian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Christian County Indonesian Population: This tells us how much of the entire Christian County, KY Indonesian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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.

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.

Dataset

This dataset was created by Sofyan Uli

Contents

Data were collected through a Google form platform involving 4,197 secondary school student respondents in the province of Bangka Belitung Islands, Indonesia. The survey used a student religious moderation scale consisting of demographic information (Table 1) such as gender, religion, parents' employment status, school type, school status, and city of origin. In addition, the survey also collected responses from students regarding religious moderation attitudes, consisting of 1) national commitment, 2) tolerance, 3) anti-violence, and 4) accommodating to local culture.

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 coverage

Analysis unit

• Communities
• Facilities
• Households
• Individuals

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

The Household Survey Sampling Procedure

The household survey component of the 1993 IFLS was designed to collect contemporaneous and retrospective information on a wide array of family life topics for a representative sample of the Indonesian population. In IFLS1 it was determined to be too costly to interview all household members, so a sampling scheme was used to randomly select several members within a household to provide detailed individual information. IFLS1 conducted detailed interviews with the following household members: - the household head and his/her spouse - two randomly selected children of the head and spouse age 0 to 14 - an individual age 50 or older and his/her spouse, randomly selected from remaining members, and - for a randomly selected 25% of the households, an individual age 15 to 49 and his/her spouse, randomly selected from remaining members.

Household Selection 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 urbanrural 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.

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.

The Community Survey Sampling Procedure

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

In 2023, it was estimated that approximately ** percent of the Indonesian population were Muslim, accounting for the highest share of Muslims in any Southeast Asian country. Indonesia also has the world's largest Muslim population, with an estimated *** million Muslims. Demographics of Indonesia The total population of Indonesia was estimated to reach around *** million in 2028. The median age of the population in the country was at an all-time high in 2020 and was projected to increase continuously until the end of the century. In 2020, the population density in Indonesia reached its highest value recorded at about ***** people per square kilometer. Shopping behavior during Ramadan in Indonesia Nearly all Muslims in Indonesia celebrated Ramadan in 2022. During the month of Ramadan, ** percent of Indonesian users utilized online applications to order food. Many Indonesians planned to shop online or offline during Ramadan, with around ** percent of online users planning to purchase fashion wear and accessories. Shopee was the most used app for shopping purposes during that period.

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

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

“Interreligious Conflicts in Indonesia 2017” provides documentation of a cross-religious dataset among the general population in six potential conflict regions in Indonesia. The Dans Data Guide 15 (meta-data) contains the research topic, theoretical framework, relevant concepts and measurements, the purposive sampling of locations, data collection procedures, the random selection of respondents and the response rates. The data were collected to investigate the relationship of ethno-religious identification with support for interreligious violence among the general population in carefully selected areas of latent and manifest conflict in Indonesia: Bekasi, South Lampung, Singkil-Aceh, Poso, Kupang, and Sampang-Madura. This research applies and further develops an integrated theory of intergroup conflict, in formulating and empirically testing hypotheses on cross-cultural and inter-individual differences of latent conflict, more specifically inter-group contact avoidance and support of interreligious protests and interreligious violence. The research is funded by the Indonesia Endowment Fund for Education (LPDP). Valid: 2017-11-10

Indonesia Bible crawled from http://alkitab.mobi.com Other version of bible coming soon