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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Matematika Media Statistika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paediatrica Indonesiana BAREKENG: Jurnal Ilmu Matematika dan Terapan Economics and Finance in Indonesia Jurnal Aplikasi Statistika & Komputasi Statistik Indonesian of Health Information Management Journal (INOHIM) Journal of Data Science and Its Applications Jurnal Kepariwisataan Indonesia: Jurnal Penelitian dan Pengembangan Kepariwisataan Indonesia The Journal of Indonesia Sustainable Development Planning (JISDeP) Jambura Journal of Biomathematics (JJBM) Jurnal Ekonomi Indonesia Narra J Indonesian Journal of Tourism and Leisure Jurnal Ketenagakerjaan Proceedings of International Conference on Multidiciplinary Research Enthusiastic : International Journal of Applied Statistics and Data Science JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Of Computer, Network Security and Information System (IJCONSIST) Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Economic and Finance in Indonesia Indonesian Journal of Statistics and Its Applications
Pramana, Setia
Politeknik Statistika STIS
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KAJIAN PEMANFAATAN DATA GOOGLE MAPS DALAM OFFICIAL STATISTICS Cholifa Fitri Annisa; Setia Pramana
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.635 KB) | DOI: 10.34123/semnasoffstat.v2020i1.614

Abstract

Publication of food and drink supply business statistics published by BPS cannot facilitate businessmen in identifying areas that have the potential to develop businesses in the food and drink supply sector. In addition, there are limitations in time, cost, and manpower in data collection by the BPS Tourism Sub-directorate on the VREST survey, so that the food and drink supply statistics cannot be published according to the methodology, namely every year. This study utilizes the web scraping method to obtain business data on food and drink providers from the google maps website. The amount of data collected is as many as 34,526 food and drink providers in Java and Bali. The results of the matching value of web scraping data with BPS data frames show a match percentage of 68.22%. Bali Province is an area that has the potential to develop food and beverage supply businesses, especially in the City / Regency of Jembrana, Buleleng, Tabanan, Karangasem, and Klungkung. Meanwhile, Central Java province is an area that has the potential to develop accommodation businesses, especially in the cities / regencies of Cilacap, Blora, Grobogan, Batang, and Kendal.
Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data Salwa Rizqina Putri; Thosan Girisona Suganda; Setia Pramana
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (681.519 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1023

Abstract

To support Indonesia's green economic growth, further analysis is needed regarding economic activity during the pandemic and its relationship to environmental conditions. This study aims to apply the Bayesian Network approach in modeling Indonesia's green economy conditions during the pandemic based on variables that are allegedly influential, such as economic activity, air quality, population mobility levels, and positive cases of COVID-19 obtained through big data. The Bayesian Network model that was constructed manually with the Maximum Spanning Tree algorithm was chosen as the best model with an average 5-cross validation accuracy in predicting four classes of GRDP is 0.83. The best model chosen shows that Indonesia's economic conditions in the pandemic era are directly influenced by the intensity of night light (NTL) which shows economic activity, air quality (AQI), and positive cases of COVID-19. Analysis of parameter learning shows that the economic growth of the Indonesian provinces still tends not to be in line with the maintenance of air quality so that efforts to achieve a green economy condition still have to be improved.
Hubungan Jumlah Tayangan Iklan Penawaran Penjualan dan Penyewaan Properti dengan PDRB Provinsi Bali Tahun 2019-2021 Dengan Menggunakan Big Data : Web Scraping Muhammad Tharif Arkandana; Thosan Girisona Suganda; Setia Pramana
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.281 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1056

Abstract

Ekonomi sangat berpengaruh untuk memajukan kehi dupan di suatu negara, namun diakibatkan oleh pandemi virus Covid-19 kondisi ekonomi negara menjadi tidak stabil. Beberapa sektor seperti pariwisata, penurunan pertumbuhan ekonomi seperti PDRB. Berdasarkan data Badan Pusat Statistik (BPS) Provinsi Bali PDRB menurun secara signifikan pada tahun 2020 triwulan keempat yaitu hingga -12,21%. Penelitian ini menggunakan metode analisis hubungan atau korelasi antara data PDRB dan TPK hotel dengan data tayangan iklan penjualan dan penyewaan properti di Provinsi Bali, dimana data didapatkan dengan menggunakan Big Data dengan metode web scraping salah satu situs web properti di Indonesia (rumah123.com). Secara hubungan antara PDRB dan tayangan iklan didapatkan hasil -0.64 dan -0.67 menunjukkan harga jual dan sewa sektor properti mengalami tekanan dan berdampak pada pemulihan pasar properti dan tayangan iklan menaik, sedangkan pada TPK dihasilkan -0.53 menunjukkan pendapatan pada sektor pariwisata sudah cukup naik maka iklan penjualan akan menurun, karena pariwisata adalah sumber pendapatan yang cukup potensial di Bali.
Klasterisasi Wilayah Rentan Bencana Alam Berupa Gerakan Tanah Dan Gempa Bumi Di Indonesia I Nyoman Setiawan; Dewi Krismawati; Setia Pramana; Erwin Tanur
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.82 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1538

Abstract

Indonesia is one of the countries prone to natural disasters, such as soil movements and earthquakes. The people of Indonesia have felt various kinds of impacts caused by the disaster, both in the form of losing their jobs, their homes, and even their beloved family members. However, this impact can certainly be minimized with good disaster management. Therefore, the author focuses on the clustering of earthquake-prone areas in Indonesia using Density-based Spatial Clustering of Application with Noise (DBSCAN), Common Nearest Neighbor Clustering (CNN), and K-Medoids. The results of the clustering show that the soil movement-prone cluster formed from the DBSCAN algorithm is centered on the islands of Java and Bali, as well as along the western part of North Sumatra to Lampung, while the earthquake-prone areas formed from the K-Medoids algorithm are spread over the area traversed by the Pacific Ring of Fire.
Pemetaan Kesiapan Penerapan Telemedika di Indonesia Nora Dzulvawan; Setia Pramana
Indonesian of Health Information Management Journal (INOHIM) Vol 10, No 2 (2022): INOHIM
Publisher : Lembaga Penerbitan Universitas Esa Unggul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47007/inohim.v10i2.436

Abstract

AbstractTelemedicine is a form of technology development that allows people to conduct health consultations without having to physically visit the hospital. The COVID-19 pandemic that has spread throughout the world has increasingly limited human mobility and has implications for the application of technology. In addition, the vulnerability of public health during the pandemic has caused hospitals to treat patients with quite limited medical personnel. One of the solutions to overcome the crisis is to use technology in the health sector. However, Indonesia's diverse geographical situation presents its own challenges for the development of telemedicine. In addition, health facilities that are not evenly distributed also have a significant influence on the development of telemedicine. This study aims to prepare provinces in Indonesia for the development of telemedicine in terms of medical infrastructure and Information and Communications Technology (ICT). The K-Means Cluster is used as the primary method for grouping each province based on these two aspects. The result is 3 clusters of Indonesian provinces which are then defined as regions with a very ready status, ready, and not ready for the development of telemedicine in Indonesia. From the results of the analysis, it was found that the most influential variable in the development of telemedicine in Indonesia is the readiness of the ICT and health workers. Thus, it can be said to develop telemedicine or e-health in Indonesia, the improvement of Information and Communication Technology infrastructure, and the increment in natural resources and human resources in the medical field are needed, so the development of telemedicine can support improving the health status of Indonesian people.Keyword: digital health, K-Means cluster, ICT AbstrakTelemedika adalah bentuk pengembangan teknologi yang memungkinkan seseorang melakukan konsultasi kesehatan tanpa perlu mendatangi rumah sakit secara fisik. Pandemi COVID-19 yang menyebar di seluruh dunia semakin membatasi ruang gerak manusia dan berimplikasi pada pemanfaatan teknologi yang semakin tinggi. Selain itu rentannya kesehatan masyarakat di masa pandemi menyebabkan rumah sakit mengalami lonjakan pasien dengan tenaga medis yang cukup terbatas. Salah satu solusi untuk mengatasi krisis tersebut adalah dengan pemanfaatan tekonologi di bidang kesehatan. Namun keadaan geografis Indonesia yang cukup beragam memberikan tantangan tersendiri terhadap perkembangan telemedika. Selain itu, fasilitas kesehatan yang belum merata juga memberikan pengaruh cukup signifikan terhadap kemajuan telemedika. Penelitian ini bertujuan untuk memetakan kesiapan provinsi di Indonesia dalam pengembangan telemedika dilihat dari aspek infrastruktur medis dan Information and Communications Technology (ICT). Metode K-Means Clustering dijadikan sebagai dasar pengelompokan provinsi berdasarkan kedua aspek tersebut. Hasilnya adalah didapatkan tiga klaster provinsi Indonesia yang kemudian didefinisikan menjadi wilayah dengan status sangat siap, siap, dan tidak siap dalam perkembangan telemedika di Indonesia. Dari hasil analisis didapatkan variabel yang paling berpengaruh terhadap perkembangan telemedika di Indonesia adalah kesiapan dari sisi ICT dan tenaga kesehatan. Dengan demikian dapat disimpulkan untuk melakukan pengembangan telemedika atau e-health di Indonesia, perbaikan infrastruktur Teknologi Informasi dan Komunikasi dan peningkatan SDA maupun SDM di bidang medis sangat dibutuhkan sehingga pengembangan telemedika dapat menjadi salah satu penunjang perbaikan status kesehatan masyarakat Indonesia.Kata Kunci: digital health, K-Means Cluster, ICT
Analisis Teks Pemberitaan Telemedicine di Indonesia: Pendekatan Sentimen, NER, Topic Modeling, dan Social Network dalam Memahami Isu dan Persepsi Satria Bagus Panuntun; Dewi Krismawati; Setia Pramana; Erni Tri Astuti
Indonesian of Health Information Management Journal (INOHIM) Vol 11, No 1 (2023): INOHIM
Publisher : Lembaga Penerbitan Universitas Esa Unggul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47007/inohim.v11i1.500

Abstract

AbstractTelemedicine is becoming an increasingly relevant phenomenon in the health sector in Indonesia, especially with the emergence of the COVID-19 Pandemic. This study examines text analysis of telemedicine news coverage during the COVID-19 pandemic in Indonesia using sentiment analysis, Named Entity Recognition (NER), topic modeling, and Social Network Analysis (SNA). This research aims to gain an in-depth understanding of issues, public perceptions, social networks, and topics related to the use of telemedicine in dealing with a pandemic. This study provides a comprehensive understanding of telemedicine coverage during the COVID-19 pandemic in Indonesia by combining four methods. The findings of this research can provide valuable insights for stakeholders in optimizing the use of telemedicine, understanding public perceptions, and building effective collaborations in handling pandemics.Keywords: telemedicine, sentiment analysis, Named Entity Recognition (NER), topic modeling, social network analysis, COVID-19 AbstrakTelemedicine menjadi fenomena yang semakin relevan dalam sektor kesehatan di Indonesia, terutama dengan munculnya Pandemi COVID-19. Penelitian ini mengkaji analisis teks pemberitaan telemedicine selama pandemi COVID-19 di Indonesia dengan menggunakan analisis sentimen, Named Entity Recognition (NER), Topic Modeling, dan Social Network Analysis (SNA). Tujuan penelitian ini adalah untuk memperoleh pemahaman yang mendalam tentang isu-isu, persepsi masyarakat, jaringan sosial, dan topik-topik yang terkait dengan pemanfaatan telemedicine dalam menghadapi masalah kesehatan di masa pandemi. Penggunaan gabungan empat metode analisis agar dapat menyajikan pemahaman yang komprehensif tentang pemberitaan telemedicine selama pandemi COVID-19 di Indonesia. Hasil penelitian menunjukkan adanya kecenderungan sentimen positif dan netral terhadap telemedicine dan keberadaannya sangat membantu masalah kesehatan di masa Pandemi COVID-19. Selain itu pejabat pemerintah adalah nama yang paling sering muncul dalam pemberitaan telemedicine  yang memiliki makna peranan sentral pemerintah dalam masalah kesehatan sangat dibutuhkan. Penelitian ini diharapkan dapat memberikan wawasan berharga bagi para pemangku kepentingan dalam mengoptimalkan pemanfaatan telemedicine, memahami persepsi masyarakat, dan membangun kolaborasi yang efektif dalam penanganan pandemi.Kata Kunci: telemedicine, analisis sentimen, Named Entity Recognition (NER), social network analysis, topic modelling, COVID-19
Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions Rifqi Ramadhan; Arie Wahyu Wijayanto; Setia Pramana
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.359

Abstract

One of the economic development the focus of the Indonesian government's efforts is for reducing poverty. In Indonesia, collecting poverty data uses the conventional method, the name is National Socio-Economic Survey (SUSENAS) which takes a large cost, time, and effort. To overcome these limitations, there is a need for additional data to provide more detailed poverty data. Recent studies show that the use of geospatial big data could identify poverty at a granular level, with a lower cost and faster update because of their unique and unbiased capacity to identify physical and socioeconomic phenomena. The integrated multi-source satellite imagery data such as the normalized difference vegetation index (NDVI) for detecting rural areas based on vegetation, built-up index (BUI) for identifying urban areas through building distribution, normalized difference water index (NDWI) for land cover detection, day time land surface temperature (LST) for identifying urban regions based on surface temperature, and pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) to evaluate economic activities based on pollution. Additionally, point of interest (POI) density and minimum POI distance are used to measure area accessibility. Therefore, the contribution of this research is to implement the utilization of geospatial big data to estimate the numbers of poverties at a granular level to the 666 sub-districts in East Java Province using machine learning and deep learning regression models. The evaluation results to estimate sub-district level poverty shows that the best model development using Support Vector Regression (SVR) in machine learning was the best root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of 0.365, 0.293, and 0.032 with R-squared of 0.59 and MLP in deep learning algorithm with 0.444, 0.345, and 0.039 values of RMSE, MAE, and MAPE with R2 0.52. In addition, the results of visual identification revealed that high estimates of lower poverty are typically found in urban areas with high accessibility, and these areas are not spatially deprived areas with limited accessibility.
Time-Series Clustering of the Regencies Hotel Room Occupancy Rate in Indonesia after the COVID-19 Pandemic Ladisa Busaina; Setia Pramana; Satria Bagus Panuntun
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.387

Abstract

After COVID-19 pandemic, Indonesia entering the recovery era. The government provides incentives for tourism industry recovery. This policy was created because the impact of COVID-19 pandemic on tourism industry at each regencies/cities are different. This study investigates a different recovery pattern at regencies/cities across Indonesia. The data of this study consist of the room occupancy rate (ROR) from Badan Pusat Statistik (BPS) Indonesia and from web scraping monthly data from Agoda website between 1 January 2021 until 1 August 2023. The regencies/cities are clustered by ROR category using the dynamic time warping method. The result of study, there is a difference of tourism industry recovery at regencies/cities across Indonesia, which is the speed are fast, medium, or slow. This could be the result of differences of different policy in each regency/city to respond COVID-19 pandemic on their tourism industry.
Mobility Pattern Changes in Indonesia in Response to COVID-19 Pramana, Setia; Cahyono, Bintang Dwitya; Novandra, Rio
Economics and Finance in Indonesia Vol. 67, No. 1
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

All countries affected by the COVID-19 pandemic have established several policies to control the spread of the disease. The government of Indonesia has enforced a work-from-home policy and large-scale social restrictions in most regions that result in the changes in community mobility in various categories of places. This study aims to (1) investigate the impact of large-scale restrictions on provincial-level mobility in Indonesia, (2) categorize provinces based on mobility patterns, and (3) investigate regional socio-economic characteristics that may lead to different mobility patterns. This study utilized Provincial-level Google Mobility Index, Flight data scraped from daily web, and regional characteristics (e.g., poverty rate, percentages of informal workers). A Dynamic Time Warping method was employed to investigate the clusters of mobility. The study shows an intense trade-off of mobility pattern between residential areas and public areas. In general, during the first 2.5 months of the pandemic, people had reduced their activities in public areas and preferred to stay at home. Meanwhile, provinces have different mobility patterns from each other during the period of the large-scale restrictions. The differences in mobility are mainly led by the percentage of formal workers in each region.
Children's Resilience to Not Working Before and During the Pandemic in Rural Indonesia Hanafi, Zulfaning Tyas; Pramana, Setia
Jurnal Ketenagakerjaan Vol 18 No 3 (2023)
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v18i3.261

Abstract

Children are an asset to a country and a resource that will further national and international development goals. Children who work will be threatened by their health, safety, education, and development. In the last 10 years, BPS has recorded the percentage of child labor fluctuating with the latest record in 2021 at 2.63 percent. The high percentage makes the phenomenon of child labor still a global concern to be addressed as contained in the Sustainable Development Goals. Therefore, the purpose of this study is to determine the determinants of children's resilience not to work in rural areas during the observation period before and during the COVID-19 pandemic. This study uses a three-level survival analysis method by utilizing Sakernas data for August 2019-2020 and data from the BPS website. The results of this study obtained that the percentage of children aged 5-17 years who worked increased during the pandemic by 5.62 percent to 18.88 percent in rural areas. The variables of child gender, child education, child status at individual level; household head gender, household head occupation sector, and household poverty status at household level; and percentage of poor population at districts level significantly affect the resilience of children aged 5-17 not to work in the period before and during the COVID-19 pandemic.
Co-Authors ., Yunofri Achmad Fauzi Bagus Firmansyah Addin Maulana Aditama, Farhan Satria Aini Izzati, Fitri Alifatri, La Ode Alistin, Zharifah Dhiya Ayu Amnur, Muh. Alfian Ana Lailatul Fitriyani Ana Lailatul Fitriyani Anang Kurnia Arie Wahyu Wijayanto Arif Handoyo Marsuhandi Ariya Jalaksana, Faruq Arkandana, M. Tharif Astrinariswari Rahmadian Prasetyo Astuti, Erni Tri Bintang Yuliani Manalu, Jernita Busaina, Ladisa Cahyono, Bintang Dwitya Charvia Ismi Zahrani Cholifa Fitri Annisa Dandy Adetiar Al Rizki Dede Yoga Paramartha Dede Yoga Paramartha Deli, Nensi Fitria Dewi Krismawati Dewi Krismawati Dhiar Niken Larasati Diory Paulus Pamanik Erni Tri Astuti Erwin Tanur Fadila Utami, Nurul Fajar Fathur Rachman Fajar Fatur Rachman Farakh Khoirotun Nasida Farhan Y. Hidayat Fitriyani, Ana Lailatul Fitriyyah, Nur Retno Geri Yesa Ermawan Gilang Hidayat, Muhammad Hady Suryono Hanafi, Zulfaning Tyas Hardiyanta, I Komang Y. Hendrawan, Daffa Hidayat, Farhan Y. Hizir Sofyan Hulliyyatus Suadaa, Lya I Komang Y. Hardiyanta I Nyoman Setiawan Imam Habib Pamungkas Jane, Giani Jovita Khairani, Fitri Krisela Fabrianne, Elisse Krismawati, Dewi Ladisa Busaina Linta Ifada Linta Ifada Maftukhatul Qomariyah Virati Magfirah, Deanty Fatihatul Mariel, Wahyu Calvin Frans Maulana Faris Median Ramadhan, Alif Muhammad Farhan Muhammad Gazali, La Ode Muhammad Nur Aidi Muhammad Tharif Arkandana Mumtaz Siregar, Amir Munaf, Alfatihah Reno Maulani Nuryaningsih Soekri Putri Nasiya Alifah Utami Nazuli, Muhammad Fachry Nensi Fitria Deli Nisa Rahayu Ananda Suwendra, Made Nora Dzulvawan Novandra, Rio Nur Retno Fitriyyah Nurmalasari, Mieke Nurtia Nurtia Nurwijayanti Oktari, Rina S. Panuntun, Satria Bagus Paramartha, Dede Yoga Putro, Dimas Hutomo Rahman, Dimas Haafizh Rahmaniar, Masna Novita Rifqi Ramadhan Rimadeni, Yeni Rina S. Oktari Rini Rahani Rutba, Sita Aliya Safrizal Rahman Safrizal Rahman, Safrizal Salim Satriajati Salwa Rizqina Putri Satria Bagus Panuntun Satria Bagus Panuntun Satria Bagus Panuntun Satria Bagus Panuntun Silalahi, Agatha Siswantining, Titin Siti Mariyah SITI MARIYAH Soemarso, Ditoprasetyo Rusharsono Suadaa, Lya Hulliyyatus Sugiri Suhendra Widi Prayoga Takdir Tasriah, Etjih Thosan Girisona Suganda Thosan Girisona Suganda Tigor Nirman Simanjuntak Titin Siswantining Usman Bustaman Usman Bustaman Utami, Nandya Rezky Wahyu Calvin Frans Mariel Wirata Raja Panjaitan, Eurorea Wiwin Srimulyani Yuniarti Yuniarti Yuniarto, Budi Zen, Rizqi Annisa