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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
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The Impact During of Pandemic COVID-19 on Property Sector Case Study: DKI Jakarta Province Arkandana, M. Tharif; Mariel, Wahyu Calvin Frans; Pramana, Setia
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 11 No 3 (2022): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v11i3.216

Abstract

Since the COVID-19 pandemic in Indonesia, economic conditions have declined, especially in the property sector but if we look at the distribution value of the real estate sector in 2020 during the pandemic, its contribution has increased to 6.31% after falling by 5.94% in 2019. Another impact is that the Property Price Index growth value decreases over the years, but in a big city like DKI Jakarta, it does not affect the pandemic condition. Big Data has the potential to produce useful and useful statistics and assist in the collection of Official Statistics data. This study aims to see the condition of the number of advertisings on the property side and the selling price of houses and apartments that can be impacted by the pandemic COVID-19. Based on data from one property site in Indonesia, the condition of ad serving during the pandemic is very influential, with the highest serving during the Micro Indonesia large-scale social restrictions (PSBB).
The Implementation of Geospatial Analysis on Hotel Occupancy Rate Nazuli, Muhammad Fachry; Panuntun, Satria Bagus; Maulana, Addin; Takdir; Pramana, Setia
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.797

Abstract

Introduction/Main Objectives: One of the main attributes of hotel selection and customer satisfaction is its location. Background Problems: Strategic location leads to higher demand for accommodation. Accommodation demand is reflected in hotel occupancy levels, which indicate the percentage of reserved rooms at a specific period. Novelty: This study aims to investigate the effect of spatial location on hotel occupancy rates by analyzing data collected in online hotel reservation applications. A study related to the effects of location and hotel occupancy has never been conducted in Indonesia. Research Methods: We use data from hotels located in the province of Yogyakarta, which contains 245 hotels spread over three regencies/cities, namely Yogyakarta City, Sleman Regency, and Bantul Regency. We conducted a spatial regression analysis, namely the Spatial Error Model (SEM), with a spatial weight matrix using a radius of 3.2 km. Finding/Results:  We found that spatial locations affect the occupancy rates of hotels based on the online hotel reservation application that we observed. These spatial locations include the distance from the hotel to the airport, the distance from the hotel to the bus stop, and the number of nearby restaurants, offices, and hotels.
Measuring ocean physical asset account using machine learning approaches Jane, Giani Jovita; Tasriah, Etjih; Pramana, Setia
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 13 No 3 (2024): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v13i3.563

Abstract

The blue economy concept has been adapted as a strategy in setting development programmes and public policies in managing Indonesia’s marine resources. As a supporting instrument, accurate field data is needed when compiling the ocean account. Meanwhile, the support of qualified resources is needed during the field data collection process. Research on mapping water areas using satellite technology and machine learning techniques in producing water maps, especially in coastal areas. The approach is suitable for arranging a physical asset account, which is a component of the ocean account framework. So far, no research has implemented these developments to produce ocean physical asset account. Therefore, this study will cover in arranging the account by utilising Sentinel-2 imagery and implementing Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost) machine learning methods, which according to previous studies are superior methods for mapping water areas. The modelling results show that there isan extensive change in coral, seagrass, and mixed ecosystem types (a combination of coral, seagrass, and macroalgae ecosystems) between 2020 and 2023.
Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia Tigor Nirman Simanjuntak; Setia Pramana
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p304-313

Abstract

This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the user accounts. The number of tweets obtained was 8,740 in text format, with a total engagement of 217,316. The data was sorted from the tweets with the largest to smallest engagement, then cleaned from unnecessary fonts and symbols as well as typo words and abbreviations. The sentiment classification was carried out by analytical tools, extracting information with text mining, into positive, negative, and neutral polarity. To sharpen the analysis, the cleaned data was selected only with the largest engagement until those with 100 engagements; then was grouped into 30 sub-topics to be analyzed. The interesting facts are found that most tweets and sub-topics were dominated by the negative sentiment; and some unthinkable sub-topics were talked by many users.
Development of Automated Environmental Data Collection System and Environment Statistics Dashboard Dede Yoga Paramartha; Ana Lailatul Fitriyani; Setia Pramana
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p314-325

Abstract

Environmental data such as pollutants, temperature, and humidity are data that have a role in the agricultural sector in predicting rainfall conditions. In fact, pollutant data is common to be used as a proxy to see the density of industry and transportation. With this need, it is necessary to have automated data from outside websites that are able to provide data faster than satellite confirmation. Data sourced from IQair, can be used as a benchmark or confirmative data for weather and environmental statistics in Indonesia. Data is taken by scraping method on the website. Scraping is done on the API available on the website. Scraping is divided into 2 stages, the first is to determine the location in Indonesia, the second is to collect statistics such as temperature, humidity, and pollutant data (AQI). The module used in python is the scrapy module, where the crawling is effective starting from May 2020. The data is recorded every three hours for all regions of Indonesia and directly displayed by the Power BI-based dashboard. We also illustrated that AQI data can be used as a proxy for socio-economic activity and also as an indicator in monitoring green growth in Indonesia.
Online Marketplace Data to Figure COVID-19 Impact on Micro and Small Retailers in Indonesia Dhiar Niken Larasati; Usman Bustaman; Setia Pramana
Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i2p333-342

Abstract

The COVID-19 outbreak is not only talking about health crises but also social and economic crises all over the world. In Indonesia, the outbreak has shaken almost all business sectors, however it seems to bring a silver lining for e-commerce sectors since the pandemic has developed online shopping habits. During the pandemic, the impact of COVID-19 on the Indonesian economy needs to be updated from time to time to be used on quick policymaking. Therefore, big data plays an important role to provide the information relatively fast. This paper aims to describe how big data i.e., marketplace data, could be used to figure the impact of COVID-19 outbreak on micro and small retailers in Indonesia. The dataset was collected regularly from a marketplace website in Indonesia from January to June 2020. To see the changing of sales during the COVID-19 period, the sales before and after social distancing policy implementation are compared. The result showed that the online marketplace in Indonesia is dominated by micro retailers based on the number of products sold in the marketplace. The total revenue of micro retailers gives a significant increase during the pandemic. Whereas for medium retailers, the increase in total revenue is seen to be lower than micro retailers’ total revenue. It indicates a positive sign for the growth of micro retailers in the online marketplace.
Green Spaces and Crime: Spatial Modeling of Socio-Economic Influences in Jakarta's Urban Areas, 2022 Nur Retno Fitriyyah; Setia Pramana
The Journal of Indonesia Sustainable Development Planning Vol 6 No 1 (2025)
Publisher : Pusbindiklatren Bappenas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46456/jisdep.v6i1.609

Abstract

Urban crime is a multidimensional issue influenced by environmental, economic, and social interactions. This study investigates factors affecting crime rates in DKI Jakarta, including green open space (RTH), night light intensity (NTL), security services and worship facilities, extreme poverty, relative wealth index (RWI), and population density. Using remote sensing and spectral indices, green open spaces were identified and classified with a random forest model, achieving 95.53% overall accuracy and a kappa coefficient of 94.19%. Spatial regression analysis with Queen Contiguity weights was employed to examine the influence of these factors on crime rates. Results from the Spatial Autoregressive Moving Average (SARMA) model show that green space area, NTL, and extreme poverty significantly impact crime rates. Districts with more green spaces, such as South Jakarta, experienced lower crime rates, while densely populated and impoverished areas, such as North Jakarta, exhibited higher crime rates. The study highlights the importance of ecological factors in crime prevention, emphasizing the integration of green space planning and big data analytics. These findings provide actionable insights for policymakers to develop safer urban environments and support Indonesia’s efforts toward achieving SDG 16 on peace and justice.
Evaluation of Biclustering Imputation Methods for Glioblastoma Gene Expression Data Silalahi, Agatha; Titin Siswantining; Setia Pramana
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art7

Abstract

Glioblastoma is a highly aggressive primary brain tumor with a low survival rate. One of the main challenges in analyzing glioblastoma gene expression data is the presence of missing values, which can reduce biclustering accuracy and affect biological interpretation. This research compared six imputation methods k-nearest neighbors (KNN), mean imputation, singular value decomposition, nonnegative matrix factorization, soft impute, and autoencoderon the GSE4290 gene expression dataset with missing values ranging from 5% to 50%. An evaluation using root mean square error (RMSE), mean absolute error (MAE), and structural similarity index measure (SSIM) showed that soft impute provided the best performance at all levels of missing values, with RMSE of 0.0076, MAE of 0.0073, and perfect SSIM of 1.0000 at 50% missing values. Meanwhile, deep learning-based autoencoder experienced significant performance degradation at high missing values. These findings indicate that more complex models are not always superior, and regularization-based approaches like soft impute are more effective in preserving the biological structure of the data. The results of this research contribute to the optimization of imputation strategies to improve the accuracy of biclustering analysis in glioblastoma studies.
Socioeconomic impact of COVID-19 restrictions in Bali: A nighttime light analysis Putro, Dimas Hutomo; Pramana, Setia; Hendrawan, Daffa
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 14 No 1 (2025): April
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v14i1.220

Abstract

This study investigates the socio-economic impacts of COVID-19 pandemic restrictions in Bali using nighttime light remote sensing as a proxy for socio-economic activity. The monthly NTL data from the Suomi-NPP VIIRS instrument, spanning from 2014 to 2021, are analyzed. This study focuses on changes in NTL trends before and after the restrictions, specifically the Large-Scale Social Restriction and Welfare Activity Restriction programs. To ensure that the NTL used in this study accurately measures human activity, we integrate the data with built-up area maps from the Global Human Settlement Layer. An ARIMA intervention model is employed to assess the impact of the restrictions on NTL, revealing a significant decrease in certain regions. Furthermore, we find a moderate correlation between NTL and Bali's quarterly GDP data. This study also highlights the potential of NTL remote sensing as a near-real-time proxy for socioeconomic change, allowing for the early evaluation of policy effectiveness. Keywords: nighttime light, COVID-19, proxy indicator, ARIMA intervention JEL Classification: C22; I18; R11
Aspect-based Sentiment Analysis and Topic Modelling of International Media on Indonesia Tourism Sector Recovery Rutba, Sita Aliya; Pramana, Setia
Indonesian Journal of Tourism and Leisure Vol 6, No 1 (2025)
Publisher : Lasigo Akademia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36256/ijtl.v6i1.502

Abstract

The international world's perception of a country is essential. In 2022, Indonesia attracted international media attention, which can form the nation’s image of Indonesia in the international public perception. Therefore, this study analyses international media, online news, and social media Twitter perceptions towards Indonesia. For the news, aspect-based sentiment analysis is carried out, and for Twitter opinion, sentiment classification using multiple classification algorithms, and topic modeling related to these sentiments are carried out. Furthermore, this research classifies news sentiment based on aspects of forming the country's image, such as tourism, exports, diplomacy, government policies, and people's behavior. It was obtained that aspects of people, policy, and tourism, besides being classified as “none” class, mostly classified in negative sentiment. While diplomacy and export are mostly positive sentiment. One limitation of this classifier is the insufficient number of cases in the training data, which led to relatively low accuracy and precision in this study.  On Twitter opinion, it was found that Twitter's positive sentiment about Indonesia is associated with tourism recovery. The topic modeling of positive tweets highlighted international interest in Indonesia's tourism. This study's findings can provide valuable insights for the government on boosting foreign tourism to support economic growth. Additionally, policymakers should focus on addressing issues that attract foreign media attention. By effectively managing these concerns, Indonesia’s branding can be enhanced, potentially leading to an increase in tourist arrivals.
Co-Authors Achmad Fauzi Bagus Firmansyah Addin Maulana Aditama, Farhan Satria Alifatri, La Ode Ana Lailatul Fitriyani Ana Lailatul Fitriyani Anang Kurnia Arie Wahyu Wijayanto Arif Handoyo Marsuhandi Arkandana, M. Tharif Astrinariswari Rahmadian Prasetyo Astuti, Erni Tri 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 Fajar Fathur Rachman Fajar Fatur Rachman Farakh Khoirotun Nasida Farhan Y. Hidayat Fitriyani, Ana Lailatul Fitriyyah, Nur Retno Geri Yesa Ermawan Hady Suryono Hanafi, Zulfaning Tyas Hardiyanta, I Komang Y. Hendrawan, Daffa Hidayat, Farhan Y. Hizir Sofyan I Komang Y. Hardiyanta I Nyoman Setiawan Imam Habib Pamungkas Jane, Giani Jovita Khairani, Fitri Krismawati, Dewi Ladisa Busaina Linta Ifada Linta Ifada Maftukhatul Qomariyah Virati Magfirah, Deanty Fatihatul Mariel, Wahyu Calvin Frans Maulana Faris Muhammad Farhan Muhammad Nur Aidi Muhammad Tharif Arkandana Munaf, Alfatihah Reno Maulani Nuryaningsih Soekri Putri Nasiya Alifah Utami Nazuli, Muhammad Fachry Nensi Fitria Deli Nora Dzulvawan Novandra, Rio Nur Retno Fitriyyah Nurmalasari, Mieke Nurtia Nurtia Nurwijayanti Oktari, Rina S. Panuntun, Satria Bagus Paramartha, Dede Yoga Putro, Dimas Hutomo 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 Wiwin Srimulyani Yuniarti Yuniarti Zen, Rizqi Annisa