Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal Jurnal Ekonomi Pembangunan Jurnal Ilmu Sosial dan Humaniora PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Nucke Widowati Kusumo Projo
Politeknik Statistika STIS
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS

 1 
Topic Modelling in Knowledge Management Documents BPS Statistics Indonesia Muhammad Yunus Hendrawan; Nucke Widowati Kusumo Projo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.52

Abstract

Knowledge management is an important activity in improving the performance an organization. BPS Statistics Indonesia has recently implemented such a system to improve the quality and efficiency of business processes. The purposes of this research are: 1) implementing topic modelling on BPS Knowledge Management System to identify groups of document topics; 2) providing recommendations on which the best topic modelling; 3) building a web service function of topic modelling for BPS that includes data preprocessing function and topic group recommendation function. This study applies the Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) topic modelling methods to determine the best grouping techniques for knowledge management systems in BPS Statistics Indonesia. The results show that the LDA model using Mallet is the best model with 25 topic groups and a coherence score of 0.4803. The performance result suggest that the best modelling method is the LDA. The LDA model is then successfully implemented in RESTful web service to provide services in the preprocessing function and topic recommendations on documents entered into the Knowledge Management System BPS.
What We Know from Telemedicine Data in Indonesia? Study case using Alodokter, Dokter.id, and Honestdocs Faza Nur Fuadina; Nucke Widowati Kusumo Projo; Siti Mariyah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.53

Abstract

The internet and technology development arise in various aspects of life in Indonesia, including in the health sector with e-health. Telemedicine utilization as a form of e-health was still rare among Indonesians because its existence is not as much as e-commerce that is more related to the economic sector. The COVID-19 pandemic has limited people's movement to get health care, but it made people use telemedicine in Indonesia. This research aims to analyze telemedicine utilization in Indonesia and see the health phenomena captured in the data. This research uses descriptive analysis and text mining to determine the utilization of telemedicine with the Named Entity Recognition (NER) and Latent Dirichlet Allocation (LDA) methods. In addition, a literature review is also used to identify the potential use of telemedicine data in collecting health statistics in Indonesia. The results show that telemedicine has been widely used in Indonesia. The clinical teleconsultation data and article titles on telemedicine produce various health topics. Therefore, telemedicine data can potentially be used as a source for collecting health statistics.
Do Tourist Attraction Objects Implement Health Protocols? Analysis of Tourist Attraction Object in East Java Province Using Google Maps Review Disya Pratistaning Ratriatmaja; Nucke Widowati Kusumo Projo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.64

Abstract

The COVID-19 pandemic has impacted the tourism sector, particularly the Tourist Attraction Object (TAO) in Indonesia. This research aims: to analyse the implementation of health protocols and facility conditions at TAO, to analyse the change in visitor sentiment and rating towards TAO before and during the COVID-19 pandemic, to analyse the close relationship between ratings and reviews of visitor sentiment on TAO, to analyse the possibility of web scraping data to complement tourism data from BPS Statistics Indonesia. Using Google Maps review, this research uses the Multinomial Naïve Bayes (MNB), Term Frequency-Inverse Document Frequency (TF-IDF), pseudo-labelling, and word association methods. The results show that the health protocol has been implemented in TAO of East Java province, the available facilities are good, and there is no change in reviews during the TAO pandemic. The Stuart-Kendall Tau-c value shows a weak relationship in a positive direction between rating and review sentiment. According to Haversine, Jaro Winkler, and Levenshtein, the data calculation indicates that web scraping data can complement tourism data for BPS-Statistics Indonesia.
Nowcasting of Chili Pepper (Capsicum frutescens L.) Prices in East Java Province Using Multi-Layer Perceptron Method Mohamad Choirul Zamzami; Nucke Widowati Kusumo Projo
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.v2023i1.274

Abstract

The aims of study is to predict the price of chili pepper at the provincial level in East Java by looking for the best input variable from three types of input variables, price of chili pepper at the regency and city levels, natural factors, and word search index on Google Trends as an approach to the causes of chili pepper price fluctuations. The Multi-Layer Perceptron method, accompanied by a search for the best combination of model parameters is selected to get the model with the best nowcasting ability. The result shows that the best model for nowcasting is characterized by: the input variable is price of chili pepper at the regency and city levels with three hidden layers and 32, 45, and 51 neurons in each hidden layer, maximum iteration is 200 iterations, maximum iteration when the model not increase in performance for applying early stopping is 20 iterations, non-linear activation used is RELU (Rectified Linear Unit), and optimization function used is ADAM optimizer. The accuracy of nowcasting in this study is highly accurated with MAPE smaller than 10%.
Co-Authors Desy Wasani Disya Pratistaning Ratriatmaja Faza Nur Fuadina Mohamad Choirul Zamzami Mohammad Rifky Pontoh Muhammad Yunus Hendrawan Siti Mariyah