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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

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FENOMENA PEKERJA TIDAK TETAP (PRECARIOUS EMPLOYEE) DI INDONESIA DAN FAKTOR-FAKTOR PENENTUNYA Nucke Widowati Kusumo Projo; Mohammad Rifky Pontoh
Jurnal Ilmu Sosial dan Humaniora Vol. 11 No. 3 (2022)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jish.v11i3.45516

Abstract

Indonesia mengalami peningkatan jumlah pekerja tidak tetap (precarious employee) sejak tahun 2016. Hal ini menunjukkan bahwa semakin banyak pekerja yang tidak mendapatkan pekerjaan yang layak. Penelitian ini bertujuan untuk mengidentifikasikan karakteristik pekerja dengan status precarious employee dan faktor-faktor yang menyebabkan seorang pekerja berstatus sebagai precarious employee. Penelitian ini menggunakan data Survei Angkatan Kerja Nasional (SAKERNAS) periode Agustus 2019 dengan metode regresi logistik biner. Hasil penelitian menunjukkan bahwa pekerja laki-laki, kawin atau pernah kawin, memiliki pendidikan hingga maksimal SMA, tinggal di wilayah perdesaan, pernah memiliki pekerjaan sebelumnya, tidak terdaftar atau tidak mengetahui serikat pekerja, dan bekerja di sektor pertanian memiliki kecenderungan yang lebih besar untuk berstatus sebagai precarious employee. Sementara pekerja usia muda lebih berpeluang untuk berstatus menjadi precarious employee. Semakin kecil pendapatan pekerja maka menunjukkan bahwa pekerjaannya merupakan pekerjaan tidak tetap. Pemerintah dapat berkonsentrasi memberikan bantuan pada kelompok pekerja yang memiliki karakteristik rentan menjadi pekerja tidak tetap seperti menambah program peningkatan pendidikan dan kemampuan kerja pada pekerja dengan pendidikan SMA ke bawah serta memperluas jaminan kesejahteraan pekerja di wilayah perdesaan dan pekerja di sektor pertanian.
The Impact of Fuel Prices Increasing on Inflation in South Sulawesi using Pulse Function Intervention Analysis Desy Wasani; Nucke Widowati Kusumo Projo
Jurnal Ekonomi Pembangunan Vol. 20 No. 02 (2022): Jurnal Ekonomi Pembangunan
Publisher : Pusat Pengkajian Ekonomi dan Kebijakan Publik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jep.v21i02.23202

Abstract

South Sulawesi Province is considered capable of controlling inflation, as seen from its not very large volatility. However, this does not mean that the increase in fuel prices does not impact inflation in South Sulawesi. This study aims to obtain the best model to analyze the impact of rising fuel prices on inflation in South Sulawesi using Pulse Function Intervention Analysis and to find out how significant the impact of rising fuel prices on inflation in South Sulawesi is. The data in this study were obtained from the Central Statistics Agency (BPS) of South Sulawesi Province, namely the monthly inflation of South Sulawesi Province for the period January 2014 – September 2022, with a total of 105 observations. The best model in this study is the MA ([12]) bbm1, bbm5, and bbm6 models with AIC 104.09. Statistical test results show that 3 of 6 times increases in fuel prices since 2014 still influence the rising inflation, namely in November 2014, April 2022, and September 2022. The increase in fuel prices in 2014 impacted rising inflation by 1.97 percent, while in 2022, it increased inflation by 0.92 and 0.99 percent.
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