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All Journal Indonesian Journal of Applied Environmental Studies Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Efficient: Indonesian Journal of Development Economics
Aditya, Randy Daffa
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Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Social Sciences

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Analysis of Carbon Emissions in ASEAN Manufacturing: Input-Output and Panel Data Approach Sandi, Imella Mendita; Abioga, Naufal Raffie; Aditya, Randy Daffa; Sabrina, Rizka; Kartiasih, Fitri
Efficient: Indonesian Journal of Development Economics Vol. 8 No. 1 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/efficient.v8i1.14301

Abstract

This research aims to identify the leading sectors in four ASEAN countries and analyze the influence of production in these sectors and the consumption of renewable energy on carbon dioxide emissions in these countries. The analytical methods used include the analysis of inter-sector linkages and identifying leading sectors using input-output tables, followed by further analysis with panel data regression. The results indicate that while the leading sectors vary among the four countries, the manufacturing sector predominantly leads. Additionally, GDP in the manufacturing sector and renewable energy consumption significantly affect carbon dioxide emissions in the four ASEAN countries
Analysis of flood-prone areas in DKI Jakarta Province using Clustering Method Aditya, Randy Daffa; Habibi, Muhammad Abdul Aziz
Indonesian Journal of Applied Environmental Studies Vol 5, No 1 (2024): Volume 5 Number 1 April 2024
Publisher : Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/injast.v5i1.10259

Abstract

The objective of this research is to ascertain the patterns and organization of flood-affected areas in Jakarta. The dataset of flood incidents in the DKI Jakarta Province in 2020 served as the data source for this study. The research employed three methods: K-Means, K-Medoid, and Hierarchical Clustering. Of these, Hierarchical Clustering produced the best grouping in comparison to the other methods. The findings of the study show that the flood-affected areas in DKI Jakarta are classified into three groups: safe (cluster 1), moderate (cluster 2), and vulnerable (cluster 3). The districts of Cengkareng, Jatinegara, and Pulogadung are among the vulnerable areas. ABSTRAK Tujuan penelitian ini adalah untuk mengetahui pola dan penataan wilayah terdampak banjir di Jakarta. Dataset kejadian banjir di Provinsi DKI Jakarta tahun 2020 dijadikan sebagai sumber data penelitian ini. Penelitian ini menggunakan tiga metode: K-Means, K-Medoid, dan Hierarchical Clustering. Dari ketiga metode tersebut, Hierarchical Clustering menghasilkan pengelompokan terbaik dibandingkan dengan metode lainnya. Temuan penelitian menunjukkan bahwa wilayah terdampak banjir di DKI Jakarta diklasifikasikan menjadi tiga kelompok: aman (kluster 1), sedang (kluster 2), dan rentan (kluster 3). Kecamatan Cengkareng, Jatinegara, dan Pulogadung termasuk wilayah yang rentan.
Penerapan Stochastic Frontier Analysis untuk Estimasi Efisiensi Industri Manufaktur di Indonesia Tahun 2022 Parulian, Firman Emmanuel Declarantius; Harum, Nisrina Sekar; Aditya, Randy Daffa; Yuliana, Rita
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2492

Abstract

The manufacturing industry plays a crucial role in Indonesia’s economic system due to its significant contribution to GDP. However, the sector’s share of GDP has been declining over the years. Therefore, efficiency and productivity levels remain key challenges that must be addressed. This study aims to estimate the technical efficiency of the manufacturing industry and analyze the factors affecting its inefficiency across 34 provinces in Indonesia using Stochastic Frontier Analysis (SFA) in 2022. Unlike previous studies that focus on inter-industry or industrial classification analysis, this research adopts a regional perspective. The results indicate that capital and labor have a positive and significant effect. Furthermore, average years of schooling and fuel consumption in the manufacturing industry significantly affect technical inefficiency. Province with the highest technical efficiency is West Java, while the lowest is in East Nusa Tenggara.
Estimating the Unemployment Rate at Sub-District Level in West Java Province in 2024 Using Hierarchical Bayesian Approach with Cluster Information Aditya, Randy Daffa; Zukhrufah, Awika; Auliya, Eksis; Widyastuti, Dyah; Lubis, Adrian; Nugraha, Anggie; Muchlisoh, Siti
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 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.v2025i1.518

Abstract

Unemployment is a substantial obstacle to growth in Indonesia, affecting both socialand economic stability. The Unemployment Rate is a crucial metric that quantifies the proportionof the labor force actively pursuing work opportunities. The unemployment rate serves as acritical indicator of labor market imbalances, essential for labor policy formulation andassessment. Nonetheless, unemployment data has limitations, particularly at the micro-level,owing to sample constraints. Small Area Estimation (SAE) can address these constraints. Thisstudy estimates the unemployment rate at the sub-district level in West Java province for 2024utilizing the Hierarchical Bayes Beta methodology and clustering techniques. The modelingresults indicate that most sub-districts exhibit a low to medium unemployment rate, however 21locations demonstrate a very high unemployment rate, ranging from 23.00 percent to 48.06percent.
Co-Authors Abioga, Naufal Raffie Auliya, Eksis Dyah Widyastuti Fitri Kartiasih Habibi, Muhammad Abdul Aziz Harum, Nisrina Sekar Lubis, Adrian Muchlisoh, Siti Nugraha, Anggie Parulian, Firman Emmanuel Declarantius Rita Yuliana Sabrina, Rizka Sandi, Imella Mendita Zukhrufah, Awika