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All Journal Prosiding Seminar Nasional Official Statistics
Zareka, Andi Muh. Zulfadhil
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Pendeteksian dan Determinan Overfishing di Indonesia: Penerapan Analisis Klaster dan Regresi Logistik Biner Johan, Muhammad Fazlan; Zareka, Andi Muh. Zulfadhil; Kesumawijaya, Anak Agung Istri Anggita; Kurniasari, Agustin; Maharani, Jessica; Putri, Nimas Ayu Eka
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2096

Abstract

Overfishing causes a decline in fish stocks, imbalance in marine ecosystems, and economic losses for the fisheries sector. This study aims to obtain a model that is able to detect overfishing in various provinces in Indonesia using a combination of cluster analysis and logistic regression. The data used in this research is secondary data obtained from the Central Statistics Agency (BPS) and the Indonesian Ministry of Maritime Affairs and Fisheries (KKP) which includes information related to marine fish production in Indonesia in 2022. From the study conducted, it was found that the provincial data are classified into two clusters where the second cluster was classified as overfished provinces. Based on the analysis carried out, the best model for modeling overfishing is a logistic regression model with two predictor variables, which are exports and fish consumption rates. Thus, it is hoped that this research can serve as a guide for the government in formulating sustainable policies to reduce the number of overfishing in Indonesia in the following years.
Nowcasting Pergerakan Indeks Saham Lingkungan Berdasarkan Minat Publik terhadap Isu Lingkungan Zareka, Andi Muh. Zulfadhil; Ayuningrum, Adinda Safira Santoso; Adnyana, I Kadek Surya Wisesa; Kurniawan, Robert
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.v2025i1.2585

Abstract

The growing awareness among investors regarding environmental, social, and governance (ESG) aspects has increased attention toward the performance of environmentally-based stock indices. This condition has created a need for a nowcasting approach that is responsive to real-time public interest dynamics and market sentiment. This study aims to analyze public interest in environmental issues measured using Google Trends web search volume as a proxy for collective sentiment in predicting the movement of environmental stock indices. ARIMAX, SARIMAX, Random Forest, SVR, and XGBoost models are implemented and evaluated for their performance in predicting index movements. The results show that SVR, with an RMSE of 20.3646, is the best-performing model. These findings indicate that public interest in environmental issues has significant potential as an effective indicator for real-time prediction of environmental stock index movements, offering valuable insights for investors and market analysts in developing investment strategies that are more responsive to market dynamics influenced by sustainability factors.
Co-Authors Adnyana, I Kadek Surya Wisesa Ayuningrum, Adinda Safira Santoso Johan, Muhammad Fazlan Kesumawijaya, Anak Agung Istri Anggita Kurniasari, Agustin Maharani, Jessica Putri, Nimas Ayu Eka Robert Kurniawan, Robert