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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Official Statistics
Agustini, Peni
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Simulasi Dampak Kebijakan Moneter terhadap Perekonomian dan Emisi CO2 Per Kapita di Indonesia Ningrum, Icha Wahyu Kusuma; Agustini, Peni; Nabilah, Yasmin Nur Alya
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.v2024i1.2247

Abstract

In running an economy, energy is needed as an input for the production process. However, energy needs are still dominated by energy from fossils that produce CO2 emissions. CO2 emissions can cause global climate change where the United Nations (UN) is struggling to combat climate change and its impacts through the 13th Sustainable Development Goals. This study aims to examine the simultaneous relationship between gross domestic product, CO2 emissions, and gross fixed capital formation in Indonesia and the variables that influence the three indicators using a simultaneous equation model with the two stage least squares (2SLS) method. In addition, a simulation will be carried out when an intervention is made on monetary policy against the three indicators. As a result, the scenario that can improve the economy and CO₂ emissions per capita is by lowering interest rates. While the scenario that can reduce the economy and CO2 emissions per capita is by raising interest rates.
Perbandingan Algoritma Deep Learning untuk Analisis Sentimen Ekowisata di Bogor: Comparison of Deep Learning Algorithm in Sentiment Analysis Ecotourism in Bogor Agustini, Peni; Iqbal, Muhammad; Akbar, Vicha Amalia; Kurniawan, Robert
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2191

Abstract

Bogor memiliki destinasi ekowisata unggulan di Indonesia yang menawarkan keasrian alam dan kemudahan akses dari Jakarta. Namun, peningkatan jumlah wisatawan menimbulkan hambatan terhadap pengelolaan lingkungan, seperti pengelolaan sampah dan tekanan terhadap sumber daya alam. Media sosial, khususnya Google Maps, berperan penting dalam promosi dan memahami perilaku wisatawan melalui fitur ulasan. Studi ini bertujuan melakukan analisis sentimen mengenai ulasan ekowisata di Bogor yang diambil dari Google Maps, menggunakan metode Deep Learning berbasis neural network, yaitu Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM), dan membandingkan performa ketiga model tersebut untuk menentukan metode terbaik dalam mengklasifikasikan sentimen pengunjung. Hasil studi ini menunjukkan, model CNN memiliki akurasi tertinggi yaitu sebesar 72 persen dan lebih unggul dibanding model RNN dan LSTM. Model CNN dapat digunakan sebagai acuan utama dalam menerapkan analisis sentimen pada topik yang sejenis.
Co-Authors Akbar, Vicha Amalia Muhammad Iqbal Nabilah, Yasmin Nur Alya Ningrum, Icha Wahyu Kusuma Robert Kurniawan, Robert