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All Journal Journal of Applied Data Sciences Proceeding of Informatics Collaborations and Dessimenation Meeting (Infocoding)
Ahmad Musthafa Masyhur
Universitas Muhammadiyah Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Pengaruh Perkembangan Harga Emas terhadap Inflasi di Indonesia Menggunakan Regresi Linear Husna Kamila Syahida; Ahmad Musthafa Masyhur; Slamet Riyadi
Proceeding of Informatics Collaborations and Dessimenation Meeting Vol. 1 No. 1 (2025)
Publisher : Universitas Muhammadiyah Surakarta

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Abstract

Emas merupakan salah satu aset investasi yang diminati banyak orang. Nilainya yang cukup stabil seringkali digunakan sebagai aset lindung nilai (safe haven) untuk melawan inflasi. Oleh karena itu, penelitian ini bertujuan untuk menganalisis hubungan antara harga emas dan tingkat inflasi. Data yang digunakan dalam penelitian ini diambil dari data sekunder, baik dari website resmi harga emas maupun Bank Indonesia. Proses analisis dilakukan menggunakan pendekatan regresi linier sederhana untuk menghitung hubungan antara satu variable dependen dan independen. Hasil penelitian menunjukkan bahwa variasi data pada harga emas (Sxx) cenderung fluaktif dan jauh dari rata-rata dengan nilainya yang sangat besar, sedangkan variasi data pada tingkat inflasi (Syy) cenderung stabil dan tidak jauh dari rata-ratanya karena nilainya yang sangat kecil. Untuk keterkaitan antara harga emas dan tingkat inflasi (Sxy) menunjukkan bahwa keduanya memiliki keterkaitan yang berlawanan arah dengan hasil angka negatif, yakni -6.219. Selain itu, terdapat hubungan negatif yang kuat, dengan koefisien korelasi ????=-0,81, yang menunjukkan bahwa peningkatan harga emas berkontribusi terhadap penurunan tingkat inflasi. Hal ini didukung dengan adanya perhitungan koefisien determinasi, yang menunjukkan bahwa 65% variasi dalam penurunan tingkat inflasi dapat dijelaskan oleh harga emas. Adapun hasil persamaan regresi pada penelitian ini adalah .
Classification of Political Party Conflicts and Their Mediation Using Modified Recurrent Convolutional Neural Network Slamet Riyadi; Muhamad Arief Previasakti Suradi; Cahya Damarjati; Hsing-Chung Chen; Ridho Al-Hamdi; Ahmad Musthafa Masyhur
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.513

Abstract

The rapid proliferation of political information on the internet has exacerbated conflicts within political parties, including elite disputes, dualism, candidate controversies, and management issues, which can undermine political stability and public trust. To address these challenges, this study introduces the Modified Recurrent Convolutional Neural Network (M-RCNN), an enhanced RCNN model designed to improve classification accuracy and mitigate overfitting by incorporating additional layers and dropout mechanisms. The primary objective of this research is to provide an efficient and accurate framework for classifying political conflicts and mediation strategies, overcoming the limitations of traditional methods, particularly in handling imbalanced datasets and intricate data patterns. Using a dataset of 1,106 Indonesian news articles categorized into four conflict types—elite disputes, management, presidential, and legislative candidate conflicts—and four mediation strategies—leadership decisions, deliberation, legal channels, and none—the data underwent extensive preprocessing, tokenization, and an 80:20 training-testing split. The M-RCNN achieved a conflict classification accuracy of 98.0%, a precision of 99.0%, and a loss of 0.03, significantly outperforming baseline models, including CNN (85.0% accuracy), RNN with LSTM (88.0%), and standard RCNN (85.0%). For mediation strategy classification, the model demonstrated exceptional performance with an accuracy of 99.0%, a precision of 99.0%, and a loss of 0.01, highlighting its robustness and scalability. This study’s novelty lies in its ability to process imbalanced and complex datasets with unparalleled precision and efficiency, providing a practical framework for automated political conflict analysis and mediation. The findings underline the potential of the M-RCNN model to revolutionize political science applications by delivering reliable, fast, and accurate tools for analyzing and resolving political conflicts, thereby contributing to the advancement of artificial intelligence in promoting political stability and fostering public trust.
Co-Authors Cahya Damarjati Hsing-Chung Chen Husna Kamila Syahida Muhamad Arief Previasakti Suradi Ridho Al-Hamdi Slamet Riyadi