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All Journal Jurnal Sintak STATISTIKA Journal of Mathematics, Computation and Statistics (JMATHCOS)
Arrafi, Adamsyam
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Mathematics

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The Influence of Post Frequency and Type of Platform Used on Follower Interest Levels Azzahra, Nabila; Utami, Bulan Purnama; Arrafi, Adamsyam; Handayani, Vitri Aprilla
JURNAL SINTAK Vol. 3 No. 1 (2024): SEPTEMBER 2024
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/jsintak.v3i1.381

Abstract

This study examines the influence of social media platform types and posting frequency on the level of follower interest in a content creator's account. The analysis results show that the type of platform (Instagram or TikTok) does not have a significant impact on follower interest. However, posting frequency is proven to have a substantial influence, with weekly frequency showing the most positive impact. The interaction between platform type and posting frequency does not demonstrate a significant effect. These findings suggest that content creators can focus on adjusting their posting frequency, particularly with a weekly schedule, to increase follower interest, without needing to worry about choosing between Instagram or TikTok platforms.
Analysis of International Tourist Visits Based on Nationality and Tourism Travel Characteristics Using Complete Linkage Handayani, Vitri Aprilla; Sulistyono, Eko; Arrafi, Adamsyam; Hayati, Nahrul
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5801

Abstract

Abstract. This research aims to analyze the characteristics of international tourists in Indonesia using Clustering Method: Complete Linkage. The study successfully identified 5 distinct clusters based on nationality and tourism travel characteristics. The analysis showed significant differences between clusters in terms of country of origin, travel patterns, preferences, expenditure, and tourist activities. Cluster 1 was dominated by ASEAN and Middle Eastern countries with stable visitation patterns influenced by geographical proximity and cultural and business relationships. Cluster 2 was the largest group, encompassing various countries with holiday and business tourism characteristics, longer stays, and higher expenditure. Clusters 3, 4, and 5 each consisted of a single country: Timor Leste, Hong Kong, and Papua New Guinea respectively, with unique visitation patterns. Each cluster showed differences in travel purposes, length of stay, expenditure, and activities of interest. A deep understanding of each tourist group’s characteristics was crucial for developing more targeted tourism marketing strategies. The clustering results could be utilized for infrastructure planning, resource allocation, promotional strategies, and service improvements tailored to each group’s characteristics, thereby enhancing tourist experiences and Indonesia’s overall tourism competitiveness.
Comparative Analysis: Multiple Regression and Random Forest Regression in Predicting Food Security Index in Indonesia Handayani, Vitri Aprilla; Rahmiati, Sari; Varischa, Bintang; Arrafi, Adamsyam
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8593

Abstract

Food security was an important issue influenced by production, access, prices, and socio-economic conditions. In Indonesia, the Food Security Index (IKP) was used as the main indicator. However, prediction methods such as multiple linear regression often failed to capture the complex relationships between variables. Machine learning methods, such as random forest regression, offered a more suitable alternative for non-linear and large-scale data. Nevertheless, few studies in Indonesia compared the effectiveness of these two methods. Therefore, this study aimed to compare the performance of linear regression and random forest in predicting the IKP, in order to support more accurate and sustainable food security planning. The analysis results showed that the forecasting method with better performance in predicting the IKP in Indonesia was Random Forest Regression. This study made a significant contribution by empirically comparing multiple regression and Random Forest in predicting the Food Security Index (IKP) using big data. The results showed that Random Forest performed better in terms of MSE (5.5431) and RMSE (57.7242), indicating higher overall accuracy, while multiple regression had lower MAE (6.0805) and slightly higher R² (68.21%), suggesting more stable predictions and better explanatory power. Random Forest also identified key influencing variables, such as poverty rate and health worker ratio, and provided clearer insights through decision tree visualization. Overall, the findings demonstrated that while no model was entirely dominant, Random Forest offered greater flexibility and predictive strength for complex, large-scale data, supporting its potential use in formulating data-driven food security policies in Indonesia
Co-Authors Azzahra, Nabila Eko Sulistyono Sulistyono Handayani, Vitri Aprilla Nahrul Hayati Sari Rahmiati Utami, Bulan Purnama Varischa, Bintang