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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science Journal of Applied Agricultural Science and Technology
Subarkah, Muhammad Zidni
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Modeling the Number of Foreign Tourist Visits to Indonesia in 2020 Using GWPR Method Subarkah, Muhammad Zidni; Wahyuningtia , Rizki; Hildha , Martina; Sulandari , Winita
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 2, October 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss2.art6

Abstract

In December 2020, the number of foreign tourists visiting Indonesia experienced a sharp decline of 88.08% compared to the number of visits in December 2019. However, compared to the previous month, November 2020, this number increased by 13.58%. Modeling the number of foreign tourists visiting Indonesia in 2020 using the Geographically Weighted Poisson Regression (GWPR) method is needed to elaborate on the Indonesian government’s policy decisions, especially in the tourism sector. The results showed that the GWPR model with the Kernel fixed Gaussian weighted function had an AIC value of 1,521,240.873, deviance of 1,521,196.695, and deviance-R2 of 0.741 or 74.1%. This model produced two different clusters of characteristics of foreign tourists’ country of origin based on the variable’s significance. Cluster one consisted of Finland and Qatar and the rest were in cluster two. The characteristics of cluster two were influenced by the rupiah exchange rate variable, short stay visa free (Bebas Visa Kunjungan Singkat, BVKS), Consumer Price Index (CPI), economic growth, total imports, and the distance of CGK to the international airport. Meanwhile, cluster one had almost the same characteristics as cluster two but was not influenced by the BVKS factor variables.
The Analysis of Architectural YOLOv5 Convolutional Neural Networks for Detecting Apple Leaf Diseases Erkamim, Moh.; Subarkah, Muhammad Zidni; Soelistijono, R.
Journal of Applied Agricultural Science and Technology Vol. 9 No. 1 (2025): Journal of Applied Agricultural Science and Technology
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/jaast.v9i1.251

Abstract

Apple cultivation is crucial to agricultural economies, particularly in regions with sub-tropical climates, such as Indonesia, where apple farming is expanding rapidly. However, managing diseases and pests is essential for maintaining optimal crop yields, as they can significantly reduce production. Among the common diseases affecting apple trees are Scab, Black Rot, and Cedar Apple Rust, which primarily impact leaves and threaten the total health of the plant. Therefore, this research aimed to develop an effective model for detecting apple leaf diseases using the architectural YOLOv5 Convolutional Neural Networks (CNNs). The analysis was conducted between November 2022 and February 2023 at the Smart City Information System (SIKC) laboratory, including 120 apple leaf samples collected from Tawangmangu. Additionally, secondary data containing 30 images for each disease category, consisting of Healthy, Scab, Black Rot, and Cedar Apple Rust, were used as a benchmark. The performance of YOLOv5 was evaluated based on several metrics, including Precision, Recall, mAP@0.5, and mAP@0.5:0.95. The results showed that Cedar Apple Rust was the most prevalent disease identified among the samples. YOLOv5 performed exceptionally well in detecting disease symptoms, achieving a Precision score of 0.810, Recall of 0.981, mAP@0.5 of 0.950, and mAP@0.5:0.95 of 0.765 on the test dataset. These results showed that the proposed model was highly accurate and reliable for the early detection of apple leaf diseases, offering significant potential for improving disease management strategies and increasing the efficiency of apple production.
Co-Authors Dwi Susilo Utami Hildha , Martina Moh. Erkamim Sulandari , Winita Wahyuningtia , Rizki