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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF)
Anantyo, Andika
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementasi Metode Holt-Winters dan Deseasonalized Untuk Prediksi Penumpang Bandara Soekarno-Hatta Arief, Ulfah Mediaty; Sukamta, Sri; Anantyo, Andika; Wafa', Almas Diqya; Maulana, Alfan; Febianingrum, Anggun Fia; Praditya, Ambrosius Lingga; Putra, Ade
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9715

Abstract

Bandar udara menjadi elemen infrastruktur yang memiliki peran signifikan dalam perjalanan udara. Manajemen bandara yang kurang baik dapat menyebabkan berbagai permasalahan, salah satunya adalah fluktuasi penumpang pada tiap tahun yang menimbulkan kerugian jika tidak sesuai dengan perencanaan dan operasional bandara. Dengan permasalahan yang ada, penelitian ini dilakukan dengan tujuan untuk melakukan prediksi jumlah penumpang pada Bandara Soekarno-Hatta untuk mengetahui perencanaan dan manajemen bandara yang lebih baik pada beberapa periode waktu kedepan. Metode Holt-Winters dan Deseasonalized digunakan untuk melakukan prediksi jumlah penumpang. Berdasarkan pengujian yang telah dilakukan menggunakan data jumlah penumpang Bandara Soekarno-Hatta tahun 2015-2022 yang berasal dari BPS, didapatkan hasil prediksi menggunakan Metode Holt-Winters lebih akurat dengan nilai RMSE sebesar 224.215,83 yang lebih kecil dibandingkan Metode Deseasonalized dengan nilai RMSE sebesar 416.078,74. Hasil tersebut menunjukkan bahwa metode Holt-Winters lebih cocok digunakan untuk memprediksi jumlah penumpang Bandara Soekarno-Hatta dengan kesalahan prediksi yang lebih rendah dan dapat berjalan dengan baik dengan data yang memiliki fluktuasi tinggi.
Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system Subiyanto, Subiyanto; Prajanti, Sucihatiningsih Dian Wisika; Salim, Nur Azis; Prabowo, Setya Budi Arif; Sutrisno, Deyndrawan; Anantyo, Andika; Anggriani, Dewi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1968-1977

Abstract

In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models.
Insulator Defect Detection Based On Image Processing Using A Modified YOLOv8n Model Muzaki, Muchamad Arfim; Subiyanto, Subiyanto; Anantyo, Andika
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4679

Abstract

Insulators are critical components in power transmission and distribution systems, where any defects can lead to severe operational failures and power outages. To enhance inspection efficiency, unmanned aerial vehicles (UAVs) are increasingly used for aerial monitoring. However, the quality of images captured by drones is often compromised due to hardware limitations, motion blur, and complex environmental backgrounds, which significantly reduces the performance of deep learning-based defect detection methods. This study proposes an improved insulator defect detection model based on the YOLOv8n architecture, optimized for accuracy and efficiency in low-quality image scenarios and suitable for deployment in resource-constrained environments. The model introduces two major modifications. First, a Slim-Neck module employing Ghost-Shuffle Convolution (GSConv) replaces standard convolutions to substantially reduce computational cost while preserving rich feature representations. Second, an Efficient Multi-Scale Attention (EMA) module is integrated into the neck to enhance multi-scale feature fusion by maintaining per-channel information without dimensionality reduction, improving the model’s ability to extract discriminative features. Experimental results demonstrate that the proposed model achieves a precision of 92.0%, recall of 88.6%, mAP@0.5 of 92.1%, and an inference speed of 161.29 FPS. Furthermore, it reduces parameter count by 10.8% and computational load by 8.6% compared to the baseline, validating its suitability for real-time UAV-based inspections. The model also outperforms existing methods in detecting insulator defects, particularly in challenging conditions involving blur and complex backgrounds.
Co-Authors Ade Putra Anggriani, Dewi Febianingrum, Anggun Fia Maulana, Alfan Muzaki, Muchamad Arfim Prabowo, Setya Budi Arif Praditya, Ambrosius Lingga Salim, Nur Azis Sri Sukamta Sucihatiningsih Dian Wisika Prajanti Sutrisno, Deyndrawan Ulfah Mediaty Arief Wafa', Almas Diqya