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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal
Ashafidz Fauzan Dianta
Politeknik Elektronika Negeri Surabaya, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Pengembangan Aplikasi Virtual Tour Taman Harmoni Sebagai Sarana Promosi Wisata di Kota Surabaya Zakha Maisat Eka Darmawan; Ashafidz Fauzan Dianta; Akhmad Alimudin; Aliv Faizal Muhammad; Dwi Susanto; Hestiasari Rante; Irma Wulandari; Kholid Fathoni; Moh Zikky; Muhammad Agus Zainuddin; Mifta Nauval Harizy; Rendra Suprobo Aji; Rosiyah Faradisa; Sritrusta Sukaridhoto; Tri Budi Santoso; Widi Sarinastiti
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.5104

Abstract

Pada era digital sekarang, pemanfaatan teknologi multimedia digunakan pada berbagai bidang, terutama pariwisata. Virtual tour yang merupakan bagian dari virtual reality hadir untuk memberi manfaat ke masyarakat untuk mendapatkan pengalaman baru dengan melakukan tur secara dunia maya ke tempat wisata tertentu. Kegiatan ini menyajikan proses dan hasil dari kegiatan pengabdian masyarakat yang bekerja sama dengan Pemerintah Kota Surabaya untuk mengembangkan aplikasi virtual tour Taman Harmoni berbasis website. Dengan lima tahapan pelaksanaan, dari observasi lapangan hingga dokumentasi, kami dapat mempublikasikan aplikasi tersebut dengan laman web yang bisa diakses oleh masyarakat dimanapun dan kapanpun. Sebanyak 71,4% pengunjung aplikasi yang telah mengeksplorasi fitur-fitur yang disediakan memberikan umpan balik yang baik terhadap keberadaan aplikasi ini.
Comparison of Machine Learning Classification Methods for Weather Prediction: A Performance Analysis Zakha Maisat Eka Darmawan; Ashafidz Fauzan Dianta; Kholid Fathoni; Oktavia Citra Resmi Rachmawati; Kevin Ilham Apriandy
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6649

Abstract

Weather classification is crucial in various sectors, including agriculture, transportation, and disaster management. Accurate weather prediction can help mitigate risks and improve decision-making in these fields. However, classifying weather conditions remains challenging due to the complex and dynamic nature of meteorological data. This study aims to compare different machine learning classification methods to determine the most effective model for weather classification. The research employs a structured methodology consisting of seven key steps: literature study, data understanding, exploratory data analysis, data preparation, modeling, evaluation, and hyperparameter tuning. The study used Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting, AdaBoost, and Extra Trees to identify the best-performing classifier. Model evaluation was conducted using accuracy, precision, recall, and F1-score. The results indicate that Gradient Boosting achieved the highest performance, surpassing other models with an accuracy of 90.15%. To optimize the model further, hyperparameter tuning was conducted using GridSearchCV, and feature selection was done using SelectKBest. This process resulted in an improved accuracy of 90.22%, demonstrating the effectiveness of model optimization.
Co-Authors Akhmad Alimudin Aliv Faizal Muhammad Darmawan, Zakha Maisat Eka Dwi Susanto Hestiasari Rante Irma Wulandari Kevin Ilham Apriandy Kholid Fathoni Mifta Nauval Harizy Moh Zikky Muhammad Agus Zainuddin Oktavia Citra Resmi Rachmawati Rendra Suprobo Aji Rosiyah Faradisa Tri Budi Santoso Widi Sarinastiti