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Muhammad Afrizal Amrustian
Institut Teknologi Telkom Purwokerto
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Pelatihan Konten Digital Desa Wisata di Desa Karanggayam Menggunakan Canva Diandra Chika Fransisca; Muhammad Afrizal Amrustian
Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Vol. 3 No. 1 (2023): Mei 2023 - Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/abdiformatika.v3i1.177

Abstract

Desa Karanggayam, Kabupaten Kebumen adalah salah satu desa yang memiliki keindahan alam perbuktian dan curug sebagai desatinasi wisata. Namun, sejak adanya covid-19 pengunjung desa wisata di Desa Karanggayam menurun drastis dan akhirnya ditutup. Hal ini dikarenakan tidak adanya pemasaran desa wisata secara online di Desa Karanggayam, mengingat dengan adanya covid-19 semua akses promosi wisata tidak dibatasi secara offline. Oleh karena itu, pengabdian masyarakat ini penting sekali untuk memanfaatkan konten digital dalam hal ini aplikasi Canva sebagai media promosi secara online yang menarik. Ada tiga tahapan metode dalam pengabdian masyarakat ini yaitu, persiapan, pelaksanaan dan evaluasi. Hasil pelatihan canva menunjukan para peserta semakin tertarik untuk mempromosikan desa wisatanya karena akses edit konten digital yang praktis dan mudah dipahami. Mitra yang terlibat juga menginginkan pelatihan dengan durasi yang lebih lama.
Multivariate Forecasting of Paddy Production: A Comparative Study of Machine Learning Models Yasin, Feri; Firmansyah, Muhammad Raafi'u; Aldo, Dasril; Amrustian, Muhammad Afrizal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4681

Abstract

Accurate rice production forecasting plays an important role in supporting national food security planning. This study aims to evaluate the performance of four machine learning algorithms, namely Random Forest, XGBoost, Support Vector Regression (SVR), and Linear Regression, in predicting three target variables simultaneously: harvest area, productivity, and production. The dataset used includes annual data per province in Indonesia from 2018 to 2024 obtained from the Central Statistics Agency (BPS). Evaluation was conducted using five metrics: MAE, RMSE, MAPE, R², and training time. The results of the experiment showed that the Random Forest Regressor performed best in the 80:20 scenario, with an MAE of 76,259.52, an RMSE of 154,036.91, a MAPE of 0.61%, and an R² of 0.997. XGBoost showed a competitive performance with an MAE of 79,381.44 and faster training times. In contrast, the SVR showed the worst performance with the MAPE reaching 198.56% and the R² of 0.209. Linear Regression as baseline recorded an MAE of 1,194,355.28 and an R² of 0.503, indicating that the linear model is not effective enough for this data. The 80:20 scenario is considered the best configuration because it is able to balance the accuracy and generalization of the model. These findings show that the use of ensemble algorithms, especially Random Forest and XGBoost, has the potential to be applied practically by agricultural agencies or local governments in designing data-driven policies for more proactive and predictive rice production management. Furthermore, this study contributes to the advancement of applied informatics by demonstrating how machine learning models can be effectively used in multivariate forecasting for complex, real-world problems, thereby supporting the development of intelligent decision-support systems in the agricultural domain.
Co-Authors Agatha Dinarah Sri Rumestri Arif Wirawan Muhammad Awal, Elsa Elvira Bita Parga Zen Dasril Aldo Diandra Chika Fransisca Dina Rachmawaty Fikra Titan Syifa Firmansyah, Muhammad Raafi'u Indriyanto, Slamet Karima, Halim Qista Maliana Puspa Arum Merlinda Wibowo Merlinda Wibowo Merlinda Wibowo Muliati, Vika Febri Paradise, Paradise Wahyu Adi Prabowo Widi Widayat Yasin, Feri Yosita Dwiani Suryaningtiyas