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All Journal Jurnal Pengabdian UntukMu NegeRI Indonesian Journal of Electrical Engineering and Computer Science Jurnal Computer Science and Information Technology (CoSciTech)
Amran, Hasanatul Fu'adah
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Religion Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

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Advanced tourist arrival forecasting: a synergistic approach using LSTM, Hilbert-Huang transform, and random forest Mukhtar, Harun; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Wan Salihin Wong, Khairul Nizar Syazwan; Ridhollah, Farhan; Deprizon, Deprizon; Soni, Soni; Lisman, Muhammad; Amran, Hasanatul Fu'adah; Sunanto, Sunanto; Ismanto, Edi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp517-526

Abstract

An advanced synergistic approach for forecasting tourist arrivals is presented, integrating long short-term memory (LSTM), Hilbert-Huang transform (HHT), and random forest (RF). LSTM is leveraged for its capability to capture long-term dependencies in sequential data. Additional data from Google Trends (GT) is processed with HHT for feature extraction, followed by feature selection using the RF algorithm. The combined HHT-RF-LSTM model delivers highly accurate forecasts. Evaluation employs regression analysis with metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE), highlighting the effectiveness of this innovative approach in predicting tourist arrivals. This methodology provides a robust framework for handling limited datasets and improving forecast reliability. By incorporating diverse data sources and advanced preprocessing techniques, the model enhances prediction performance, demonstrating the strong performance of RF in feature selection.
Edukasi Antibiotik Untuk Keluarga Sebagai Upaya Preventif Terhadap Risiko Stunting Pada Anak Desa Sungai Kayu Ara Anugerah Putra, Bayu; Dian Utami; Rico Apriandika; Hanum Salsabila; Fakhira Frisya Ramadhani; Alris Gusnanda; Septiana Srinandini; Jihan Aulia; Soni, Soni; Firdaus, Rahmad; Mukhtar, Harun; Br Bangun, Elsi Titasari; Handayani, Fitri; Amran, Hasanatul Fu'adah
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10669

Abstract

Stunting masih menjadi salah satu masalah kesehatan masyarakat di Indonesia dengan prevalensi yang cukup tinggi. Salah satu faktor risiko penting terjadinya stunting adalah infeksi berulang pada anak, yang sering kali ditangani dengan pemberian antibiotik. Penggunaan antibiotik yang tepat dapat membantu mencegah stunting dengan cara menekan beban penyakit, namun penggunaan yang tidak rasional berisiko menimbulkan resistensi serta gangguan keseimbangan mikrobiota usus yang berdampak pada penyerapan gizi. Rendahnya literasi penggunaan antibiotik di masyarakat, khususnya di kalangan ibu rumah tangga dan lansia sebagai pengasuh utama dalam keluarga, menjadi tantangan tersendiri. Kegiatan sosialisasi ini bertujuan untuk meningkatkan pemahaman masyarakat mengenai penggunaan antibiotik rasional serta mengingatkannya terhadap pencegahan stunting. Metode yang digunakan adalah ceramah interaktif, diskusi, dan pembagian media edukasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta mengenai pentingnya penggunaan antibiotik sesuai anjuran tenaga medis. Oleh karena itu, sosialisasi antibiotik kepada ibu dan lansia memiliki peran penting sebagai upaya preventif yang secara tidak langsung dapat mendukung pencegahan stunting pada anak.
Penerapan Algoritma Klasifikasi dalam Data Mining pada Berbagai Studi Kasus: Literature Review Hibatullah Naufal Ramadhan; Amran, Hasanatul Fu'adah
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11101

Abstract

Data mining is one of the most widely used approaches in the field of Information Technology to extract knowledge from large data sets. One of the main techniques in data mining is the classification method, which aims to predict a particular class or category based on available attributes. Various classification algorithms such as Naïve Bayes, Decision Tree (C4.5), Random Forest, Support Vector Machine, and Artificial Neural Network have been applied in various research domains, including health, education, government, agriculture, and cybersecurity. Differences in data characteristics and methods used cause variations in performance in each study. Therefore, this study aims to conduct a literature review on the application of data mining with classification methods in various data prediction and classification cases. The research method used is a literature review by examining eleven scientific articles from accredited national journals. The results of the study show that the Naïve Bayes and Decision Tree algorithms are the most frequently used methods due to their ease of implementation and interpretation, while Random Forest and Support Vector Machine tend to provide more stable performance on data with high complexity.
Co-Authors Alris Gusnanda Bayu Anugerah Putra Br Bangun, Elsi Titasari Deprizon, Deprizon Dian Utami Edi Ismanto Fakhira Frisya Ramadhani Fitri Handayani Hanum Salsabila Harun Mukhtar Hibatullah Naufal Ramadhan Jihan Aulia Lisman, Muhammad Mohamad, Mohd Saberi Rahmad Firdaus Remli, Muhammad Akmal Rico Apriandika Ridhollah, Farhan Septiana Srinandini Soni Sunanto Sunanto Wan Salihin Wong, Khairul Nizar Syazwan