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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Manajemen dan Organisasi Jurnal Manajemen dan Agribisnis Jurnal Sosio Ekonomika Bisnis JURNAL SISTEM INFORMASI BISNIS Jurnal Manajemen Teknologi Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JAM : Jurnal Aplikasi Manajemen Indonesian Journal of Business and Entrepreneurship (IJBE) Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Journal of Consumer Science Jurnal Manajemen Teori dan Terapan Journal of Information Systems Engineering and Business Intelligence Cosmogov: Jurnal Ilmu Pemerintahan Proceeding of the Electrical Engineering Computer Science and Informatics International Research Journal of Business Studies (E-Journal) Agregat: Jurnal Ekonomi dan Bisnis Journal of Regional and Rural Development Planning Jurnal Organisasi Dan Manajemen Buletin Ilmiah Litbang Perdagangan JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK Applied Information System and Management Petir Aptisi Transactions on Technopreneurship (ATT) International Journal of Business Studies Journal of Applied Data Sciences Business Review and Case Studies International Journal of Engineering, Science and Information Technology Indonesian Journal of Multidisciplinary Science AGRARIS: Journal of Agribusiness and Rural Development Research Asian Journal of Social and Humanities International Research Journal of Business Studies
Arif Imam Suroso
School Of Business, IPB University, Jl. Raya Pajajaran, RT.03/RW.06, Babakan, Kecamatan Bogor Tengah, Kota Bogor, Jawa Barat 16128
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Journal : Journal of Applied Data Sciences

 1 
Price Prediction of Aglaonema Ornamental Plants Using the Long Short-Term Memory (LSTM) Algorithm Sugiarti, Yuni; Suroso, Arif Imam; Hermadi, Irman; Sunarti, Euis; Yamin, Fadhilah Bt Mat
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.640

Abstract

The Aglaonema ornamental plant is a horticultural commodity with high economic value and promising prospects. It is well known for its attractive leaf variations, earning it the nickname "Queen of Leaves." However, unpredictable price fluctuations make investing in Aglaonema speculative and high-risk. This research aims to predict the price of Aglaonema over the next five years using the Long Short-Term Memory (LSTM) algorithm. LSTM is considered superior to other algorithms in handling time series data. The model's performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) on a weekly Aglaonema price dataset covering the period from January 2012 to December 2023. The results demonstrate that the LSTM algorithm can predict Aglaonema prices with high accuracy, as indicated by the following metrics: MSE: 0.005 – Represents the average squared difference between predicted and actual prices. A lower MSE indicates higher model accuracy. RMSE: 0.07-RMSE provides a more interpretable error measurement as it retains the same units as the original data. A low RMSE signifies that the model's predictions closely align with actual values. MAE: 0.04 – Measures the absolute average difference between predicted and actual prices. A lower MAE value reflects a smaller prediction error. Thus, this research makes a significant contribution to the development of a machine learning-based price prediction system for the ornamental plant industry.
Time Series Forecasting of Environmental Dynamics in Urban Ecotourism Forest Using Deep Learning Iskandar, Ade Rahmat; Suroso, Arif Imam; Hermadi, Irman; Prasetyo, Lilik Budi
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1029

Abstract

Time Series Forecasting of Environmental Dynamics in urban forests is quite challenging, unless new approaches such as deep learning and remote sensing are employed. Deep learning-based time series algorithms offer robust scientific capabilities for forecasting and assessing sustainability trends using sequential data. Among these, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) have gained widespread adoption across various predictive modeling domains. In the present research, these algorithms are employed to analyze urban forest raster data derived from the Srengseng Ecotourism Forest, located in West Jakarta, Indonesia. The present study focuses on predicting the temporal patterns of key spatial indicators: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Forest Cover Density (FCD) in the Srengseng urban ecotourism forest area, spanning the years 2014 to 2024, through the application of LSTM, GRU, and BiLSTM deep learning architectures. The methodology used in this study is a combined approach involving remote sensing and deep learning. Spatial data were acquired through the delineation of a high-precision polygon of Srengseng Urban Forest using Google Earth Pro and Google Earth Engine (GEE). GeoTIFF datasets of NDVI, LST, and FCD for the years 2014–2024 were processed using Python-based modeling scripts. Model performance was evaluated through a comparative analysis of LSTM, GRU, and BiLSTM in predicting temporal trends in these ecological indicators. The results of this study show that the Bidirectional LSTM (BiLSTM) consistently demonstrated superior performance to predict all the data spatially, with scores of 0.94 for NDVI, 0.90 for FCD, and 0.85 for LST. Followed by LSTM that predicts NDVI (0.87), FCD (0.89), LST (0.83), as well as GRU, which can estimate spatial data NDVI (0.86), FCD (0.89), and LST (0.85). These results outperformed the predictive accuracy of both the standard LSTM and GRU models.
Co-Authors Abdul Mufti Ade Rahmat Iskandar Adler Haymans Manurung Agustian, Fajar Aida Vitayala Hubeis Aji Hermawan Akbar, M Lucky Alfy Sukma Amru Sahmono Boang Manalu Andi Tenri Abeng Anggraini Sukmawati Anggraini, Raden Isma Arief Ramadhan Arief Ramadhan Arif Wicaksono Arkeman, Yandra Arsyanur, Mustika Retno Asep Nurhalim Azka Bazil Danish Rahmat Bachtiar, Muchamad Bambang Juanda Bandono, Bayu Bernadus, Benny Bimo Andono Budi Setiawan Chandra, Mohamad Citra, Suryati Oka Dewi Suryani Dewi, Nina Kurnia Dharmawan, Rafi Dirdjosaputro, Sukiswo Edi Suryanto Eko Agus Prasetio Eko Agus Prasetio Eko Agus Prasetio Endang Gumbira Sa'id Erliza Noor Euis Sunarti Fadhil Muhammad Fahrillah, Ahmad Ariq Fajar Agustian Febriyana, Alfonsia Ferdi Novalendo Fety Nurlia Muzayanah Ghifari, Nurul Afiifah Hadiwidjaja, Rini Dwiyani Hafizh, Muhammad Damar Hanindipto, Fasa Aditya Hansen Tandra Hansen Tandra Hariandja, Nancy Megawati Haryono, Adi Hendro Sasongko Heny Kuswanti Suwarsinah HERMANTO SIREGAR Hermanto Siregar Hilda Puspita Pratyaharani I Gusti Ayu Indira Maharani Idqan Fahmi Ilham Ananto Yuwono Illah Sailah Irman Hermadi Iyung Pahan Jaman, Jajam Haerul Joko Ratono Joko Ratono Joko Ratono, Joko Juniar Prayogi Juniar Prayogi Khairiyah Kamilah Kudang B. Seminar Kudang Boro Seminar LILIK BUDIPRASETYO Lilik Noor Yuliati Loeis, Minaldi Lukman M. Baga M, Misbahudddin M. Syamsul Maarif Maesaroh, Syti Sarah Marimin , Meilani, Lala Meta, Fildza Shabrina Meuthia Rachmaniah Misbahuddin Mohammad Syamsul Maarif Muhamad Syukur Mukhamad Najib MULYANI Musa Hubeis Neni Seliana Ninuk Purnaningsih Nugraha, Herry Nugraha, Windy Maudy Atiah Putri Nur Adhita Rahmawati Nur Hasanah Nur Hasanah NUR HASANAH Nurhidayat, Ridwan Pahan, Iyung Permasih, Desta Popong Nurhayati Prabantarikso, Mahelan Pramadia Satriawan Priastuti, Dila Rahmana, Adhitya Rahmanto, Mohamad Ridwan Rini Dwiyani Hadiwidjaja Rinova Budiman Rita Nurmalina Rivira Yuana Rivira Yuana Rizal Broer Bahaweres Rizal Syarief Rizal Syarief Rizal Syarief Rizal Syarief Rizal Syarief Rizki, Yanis Aulia Rokhani Hasbullah Romayah, Siti Rotua Siahaan RR. Kathrin Irviana Ruth Johana Angelina Safari, Arief Saiful Bahri Seliana, Neni Setiadi Djohar Setiadi Djohar Setyo Wibowo Shalihati, Fithriyyah Siti Jahroh Siti Romayah Suci Ramadhani, Suci Sufrin Hannan Sugiarto, Anto Tri Sugiharto Soeleman Suprehatin Suprehatin Supriyadi Supriyadi Sutriono Edi Tandra, Hansen Tendy Arya Pranata Trias Andati Ujang Sumarwan Utami, Yulistiana Endah Widhiani, Anita Primaswari Widia Citra Anggundari Yamin, Fadhilah Bt Mat Yandra Arkeman Yandra Arkeman Yandra Arkeman Yani Nurhadryani Yantama, Rian Rizki YELLY REFITA Yuana, Rivira Yuni Sugiarti, Yuni Yunus Triyonggo Yunus Triyonggo, Yunus Yusman Syaukat Zahid, Rifqi Az