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All Journal Jurnal Pusat Inovasi Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Febyan, Ardelia Rahma
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Computer Science & IT Social Sciences

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Edible Garden Tower: Solusi Optimalisasi Lahan Pekarangan dan Peningkatan Kemandirian Pangan di Desa Slogoretno, Wonogiri Nazalti, Zulfa; Komala, Lintang Afifatz; Almira, Andrea Keysha; Febyan, Ardelia Rahma; Athallah, Muhammad Eishaf; Dewi, Reisha Karina; Candra, Daniel Ritchie; Wirawan, Bima; M. Shohibuddin
Jurnal Pusat Inovasi Masyarakat Vol. 7 No. 2 (2025): Oktober 2025
Publisher : Direktorat Pengembangan Masyarakat Agromaritim, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpim.7.2.288-297

Abstract

The majority of the residents of Slogoretno Village have large yards that are not being used optimally. The Edible Garden Tower can be a solution to maximize the use of residents' yards in Slogoretno Village for productive agriculture. This activity aims to provide comprehensive training on the application of the Edible Garden Tower to the community. The Edible Garden Tower training activity is conducted through interactive socialization and practice. The method used is a pre-test and post-test analyzed through frequency distribution to measure the program's effectiveness. The economic aspects of implementing the Edible Garden Tower are analyzed through economic profit projections. The results of the Edible Garden Tower implementation show improvements in cognitive (87.5%), affective (100%), and psychomotor (100%) domains. Economic projections indicate commercial profits from the implementation of the Edible Garden Tower ranging from Rp1.140.480 to Rp3.024.000. The Edible Garden Tower training program is effectively implemented among the community to enhance land productivity and food self-reliance. The long-term implementation of the Edible Garden Tower provides economic benefits both subsistence and commercial.  
Penerapan Pemodelan Konvensional dan Deep Learning pada Data Saham dengan Pencilan Maulana, Muhammad Firlan; Fayiza, Salsabila; Suhaeri, Bulan Cahyani; Febyan, Ardelia Rahma; Hambali, Thariq; Angraini, Yenni; Nurhambali, Muhammad Rizky
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.10587

Abstract

Apple Inc. stock (AAPL), one of the leading technology companies, is one of the concerns of investors as it continues to see an increase in the number of users every year. Therefore, forecasting Apple's stock price is important to help investors mitigate risks and optimize investment decisions. This forecasting can be done using two main approaches, namely conventional approaches such as Autoregressive Integrated Moving Average (ARIMA) and deep learning-based approaches such as Long Short-term Memory Network (LSTM). This study aims to find the best model using both methods, as well as compare the accuracy of the models based on datasets with outliers and datasets with handled outliers. The dataset analyzed in this study comes from weekly AAPL stock closing price data for 500 periods, from January 26, 2015 to August 19, 2024 obtained from Yahoo Finance. This study obtained the ARIMA(1,1,1) model as the best model for both datasets, with the outlier-handled dataset producing better test MAPE, while the dataset with outliers had better training MAPE. The LSTM method produced smaller MAPE values than ARIMA, demonstrating its superiority in capturing the fluctuating patterns of the AAPL stock data. Outlier handling was shown to improve model accuracy, as seen in the outlier-handled dataset. This research provides insight into the effectiveness of statistical and deep learning methods in modeling stock prices, and emphasizes the importance of outlier handling in financial data analysis.
Co-Authors Almira, Andrea Keysha Athallah, Muhammad Eishaf Candra, Daniel Ritchie Dewi, Reisha Karina Fayiza, Salsabila Hambali, Thariq Komala, Lintang Afifatz M. Shohibuddin Maulana, Muhammad Firlan Nazalti, Zulfa Nurhambali, Muhammad Rizky Suhaeri, Bulan Cahyani Wirawan, Bima Yenni Angraini