Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Pusat Inovasi Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Maulana, Muhammad Firlan
Unknown Affiliation
Computer Science & IT Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Penguatan Ketahanan Keluarga melalui Edukasi Bahaya Judi Online di Desa Kopen, Wonogiri Hidayah, Safina Octavia; Fayola, Tabina Triadinda; Riyani, Adelweiss Putri; Rustandi, Agniyya Muhshi; Nursofyani, Aziizah; Samara, Farhan Habibie; Maulana, Muhammad Firlan; Qodari, Sahrul; Arif, Ridi
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.7.2.422-433

Abstract

  The community of Kopen Village faces challenges to family resilience, particularly in family resource management, communication, and digital risk control such as online gambling. This community service program aimed to enhance participants' understanding of family resilience and mitigate the dangers of online gambling that threaten family harmony. The activity was conducted in collaboration with the TP-PKK of Kopen Village and involved 16 participants representing all hamlets.  The methods used were interactive socialization, discussions supported by a pop-up family resilience house visual media, and quantitative measurement through pre-tests and post-tests. A guide materials booklet was also developed for the sustainability program. The results demonstrated a significant increase in understanding, with all participants scoring in the high category after the activity compared to the pre-test scores. The interactive approach, coupled with real-life examples, successfully strengthened participants’ awareness of the strategic role of the family in managing digital risks. The guide material produced ensures the program has a sustainable impact on literacy and strengthen family resilience in Kopen Village.
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 Fayiza, Salsabila Fayola, Tabina Triadinda Febyan, Ardelia Rahma Hambali, Thariq Hidayah, Safina Octavia Nurhambali, Muhammad Rizky Nursofyani, Aziizah Qodari, Sahrul Ridi Arif Riyani, Adelweiss Putri Rustandi, Agniyya Muhshi Samara, Farhan Habibie Suhaeri, Bulan Cahyani Yenni Angraini