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All Journal Inovasi: Jurnal Ilmiah Ilmu Manajemen Jurnal Pengabdian Dharma Laksana JIMF (JURNAL ILMIAH MANAJEMEN FORKAMMA) Penentuan harga obligasi dan teori tingkat bunga JURNAL PEMASARAN KOMPETITIF JURNAL ABDIMAS TRIDHARMA MANAJEMEN International Journal of Cyber and IT Service Management (IJCITSM) Jurnal Abdi Masyarakat Humanis
Rosa Lesmana
Universitas Pamulang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Social Sciences

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Pelaksanaan Konsep Ice Power Untuk Meningkatkan Perekonomian Masyarakat Desa Cihambulu, Pabuaran, Subang Rosa Lesmana; Wiwiek Hasbiyah; Yuga Pratama
Jurnal Abdi Masyarakat Humanis Vol 4, No 2 (2023): Jurnal Abdi Masyarakat Humanis
Publisher : LPPM Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/%JAMH.v4i2.29455

Abstract

Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan guna mengimplementasikan konsep wirausaha terpadu (ice power concept) didalam usaha meningkatkan inovasi pada masyarakat wirausaha di desa Cihambulu, Pabuaran, Subang, Jawa Barat. Objek pengabdian kepada masyarakat ini adalah warga desa Cihambulu, Pabuaran, Subang, Jawa Barat. Metode yang digunakan adalah pelatihan dan penyampaian materi melalui ceramah dan diskusi. Hasil pengabdian ini menciptakan masyarakata wirausaha terpadu didesa Cihambulu, Pabuaran, Subang, Jawa Barat guna meningkatkan perekonomian masyarakat desa Cihambulu, Pabuaran, Subang, Jawa Barat. 
Build Entrepreneurial Interest Through PEKA Analysis on Increasing Students Income of Management Study Program Pamulang University Nardi Sunardi; Rosa Lesmana
Jurnal SEKURITAS (Saham, Ekonomi, Keuangan dan Investasi) Vol 6, No 3 (2023): Jurnal SEKURITAS
Publisher : Prodi Manajemen Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/skt.v6i3.30278

Abstract

The purpose of this study is to determine the influence of PEKA analysis on increasing the income of the students of management study program at Pamulang University, the influence of PEKA analysis on the interest in entrepreneurship of students of management study program at Pamulang University and the influence of interest in entrepreneurship on increasing the income of the students of management study program Pamulang University. This type of research is quantitative. The object of analysis is students of the management study program at Pamulang University for the year of 2022, data were collected through a questionnaire. The analytical method uses SEM analysis and the tool used is lisrel 8.70. The results showed that the PEKA analysis had no effect on increasing the income of the students of management study program Pamulang University, but the PEKA analysis had an effect on increasing the entrepreneurial interest of the students of management study program Pamulang University, and interest in entrepreneurship has no  effect on income increasing of the students  of management study program Pamulang University
Enhancing Market Trend Analysis Through AI Forecasting Models Rosa Lesmana; Indra Wijaya; Efa Ayu Nabila; Harry Agustian; Sipah Audiah; Adam Faturahman
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.162

Abstract

Accurate market trend analysis is crucial for strategic decision making in industries, yet traditional forecasting models often struggle to provide reliable predictions in rapidly changing environments. This study investigates the application of advanced Artificial Intelligence (AI) models Long Short Term Memory (LSTM), Random Forest, Decision Trees, and Support Vector Machines (SVM) to improve the accuracy and robustness of market forecasting. Data was collected from sources like Bloomberg and Yahoo Finance, encompassing stock prices, economic indicators, and sector specific trends over five years. The models were evaluated using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess their predictive performance. Results show that AI models, especially LSTM, outperform traditional models like Auto Regressive Integrated Moving Average (ARIMA), offering superior handling of complex temporal dependencies and short term market fluctuations. For instance, LSTM achieved a MAPE of 1.8% and RMSE of 0.045, significantly improving forecast precision over ARIMA. Random Forest and Decision Trees also provided valuable insights into market drivers, adding interpretability to the forecasting process. This research highlights the potential of AI to enhance decision making by offering more accurate, data driven predictions and provides practical guidelines for implementing these models in real world market forecasting. Future research should explore hybrid AI approaches and broader datasets to further enhance forecasting adaptability across diverse market conditions.
Enhancing Market Trend Analysis Through AI Forecasting Models Rosa Lesmana; Indra Wijaya; Efa Ayu Nabila; Harry Agustian; Sipah Audiah; Adam Faturahman
International Journal of Cyber ​​and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.162

Abstract

Accurate market trend analysis is crucial for strategic decision making in industries, yet traditional forecasting models often struggle to provide reliable predictions in rapidly changing environments. This study investigates the application of advanced Artificial Intelligence (AI) models Long Short Term Memory (LSTM), Random Forest, Decision Trees, and Support Vector Machines (SVM) to improve the accuracy and robustness of market forecasting. Data was collected from sources like Bloomberg and Yahoo Finance, encompassing stock prices, economic indicators, and sector specific trends over five years. The models were evaluated using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) to assess their predictive performance. Results show that AI models, especially LSTM, outperform traditional models like Auto Regressive Integrated Moving Average (ARIMA), offering superior handling of complex temporal dependencies and short term market fluctuations. For instance, LSTM achieved a MAPE of 1.8% and RMSE of 0.045, significantly improving forecast precision over ARIMA. Random Forest and Decision Trees also provided valuable insights into market drivers, adding interpretability to the forecasting process. This research highlights the potential of AI to enhance decision making by offering more accurate, data driven predictions and provides practical guidelines for implementing these models in real world market forecasting. Future research should explore hybrid AI approaches and broader datasets to further enhance forecasting adaptability across diverse market conditions.
Co-Authors A Kadim A Kadim Adam Faturahman Arif Siaha Widodo Asep Sutarman E, Nurzaman Efa Ayu Nabila Endang Nurita Furyanah, Anah Harry Agustian Hastono Hastono Indra Wijaya Jeni Andriani Kartono Kartono Kartono Kartono Khotimatus Sadiyah Nardi Sunardi Novia Susanti Nuzul Inas Nabila Raden Yeti Sumiaty Rudy Rudy Sarwani Sarwani Sipah Audiah Siti Alfiah Suci Dara Ayu Wiwiek Hasbiyah Wiwik Habiyah Wiwik Hasbiayah Wiwik Hasbiyah Yuga Pratama Zaenuddin Thahira Bukhani