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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

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

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

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

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.

Analisis Promosi, Harga dan Lokasi terhadap Kepuasan Konsumen Melalui Keputusan Pembelian pada Gerai Ritel Modern Alfamart Karawaci Residence Bojong Nangka, Kelapa Dua, Kabupaten Tangerang Abu Amar; Nurmin Arianto; Rosa Lesmana
Journal of Economics and Management Scienties Volume 8 No. 3, June 2026 (Accepted)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jems.v8i3.392

Pertumbuhan ritel modern di Indonesia mendorong persaingan ketat antar jaringan minimarket, seperti Alfamart dan Indomaret, sehingga pemahaman faktor pemasaran menjadi krusial. Tujuan dari penelitian ini adalah untuk menganalisis dampak promosi, harga, dan lokasi terhadap keputusan pembelian pelanggan terkait dengan alasan pembelian mereka di area Alfamart Karawaci Residence, yang terletak di Kabupaten Tangerang. Penelitian menggunakan pendekatan kuantitatif asosiatif dengan 399 responden yang dipilih melalui purposive sampling. Data dikumpulkan melalui kuesioner yang valid dan reliabel, kemudian dianalisis menggunakan SEM-PLS. Hasil penelitian menunjukkan bahwa promosi, harga, dan lokasi berpengaruh positif dan signifikan terhadap keputusan pembelian dan kepuasan konsumen. Harga menjadi faktor paling dominan dalam memengaruhi keputusan pembelian, sedangkan keputusan pembelian berperan utama dalam meningkatkan kepuasan konsumen. Selain itu, keputusan pembelian terbukti memediasi secara signifikan hubungan antara promosi, harga, dan lokasi terhadap kepuasan konsumen. Model memiliki kemampuan prediktif moderat-kuat dengan nilai R² sebesar 0,664 untuk keputusan pembelian dan 0,491 untuk kepuasan konsumen, serta Goodness of Fit sebesar 0,626. Penelitian ini mengimplikasikan bahwa keberhasilan strategi pemasaran dalam mendorong keputusan pembelian akan meningkatkan kepuasan konsumen secara signifikan apabila terdapat keselarasan antara ekspektasi harga dan layanan dengan manfaat nyata yang diterima konsumen selama proses berbelanja.

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