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All Journal JURNAL ELEKTRO CogITo Smart Journal Jurnal Pengabdian kepada Masyarakat Nusantara
Rizki Surya Permana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Susunan Redaksi Jurnal Elektro Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (38.52 KB)

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Indek Pengarang Jurnal Vol 12.2 Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (21.603 KB)

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Indek Subjek Jurnal Elektro Vol 12.2 Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.717 KB)

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Template Jurnal Elektro Rizki Surya Permana
Jurnal Elektro Vol 12 No 2 (2019): Oktober 2019
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

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Predicting Stock Market Trends Based on Moving Average Using LSTM Algorithm Permana, Rizki Surya; Mahyastuty, Veronica Windha; Budiyanta, Nova Eka; Bachri, Karel Octavianus; Kartawidjaja, Maria Angela
CogITo Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v10i2.648.486-495

Abstract

Prediction of the stock market is highly needed to assist traders in making decisions. Many methods are used by traders to predict this such as technical analysis and moving averages. Moving averages predict stock trends based on the past data of the stock. The disadvantage of using a moving average analysis is the delay in crossover signals. As a solution, a deep learning technique known as LSTM is applied to the moving average strategy in this paper. In this research, the BBCA stock dataset spanning from 2010 to 2018 was utilized. The data was segmented into two parts: 2010-2017 for training data and 2018 for testing data. The training process employed Long Short-Term Memory (LSTM) networks, with the subsequent results being combined with moving average crossover techniques. Validation results indicate that BBCA shows a relatively minimal error. BBCA's average MAPE is 1.1%, and its RMSE is 65.402, classifying it within the "Highly Accurate Forecasting" category. Various combinations of moving average crossovers were tested during model training, with the combination of SMA05 and SMA50 for BBCA yielding the highest profit potential. Stocks that exhibit a downward trend are more likely to incur substantial losses. The model can predict the reversal of trends by predicting the trading signal given by the moving averages.
Pelatihan Pengolahan Citra Digital Berbasis Generative AI dan Pemrograman Ardiansyah, Muhammad; Rizqiyah, Putri; Permana, Rizki Surya; Maulana, Muhammad Farhan; Juliantara, I Putu Eka
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 1 (2026): Edisi Januari - Maret
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i1.8132

Abstract

Pelatihan pengolahan citra digital berbasis generative AI dan pemrograman telah dilaksanakan di Pondok Pesantren Miftahul Khaer. Latar belakang kegiatan ini adalah rendahnya pemahaman siswa terhadap konsep pemrograman dan kecerdasan buatan akibat keterbatasan praktik langsung serta sarana pendukung pembelajaran. Metode pelatihan mengombinasikan pendekatan naratif berbasis cerita edukatif dengan pembelajaran berbasis praktikum menggunakan bahasa pemrograman Python dan generative AI tools pada studi kasus pengolahan citra CCTV lalu lintas. Pelatihan dilaksanakan selama dua jam dengan pendampingan intensif oleh tim pengabdi dan diikuti oleh 25 peserta. Evaluasi dilakukan menggunakan kuesioner skala Likert yang mencakup lima aspek penilaian. Hasil evaluasi menunjukkan nilai rata-rata keseluruhan sebesar 4,33 yang termasuk kategori sangat berhasil. Peserta menunjukkan antusiasme tinggi serta mampu mengikuti alur pengolahan citra hingga menghasilkan identifikasi objek. Kendala utama yang ditemukan adalah keterbatasan kemampuan mengetik dan tingkat kompleksitas materi bagi sebagian peserta. Secara keseluruhan, kegiatan ini efektif dalam meningkatkan pemahaman awal pengolahan citra dan kecerdasan buatan, serta berpotensi dikembangkan sebagai model pelatihan berkelanjutan di lingkungan pendidikan vokasi berbasis pesantren.
Co-Authors Bachri, Karel Octavianus I Putu Eka Juliantara Maria Angela Kartawidjaja Maulana, Muhammad Farhan Muhammad Ardiansyah Nova Eka Budiyanta Putri Rizqiyah Veronica Windha Mahyastuty