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All Journal JURNAL ELEKTRO CogITo Smart Journal Jurnal Pengabdian kepada Masyarakat Nusantara SOROT: Jurnal Pengabdian Kepada Masyarakat Computer Journal
Rizki Surya Permana
Teknik Telekomunikasi, Telkom University, Jakarta, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Other

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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

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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

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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

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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

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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 - April
Publisher : Lembaga Dongan Dosen

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

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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.
Enhancing Stock Price Prediction Using Temporal Convolutional Network with Moving Average Features Rizki Surya Permana; Aisyah Novfitri; Putri Rahmawati
Jurnal Elektro Vol 19 No 1 (2026): Jurnal Elektro : April 2026
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v19i1.7882

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Stock price prediction is a complex problem in the financial domain due to the non-linear, dynamic nature of the data and its dependence on various external factors. This study proposes a deep learning–based approach using a Temporal Convolutional Network (TCN) to predict the stock price of Bank Central Asia Tbk. The model is evaluated under two scenarios: using a single feature (close price) and using three features MA5, and MA10. The dataset consists of historical BBCA stock data from 2010 to 2018, split into 80% training data and 20% testing data. The model is trained with 100 epochs, a window size of 60, batch size of 32, the Adam optimizer, and a learning rate of 0.0005. Experimental results show that the TCN model using a single feature achieves an RMSE of 227.1920, MAE of 192.8089, and MAPE of 4.3411%. Meanwhile, the TCN model with additional features (MA5 and MA10) demonstrates improved performance, achieving an RMSE of 176.4599, MAE of 145.7689, and MAPE of 3.2750%, indicating an accuracy improvement of more than 20%.These findings indicate that TCN is effective in capturing temporal patterns in financial time series data, and that incorporating simple technical indicators such as Moving Averages can significantly enhance model performance. This study contributes to the development of efficient and practical stock price prediction methods, particularly in the Indonesian stock market context.
Implementasi Smart Trash Bin Pemilah Logam dan Nonlogam untuk Meningkatkan Efisiensi Pengelolaan Sampah Bank Sampah Al-Furqon Devan Junesco Vresdian; Widang Muttaqin; Annisa Desianty; Rizki Surya Permana; Farhan Dwi Ananda Putra; Marcelino Setiyo Mufti; Alviandra Naufal Azis Caesarea; Quinnsha Thara Diva
SOROT : Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 2 (2026): Juli
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/yzz43555

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Timbulan sampah nasional Indonesia mencapai 21,1 juta ton pada 2022, dengan 34,29% belum terkelola dengan baik akibat rendahnya tingkat pemilahan di sumber (KLHK, 2022). Bank Sampah Al-Furqon di RT 04 RW 05, Kelurahan Pondokcina, Kota Depok, merupakan salah satu unit bank sampah aktif yang masih mengandalkan pemilahan manual, sehingga rentan terhadap inkonsistensi dan inefisiensi, khususnya dalam memisahkan sampah logam dan nonlogam. Program pengabdian ini mengembangkan smart trash bin berbiaya rendah (di bawah Rp500.000,-) berbasis sensor induktif yang diintegrasikan dengan pendekatan Asset Based Community Development (ABCD)—suatu kombinasi yang belum pernah dilaporkan sebelumnya dalam konteks bank sampah komunitas di Indonesia. Keunggulan alat ini dibanding penelitian sebelumnya meliputi biaya rendah, kemudahan operasional tanpa konektivitas internet, dan proses pengembangan partisipatif bersama mitra. Kegiatan pelatihan dan pendampingan melibatkan 22 peserta. Evaluasi kuesioner skala Likert menunjukkan indeks kepuasan 83,82% (mean 4,19/5,00), dengan 97,27% responden menyatakan setuju dan sangat setuju. Pengujian kinerja alat menunjukkan peningkatan akurasi pemilahan dari 72,4% menjadi 91,3%, efisiensi waktu 57,3% lebih cepat, dan volume sampah terpilah meningkat 74,4%. Program ini menyediakan model replikasi teknologi tepat guna yang dapat diadopsi oleh bank sampah komunitas lain di Indonesia.
Penerapan Metode OWASP IoT Top 10 dalam Analisis Kerentanan Keamanan Perangkat Internet of Things: Studi Kasus Smart Waste Arif Rahman Hakim; Rizki Surya Permana; Demi Adidrana; Hertanto Suryoprayogo; Deny Haryadi
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.471

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This study examines security vulnerabilities in a publicly accessible IoT-based smart waste system using Nmap, Wireshark, and OWASP ZAP to assess network services, packet traffic, and the web application layer. Results were mapped to the OWASP IoT Top 10 (2018). Because the assessment was external black-box testing without exploitation, the mapping is indicative rather than comprehensive. Nmap identified several active TCP ports, although only six open ports were explicitly documented. Wireshark captured 62,591 packets, indicating port-scanning activity and ongoing TCP communication. OWASP ZAP identified 14 web application weaknesses: six medium, five low, and three informational, with no high-risk findings. Four OWASP IoT Top 10 categories (I2, I3, I7, and I9) were supported by direct evidence, while I1 and I5 require further verification and I4, I6, I8, and I10 were outside the testing scope. Key risks involved insecure network services, default settings, and inadequate data protection during transmission and storage.
Co-Authors Aisyah Novfitri Alviandra Naufal Azis Caesarea Annisa Desianty Arif Rahman Hakim Bachri, Karel Octavianus Demi Adidrana Deny Haryadi Devan Junesco Vresdian Farhan Dwi Ananda Putra Hertanto Suryoprayogo I Putu Eka Juliantara Marcelino Setiyo Mufti Maria Angela Kartawidjaja Maulana, Muhammad Farhan Muhammad Ardiansyah Nova Eka Budiyanta Putri Rahmawati Putri Rizqiyah Quinnsha Thara Diva Veronica Windha Mahyastuty Widang Muttaqin