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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Edukasi dan Penelitian Informatika (JEPIN) SMATIKA JOIN (Jurnal Online Informatika) Jurnal Sisfokom (Sistem Informasi dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Computer Science and Information Technologies Online Learning in Educational Research Coreid Journal Jurnal Abdimas Peradaban ISTEK
Yana Aditia Gerhana, Yana Aditia
Teknik Informatika Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences

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Journal : Coreid Journal

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Academic Data Quality Measurement in SALAM Application Using Six Sigma Method Firdaus, Imam; Alam, Cecep Nurul; Gerhana, Yana Aditia; Irfan, Mohamad; Iskandar, Ibrahim
CoreID Journal Vol. 3 No. 2 (2025): July 2025
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v3i2.136

Abstract

Data quality plays a critical role in ensuring the reliability and usefulness of information for decision making in higher education institutions. However, academic data within the SALAM application at UIN Sunan Gunung Djati Bandung has not previously undergone a systematic quality assessment, leading to uncertainty in several managerial and academic decisions. This study aims to evaluate the quality of academic data in the SALAM application using the Six Sigma method with the DMAIC (Define–Measure–Analyze–Improve–Control) framework. Five data quality dimensions completeness, consistency, conformity, uniqueness, and timeliness are employed to measure and analyze data quality performance. The measurement process begins with data definition and extraction, followed by quantitative analysis using sigma metrics. The results indicate that the overall quality of academic data is at a moderate level, with an average sigma score of approximately 3, primarily influenced by incomplete and inconsistent data. In contrast, the timeliness dimension demonstrates excellent performance, achieving a sigma metric of 6 due to the long-term availability of data over more than ten years. This study contributes by providing an empirical, data-driven evaluation of academic data quality and offers practical insights for implementing continuous monitoring and improvement strategies to enhance data reliability and support more effective decision making in higher education institutions.
Comparison of Long Short-Term Memory and Recurrent Neural Network For Stock Market Price Movement Classification in Islamic Bank Finance Rijki, Rijki; Gerhana, Yana Aditia; Sandi, Gitarja; Firdaus, Muhammad Deden; Nurlatifah, Eva
CoreID Journal Vol. 4 No. 1 (2026): March 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i1.152

Abstract

This study addresses the importance of accurate stock price prediction in the Islamic finance sector, where reliable forecasting supports better investment decisions and market stability. Despite the growing use of deep learning methods, comparative studies on sequential models in this domain remain limited. Therefore, this research compares the performance of Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) models for classifying stock price movement direction of Islamic banks in Indonesia. The dataset was sourced from two Islamic banks in Indonesia, covering the period from 2022 to mid-2024, with features such as Open, High, Low, Close, Adjusted Close, and Volume. The CRISP-DM method was applied for data processing, and testing was performed with data splits of 60:40, 70:30, and 80:20, as well as epoch variations (30, 50, 80). Results indicate that RNN outperforms LSTM, with the highest accuracy of 58% for RNN and 53% for LSTM. Evaluation metrics also included precision, recall, and F1-score. In conclusion, RNN performs better for stock movement classification direction, while LSTM is more effective for minimizing prediction error.
Co-Authors Agra, Agra Agung Wahana ALAWIYAH, RIZKA Aldy Rialdy Atmadja Anggayana, Denta Pratama Anwar, Muhammad Afian Arief Fatchul Huda Aulia, Salman Fauzan Fahri Ayu Puji Rahayu Azis, Aaz Muhammad Hafidz Beki Subaeki, Fatkhan Gunawan, Aldy Rialdy Atmadja, Beki Cecep Nurul Alam Cecep Nurul Alam, Cecep Nurul Cepy Slamet Dahriah, Iis Deden Suparman, Deden Devi Novitasari Dewi, Citra Deliana Dhanis Al Ghifari Diena Rauda Ramdania Dikdik Firman Sidik Dzulfikar, Wildan Budiawan Erwin Setiawan, Erwin Faiz M. Kaffah Firdaus, Imam Firdaus, Muhammad Deden Heryanto, Rafi Rai Ichsan Taufik, Ichsan Iskandar, Ibrahim Khaerul Manaf Khaerunnisa, Rifa Kusuma Pradana, Galih Maylawati, Dian Sa'adillah Mintarsih, Mimin Mohamad Irfan, Mohamad Muhammad Ali Ramdhani Muharam, Aldi Fahluzi Nur Lukman Nurlatifah, Eva Padilah, Muhamad Farid Pitriani, Pitriani Qomaruddin, Nurhadi Rahman, Titik Khawa Abdul Rijki, Rijki Rusyana, Meta Barokatul Karomah Sandi, Gitarja Solehudin, Muhammad Alwy Somantri, E. Aris Taufik, Ichsan Undang Syaripudin, Undang