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All Journal Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Computer Science and Information Technologies Online Learning in Educational Research Coreid Journal
Yana Aditia Gerhana
UIN Sunan Gunung Djati Bandung
Religion Humanities Computer Science & IT Education Engineering Social Sciences Other

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DIGITALISASI LAYANAN MINIMUN DESA SEBAGAI UPAYA PENINGKATAN LAYANAN DESA BINAAN Nur Lukman; Fatimah Umar; Yana Aditia Gerhana
Al-Khidmat Vol 5, No 1 (2022): Jurnal Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat
Publisher : Pusat Pengabdian kepada Masyarakat LP2M UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jak.v5i1.17431

Abstract

Perkembangan teknologi informasi dan munculnya industry 4.0 merupakan tantangan besar bagi seluruh masyarakat Indonesia termasuk Desa Sindangsuka Kecamatan Cibatu Garut, yang harus menyesuaikan dengan perkembangan ini, tahun 2019 merupakan awal dari Desa Sindangsuka munculnya komitmen untuk mengikuti perkembangan teknologi dengan mendigitalisaasi layanan minimum desa, karena pada tahun ini lahir covid-19 yang membuat layanan desa yang tadinya berjalan normal menjadi tehambat, karena protocol harus diterapkan dan pelayanan desapun tidak berjalan dengan semestinyam, sehingga pemanfaatan teknologi menjadi pilihan sebagai solusi untuk masalah tersebut. Terdapat dua kegiatan besar dalam pengabdian ini, diantaranya adalah perancangan dan pembuatan sistem sesuai dengan analisis kebutuhan pengguna dilihat dari urgensi desa terkait, dan yang kedua adalah pelatihan aplikasi layanan minimun desa yang telah dibangun melalui beberapa tahap, tahap pertama merupakan tahap pelatihan terhadap admin sistem, yaitu aparat desa yang ditugasi sebagai admin aplikasi, pelatihan terhadap perwakilan wagra yang diwakili oleh Ketua RW, untuk rekomendasi kedepannya sesuai dengan roadmap yang telah dibuat sebelumnya, yaitu pelatihan yang dilakukan oleh desa dengan instruktur setiap ketua RW yang telah melakukan pelatihan sebelumnya dan dijadwalkan oleh pihak desa.
Acrachemmics (Acid Rain in Chemical Comics) Oriented to Chemical Literacy Rifa Khaerunnisa; Ferli Septi Irwansyah; Citra Deliana Dewi; Iis Dahriah; Yana Aditia Gerhana
Online Learning In Educational Research (OLER) Vol. 5 No. 1 (2025): Online Learning in Educational Research
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/oler.v5i1.721

Abstract

Understanding chemical concepts such as acid rain can be challenging for students due to their abstract and complex nature. To address this, a chemical literacy-based learning approach using comic media. Acrachemmics (Acid Rain in Chemical Comics) was developed to enhance student comprehension. This research aims to develop and evaluate electronic comic media especially Acrachemmics (Acid Rain in Chemical Comics). A Design-Based Research (DBR) methodology was employed, comprising three phases: analysis, design, and development. The validation test yielded a Pearson correlation coefficient (r) of 0.88, indicating a high level of content validity. Additionally, the feasibility assessment, which examined aspects of material content, visual communication, software engineering, and language, produced an average score of 84 percent, reflecting a high level of practical usability. These results suggest that Acrachemmics is both a valid and feasible educational media for teaching acid rain concepts. By presenting scientific content through engaging visuals and narrative storytelling, Acrachemmics facilitates student understanding in a more accessible and enjoyable format
Vector space model, term frequency-inverse document frequency with linear search, and object-relational mapping Django on hadith data search Ichsan Taufik; Agra Agra; Yana Aditia Gerhana
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p306-314

Abstract

For Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams’ compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly.
Academic Data Quality Measurement in SALAM Application Using Six Sigma Method Imam Firdaus; Cecep Nurul Alam; Yana Aditia Gerhana; Mohamad Irfan; Ibrahim Iskandar
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; Yana Aditia Gerhana; Gitarja Sandi; Muhammad Deden Firdaus; Eva Nurlatifah
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 Cecep Nurul Alam Citra Deliana Dewi Eva Nurlatifah Fatimah Umar Gitarja Sandi Ibrahim Iskandar Iis Dahriah Imam Firdaus Mohamad Irfan Muhammad Deden Firdaus Nur Lukman Rifa Khaerunnisa Rijki Rijki Taufik, Ichsan