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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengabdian Masyarakat Bhinneka International Journal of Health, Engineering and Technology Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah
Astie Darmayantie
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Pengelolaan dan Kuantisasi Umpan Balik Terbuka dalam Evaluasi Kepuasan Pengguna Aplikasi Darisini Aisyah Nurul Afyna; Astie Darmayantie
Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah Vol. 8 No. 4 (2026): Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/alkharaj.v8i4.11644

Abstract

Darisini is a digital platform based on Islamic values that provides various da’wa activities, communities, and collaborative initiatives for social good. Evaluating user satisfaction is essential to ensure that the application meets user needs. This study aims to analyze the level of user satisfaction with the Darisini application and to quantify open-ended feedback in order to identify aspects that require improvement. This research adopts a mixed-method approach with a convergent design. Quantitative data were collected through a structured questionnaire based on the Technology Acceptance Model (TAM) and End User Computing Satisfaction (EUCS). Meanwhile, qualitative data were obtained from open-ended feedback provided by respondents, particularly for indicators with low satisfaction scores. Quantitative data were analyzed using descriptive statistics, while qualitative data were examined through text preprocessing, aspect grouping, sentiment identification, and frequency quantification of feedback for each aspect. The results indicate that overall user satisfaction with the Darisini application falls within the very satisfied category. However, the quantification of open-ended feedback reveals several aspects that still require improvement, particularly in terms of usability and content relevance. These findings are expected to serve as a basis for feedback management and future development of the Darisini application.
Pelatihan Pemanfaatan Generative AI untuk Edukasi Keamanan Jaringan bagi Pelajar SMK Yadika 13 Bekasi Tambun dengan Universitas Gunadarma Amrin Fakhruddin Jauhari; Budi Utami Fahnun; Anneke Annassia Putri Siswadi; Khairunnisa Raihani; Ertie Nur Hartiwati; Indra Adi Permana; Sindy Nova; Widiastuti Widiastuti; Astie Darmayantie
Jurnal Pengabdian Masyarakat Bhinneka Vol. 4 No. 4 (2026): Juli
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v4i4.1353

Abstract

Perkembangan pesat Generative Artificial Intelligence (Gen-AI) memunculkan peluang sekaligus tantangan bagi dunia pendidikan, khususnya dalam mempersiapkan siswa vokasi menghadapi transformasi teknologi. Kegiatan pengabdian kepada masyarakat ini bertujuan memberikan pelatihan pemanfaatan GenAI untuk edukasi keamanan jaringan kepada siswa kelas X jurusan Teknik Komputer dan Jaringan (TKJ) SMK Yadika 13 Bekasi Tambun. Kegiatan dilaksanakan pada 30 April 2026 dengan metode yang menggabungkan penyampaian materi teoretis, praktik langsung, dan evaluasi partisipatif. Materi mencakup klarifikasi isu technological unemployment, etika penggunaan AI, teknik prompting, serta aplikasi tiga jenis GenAI (generative text, generative art, dan generative music) untuk pembuatan konten edukasi keamanan jaringan. Sebanyak 19 siswa berpartisipasi aktif dalam kegiatan, dengan 63% memilih generative art sebagai media realisasi ide. Hasil evaluasi menunjukkan peningkatan pemahaman siswa terhadap konsep GenAI dan kemampuan mengaplikasikannya secara bertanggung jawab. Kegiatan ini berkontribusi terhadap penguatan literasi digital siswa SMK dan direkomendasikan untuk dilaksanakan secara berkelanjutan dengan pendekatan project-based learning serta pelibatan guru sebagai pendamping.
Analysis Of Financing Risk Optimization At XYZ Multifinance Company Through A Credit Score Clustering Approach Ranti Ramayanti Haris; Astie Darmayantie
International Journal of Health Engineering and Technology Vol. 5 No. 2 (2026): JULY 2026
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v5i2.918

Abstract

This research a credit scoring model to optimize financing risk in Multifinance Company XYZ, which has a Non-Performing Financing (NPF) rate of 2.8%, exceeding the company’s target of 2%. The development is carried out using Oracle Development Suite with the CRISP-DM methodology, covering the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset consists of 250 customer records from the period 2015–2025, with 101 initial variables that are subsequently reduced to 14 predictive variables based on correlation analysis and credit scoring domain knowledge. A K-Nearest Neighbor (KNN) classification model with K = 3 is developed using Oracle Data Miner to classify borrower risk into three categories: LOW, MEDIUM, and HIGH. Testing results on 50 testing data show that the model achieves an accuracy of 98%, precision of 97.5%, and recall of 100% for the HIGH-risk class. A 5-fold cross-validation confirms the model’s consistency with a mean accuracy of 97.6% (±1.1%). Implementation using PL/SQL and Oracle Forms produces a real-time scoring system with a processing time of less than 5 seconds per application, compared to 5–7 days in the previous manual proces.
Comparative Analysis of Indonesian Pre-trained BERT Models for the Extractive Question Answering Task on an Indonesian-Translated SQuAD Dataset Fattah Al Ilmi Suhendra; Astie Darmayantie; Adang Suhendra Suhendra; Pa Pa Min
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5847

Abstract

Transformer-based architectures have significantly advanced Natural Language Processing (NLP), with Bidirectional Encoder Representations from Transformers (BERT) serving as a strong baseline for extractive Question Answering (QA). This study aims to evaluate the performance of Indonesian BERT models on extractive QA tasks and to identify the most effective model for low-resource language settings. This research employed a comparative experimental method using two Indonesian BERT variants: indobert-base- ncased (IndoLEM) and indobert-base-p1 (IndoNLU/IndoBenchmark). Both models were fine-tuned on an Indonesian version of SQuAD 2.0, automatically translated via the Google Translate API. Answer-span alignment errors caused by translation were corrected using fuzzy string matching. Evaluation was conducted under identical hyperparameter settings and training schemes, using Exact Match (EM) and F1-score as performance metrics. The results indicate that IndoLEM achieved superior performance, with better loss convergence and a higher F1-score (71.58) than IndoNLU (63.59), and the difference was statistically significant (p < 0.001). In conclusion, IndoLEM is a more effective baseline model for Indonesian extractive QA systems. The findings also demonstrate that the composition and scale of pre-trained corpora substantially influence model performance in low-resource language contexts and highlight the importance of transfer learning for advancing NLP in underrepresented languages.
Co-Authors Adang Suhendra Suhendra Aisyah Nurul Afyna Amrin Fakhruddin Jauhari Anneke Annassia Putri Siswadi Budi Utami Fahnun Ertie Nur Hartiwati Fattah Al Ilmi Suhendra Indra Adi Permana Khairunnisa Raihani Pa Pa Min Ranti Ramayanti Haris Sindy Nova Widiastuti Widiastuti