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All Journal Jurnal Ilmiah Universitas Batanghari Jambi Jurnal Pilar Nusa Mandiri Jurnal Pengabdian kepada Masyarakat Nusantara
Andra, Muhammad Bagus
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

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Penerapan Artificial Intelligence untuk Meningkatkan Efisiensi Pengelolaan Database Administrasi Remaja Masjid Andra, Muhammad Bagus; Hermaliani, Eni Heni; Subekti, Agus; Haris, Muhammad
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi Januari - Maret
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Menurut studi terkini, administrasi masjid yang dikelola oleh remaja masjid sering menghadapi masalah seperti alur kerja yang lambat, pencatatan yang buruk dan kesalahan pengetikan, serta ketidakmampuan untuk mengambil informasi dengan cara yang tepat waktu dan akurat. Bertujuan untuk menyelesaikan masalah ini, kegiatan pengabdian ini meninjau integrasi Artificial Intelligence (AI) dalam manajemen administrasi masjid. Kegiatan spesifik pada Pengabdian kepada Masyarakat (PkM) meliputi analisis kebutuhan, pengembangan perangkat lunak berbasis AI, sesi pelatihan untuk staf, dan evaluasi sistem manajemen. Temuan lebih lanjut menunjukkan adanya penerapan AI untuk pengambilan keputusan menaikkan akurasi, tepat guna dan kecepatan pengambilan informasi selama proses administrasi. Disimpulkan bahwa penggunaan teknologi canggih yang diperlukan untuk organisasi berbasis komunitas mendukung pertumbuhan produktivitas. Dengan cara yang sama, inisiatif ini menetapkan preseden bagi organisasi lain untuk menggunakan solusi teknologi guna meningkatkan operasi administrasi.
EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS Marcelly, Frizca Fellicita; Saputra, Irwansyah; Andra, Muhammad Bagus
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6884

Abstract

Information Retrieval (IR) systems are pivotal for efficient data management, particularly in tasks involving name searches and entity identification. This study evaluates text preprocessing techniques, including case folding, phonetic normalization, and gender tagging, that affect the performance of classical (TF-IDF, LSI) and CNN-based retrieval models for multilingual name matching. Using a dataset of 365,468 globally diverse names, this study implements a preprocessing pipeline featuring: Double Metaphone phonetic preprocessing (92% validation accuracy), gender disambiguation for unisex names (92% accuracy), and optimized n-gram tokenization for short names. Evaluation metrics include precision, recall, F1-score, and our novel Name Similarity Score (NSS), combining orthographic and phonetic preprocessing. Results show our full pipeline improves recall to 1.00 and F1-score by 37% while reducing false negatives by 63%. Key findings reveal: TF-IDF achieves superior recall (0.98 vs CNN’s 0.85), LSI handles cultural variants effectively, and CNNs deliver the highest precision (0.91 vs TF-IDF’s 0.70), particularly for unisex names. This work contributes both a scalable multilingual preprocessing framework and the NSS evaluation metric for robust name retrieval systems.
EVALUATING PREPROCESSING EFFECTS IN NAME RETRIEVAL USING CLASSICAL IR AND CNN-BASED MODELS Marcelly, Frizca Fellicita; Saputra, Irwansyah; Andra, Muhammad Bagus
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6884

Abstract

Information Retrieval (IR) systems are pivotal for efficient data management, particularly in tasks involving name searches and entity identification. This study evaluates text preprocessing techniques, including case folding, phonetic normalization, and gender tagging, that affect the performance of classical (TF-IDF, LSI) and CNN-based retrieval models for multilingual name matching. Using a dataset of 365,468 globally diverse names, this study implements a preprocessing pipeline featuring: Double Metaphone phonetic preprocessing (92% validation accuracy), gender disambiguation for unisex names (92% accuracy), and optimized n-gram tokenization for short names. Evaluation metrics include precision, recall, F1-score, and our novel Name Similarity Score (NSS), combining orthographic and phonetic preprocessing. Results show our full pipeline improves recall to 1.00 and F1-score by 37% while reducing false negatives by 63%. Key findings reveal: TF-IDF achieves superior recall (0.98 vs CNN’s 0.85), LSI handles cultural variants effectively, and CNNs deliver the highest precision (0.91 vs TF-IDF’s 0.70), particularly for unisex names. This work contributes both a scalable multilingual preprocessing framework and the NSS evaluation metric for robust name retrieval systems.
Dokumentasi Kode Otomatis Menggunakan AI Taskia, Ryeisa; Kurniawati, Laela; Andra, Muhammad Bagus
Jurnal Ilmiah Universitas Batanghari Jambi Vol 26, No 1 (2026): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v26i1.6385

Abstract

This study aims to evaluate the performance of four artificial intelligence models—CodeT5, CodeBERT, StarCoder, and GPT-4 (simulated)—in the code summarization task, which involves generating summaries or documentation for simple Python code snippets. The dataset consists of Python comment and code pairs, processed into documentation–code format to support the summarization process. The evaluation was conducted using BLEU and ROUGE-L metrics to measure the agreement between the model-generated summaries and the original documentation. The results show that GPT-4 (simulated) performed best with a BLEU score of 0.61 and ROUGE-L of 0.72, indicating superior context understanding capabilities. Among the open-source models, CodeT5 achieved the highest performance (BLEU 0.42 and ROUGE-L 0.55). CodeBERT produced an intermediate score, while StarCoder obtained the lowest score because its optimization is more geared towards code completion than code summarization. This study concludes that model selection should be tailored to the needs. CodeT5 is recommended for implementing open-source automated documentation systems, offering a good balance between performance and accessibility. Meanwhile, GPT-4 can be used as a reference model for high-accuracy applications. This research contributes to the field of software engineering by highlighting the potential of AI models to improve the efficiency and automation of code documentation processes.
Co-Authors Agus Subekti Eni Heni Hermaliani, Eni Heni Laela Kurniawati, Laela Marcelly, Frizca Fellicita MUHAMMAD HARIS Saputra, Irwansyah Taskia, Ryeisa