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All Journal JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TECHSI - Jurnal Teknik Informatika Lok Seva: Journal of Contemporary Community Service Jurnal Pengabdian Sosial
Hamdhana, Defry
Universitas Malikussaleh
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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A LOW CODE APPROACH TO Q&A ON CARE RECORDS USING FLOWISE AI WITH LLM INTEGRATION AND RAG METHOD Hamdhana, Defry
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.6978

Abstract

Care records are vital for monitoring patient conditions and supporting clinical decision-making, but their diverse formats—such as tables, narrative sentences, checklists, and fill-in-the-blank fields—present challenges for efficient information retrieval. Traditional retrieval methods are often time-consuming and error-prone, while automated systems struggle with contextual accuracy in complex medical language. This study proposes a low-code approach to develop a question-and-answer (QA) system for care records using Flowise AI integrated with Retrieval-Augmented Generation (RAG) methodology. By utilizing LangChain and OpenAI’s language models, Flowise AI provides a framework for constructing a QA system that retrieves information accurately across different documentation formats. The system employs components such as Recursive Character Text Splitter, PDF processing, OpenAI Embeddings, In-Memory Vector Store, and a Conversational Retrieval QA Chain, ensuring efficient retrieval with contextual relevance. Our results demonstrate high accuracy in aligning the QA responses with ground truth data, validating the system's effectiveness in healthcare documentation retrieval. This low-code solution not only enhances accessibility for non-technical users but also empowers healthcare professionals with a scalable tool for quick access to critical patient data. The findings underscore the potential of low-code AI systems like Flowise AI, utilizing RAG, to improve information retrieval in healthcare, supporting more accurate and timely clinical decisions.
Pemberdayaan Masyarakat Tani Melalui Produksi Briket Jerami sebagai Energi Alternatif Ramah Lingkungan dan Sumber Pendapatan Baru di Gampong Reulet Timu Nailufar, Fanny; Sari, Cut Putri Mellita; Fadhilah, Fadhilah; Hamdhana, Defry; Arliansyah, Arliansyah; Yusra, Muhammad
Jurnal Pengabdian Sosial Vol. 2 No. 7 (2025): Mei
Publisher : PT. Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/6h6y9442

Abstract

Pengabdian kepada masyarakat ini dilakukan di Gampong Reulet Timu dengan tujuan memberdayakan masyarakat tani melalui pemanfaatan limbah jerami menjadi briket sebagai energi alternatif ramah lingkungan dan sumber pendapatan baru. Limbah jerami yang selama ini dibakar atau dibiarkan tanpa pengelolaan diolah menjadi briket yang memiliki nilai ekonomis dan fungsi praktis sebagai bahan bakar. Kegiatan dilaksanakan melalui tiga tahap utama, yaitu sosialisasi, pelatihan teknis, dan pendampingan produksi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan masyarakat dalam memproduksi briket jerami. Satu kelompok tani telah terbentuk dan mulai menjalankan produksi mandiri. Produk yang dihasilkan dinilai layak untuk digunakan sebagai bahan bakar rumah tangga serta berpotensi dikembangkan ke pasar lokal. Kegiatan ini tidak hanya mengurangi limbah pertanian tetapi juga membuka peluang ekonomi baru. Program ini diharapkan dapat menjadi model inovasi energi lokal berkelanjutan di wilayah pedesaan.
Analisis Prediktif Intensi Berwirausaha Mahasiswa Akuntansi Menggunakan Machine Learning Hamdhana, Defry; Yusra, Muhammad
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1863

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi intensi berwirausaha di kalangan mahasiswa akuntansi melalui penerapan model machine learning, khususnya K-Nearest Neighbor (K-NN). Kewirausahaan dianggap memiliki peran penting dalam pertumbuhan ekonomi dan penciptaan lapangan kerja, terutama di negara berkembang seperti Indonesia. Namun, tidak semua mahasiswa menunjukkan minat yang kuat untuk menjadi wirausahawan setelah lulus, termasuk mahasiswa akuntansi yang umumnya memiliki prospek karier di bidang keuangan. Penelitian ini menggunakan Theory of Planned Behavior (TPB) sebagai dasar teoretis untuk memahami faktor sikap, norma subjektif, dan kontrol perilaku dalam memengaruhi intensi berwirausaha mahasiswa. Data dikumpulkan dari 30 mahasiswa akuntansi di Politeknik Negeri Lhokseumawe dan Universitas Islam Kebangsaan Indonesia melalui kuesioner terkait pelatihan kewirausahaan, kemudian dianalisis menggunakan model K-NN. Hasil penelitian menunjukkan bahwa sikap positif dan dukungan sosial memiliki pengaruh signifikan terhadap intensi berwirausaha. Model K-NN dengan parameter K = 3 menunjukkan akurasi sebesar 83%, yang mengindikasikan potensi penerapan machine learning dalam memprediksi intensi berwirausaha. Temuan ini berkontribusi pada literatur kewirausahaan serta memberikan rekomendasi untuk pengembangan program pelatihan kewirausahaan yang lebih komprehensif di lingkungan pendidikan.
Few-Shot Learning for Classifying Genuine and Bot Comments on YouTube Using Transformer Models Fikriah Nst, Nahdah; Hamdhana, Defry; Qamal, Mukti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10023

Abstract

This study aims to develop a comment classification system on the YouTube platform to distinguish between real accounts and bot accounts, addressing the challenge of limited labeled data through a few-shot learning approach. The issue of bot accounts masquerading as real users in comment sections is becoming increasingly prevalent and has the potential to spread spam, misinformation, and influence public opinion. In this study, a Transformer-based model, DistilBERT, is used, which is known for its efficiency in understanding natural language context. The model is trained in a few-shot scenario (N5 to N50) using a very limited amount of training data. Testing results show that the model maintains high and stable performance even with minimal data (N5), achieving an F1-score above 0.90. In addition, this system is implemented into a web application using Flask to enable direct and interactive comment detection. The main contribution of this research is the proof that the combination of few-shot learning and the DistilBERT model can provide a practical and efficient solution for classifying YouTube bot account comments even with limited data conditions, as well as providing a replicable approach for similar problems on other digital platforms.
Implementation of Ant Colony Optimization (ACO) Algorithm for Route Optimization of Tourist Paths in Takengon Suryana, Fitra; Nurdin, Nurdin; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9706

Abstract

This study aims to design and implement a system for determining the shortest route between tourist destinations in Takengon using the Ant Colony Optimization (ACO) algorithm. The system is developed to assist travelers in obtaining efficient visitation routes based on distance and travel time. Experiments were conducted on 20 tourist locations, resulting in an optimized route with a total travel distance of 40.40 km and an estimated travel time of 81 minutes. The computation process took only 0.024001 seconds with a memory usage of 20.23 KB. The ACO algorithm was executed using 10 ants with key parameters set to alpha (α) = 1, beta (β) = 2, and rho (ρ) = 0.5. ACO demonstrated high effectiveness in exploring route combinations and iteratively generating near-optimal solutions. The chosen parameters were determined through experimentation to balance solution quality and convergence speed. In addition to generating the optimal visitation sequence, the system also provides complete turn-by-turn navigation instructions, including major roads such as Jalan Lintas Tengah Sumatera and Jalan Lebe Kader. The actual estimated travel route based on the generated navigation covers a distance of 97.4 km with a travel duration of approximately 2 hours and 42 minutes. The results indicate that ACO is an effective and efficient approach for solving medium- to large-scale tourist route optimization problems. The developed system can serve as a practical tool in the tourism sector and has the potential to be adapted and implemented in other tourist regions with similar routing challenges.
Peningkatan Kompetensi Guru SLB dalam Pemanfaatan Teknologi Pembelajaran Digital di SLB YPAC Dewantara Aceh Utara Hamdhana, Defry; Yusra, Muhammad; Maghfirah, Fitri; Mulyati, Sri; Kembaren, Emmia Tambarta
Lok Seva: Journal of Contemporary Community Service Vol 4, No 2 (2025)
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/lokseva.v4i2.13515

Abstract

At SLB YPAC Dewantara, North Aceh, teachers' mastery of digital technology remains constrained in terms of the utilization of online applications and multimedia. The objective of this Community Service Program (PKM) is to enhance pedagogues' competencies through experiential learning, which is characterized by a participatory approach. The activity was conducted over the course of two days and involved 15 teachers. The materials covered the use of Zoom Meeting, Google Meet, Bitly, and the creation of simple animations using Canva and Powtoon. The evaluation results demonstrated a substantial increase in the utilization of digital tools, with the capacity to employ the fundamental functionalities of Zoom and Google Meet escalating from 27% to 87%, the application of Bitly rising from 13% to 80%, and the proficiency in generating animated media increasing from 7% to 73%. The challenges encountered by the participants included time constraints, variations in digital literacy, and internet infrastructure. This PKM fostered the establishment of an internal teacher learning community and underscored the significance of synergy among individual capacity, institutional support, and sustainable mentoring.
Co-Authors Arliansyah, Arliansyah Fadhilah Fadhilah Fadlisyah Fadlisyah Fanny Nailufar Fikriah Nst, Nahdah Fitri Maghfirah Kembaren, Emmia Tambarta Mardiaton, Mardiaton Martin Martin Martin Martin Maulidawati, Maulidawati Mauliza Mauliza Mauliza, Mauliza Muhammad Iqbal Muhammad Johan Setiawan Mukti Qamal Mukti Qamal Nur Afni Yunita Nurdin Nurdin Putri, Rany Gesta Rio Pratomo Rizal Rizal Rizal Rizal Safira Adani Sari, Cut Putri Mellita Sayed Fachrurrazi Sri Mulyati Suryana, Fitra Yusra, Muhammad