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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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) Vocatech : Vocational Education and Technology Journal TECHSI - Jurnal Teknik Informatika Lok Seva: Journal of Contemporary Community Service Journal Of Artificial Intelligence And Software Engineering Jurnal Pengabdian Sosial Journal of Deep Learning, Computer Vision and Digital Image Processing Journal of Advanced Computer Knowledge and Algorithms Seuraya Jurnal Pengabdian Kepada Masyarakat Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Hamdhana, Defry
Universitas Malikussaleh
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering 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.
Classification Analysis of Single Tuition Fees Using the Random Forest Method with K-Fold Cross Validation Khaidar, Al; Nurdin, Nurdin; Fajriana, Fajriana; Taufiq, Taufiq; Hamdhana, Defry
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11798

Abstract

Classification is the process of grouping data into specific categories based on their characteristics or features, which plays a crucial role in the analysis, decision-making, and prediction of new data. In academic settings, classification is used to determine the Single Tuition Fee to place students according to their economic ability. Lhokseumawe State Polytechnic has implemented the UKT system since 2020 with eight categories, but some students are still placed in UKT groups that do not match the results of the manual process, which has limited accuracy. This study uses the Random Forest method as a technology-based solution to improve the accuracy and objectivity of UKT classification. The dataset used consists of 10,000 student data with 10 variables, covering economic and social information. The research process includes data preprocessing, Random Forest model training, performance evaluation using accuracy, precision, recall, and F1-score, and model stability testing through 10-fold K-Fold Cross Validation. The results show that Random Forest is able to classify most UKT classes well, especially classes 0–5 and 7. Class 6 has lower performance with a recall of 0.39 and an F1-score of 0.56 due to the limited number of samples. The overall accuracy of the model reaches 96%, while K-Fold Cross Validation produces an average accuracy of 95.50% with a standard deviation of 0.66%, indicating the model is stable and able to generalize to new data. This study proves that Random Forest is effective in UKT classification, producing an objective, fair, and efficient system. This implementation model supports data-driven decision-making in higher education and increases transparency in UKT determination.
Analysis of Winnowing Algorithms in The Title Selection System of Job Training Report, Faculty of Engineering, Universitas Malikussaleh Rizal; Defry Hamdhana
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 1 (2018): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

The number of practical work reports that have been published by department makes a coordinator of on the job training have difficulty in determining the feasibility of the title of the on the job training report quickly. This happened because each report that was made must be unique in terms of the methods and objects that are examined in a particular institution/company. So that reports that have been published previously did not experience plagiarism. Winnowing algorithm is an algorithm used to detect the similarity of words/sentences in two or more texts that are compared. If there are two texts that are the same, a fingerprint will be formed. So that the practical work coordinator can determine the feasibility of the title of on the job training report quickly and the creation of an online-based on the job training report information system that can be accessed at any time.
Comparison of Coffee Bean Sales Predictions at the Ketiara Coffee Traders Cooperative (KOPEPI) Using Linear Regression and Random Forest Methods Syadzwina, Nada; Defry Hamdhana; Ar Razi
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25974

Abstract

Most of Indonesia's land is used for agriculture and plantations because it is an agrarian country. Harvests or agricultural products can be exported to help the country's economic recovery. Coffee, the most traded tropical crop in the world, is one of the most valuable commodities. Approximately 25 million farming households contribute up to 80% of global coffee production (FAO Organizational 2023). Indonesia's coffee industry continues to experience significant annual growth. To optimize their production and distribution, Indonesian coffee producers must understand coffee bean sales trends. This study compares two methods for predicting coffee bean sales at the KOPEPI Ketiara Aceh Tengah Cooperative using Linear Regression and Random Forest methods. The research methods used in this study are data collection and system design. The results show a comparison of the Linear Regression and Random Forest methods in predicting coffee bean sales. Linear regression provides fairly good accuracy for the price variable with low MAPE values (3.35%–4.55%) and MAE that is still within reasonable limits, but produces large prediction errors for the export variable with high MAPE (67.84%–80.65%) and large MAE (5982–7960). In contrast, Random Forest shows superior performance with very low MAPE (2.69%–3.46%) and smaller MAE (4275–6038) on price variables, as well as more stable and consistent export predictions even though the MAPE values are still quite high (54.25%–84.97%). Overall, Random Forest is a more appropriate model to use because it provides accurate price predictions and more consistent export performance compared to Linear Regression.
Student Academic Consultation Chatbot Using Meta AI Large Language Models and Retrieval-Augmented Generation Pangestu, Aridho; Hamdhana, Defry; Suwanda, Rizki
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.8875

Abstract

Academic consultation is an important service for students in obtaining information related to academic regulations, procedures, and requirements. However, the consultation process, which is still carried out manually, often causes delays in the delivery of information and limited access, especially when students need answers quickly. Therefore, a system is needed that is capable of providing academic consultation services automatically and based on official documents. This study aims to design and build a student academic consultation chatbot using Large Language Model (LLM) technology and Retrieval Augmented Generation (RAG) architecture. The methods used include calling up Academic Guidelines documents, splitting text into several parts (text splitting), creating embeddings using the HuggingFace all-MiniLM-L12-v2 model, and storing embeddings in a vector database. Next, the system performs a relevant document search process using a retriever and utilizes the LLaMA 3.1-8B-Instant model to generate answers based on the context found. The chatbot's performance was evaluated using ROUGE metrics, including ROUGE-1, ROUGE-2, and ROUGE-L, with measurements of precision, recall, and F1-score. The evaluation results showed that the chatbot was able to provide relevant answers in accordance with academic documents. The average evaluation scores obtained were precision of 47,57%, recall of 67,85%, and F1-score of 53,36%. The higher recall score indicates that the system is quite good at covering reference information, although the accuracy of word selection can still be improved.
Co-Authors Ar Razi Ar Razi Arliansyah, Arliansyah Baharuddin Ismail Dahlan Abdullah Fadhilah Fadhilah Fadlisyah Fadlisyah Fajriana, Fajriana Fanny Nailufar Fikriah Nst, Nahdah Fitri Maghfirah Hamzani Hamzani Juanda Pratama Kembaren, Emmia Tambarta Khaidar, Al Lailatul Husna Maizuar Maizuar Mardiaton, Mardiaton Martin Martin Martin Martin Maulidawati, Maulidawati Mauliza Mauliza Mauliza, Mauliza Muhammad Fikry Muhammad Iqbal Muhammad Johan Setiawan Muhammad Yusra Mukti Qamal Mukti Qamal Munirul Ula Nur Afni Yunita Nurdin Nurdin Pangestu, Aridho Putri, Rany Gesta Rio Pratomo Rizal Rizal Rizal Rizal Rizal Rizki Suwanda Safira Adani Sari, Cut Putri Mellita Sayed Fachrurrazi Sri Mulyati Sri Mulyati Suryana, Fitra Syadzwina, Nada Taufiq Taufiq Taufiq Taufiq Utomo, Muhammad Fikri Widyana Verawaty Siregar Yusra, Muhammad Zara Yunizar Zulfikar Aji Kusworo