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All Journal Jurnal Teknologi Terpadu Jurnal Informatika Terpadu Bulletin of Computer Science Research Decode: Jurnal Pendidikan Teknologi Informasi Digital Transformation Technology (Digitech) Jurnal Penelitian Sistem Informasi DBESTI: Journal of Digital Business and Technology Innovation Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Saptono, Henry
Sekolah Tinggi Teknologi Terpadu Nurul Fikri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

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Implementation of Artificial Intelligence in Health Screening Systems for Category-Based Fitness and Nutrition Recommendations Nasrul; Henry Saptono; Rusmanto
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.108

Abstract

The increasing prevalence of non-communicable diseases, such as diabetes, hypertension, and metabolic disorders, highlights the need for accessible health screening services. However, existing health screening systems generally provide examination results without offering automated educational recommendations to support preventive healthcare. This study proposes an Artificial Intelligence (AI)-based health screening information system that automatically generates fitness and nutrition recommendations based on categorized health screening results. The research employed a Research and Development (R&D) approach using the Extreme Programming (XP) software development methodology. The proposed system was developed using the Laravel framework and the Filament administration panel and integrates a Large Language Model (LLM) through the OpenAI API. The proposed architecture combines structured prompt engineering, predefined AI guardrails, SHA-256 prompt hashing, and recommendation caching to improve recommendation consistency and computational efficiency. The system was evaluated using User Acceptance Testing (UAT) and an AI Recommendation Consistency Evaluation. The UAT results showed that all functional requirements were successfully fulfilled. The consistency evaluation demonstrated that repeated processing of identical health screening data produced stable recommendations, achieving an average qualitative consistency score of 90.4% and an average TF–IDF cosine similarity of 0.784. These findings indicate that the proposed architecture is capable of generating reliable and consistent educational recommendations while reducing redundant AI requests through recommendation caching. This study contributes to the development of intelligent health information systems by introducing an efficient AI recommendation architecture that supports digital health screening and preventive healthcare services.
Implementasi Deteksi Intrusi Aplikasi Web Berbasis Supervised Machine Learning: Studi Kasus LMS STT Terpadu Nurul Fikri Henry Saptono; Yuda Fatahillah Achmar; Hendro Sasongko Hadi; Salman Fathy Shiroth; Lambang Ramadhian Putra Aria; Muhamad Masayid Alfarizqi
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 5 No. 3: NOVEMBER 2025
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v5i3.1313

Abstract

Sistem deteksi intrusi merupakan komponen penting dalam menjaga keamanan aplikasi web, terutama pada platform pembelajaran daring seperti Learning   Management   System (LMS). Penelitian ini bertujuan mengimplementasikan sistem deteksi intrusi berbasis supervised machine learning untuk mengidentifikasi serangan SQL  Injection dan Cross-Site  Scripting (XSS) melalui analisis payload HTTP yang diterima oleh LMS Moodle. Model yang digunakan adalah algoritma Random Forest dengan representasi fitur berbasis (Term Frequency–Inverse Document Frequency) TF-IDF pada level karakter. Data pelatihan berasal dari gabungan dataset publik dan log aktivitas LMS internal yang telah melalui proses preprocessing serta masking data sensitif. Arsitektur sistem dirancang menggunakan plugin middleware pada LMS untuk menangkap log secara real-time, Redis sebagai message broker, dan Flask-RQ sebagai worker pemrosesan model, serta dashboard Grafana-Loki untuk visualisasi hasil deteksi. Hasil pengujian menunjukkan bahwa model Random Forest mencapai akurasi 99,94% dengan nilai presisi, recall, dan AUC yang sangat tinggi, menunjukkan kemampuan deteksi yang andal terhadap serangan SQL  Injection dan XSS. Sistem ini mampu beroperasi secara real-time tanpa mengganggu kinerja LMS, sehingga efektif diterapkan sebagai solusi keamanan siber pada lingkungan pendidikan. Implementasi ini berpotensi dikembangkan lebih lanjut untuk mendeteksi jenis serangan web lainnya secara adaptif.
Multisensor IoT and Deep Learning Integration for Estimating Grapevine Vigor Index under Tropical Conditions Maulana Fakih Latief; Henry Saptono; Sapto Waluyo; Zahra Aulia Rahmani
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 6 No. 2: JULI 2026
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v6i2.1629

Abstract

Grapevine cultivation under tropical conditions is highly susceptible to abiotic stress, making reliable assessment of plant vigor important for precision management. This study presents an integrated multisensor Artificial Intelligence of Things (AIoT) framework combined with deep learning for estimating a relative grapevine vigor index using RGB canopy imagery. Environmental sensors provide contextual measurements of microclimatic and soil conditions, while image data are processed using a convolutional neural network–based model to estimate vigor levels derived from fractional green canopy cover. Approximately 1,600 RGB canopy images and 4,320 environmental telemetry records were used to train and evaluate the model. The results demonstrate that the proposed approach achieved a mean absolute error (MAE) of 8.73, a root mean square error (RMSE) of 11.74, and a coefficient of determination (R²) of 0.57 in predicting the relative vigor index. The integration of environmental sensing and image-based analysis provides an initial contribution to tropical grapevine vigor monitoring, although the model remains limited by single-site data and moderate predictive performance. Overall, the proposed framework provides a practical foundation for image-based vigor estimation and integrated data acquisition in tropical precision viticulture.
Co-Authors Adriansyah, Ahmad Rio Amalia Amalia Amalia Amalia April Rustianto Berniawan, Nikita Putri Christine Ayu Debi Onas Saputra Dimas Kurniawan Edi Wibowo Edi Wibowo Fariz Fariz Fathul Muiin Febriana Akbar, Rizky Hendro Sasongko Hadi Hera Karmila Husni Mubarok Karmila, Hera Lambang Ramadhian Putra Aria Lukman Rosyidi Maulana Fakih Latief Muhamad Masayid Alfarizqi MUHAMMAD AMIR Muhammad Jafier Rama Putra Nasrul Nasrul Nasrul Nuraini Nuraini Nurul Azizah Rizky Hilmiawan Anggoro Romi Wahyudi Hasibuan Rusmanto Salman Fathy Shiroth Sapto Waluyo Taufiqul Hidayah Yuda Fatahillah Achmar Yunita Surahman Yusuf, Muhamad Burhanudin Zahra Aulia Rahmani Zaida, Efrizal Zaki Imaduddin