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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat
Danny Oka Ratmana
UNIVERSITAS DIAN NUSWANTORO
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Pelatihan Implementasi Artificial Intelligence Menggunakan Teachable Machine berbasis Project-Based Learning bagi Siswa SMA/SMK Dibyo Adi Wibowo; Moch. Sjamsul Hidajat; Ricardus Anggi Pramunendar; Muhammad Syaifur Rohman; Danny Oka Ratmana; Rama Aria Megantara
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3226

Abstract

Artificial Intelligence (AI) merupakan teknologi yang berkembang pesat dan penting untuk dikenalkan sejak jenjang pendidikan menengah. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman siswa SMA/SMK di Kota dan Kabupaten Kediri terhadap konsep dasar Artificial Intelligence dan machine learning melalui pelatihan implementasi AI menggunakan Teachable Machine berbasis Project-Based Learning (PjBL). Metode pelaksanaan kegiatan mengombinasikan pendekatan PjBL dan experiential learning, di mana peserta dilibatkan secara aktif dalam pengembangan proyek AI sederhana berbasis gambar, suara, dan pose tubuh. Evaluasi pembelajaran dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman peserta. Hasil kegiatan menunjukkan adanya peningkatan yang signifikan pada seluruh kategori materi, termasuk konsep dasar AI, computational thinking, machine learning, penggunaan Teachable Machine, serta implementasi dan evaluasi model AI. Temuan ini menunjukkan bahwa penggunaan Teachable Machine yang dipadukan dengan pendekatan PjBL efektif dalam meningkatkan literasi Artificial Intelligence siswa SMA/SMK serta membantu peserta memahami konsep AI secara lebih konkret dan aplikatif.
Analisis Kesehatan Vegetasi Multi-Temporal Berbasis WebGIS Menggunakan NDVI Sentinel-2 dan Penilaian Multi-Kriteria SAW Akner Yosha Ade Saputra; Nurul Anisa Sri Winarsih; Muhammad Syaifur Rohman; Danny Oka Ratmana
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

This study integrates Sentinel-2 NDVI, the Simple Additive Weighting (SAW) method, and WebGIS to analyze vegetation health dynamics across 19 sub-districts in Grobogan Regency (±1,975.87 km²). Data were sourced from four Sentinel-2 Level-2A images (September 14–October 9, 2025) and supporting meteorological parameters. Results indicate NDVI values ranging from 0.2424 to 0.6297. On September 14, 42.1% of the areas were classified as High. However, by September 19, 12 sub-districts experienced a significant decline anomaly (), leaving only 21.1% in the High category. The SAW assessment yielded scores between 0.2793 and 0.4918. Spearman's validation demonstrated a perfect correlation () between NDVI and SAW rankings. The observed temporal fluctuations correlated with rainfall and humidity. In conclusion, this integrated approach effectively evaluates vegetation anomalies to support precise land management decisions.
Three-Tier Disaster Logistics System Integrating GIS and MILP Optimization Danny Oka Ratmana; Muhammad Syaifur Rohman; Galuh Wilujeng Saraswati; Filmada Ocky Saputra; Aprilyani Nur Safitri; Imanuel Harkespan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12727

Abstract

Effective disaster logistics management requires rapid, data-driven decision support that bridges optimization theory and operational practice. Existing systems either rely on theoretical models without implementable software, on proprietary datasets that restrict independent reconstruction, or lack validated prototypes in the Indonesian disaster context — three gaps that persist across the disaster IS literature. This study presents a three-tier web-based disaster logistics management IS integrating GIS and MILP optimization, built exclusively on public data sources (BNPB DIBI and OpenStreetMap). Using Design Science Research (DSR) across five phases, the system employs an open-source stack: Laravel 11.x presentation layer, PostgreSQL 16/PostGIS data layer, and Python FastAPI as a dedicated MILP microservice. The MILP model, a two-phase lexicographic MILP formulation with trips-aware vehicle capacity constraints is solved using the PuLP 3.3.0 + CBC solver. Three integrated modules were developed: shelter management, warehouse inventory, and logistics coordination with GIS visualization. Functional testing achieved 100% pass rate across 85 automated test cases covering all system modules, with 246ms mean response time under 50 concurrent users. The MILP solver resolved a 20-shelter problem in 0.094 seconds (99.9% below the 120-second operational planning threshold); scalability testing confirms tractability from 10 to 50 shelters (0.011–0.111 seconds), with Priority-1 shelters consistently served under both sufficient and scarce fleet conditions. Sensitivity analysis confirms lexicographic priority objectives activate correctly under resource scarcity. Comparative evaluation against heuristic and metaheuristic approaches confirms exact MILP is appropriate for the strategic planning scope of this proof-of-concept (n ≤ 50 shelters). Expert validation via ISO 25010 yielded a weighted score of 4.21/5. Usability testing with 25 participants produced a SUS score of 74.8 (Grade B, above-average per established SUS benchmarks) with 88% task completion rate. The primary contributions are a MILP-IS microservices integration pattern with explicit API specification, a comprehensively documented public-data-only implementation framework, and a proof-of-concept that closes the implementation gap between disaster logistics optimization research and operational IS deployment.
Co-Authors Akner Yosha Ade Saputra Aprilyani Nur Safitri Dibyo Adi Wibowo Filmada Ocky Saputra Imanuel Harkespan Moch. Sjamsul Hidajat Muhammad Syaifur Rohman Rama Aria Megantara Ricardus Anggi Pramunendar Saraswati, Galuh Wilujeng Winarsih, Nurul Anisa Sri