Selected Work, Essays, Designs, And Journals

Poster Juara 1 KMIPN 2025 Kategori IOT

Former mining lands in Indonesia, particularly in East Kalimantan, experience severe soil degradation characterized by high acidity, nutrient deficiency, and poor physical structure, which limit their potential for agricultural reuse. This study presents the design and implementation of an integrated Internet of Things (IoT)–based soil monitoring system combined with an artificial intelligence (AI)–driven crop recommendation module to support data-driven land reclamation and precision agriculture. The system consists of an ESP32 microcontroller, NPK soil sensors with RS485 communication, and a cloud-connected mobile application developed using Flutter, Firebase, and ThingsBoard. Soil parameters including pH, moisture, electrical conductivity, temperature, and macronutrients (nitrogen, phosphorus, and potassium) are collected in real time and analyzed using an AI-based reasoning model to generate crop suitability recommendations.System validation was conducted through black-box functional testing covering authentication, data acquisition, geotagged storage, analytics, and recommendation modules. A total of 35 test cases were executed, with 33 cases (94.3%) passing successfully. Performance evaluation shows that dashboard visualization and recommendation generation meet predefined service-level thresholds under normal network conditions. The results indicate that the proposed system is technically feasible for real-time soil monitoring and decision support on post-mining land. However, this study is limited to system-level validation and does not yet include large-scale agronomic field trials or comparative evaluation against conventional soil assessment methods. Future work will focus on improving AI model validation, expanding field deployment, and assessing agronomic impacts over longer cultivation cycles.

Membantu pengembangan sistem IT Request Monitoring berbasis web untuk mendukung pencatatan, pemantauan, dan pengelolaan permintaan layanan IT internal perusahaan. Membuat fitur pengajuan request, kategori kebutuhan IT, status permintaan, riwayat proses, dan dashboard monitoring request. Mendukung digitalisasi permintaan layanan IT agar lebih rapi, terstruktur, mudah dipantau, dan terdokumentasi dengan baik Melakukan perancangan tampilan antarmuka, pengelolaan database, implementasi fitur, pengujian sistem, dan perbaikan bug. Sistem dirancang untuk membantu tim IT memantau permintaan pengguna, menentukan prioritas pekerjaan, dan meningkatkan efisiensi layanan internal.

Mengembangkan sistem helpdesk IT berbasis web untuk membantu pencatatan, pengelolaan, dan tindak lanjut tiket kendala pengguna. Membuat fitur pembuatan tiket, kategori masalah, prioritas tiket, status penanganan, riwayat tanggapan, dashboard admin, dan manajemen pengguna. Menerapkan respon otomatis berdasarkan kategori dan kata kunci permasalahan untuk memberikan arahan awal kepada pengguna. Sistem dirancang untuk mempercepat respon awal, mempermudah monitoring tiket, dan membantu admin IT dalam mengelola laporan kendala. Mendukung dokumentasi permasalahan IT agar lebih terstruktur, mudah dipantau, dan dapat digunakan sebagai bahan evaluasi layanan.

The Field Technician Performance Monitoring and Assignment System is a web-based application designed to help companies manage technician tasks in a centralized and structured way. This system provides features for managing technician data, job assignments, assessment criteria, work progress monitoring, and dashboard reporting to track task status and technician performance more effectively. The system supports technician assignment based on several criteria, including technical skills, relevant experience, workload, work safety score, certification, and technician availability. Through this application, administrators can assign the most suitable technician for each task, monitor job progress, and improve operational efficiency, transparency, and documentation. This system was developed using Laravel as the backend framework and MySQL as the database.

Mengembangkan sistem smart agriculture berbasis IoT dan AI yang dirancang untuk menganalisis kondisi tanah pada lahan bekas tambang serta memberikan rekomendasi tanaman yang sesuai berdasarkan parameter nutrisi tanah. Sistem ini mengintegrasikan sensor tanah, cloud database, aplikasi mobile, dan analisis AI untuk mendukung reklamasi lahan berkelanjutan serta penerapan precision agriculture. Project ini dikembangkan sebagai bagian dari Project-Based Learning (PBL) dan berhasil meraih Juara 1 Kategori Poster IoT pada Kompetisi Mahasiswa Informatika Nasional (KMIPN) 2025. • Mengembangkan sistem monitoring tanah berbasis IoT menggunakan ESP32, sensor, dan integrasi data secara real-time. • Membangun fitur aplikasi mobile untuk monitoring tanah, analisis kondisi lahan, dan rekomendasi tanaman. • Mengintegrasikan analisis berbasis AI untuk merekomendasikan tanaman yang sesuai berdasarkan kondisi nutrisi tanah. • Menerapkan Firebase Realtime Database dan sistem pengelolaan data berbasis cloud. • Merancang dashboard interaktif, halaman analisis, dan tampilan rekomendasi pada aplikasi mobile. • Menggunakan teknologi IoT, Artificial Intelligence, Flutter, Firebase, REST API, dan integrasi sensor. • Berkolaborasi dalam tim multidisiplin dengan alur kerja pengembangan berbasis proyek. • Meraih Juara 1 Kategori Poster IoT pada KMIPN 2025 melalui project inovasi “Coal Co-Assistant Nutrient”.

Project Based Learning (PBL)

Project Based Learning (PBL)