Claim Missing Document
Check
Articles

Found 4 Documents
Search

RANCANG BANGUN SISTEM PEMANTAUAN DAN PENGATURAN JARAK JAUH PADA FERTIGASI TETES BERBASIS INTERNET OF THINGS Gemilang, Angga; Widhiyasana, Yudi; Hakim Firdaus, Lukmannul
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 1 (2024): JATI Vol. 8 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i1.8786

Abstract

Seiring berjalannya waktu, teknologi Internet of Things (IoT) berkembang pesat dan diterapkan dalam berbagai bidang, termasuk pertanian untuk mengotomatisasi kegiatan irigasi dan fertilisasi. Hasil survei dan literatur menunjukkan masih banyaknya penggunaan metode manual, berpotensi mengakibatkan ketidakakuratan dosis dan keterbatasan dalam menghadapi anomali. Contohnya seperti ketika kadar air dalam tanah lebih cepat habis pada malam hari atau kadar air yang sudah tercukupi karena terjangan hujan. Oleh karena itu, dikembangkan sistem fertigasi otomatis berbasis IoT dengan metode tetes untuk meningkatkan ketepatan dosis, penanganan anomali, dan memungkinkan pengaturan dinamis. Sistem ini juga memungkinkan pemantauan real-time, menampilkan notifikasi pada kondisi gangguan tertentu, serta kemampuan menjalankan irigasi dan fertilisasi secara terpisah. Pada bagian hardware, alat ini terbagi menjadi dua perangkat, yaitu perangkat pemantau dan utama. Sedangkan pada bagian software dikembangkan dengan menerapkan metodologi SDLC waterfall menggunakan Kotlin dan C sebagai bahasa pemrogramannya. Untuk media penyimpanan dan pentransmisian datanya sistem ini menggunakan Firebase. Cabai rawit merah digunakan sebagai sample pengujian, yaitu tanaman yang memiliki nilai ekonomis tinggi, sejalan dengan diperlukannya biaya pengembangan sistem awal yang tinggi.
Pelatihan Peningkatan Kompetensi Guru melalui Media Board Game sebagai Inovasi Pembelajaran Computational Thinking di Pondok Pesantren Darul Fithrah Kabupaten Bandung Fitriani, Sofy; Rachmat, Setiadi; Sari, Aprianti Nanda; Syakrani, Nurjannah; Hidayatullah, Priyanto; Soewono, Eddy Bambang; Widhiyasana, Yudi; Abdillah, Trisna Gelar; Setiarini, Siti Dwi; Sholahuddin, Muhammad Rizqi
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 10 No 1 (2026): Volume 10 Nomor 1 Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v10i1.27180

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) ini dilatarbelakangi oleh keterbatasan guru pesantren dalam memahami dan mengimplementasikan keterampilan computational thinking dalam pembelajaran. Di Pondok Pesantren Darul Fithrah, media pembelajaran inovatif berupa board game berbasis Unplugged Computational Thinking dirancang untuk menjembatani kebutuhan tersebut sekaligus memberikan alternatif metode pembelajaran yang lebih interaktif. Metode pelaksanaan meliputi perancangan board game, penyusunan instrumen pelatihan, pelaksanaan pre-test, pemberian materi dan simulasi board game, serta post-test dan observasi implementasi di kelas. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada pemahaman guru mengenai computational thinking, terlihat dari kenaikan skor rata-rata pre-test dan post-test, khususnya pada aspek pengenalan pola, penyusunan langkah pemecahan masalah, dan pemahaman istilah computational thinking. Respon kuesioner dan feedback guru juga menunjukkan bahwa board game dipandang interaktif, mudah digunakan, serta potensial untuk diterapkan dalam pembelajaran di pesantren. Dengan demikian, kegiatan PKM ini berhasil mendorong guru lebih siap mengintegrasikan keterampilan abad 21 ke dalam praktik pendidikan
Adaptive EKF-Based Ship Trajectory Estimation with Earth Curvature Modeling and Dynamic Noise Tuning Elfada, Berliana; Gardara, Suci Awalia; Soewono, Eddy Bambang; Widhiyasana, Yudi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2397

Abstract

Accurate position estimation is critical for the effectiveness of automatic weapon and navigation systems. Standard Extended Kalman Filter (EKF) models typically adopt flat-Earth assumptions and static noise covariances, which limit their accuracy in operational environments. This study proposes an optimized EKF framework that integrates two complementary approaches. First, ship trajectories are represented in Earth-Centered Earth-Fixed (ECEF) coordinates with a WGS-84 reference to account for Earth’s curvature. Second, process (Q) and measurement (R) covariances are adaptively determined using Joint Likelihood Maximization (JLM) with logarithmic scale exploration, allowing the filter to automatically identify the most accurate configuration. Each Q/R setting is evaluated within the EKF framework using root mean square error (RMSE) derived from radar data logs. The method was tested under short-history scenarios (5 and 10 data points) within an operational range of ±15 km, reflecting conditions commonly encountered in Combat Management Systems (CMS). Results show that while coordinate transformation alone provides only marginal improvements at short ranges, the combination of curvature modelling and adaptive Q/R tuning significantly reduces RMSE, achieving average errors approaching zero with high repeatability as measured by standard deviation. This research demonstrates a novel integration of geometric and statistical optimization in EKF design and highlights its applicability to ship trajectory estimation and defence systems.
A Morphology Processing Approach For Image Processing In Cancer Diagnosis Hutahaean, Jonner; Widhiyasana, Yudi; Ramdhani, Algi Fari
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4783

Abstract

Early tumor detection is critical for improving cancer treatment outcomes, enabling less invasive and more cost-effective interventions. However, limited access to pathologists and high patient volumes reduce diagnostic efficiency, particularly in underserved regions, underscoring the urgency for computational support tools. While deep learning has shown promise in tumor detection, it requires extensive annotated datasets, high computational resources, and long processing times, making it less feasible in certain contexts.This study introduces a lightweight image processing approach for detecting tumors in Hematoxylin and Eosin (H&E)–stained histopathology images without deep learning. Using data from the PAIP 2023 Tumor Cellularity challenge, the proposed method applies histogram equalization, bilateral filtering, morphological transformations, bitwise operations, and an improved algorithm adapted from prior research. The method achieves IoU (Intersection of Union) of 0.93 compared to pathologist-determined ground truth. The results indicate that this approach can serve both as a standalone segmentation tool and as a preprocessing stage for deep learning pipelines, enhancing accessibility, reducing computational costs, and supporting broader adoption of computer-aided pathology in resource-limited settings.