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All Journal Insect (Informatics and Security) : Jurnal Teknik Informatika Comunitario: Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration
Akrianto, Muhammad Ichwandar
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Optimalisasi Chat GPT dan Canva AI sebagai Asisten Cerdas Guru Dalam Penyusunan Media Ajar Hidayat, Muhamad Maksum; Syukron, Muhammad; Zuhair, Alvin; Pambudi, Kukuh Trisna; Prasetyaningtyas, Antika; Akrianto, Muhammad Ichwandar; Nugroho, Ahmad
Comunitario: Jurnal Pengabdian Masyarakat Vol. 1 No. 2 (2025): Desember
Publisher : CV. Biha Cendekia

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Abstract

In the digital era, teachers are required to produce creative and relevant teaching materials quickly. However, many teachers in rural areas, such as in Candimulyo District, still rely on manual methods that are time-consuming due to a lack of familiarity with modern technology. This community service aums to optimize the use of Artificial Intelligence (AI), specifically ChatGPT and Canva AI, as “smart assistans” to help teachers streamline their workload. The methods employed was a workshop with a hands-on training approach, guiding teachers to use AI for drafting lesson plans and designing visual media. The activity was attended by 30 teachers from LP Ma’arif. The evolution results showed a significant increase in competence. The average cognitive score rose from 83.33 (pre-test) to 100.00 (post-test) with an N-Gain of 0.48 (Medium Category). Furthermore, participants responded positively (score 4.19/5.00) regarding their ability to use these tools technically. It can be concluded that ChatGPT and Canva AI are effective in serving as smart assistants that increase teachers productivity and creativity in developing teaching materials.
Pengembangan Sistem Reservasi Lapangan Berbasis Django dengan Analisis Pola Penyewaan Menggunakan Metode K-Means Nugroho, Ahmad; Hidayat, Muhamad Maksum; Akrianto, Muhammad Ichwandar
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 12 No. 01 (2026): Maret 2026
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v12i01.5478

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem reservasi lapangan berbasis web menggunakan framework Django serta menganalisis pola penyewaan menggunakan metode K-Means. Permasalahan yang umum terjadi pada pengelolaan penyewaan lapangan meliputi konflik jadwal, pencatatan transaksi yang tidak terstruktur, serta tidak adanya analisis data historis untuk mendukung pengambilan keputusan. Sistem yang dikembangkan mengintegrasikan modul reservasi, validasi konflik jadwal otomatis, pengelolaan transaksi, serta dashboard analitik. Data historis sebanyak 150 transaksi digunakan sebagai dataset untuk proses clustering dengan variabel jam mulai, durasi, total pembayaran, hari penyewaan, dan frekuensi transaksi pelanggan. Proses clustering dilakukan melalui tahapan preprocessing, normalisasi data, penentuan jumlah cluster menggunakan Elbow Method, serta evaluasi menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa jumlah cluster optimal adalah tiga kelompok dengan nilai Silhouette Score sebesar 0,62 yang mengindikasikan kualitas pemisahan cluster yang cukup baik. Interpretasi cluster mengidentifikasi tiga pola utama penyewaan, yaitu penyewa siang singkat, penyewa malam intensif, dan penyewa sore moderat. Integrasi sistem reservasi dan analisis clustering terbukti mampu memberikan insight strategis untuk optimalisasi harga, program loyalitas, dan pengelolaan jadwal operasional.
Enhancing YOLO performance with attention module for plastic and non-plastic waste detection on water surfaces Priadana, Adri; Murdiyanto, Aris Wahyu; Akrianto, Muhammad Ichwandar; Cahyono, Heru
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.46

Abstract

The rapid accumulation of plastic waste in aquatic environments poses serious threats to ecosystems, water management systems, and human health. This growing concern creates an urgent need for efficient and accurate detection methods. To address this challenge, this work proposes an approach to enhance YOLO performance by integrating attention modules for plastic and non-plastic waste detection on water surfaces. A comprehensive evaluation is conducted on the Plastic on Water dataset, considering detection accuracy, computational complexity, and inference speed. The results identify YOLO11n as the most effective baseline, achieving a mean Average Precision (mAP) of 96.3% with 2,590,230 parameters, 6.4 GFLOPs, and an inference speed of 18.58 FPS. To further improve performance, several attention modules are integrated into the YOLO11n architecture. Among them, the Convolutional Block Attention Module (CBAM) yields the best performance, achieving an mAP of 96.7% with 2,598,520 parameters and 6.5 GFLOPs, while maintaining real-time performance at 18.26 FPS. The results demonstrate improved detection capability, particularly for small and less prominent objects, with negligible additional computational cost. These findings highlight the effectiveness of attention mechanisms, especially CBAM, in enhancing lightweight object detection models for real-time aquatic waste monitoring.
Co-Authors Adri Priadana Heru Cahyono Hidayat, Muhamad Maksum Nugroho, Ahmad Pambudi, Kukuh Trisna Prasetyaningtyas, Antika Syukron, Muhammad Zuhair, Alvin