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All Journal International Journal of Advances in Intelligent Informatics Dedikasi: Jurnal Pengabdian Masyarakat SWAGATI: Journal of Community Service
SUPRIADI RUSTAD
UNIVERSITAS DIAN NUSWANTORO
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Library & Information Science Social Sciences Other

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Utilization of Alkane Hydrocarbons in Waste Management to Support a Green Economy Movement for Students GUSTINA ALFA TRISNAPRADIKA; NOOR AGENG SETIYANTO; SUPRIADI RUSTAD; MULJONO; MUHAMMAD NAUFAL
SWAGATI : Journal of Community Service Vol. 4 No. 1 (2026): March
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2026v4i1.2645

Abstract

Household waste management, particularly used cooking oil (waste cooking oil), remains an environmental issue due to low public awareness and limited understanding of sustainable waste management practices. Improper disposal of used cooking oil can lead to environmental pollution and pose health risks. This community service activity aims to improve students’ knowledge and skills in processing used cooking oil into value-added aromatherapy candles, while fostering environmental awareness and an entrepreneurial spirit based on the green economy. The method applied is Community Based Participatory Research (CBPR), conducted in two stages: delivery of theoretical material on the 3R concept (Reduce, Reuse, Recycle) and hands-on practice in processing used cooking oil. The activity was carried out at SMK Negeri 9 Semarang involving 23 students. The results indicate an increase in participants’ understanding, as shown by improvements in scores on 3R education (from 59.5 to 83.3) and used cooking oil processing (from 50 to 85). In addition, students were able to produce aesthetically pleasing aromatherapy candles with economic value potential.
Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability Anton Anton; Supriadi Rustad; Guruh Fajar Shidik; Abdul Syukur
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2377

Abstract

Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refinement, particularly in multi-crop classification settings. This study proposes a redundancy-aware dual-attention architecture, termed ATSA-DenseNet, which integrates the spatial branch of the Convolutional Block Attention Module (CBAM-Spatial) with Efficient Channel Attention (ECA) within a DenseNet121 backbone. Unlike prior dual-attention frameworks that retain full CBAM and introduce channel-level redundancy, the proposed design isolates complementary spatial and channel mechanisms to improve representational efficiency without increasing computational complexity. The framework is evaluated on controlled multi-crop PlantVillage-derived datasets comprising tomato, potato, pepper, and maize. Across both 3-crop and 4-crop configuration, ATSA-DenseNet consistently outperforms baseline DenseNet121 and single-attention variants, achieving 99.94% accuracy and 0.9994 macro-F1 on the 4-crop setting while maintaining a lightweight footprint (6.96M parameters, 2.87G FLOPs). Grad-CAM visualizations indicate improved localization of disease-relevant regions compared to the baseline. While results are obtained under controlled imaging conditions, the findings demonstrate that redundancy-aware dual-attention enhances feature discrimination efficiency in multi-class agricultural classification tasks. Future work will extend validation to real-field datasets with natural variability.
Strategi Meaningful Learning Berbasis Google Teachable Machine untuk Penguatan Kompetensi Guru di SMK Negeri 9 Semarang Wahyu Aji Eko Prabowo; Achmad Wahid Kurniawan; Setyo Budi; Usman Sudibyo; Supriadi Rustad; Muhammad Reesa Rosyid; Lubna Mawaddah; Izulsyauki Imani
DEDIKASI: Jurnal Pengabdian Masyarakat Vol 8 No 2 (2026): Dedikasi: Jurnal Pengabdian Masyarakat, Juli - Desember 2026
Publisher : Pusat Pengabdian Masyarakat LPPM IAIN Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32332/dedikasi.v8i2.13213

Abstract

Perkembangan kecerdasan artifisial (AI) dalam pendidikan menuntut guru memiliki kesiapan pedagogis dalam memanfaatkan teknologi secara bermakna. Program kemitraan masyarakat ini bertujuan memperkuat literasi AI guru melalui pelatihan Google Teachable Machine (GTM) berbasis meaningful learning di SMKN 9 Semarang. Kegiatan dilaksanakan melalui enam tahap, meliputi sosialisasi dan identifikasi kebutuhan, pelatihan konsep, praktik GTM, penyusunan lesson plan, pendampingan dan evaluasi, serta keberlanjutan program. Peserta kegiatan berjumlah 40 guru. Data dikumpulkan melalui pre-test, post-test, observasi, refleksi peserta, dan dokumentasi, kemudian dianalisis secara deskriptif kuantitatif, inferensial menggunakan uji Wilcoxon signed-rank, dan deskriptif kualitatif. Analisis komparatif pengetahuan dibatasi pada butir yang memiliki substansi sebanding antara pre-test dan post-test (Q1, Q6-Q9). Hasil menunjukkan bahwa program terlaksana sesuai rancangan serta menghasilkan pengalaman praktik GTM, pengembangan lesson plan berbasis meaningful learning. Rata-rata skor pengetahuan meningkat dari 4,30 menjadi 4,73 dan menunjukkan perbedaan yang signifikan secara statistik (p=0,009). Sementara itu, skor persepsi/kesiapan meningkat dari 3,85 menjadi 4,25, tetapi belum menunjukkan perbedaan yang signifikan (p=0,091). Temuan ini menunjukkan bahwa dampak awal pelatihan terutama terlihat pada penguatan pengetahuan AI guru, sedangkan kesiapan implementatif masih memerlukan pendampingan lebih lanjut.
Co-Authors Abdul Syukur Achmad Wahid Kurniawan Anton Anton Guruh Fajar Shidik Gustina Alfa Trisnapradika Izulsyauki Imani Lubna Mawaddah Muhammad Naufal Muhammad Reesa Rosyid Muljono, - Noor Ageng Setiyanto, Noor Ageng Setyo Budi Usman Sudibyo Wahyu Aji Eko Prabowo