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Ariyana, Renna Yanwastika
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PEMBERDAYAAN SANTRI MELALUI KAMPANYE EDUKASI PENGELOLAAN SAMPAH BERBASIS VIDEO DI PONDOK PESANTREN KUN SHOLIHAN Nurnawati, Erna Kumalasari; Ariyana, Renna Yanwastika; Arbintarso, Ellyawan Setyo; Susanti, Erma; Almuntaha, Eska
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 3 No. 6 (2025): Desember
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v3i6.3362

Abstract

Permasalahan pengelolaan sampah masih menjadi tantangan di berbagai lembaga pendidikan, termasuk di lingkungan pondok pesantren. Di Pondok Pesantren Kun Sholihan, pengelolaan sampah belum menerapkan prinsip Reduce, Reuse, Recycle (3R) secara optimal, ditandai dengan kebiasaan santri membuang sampah tanpa pemilahan serta rendahnya kesadaran terhadap pengelolaan sampah berkelanjutan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberdayakan santri melalui kampanye edukasi pengelolaan sampah berbasis video sebagai upaya meningkatkan pemahaman dan perubahan perilaku peduli lingkungan. Metode yang digunakan adalah pemberdayaan partisipatif (Participatory Action / Community Empowerment), yang dilaksanakan melalui tahapan penyuluhan pengenalan dan pemilahan sampah berbasis 3R, pelatihan pembuatan dan pengeditan video kampanye, serta perlombaan video edukatif karya santri. Hasil kegiatan menunjukkan adanya peningkatan pemahaman santri terhadap konsep 3R, perubahan perilaku dalam praktik pemilahan sampah, serta meningkatnya partisipasi dan kreativitas santri dalam menyampaikan pesan edukasi lingkungan melalui media video. Pemanfaatan video sebagai media kampanye terbukti efektif dalam meningkatkan keterlibatan santri dan memperkuat kesadaran kolektif di lingkungan pesantren. Kegiatan ini dapat disimpulkan bahwa pendekatan pemberdayaan partisipatif berbasis kampanye video berpotensi menjadi strategi edukasi lingkungan yang efektif dan berkelanjutan dalam mendukung pengelolaan sampah di lingkungan pondok pesantren.
KLASIFIKASI TINGKAT KEMATANGAN BUAH PISANG DENGAN ALGORITMA CNN  Hamzah, Amir; Renna Yanwastika Ariyana; Untung Joko Basuki; Muhammad Sholeh; Bagas Tri Basgoro
Jurnal DutaCom Vol 19 No 1
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/xmqa2d79

Abstract

Bananas are the most widely produced fruit in Indonesia, which is around 9.69 million tons in 2024. With this amount of data, bananas have quite promising economic value. The production of large quantities of bananas according to the previous data needs to be carried out a rapid distribution process, because the time the bananas after harvesting may only last about 5 to 7 days in normal temperatures before the fruit rotting process will finally occur. For this reason, it is necessary to carry out a quick banana classification process, the process is carried out automatically using a machine. This study elaborates on the capabilities of the CNN algorithm  in the classification of bananas. Dataset was taken from Keagle's open source as many as 3000 image data. In their classification, researchers divided bananas into three levels of fruit ripeness, namely raw, ripe, and overripe. The study used a CNN model  consisting of several layers consisting of a 2D convolutional layer (Conv2D), a 2D pooling layer (MaxPooling2D), a flatten layer, a fully connected layer (Dense), and a Dropout layer. After the model creation process is complete, the model will be tested for accuracy with  the Confusion matrix method. In the 3rd experiment the model produced the highest level of accuracy in its trials, with 300 test images resulting in 290 correctly predicted images so that the accuracy reached 97%. From the results  of deploying using the website interface using flask, it was found that the classification had an accuracy of above 95% so it was good enough to be used as a prototype for classification engine applications.
Co-Authors Afifah Dzuriatun Khasanah Aji Pranoto Almuntaha, Eska Amir Hamzah Amir Hamzah Arbintarso, Ellyawan Setyo Ath-Thaariq, Muhammad Rizqy Bagas Tri Basgoro Catur Iswahyudi Dino Rahman Sya'bani Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Eko Nur Cahyo Emy Setyaningsih Erfanti Fatkhiyah Erma Susanti Erna Kumalasari Erna Kumalasari Nurnawati Erna Kumalasari Nurnawati Febrianto Ramadhan Galang Pratama Sukma Putra Hanifah Febria Mahatrisna I Nyoman Wiranata Indriani, Putri Irmah Gisfas Khasanah, Rahayu Kusumaningsih, Rr. Yuliana Rachmawati Luay Nabila El Suffa Majid, Iqbal Abdul Mansyur, Muhamad Muhammad Nurcahyo Sasongko Muhammad Rizqy Ath-Thaariq Muhammad Rizqy Ath-Thaariq MUHAMMAD SHOLEH Muhammad Sholeh Muhammad Sholihul Masnuh Nurmansyah Nurmansyah Oktavia, Yunika Putri Pradnyana, I Wayan Julianta Prita Haryani R. Ismail Nugroho Akbar Ramadhan, Muhammad Zidan Rifqi Reza Pakiding Rian Fatoni Tri Wicaksono Riki Apriadi Riki Apriadi Rochmad Suseno Rosalia Arum Kumalasanti Rosalina Elvideswita S. Sutysna Saputra, Tegar Satria Iman Satrio Muslim Wibowo Siti Saudah Suparyanto Susanto, Fx. Guntur Putra Theresia Solot Diri Uning Lestari Uning Lestari Untung Joko Basuki Wahyudi, Putu Widi