{ "date": "2026-07-15", "dir": "disk0\/00\/02\/55\/84", "contact_email": "digilib@unpkdr.ac.id", "department": "KODEPRODI55201#Teknik Informatika", "subjects": [ 457, 459 ], "metadata_visibility": "show", "userid": 64886, "rev_number": 17, "lastmod": "2026-08-04 03:25:13", "uri": "http:\/\/repository.unpkediri.ac.id\/id\/eprint\/25584", "eprint_status": "archive", "ispublished": "unpub", "status_changed": "2026-08-04 03:25:13", "referencetext": "Alim Mutasodirin, M., Miftakhul Falakh, F., & Kunci, K. (2024). Efficient Weather Classification Using DenseNet and EfficientNet. Jurnal Informatika: Jurnal Pengembangan IT, 9(2), 173–179. https:\/\/doi.org\/10.30591\/JPIT.V9I2.7539\r\nAmien, F. M., Kurniawan, D., Junaidi, A., & Hermanto, B. (2025). A Comparative Study of CNN Architectures: ConvNeXt, MobileNetV3, and EfficientNet for Oral Disease Diagnosis. Jurnal Pepadun, 6(1), 81–91. https:\/\/doi.org\/10.23960\/PEPADUN.V6I1.267\r\nChalista, N., Natun, I., Santhia, M. A., & Kaesmetan, Y. R. 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SISTEM IDENTIFIKASI SAMPAH ANORGANIK BERBASIS IOT (Internet of Things) MENGGUNAKAN METODE SAW (Simple Additive Weighting). Jurnal Teknologi Informasi Dan Komunikasi, 16(2), 93–104. https:\/\/doi.org\/10.47561\/A.V16I2.249\r\nFitri, I., Informatika, T., Ilmu Komputer, F., Raya Lubuk Begalung, J., Lubuk Begalung, K., & Padang, K. (2024). Development of Apple Fruit Classification System using Convolutional Neural Network (CNN) MobileNet Architecture on Android Platform. Sistemasi: Jurnal Sistem Informasi, 13(1), 230–243. https:\/\/doi.org\/10.32520\/STMSI.V13I1.3533\r\nHussian, D. S., Raheem, H. A., Altememe, M. S., Li, Y., & Abdulla, S. (2026). Real-Time Object Detection in Complex Environments : Leveraging Deep Learning and Computer Vision Techniques. Al-Khwarizmi Engineering Journal, 22(2), 54–71. https:\/\/doi.org\/10.22153\/kej.2026.03.001\r\nLu’ Anjeli, L. ’, & Rizki, F. (2025). Analisis Prediksi Penyakit Jantung Menggunakan Perbandingan Algoritma Machine Learning. JTINFO : Jurnal Teknik Informatika , 4(2), 262–269. https:\/\/doi.org\/10.02220\/JTINFO.V4I2.1424\r\nLuthfi Bangun Permadi, M., & Gumilang Sekolah Tinggi Teknologi Bandung, R. (2024). Penerapan Algoritma CNN (Convolutional Neural Network) Untuk Deteksi Dan Klasifikasi Target Militer Berdasarkan Citra Satelit. Jurnal Sosial Teknologi, 4(2), 134–143. https:\/\/doi.org\/10.59188\/JURNALSOSTECH.V4I2.1138\r\nMappasere, F. A., Sulfasyah, S., Rahayu, S., & Khumaera, I. (2025). Inovasi Pengelolaan Sampah Plastik: Transformasi Menjadi Produk Ekonomi di Pesantren Ummul Mukminin Makassar. Abdimas Galuh, 7(2), 1108–1120. https:\/\/doi.org\/10.25157\/AG.V7I2.18499\r\nMuchlis, M., & Sunupurwa, J. (2025). Integrasi Data “Real-Time” Untuk Asuransi Menggunakan Kafka dan “PLATFORM STREAMING.” IKRA-ITH Informatika : Jurnal Komputer Dan Informatika, 9(1), 123–133. https:\/\/doi.org\/10.37817\/ikraith-informatika.v9i1\r\nPinem, T. H., Putra, Z. P., & Korespondensi, P. (2025). Evaluasi Kinerja Algoritma Klasifikasi Deep Learning dalam Prediksi Diabetes. Jurnal Ilmiah Fifo, 17(1), 17–28. https:\/\/doi.org\/10.22441\/FIFO.2025.V17I1.003\r\nRahendaputri, C. S., Febrianti, N., Pratama, R. H., Fadliilah Noor, M., Lingkungan, T., Kebumian, J. I., Lingkungan, D., Kalimantan, I. T., Elektro, T., Industri, J. T., Proses, D., Kalimantan, T., Perkapalan, T., Sains, J., & Kemaritiman, D. (2022). PEMANFAATAN LIMBAH SISA MAKANAN SEBAGAI KOMPOS DI KELURAHAN KARANG JATI BALIKPAPAN. JABN, 3(2), 45–54. https:\/\/jabn.upnjatim.ac.id\/index.php\/jabn\/article\/view\/196\r\nReynaldi Valerian, F., Syarief, M., Abdul Fatah, D., Raya Telang, J., Kamal, K., & Timur, J. (2025). KLASIFIKASI TINGKAT OBESITAS MENGGUNAKAN METODE GBM DAN CONFUSION MATRIX. JATI (Jurnal Mahasiswa Teknik Informatika), 9(2), 2242–2249. https:\/\/doi.org\/10.36040\/JATI.V9I2.13062\r\nRianti, R., & Andarsyah, R. (2024). Memprediksi Tingkat Atrisi Karyawan Menggunakan Machine Learning. Jurnal Tekno Insentif, 18(1), 39–52. https:\/\/doi.org\/10.36787\/JTI.V18I1.1263\r\nSahamony, N. F., Terttiaavini, T., & Rianto, H. (2024). Analisis Perbandingan Kinerja Model Machine Learning untuk Memprediksi Risiko Stunting pada Pertumbuhan Anak. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(2), 413–422. https:\/\/doi.org\/10.57152\/MALCOM.V4I2.1210\r\nSari, P. D. K., Mahdiyah, U., & Wulanningrum, R. (2025). Klasifikasi Sampah Menggunakan Algoritma EfficientNet. Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 9(3), 2074–2081. https:\/\/doi.org\/10.29407\/A1ZQ6810\r\nSuherman, M. I., Febrianti, R., Fauziah, Nurhidayah, N., Khatimah, F. H., & Edy, M. R. (2024). Analisis Sentimen Ekspresi Wajah : Pendekatan Deep Learning dan OpenCV dengan Python. Information Technology Education Journal, 3(3), 162–169. https:\/\/doi.org\/10.59562\/INTEC.V3I3.5755\r\nTanmenu, C. F., Sawitri, B., & Wahyudie, T. (2024). Kajian Pembuatan Pupuk Kompos dari Kotoran Sapi dengan Bioaktivator Mikroorganisme Lokal (MOL) Bonggol Pisang di Desa Lemahbang Kecamatan Sukorejo. Biofarm : Jurnal Ilmiah Pertanian, 20(1), 104–116. https:\/\/doi.org\/10.31941\/BIOFARM.V20I1.4288\r\nZahara, N., Putri, P. B., & Fitriani, Y. (2025). PENDAMPINGAN PEMBUATAN ECOBRICK UPAYA MENUMBUHKAN SEKOLAH RAMAH LINGKUNGAN DI SMPN 1 RANTAU PANDAN. Jurnal Pengabdian Pendidikan Masyarakat (JPPM), 6(1), 190–196. https:\/\/doi.org\/10.52060\/JPPM.V6I1.2762\r\nZhao, Y., Huang, H., Li, Z., Yiwang, H., & Lu, M. (2022). Intelligent garbage classification system based on improve MobileNetV3-Large. Connection Science, 34(1), 1299–1321. https:\/\/doi.org\/10.1080\/09540091.2022.2067127\r\nZhu, F., Sun, Y., Zhang, Y., Zhang, W., & Qi, J. (2023). An Improved MobileNetV3 Mushroom Quality Classification Model Using Images with Complex Backgrounds. 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"RAMA_55201_2213020077_0729098903_0713059502_06_ref.pdf" }, { "content": "accepted", "language": "id", "license": "cc_public_domain", "formatdesc": "Lampiran", "pos": 9, "security": "validuser", "docid": 211058, "mime_type": "application\/pdf", "eprintid": 25584, "format": "text", "rev_number": 2, "placement": 9, "uri": "http:\/\/repository.unpkediri.ac.id\/id\/document\/211058", "files": [ { "hash_type": "MD5", "datasetid": "document", "filesize": 2403821, "hash": "c1bafad9d4e2f4cdc4731f2db070fe0e", "filename": "RAMA_55201_2213020077_0729098903_0713059502_07_lamp.pdf", "mime_type": "application\/pdf", "mtime": "2026-08-04 02:33:06", "fileid": 481615, "uri": "http:\/\/repository.unpkediri.ac.id\/id\/file\/481615", "objectid": 211058 } ], "main": "RAMA_55201_2213020077_0729098903_0713059502_07_lamp.pdf" } ], "keywords": "Deep Learning, Klasifikasi Sampah, MobileNetV3, Real-Time, Sampah Anorganik, Sampah Organik.", "pages": 104, "thesis_name": "dphil", "divisions": [ "sch_psy" ], "type": "thesis", "date_type": "completed", "title": "KLASIFIKASI CITRA SAMPAH ORGANIK DAN ANORGANIK MENGGUNAKAN ALGORITMA CNN SEBAGAI MEDIA BELAJAR SEKOLAH DASAR", "contributors": [ { "id": "NIDN0729098903", "name": { "lineage": null, "given": "Umi", "family": "Mahdiyah", "honourific": null }, "type": "http:\/\/www.loc.gov\/loc.terms\/relators\/THS" }, { "id": "NIDN0713059502", "name": { "lineage": null, "given": "Wahyu Cahyo", "family": "Utomo", "honourific": null }, "type": "http:\/\/www.loc.gov\/loc.terms\/relators\/THS" } ], "eprintid": 25584, "thesis_type": "other", "abstract": "Tercampurnya limbah organik dan anorganik akibat ketiadaan pemisahan wadah di ruang kelas menjadi kendala utama dalam pengelolaan sampah di SDN Payaman, sehingga proses penyortiran harus dilakukan secara manual di tempat pembuangan akhir yang memakan waktu dan rawan kekeliruan. Penelitian ini bertujuan untuk merancang sistem klasifikasi sampah berbasis web yang beroperasi secara real-time dengan memanfaatkan algoritma Convolutional Neural Network (CNN) berarsitektur MobileNetV3. Sistem ini dirancang untuk mengatasi keterbatasan aplikasi statis yang mengharuskan pengguna mengunggah citra satu per satu. Pengujian kinerja model dilakukan melalui 17 skenario eksperimen menggunakan 2.820 data citra gabungan dari akuisisi primer di sekolah dan dataset publik Kaggle. Hasil evaluasi menunjukkan kinerja model yang sangat unggul dengan tingkat akurasi rata-rata mencapai 99,08%, presisi 99,15%, recall 99,22%, serta F1-score 99,18%. Sistem juga terbukti sangat responsif untuk pemrosesan real-time dengan waktu inferensi 11,05 milidetik per frame (setara dengan 91 Frames Per Second) tanpa adanya ketimpangan prediksi antar kelas. Pengujian fungsional di lapangan mengonfirmasi bahwa aplikasi ini layak dan efektif digunakan sebagai media interaktif pendukung pembelajaran lingkungan hidup bagi siswa sekolah dasar.", "creators": [ { "id": "NPM2213020077", "name": { "lineage": null, "given": "Dwika Putra", "family": "Adinata", "honourific": null } } ], "full_text_status": "restricted", "institution": "Universitas Nusantara PGRI Kediri" }