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All Journal Jurnal Sains dan Informatika Building of Informatics, Technology and Science Abdimas Terapan: Jurnal Pengabdian Kepada Masyarakat Terapan
Julianto, Afis
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Public Health Social Sciences

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Sistem Rekomendasi Buku di Perpustakaan Menggunakan Machine Learning dan Algoritma Apriori Jannah, Miftahul; Yumami, Eva; Julianto, Afis; Rahmi, Elvi
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.1868

Abstract

Perpustakaan Politeknik Negeri Bengkalis memiliki peran penting dalam mendukung kegiatan akademik mahasiswa dan dosen. Namun, pertambahan jumlah koleksi buku sering kali menyulitkan pengguna dalam menemukan buku yang relevan secara cepat dan tepat. Permasalahan ini disebabkan oleh keterbatasan sistem pencarian konvensional yang hanya mengandalkan judul atau pengarang. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi buku berbasis algoritma Apriori guna menganalisis pola peminjaman dan preferensi pengguna. Data yang digunakan berupa riwayat transaksi peminjaman buku di perpustakaan, yang dianalisis untuk menemukan asosiasi antar buku. Hasil analisis menunjukkan adanya aturan asosiasi yang signifikan, seperti buku "Akuntansi BUMDes" yang sering dipinjam bersamaan dengan "Akuntansi Keuangan Menengah: Berbasis PSAK" dan "Analisis Laporan Keuangan". Selain itu, buku "Algoritma machine learning" kerap dipinjam bersamaan dengan "Pemrograman Python Untuk Penanganan Big Data" (confidence = 1.0, lift = 70.50) dan "Pemrograman CNC & Aplikasi Di Dunia Industri" (confidence = 1.0, lift = 47.00), menunjukkan hubungan erat antara bidang pemrograman, data, dan teknik. Nilai confidence sebesar 1.0 dan lift yang tinggi menunjukkan hubungan kuat antar buku. Temuan ini bermanfaat bagi pengelola perpustakaan dalam menyusun rekomendasi buku, strategi pengelolaan persediaan, serta pengaturan tata letak koleksi. Dengan demikian, penerapan algoritma Apriori terbukti efektif dalam meningkatkan layanan informasi dan pengalaman pengguna di perpustakaan.
Sistem Informasi Manajemen Dokumen Akreditasi Program Studi di Jurusan Teknik Informatika Julianto, Afis; Supria; Enda, Depandi; Rahmadhani, Ayu; Rimanda, Suhardianto; Juelon Sinaga, Ali; Rizki Ramadhan, Ikhsan; Abidin, Zainal
ABDIMAS TERAPAN : Jurnal Pengabdian Kepada Masyarakat Terapan Vol. 3 No. 2 (2025): Desember: ABDIMAS TERAPAN: Jurnal Pengabdian Kepada Masyarakat Terapan
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/abdimasterapan.v3i2.1266

Abstract

The accreditation process for study programs is an important step in ensuring the quality of higher education, which requires systematic and efficient management of accreditation documents. In the Department of Informatics Engineering, accreditation document management is still carried out manually, often causing difficulties in searching, storing, and coordinating between teams compiling forms. This study aims to design and develop a web-based accreditation document management information system that can facilitate the management, search, and monitoring of accreditation documents in accordance with BAN-PT or LAM INFOKOM standards. This system is designed to support multiple study programs to improve the effectiveness and efficiency of the accreditation process. With the implementation of this system, it is hoped that the accreditation process for study programs in the Department of Informatics Engineering can run faster, more structured, and more integrated.
Evaluasi Komparatif Lightweight Convolutional Neural Network Untuk Klasifikasi Penyakit Daun dan Hama Tanaman Padi Julianto, Afis; Jannah, Miftahul
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8698

Abstract

Rice is a critical commodity for national food security; however, its productivity is frequently reduced due to leaf diseases and pests. Conventional identification methods that rely on visual observation are often inefficient and prone to subjectivity, particularly given the complex and variable nature of symptoms. This study to evaluate and compare the performance of several lightweight CNN architectures in accurately and efficiently detecting rice leaf diseases and pests on resource constrained devices. This study compares four CNN lightweight architectures: MobileNetV2, EfficientNetV2-B3, NasNetMobile, and a custom CNN Lightweight Architecture, all using a 13-class dataset that underwent preprocessing, augmentation, and data balancing. The models were trained for 100 epochs using the Adam optimizer. Experimental results show that EfficientNetV2B3 achieved the best performance, with 97% accuracy, precision, recall, and F1-score, followed by MobileNetV2 and NasNetMobile, which achieved 94% accuracy. The Custom CNN lightweight model produced 91% accuracy with a model size of only 0.53 MB. Overall, this study provides recommendations for developing accurate and efficient lightweight CNN models to support rice disease and pest detection on mobile devices, IoT systems, and edge computing platforms.
Co-Authors Depandi Enda Elvi Rahmi Juelon Sinaga, Ali Miftahul Jannah Rahmadhani, Ayu Rimanda, Suhardianto Rizki Ramadhan, Ikhsan Supria Yumami, Eva Zainal Abidin