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All Journal E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Bagimu Negeri : Jurnal Pengabdian Kepada Masyarakat
Rizki Aulianita
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Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Medicine & Pharmacology Nursing Public Health Social Sciences Other

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ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Maulidah, Mawadatul; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

Abstract

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.
ALGORITMA KLASIFIKASI DECISION TREE UNTUK REKOMENDASI BUKU BERDASARKAN KATEGORI BUKU Maulidah, Mawadatul; Windu Gata; Rizki Aulianita; Cucu Ika Agustyaningrum
E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Vol 13 No 2 (2020): Jurnal Ilmiah Ekonomi dan Bisnis
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/e-bisnis.v13i2.251

Abstract

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.
PENERAPAN APLIKASI PANDUAN BUDIDAYA IKAN LELE DENGAN SISTEM BIOFLOK PADA KELOMPOK TANI LELE BP. SUMBERJAYA TAMBUN Anna Mukhayaroh; Rizki Aulianita; Khoirun Nisa
Bagimu Negeri Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengaktifkan kembali usaha budidaya ikan lele pada Kelompok Tani Lele Bp. Sumberjaya Tambun melalui penerapan aplikasi panduan budidaya ikan lele berbasis sistem bioflok. Permasalahan utama mitra meliputi terhentinya kegiatan budidaya, rendahnya pengetahuan kader remaja, serta belum adanya panduan budidaya yang terstandar dan mudah diakses. Metode pelaksanaan pengabdian dilakukan secara luring melalui tahapan observasi, wawancara, studi pustaka, serta penyuluhan dan diskusi menggunakan aplikasi panduan budidaya lele sistem bioflok yang dapat diunduh melalui Play Store. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep dan praktik budidaya lele bioflok serta kemudahan dalam mengikuti langkah-langkah operasional budidaya melalui aplikasi. Penerapan teknologi ini diharapkan mampu meningkatkan produktivitas budidaya ikan lele, mendukung ketahanan pangan masyarakat, serta memberikan dampak ekonomi bagi warga sekitar secara berkelanjutan.
Co-Authors Anna Mukhayaroh Cucu Ika Agustyaningrum Gata, Windu Khoirun Nisa Mawadatul Maulidah