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All Journal Jurnal Pengabdian UntukMu NegeRI bit-Tech Journal of Applied Engineering and Technological Science (JAETS) Jurnal Menara Medika Jurnal Media Informatika Jurnal Minfo Polgan (JMP) Jurnal Pustaka Mitra : Pusat Akses Kajian Mengabdi Terhadap Masyarakat Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Ilmu Komputer dan Teknologi (IKOMTI) J-COSCIS : Journal of Computer Science Community Service NEAR: Jurnal Pengabdian kepada Masyarakat Journal of Hospital Administration and Management (JHAM) Awal Bros Journal of Community Development
Asep Marzuki
Universitas Awal Bros
Religion Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : bit-Tech

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Model Penerapan Sistem Rekomendasi Kuliner Pada Objek Wisata Berbasis User-Based Collaborative Filtering Asep Marzuki Marzuki; Abdul Zaky; Marido Bisra
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1926

Abstract

Industri kuliner memiliki peran strategis dalam mendukung perkembangan sektor pariwisata. Namun, meskipun potensi sektor ini sangat besar, pengunjung seringkali menghadapi tantangan dalam memilih pengalaman kuliner yang sesuai dengan preferensi mereka. Hal ini disebabkan oleh kurangnya personalisasi dalam sistem rekomendasi yang ditawarkan. Akibatnya, pengunjung kerap mengalami ketidakpuasan, dan pengelola sektor kuliner kehilangan peluang untuk meningkatkan kepuasan dan loyalitas pelanggan. Penelitian ini bertujuan untuk mengembangkan model rekomendasi kuliner berbasis user-based filtering dan RecommenderNet guna memberikan rekomendasi yang lebih sesuai dengan preferensi individual pengunjung. Penelitian ini menggunakan dataset yang terdiri dari 3.200 data interaksi antara pengunjung dan delapan jenis kuliner di objek wisata Asia Heritage. Data tersebut dianalisis untuk membangun model rekomendasi yang mampu mempelajari pola preferensi pengguna. Model user-based filtering digunakan untuk mengidentifikasi kesamaan antara pengguna berdasarkan riwayat interaksi mereka, sementara RecommenderNet, yang berbasis jaringan saraf tiruan, digunakan untuk meningkatkan akurasi rekomendasi. Hasil penelitian menunjukkan bahwa model yang dikembangkan mampu memberikan rekomendasi dengan tingkat akurasi sebesar 93,63% dan rata-rata tingkat kesalahan (error) sebesar 6,36%. Hasil ini menunjukkan bahwa pendekatan berbasis machine learning dapat secara efektif meningkatkan kualitas rekomendasi kuliner. Dengan implementasi model ini, pengunjung dapat menikmati pengalaman kuliner yang lebih personal dan memuaskan, sementara pengelola objek wisata dapat meningkatkan daya tarik dan kepuasan pelanggan.
IoT-Based Reverse Vending Machine with Deep Learning for Bottle Waste Management Asep Marzuki; Abdul Zaky; Bobi Handoko; Dela Qurota Mustieni; Novita Risyadi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3183

Abstract

The escalating volume of waste, driven by insufficient public awareness and the lack of effective waste management systems, has become a significant environmental challenge. Improper waste disposal, particularly of non-biodegradable materials like plastics and metals, contributes notably to environmental pollution. In Riau Province, Indonesia, where the annual waste generation reaches 862,013 tons, only 60.71% is effectively managed. Despite the presence of 267 waste bank units, much of the plastic bottle and aluminum can waste remains improperly discarded. The high cost of commercially available Reverse Vending Machines (RVMs) further limits their widespread adoption, especially in regions like Riau. This study addresses these issues by proposing a cost-effective RVM that integrates Internet of Things (IoT) technology and a deep learning-based image classification model. The system enables users to exchange waste bottles for rewards through the Ecocycle mobile application, thus promoting waste sorting and recycling. The proposed model, tested on plastic and aluminum bottles, achieved 100% classification accuracy. Notably, this research bridges a critical gap by combining automated classification with IoT communication and incentive distribution in a low-cost, scalable system. The potential for this system to be expanded globally is evident, as it provides a feasible solution for large-scale waste management, particularly in regions lacking advanced waste infrastructure. Through both technological and behavioral approaches, this study contributes uniquely to the field of waste management by advancing accessible, effective solutions to foster environmental sustainability.
Co-Authors Abdul Zaky Adha, Afifah Cahayani Afifah Cahayani Adha Afifah Cahayani Adha Alfitri Yana Yunita Amir, Eka Fitri Azim, Ahmad Fauzan Bobi Handoko Dedy Yasriady Dedy Yasriady Dela Qurota Mustieni Desti Monika Uli Hendriani, Sisi Hendriani Ikhtiyaruddin Iswadi Iswadi Joeharsyah Reza Marido Bisra Muhammad Firdaus Muhammad Firdaus Mustieni, Dela Qurota Novita Risyadi Purnomo, Nopi Rezki Amalia Nurshal Rifaldo Pratama Septi Maisyaroh Ulina Panggabean Sherly Mutiara Sherly Mutiara Sianipar, Yola Abdila Sisi Hendriani Syabdan Dalimunthe Syahada, Jihan Syamsiar, Syamsiar Yolanda Safitri Nelaz Yoshandi, Tengku Mohammad Yunita, Jasrida