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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JURNAL MEDIA INFORMATIKA BUDIDARMA JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Madani : Indonesian Journal of Civil Society Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Agung Toto Wibowo
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Public Health Social Sciences Other

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MOVIE RECOMMENDATION SYSTEM USING HYBRID FILTERING WITH WORD2VEC AND RESTRICTED BOLTZMANN MACHINES Pradana, Muhammad Aryuska; Wibowo, Agung Toto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 1 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i1.4306

Abstract

Recommender systems are designed to provide interesting information to users and assist them in making choices. With the help of a recommender system, users can feel more comfortable using an application. In this final project, we will implement a hybrid filtering method using two techniques: Word2Vec as the algorithm for content-based filtering and Restricted Boltzmann Machine for collaborative filtering. The Word2Vec algorithm will utilize a pre-trained model provided by Google, while the Restricted Boltzmann Machine algorithm will utilize the TensorFlow library. The dataset used for this project will be Movie Lens. The goal of this final project is to evaluate the accuracy and performance of the recommender system using various metrics such as Precision and Normalized Discounted Cumulative Gain.
Sistem Rekomendasi Produk Elektronik Berbasis Collaborative Filtering Manggunakan Matrix Factorization Rajib , Ustami; Wibowo, Agung Toto
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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Abstract

Sistem rekomendasi adalah suatu program yangmelakukan prediksi suatu item, dalam pembuatan sistemrekomendasi terdapat Beberapa metode yang dapat digunakandiantaranya Collaborative Filtering karena dianggap mampumemberikan saran item yang lebih akurat. pendekatanCollaborative Filtering karena dianggap mampu memberikansaran item yang lebih akurat. Pada penelitian ini akan dibuatsistem rekomendasi menggunakan 3 Algoritma Turunan MFyaitu Singular Value Decomposition (SVD), SVD++, NonNegative Matrix Factorization NMF terhadap dataset AmazonReview dengan Studi Kasus Elektronik, ini perlu diaplikasikandalam penelitian sistem rekomendasi, karena data Elektronikini mempunyai jumlah data yang sangat besar. Dalampenelitian ini akan dilakukan uji coba terhadap beberapaparameter yang meliputi n-epochs, n-factor dalam mekanisme5-fold cross-validation. Untuk menangani data yang terlalubesar, penulis melakukan random sampling sebesar 25% daritotal dataset untuk mengurangi beban komputasi. Dari hasil ujicoba didapatkan performansi rata-rata terbaik MAE = 1.0384dan RMSE = 1.3139 yaitu pada Algoritma SVD. Kata kunci— Produk Elektronik, Sistem Rekomendasi,Collaborative Filtering, Matrix Factorization, Cross Validation
Sistem Pemberi Rekomendasi Artis Berdasarkan Jumlah Interaksi Menggunakan Metode Collaborative Filtering Yuliarta, Chara Maria Emmanuel; Wibowo, Agung Toto
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Sistem rekomendasi adalah suatu sistem penyaringan yang bertujuan untuk memprediksi preferensi yang diberikan oleh pengguna terhadap suatu elemen tertentu, pada penelitian ini terhadap sebuah artis. Penelitian ini memiliki tujuan untuk meningkatkan hasil performansi sistem rekomendasi artis menggunakan metode collaborative filtering. Metode collaborative filtering menggunakan informasi pengguna dan artis dalam membangun rekomendasi. Dataset yang digunakan mencakup jumlah pemutaran lagu oleh pengguna. Metode collaborative filtering diimplementasikan dengan melakukan perhitungan similarity antar pengguna dan antar artis. Perhitungan similarity yang digunakan, menggunakan cosine similarity. Setelah dilakukan perhitungan kesamaan, dilakukan perhitungan weighted sum dan menghasilkan prediksi. Evaluasi performansi dihitung menggunakan MAE (Mean Absolute Error), MSE (Mean Squared Error), dan RMSE (Root Mean Squared Error). Hasil evaluasi yang didapatkan pada penelitian ini adalah MAE 9,474, MSE 52.653,40 dan RMSE 229,208 pada perbandingan 70:30. Sedangkan pada perbandingan 75:25 menghasilkan MAE 9,902, MSE 45.914,85 dan RMSE 210,017. Pada perbandingan 80:20 hasil yang didapatkan adalah MAE 10,486, MSE 48.764,51 dan RMSE 217,416. Hasil tersebut menunjukkan bahwa, semakin besar rasio data train terhadap data test, nilai MAE, MSE dan RMSE cenderung meningkat. Kata kunci: Sistem Rekomendasi, Collaborative filtering, Cosine similarity, MAE, MSE, RMSE.
Sistem Pemberi Rekomendasi Pakaian Menggunakan Metode Content-Based Filtering Ekasanjaya , Ridho Bagus; Wibowo, Agung Toto
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Pakaian merupakan suatu yang menunjukan suatu identitas seseorang. Melalui pakaian seseorang dapat menilai suatu kepribadian, iman, profesi dan status sosial. Perkembangan teknologi menyebabkan mudahnya suatu informasi tidak terkecuali informasi mengenai trend pakaian. Hal tersebut menyebabkan banyaknya desain pakaian sehingga mempersulit memilih mana pakaian yang sesuai untuk konsumen. Oleh karena itu dibangun suatu sistem yang mempermudah calon konsumen untuk memilih pakaian. Sistem rekomendasi pakaian menggunakan metode content-based filtering akan membantu calon konsumen untuk memilih pakaian yang sesuai berdasarkan yang disukai oleh pengguna. Kata Kunci - Sistem rekomendasi; Content-based filtering; Pakaian
Peningkatan Kreativitas dan Keterampilan Digital Pemuda Karang Taruna Kampung Karasak Wibowo, Agung Toto; Fahlena, Hilda; Maharani, Warih; Ramadhan, Nur Ghaniaviyanto
Madani : Indonesian Journal of Civil Society Vol. 7 No. 2 (2025): Madani : Agustus 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v7i2.2832

Abstract

Karasak Village, Ciheulang Village, Ciparay District, Bandung Regency, still faces challenges, including low digital literacy and limited use of information technology to support the development of the village's potential. This condition has implications for the limited ability of the younger generation to produce and distribute creative content that can strengthen local identity and increase village competitiveness in the digital realm. To address these challenges, the community service team implemented a training program targeting Youth Karang Taruna on December 15, 2024. The training materials were comprehensively designed, covering the use of social media, talent modules, storytelling, on-camera communication, video shooting techniques, and music and video editing. The method employed was a combination of theoretical instruction, direct practice, and interactive mentoring, enabling participants to produce digital content products independently. Evaluation of the activity was conducted through the distribution of questionnaires, with the results showing that 94% of participants agreed or strongly agreed with the usefulness of the training. These findings confirm that the activity was effective, well-received by participants, and has the potential to encourage increased digital literacy capacity and creativity of youth in creating content based on local potential, ready for publication on social media.
Tourism Recommender System using Weighted Parallel Hybrid Method with Singular Value Decomposition Akbar, Yoan Amri; baizal, zk abdurahman; Wibowo, Agung Toto
Indonesian Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.579

Abstract

Presently, we often get suggestions for recommendations for tourist attractions from various sources such as the internet, magazines, newspapers, or travel agencies. Because there is numerous information, tourists become difficult to determine the tourism destination that suits their wishes. We created a tourism recommender system that can provide information in the form of recommendations for tourist attractions by the preference of tourists. The method used is a hybrid method that combines several recommendation methods, which are Content-Based Filtering (CB) and Collaborative Filtering (CF). We use tourism data of Lombok Island, West Nusa Tenggara, which will be taken from the TripAdvisor site. We apply the Singular Value Decomposition algorithm on CF and CB. The Hybrid Weighted Parallel Technique is used for Hybrid Method. The results of the experiment show that the weighting technique hybrid method provides higher prediction accuracy than when undergoing the recommender system method separately. The average results of Mean Square Error were obtained 0.7275 (CF), 0 .4583 (CB), and 0.2548 (Hybrid Method). The result indicates that the Hybrid Method with the Weighting Technique has the highest accuracy of another method.
Evaluating Non-Negative Matrix Factorization and Singular Value Decomposition for Skincare Recommendation Systems Ahmad Indra Nurfauzi; Agung Toto Wibowo
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 3 (2024): December, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.3.983

Abstract

Facial skincare plays a crucial role in maintaining clean, healthy, and radiant skin. Recommendation systems, such as Collaborative Filtering and Content-Based Filtering, can help users discover suitable skincare products based on their preferences and reviews. This study compares two Matrix Factorization techniques Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) to enhance the accuracy and relevance of skincare product recommendations. The results reveal that the SVD model outperforms NMF, achieving a Mean Absolute Error (MAE) of 0.7190, Root Mean Squared Error (RMSE) of 1.0104, Precision of 0.8054, Recall of 0.8144, and an F-1 score of 0.8099. In contrast, the NMF model produced an MAE of 0.7074, RMSE of 1.1052, Precision of 0.7865, Recall of 0.7987, and an F-1 score of 0.7926. These findings demonstrate that both models provide accurate recommendations, with SVD offering more precise and relevant predictions for skincare product recommendations.
Movie Recommendation System Based on Synopsis Using Content-Based Filtering with TF-IDF and Cosine Similarity Juni Permana, Armadhani Hiro Juni Permana; Agung Toto Wibowo
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.747

Abstract

Recommendation systems have become an interesting topic in the field of artificial intelligence and data analysis. In the current era of technological advancement, the entertainment industry is rapidly growing, particularly the film industry, which is highly popular among the public due to their enthusiasm for watching movies. The increasing number and variety of films with various genres and titles have made it challenging for users to choose a film. To assist them in selecting movies, the presence of a recommendation system is necessary to provide information or film recommendations based on user interests and preferences. In this research, the development of the recommendation system will utilize the content-based filtering method, employing the TF-IDF algorithm and cosine similarity. The dataset used in this study is derived from publicly available data (MovieLens). The results of this research demonstrate that the TF-IDF and cosine similarity algorithms provide recommendations that align with the viewers' interests, as measured by precision, recall, and f1-score calculations.
Anime Rekomendasi Menggunakan Collaborative Filtering Jayaperwira, Iklil; Wibowo, Agung Toto; Nurjanah, Dade
eProceedings of Engineering Vol. 10 No. 3 (2023): Juni 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak-Di era digital ini orang-orang semakin mudah mendapatkan hiburan yang mereka perlukan salah satunya adalah anime[1]. Anime merupakan animasi khas dari jepang anime bisa di buat baik di gambar menggunakan tangan atau menggunakan komputer. Anime menjadi salah satu hiburan yang banyak di sukai orang-orang di dunia, hal ini bisa di lihat dari Netflix salah satu layan streaming yang besar mulai memasukkan anime ke dalam aplikasi dan situs mereka. Pada tahun 2021 sekarang terdapat kurang lebih 18350 anime baik yang sudah selesai maupun yang masih berlanjut[2]. hal ini membuat orang-orang yang sudah menyukai anime ataupun orang-orang yang baru ingin menonton anime kebingungan mencari anime yang seusai dengan selera mereka karena itulah kita memerlukan sistem rekomendasi. Sistem rekomendasi merupakan sistem yang dibuat untuk membantu pengguna mendapatkan rekomendasi sebuah barang/informasi yang pengguna sukai/butuhkah dari banyaknya barang ataupun informasi yang ada. Rekomendasi yang di berikan di harapkan bisa memberikan bantuan pada pengguna untuk dapat menentukan pilihan yang akan di ambil. Dalam sistem rekomendasi sendiri terdapat banyak metode yang bisa di gunakan salah satunya adalah metode collaborative filtering yang di gunakan untuk mencari kesamaan item/ barang yang di carik oleh user lain[3] dengan algoritma yang digunakan adalah KNNWithMeans yang berupakan salah satu basic algoritma collaborative filtering[4], [5].Pada penelitian ini dilakukan tiga skenario pengujian yang bergguna untuk mendapatkan hasil rekomendasi terbaik dengan melakukan pengukuran MAE dan NDCG.Dapat di simpulkan metode collaboratif filtering dengan menggunakan algoritma KNNWithMeans mendapatkan rekomendasi yang cukup akurat dengan hasil MAE terbaik sebesar 0.8989 dan NDCG sebesar 0.2028.Kata kunci-sistem rekomendasi, collaborative filtering
Co-Authors Ade Romadhony Ahmad Indra Nurfauzi Akbar, Yoan Amri Alfiansyah Nur Abadi Anditya Arifianto Arie Lasaprima Armadhani Hiro Juni Permana Juni Permana Asriyanti Indah Pratiwi Bambang Pudjoatmodjo Bambang Pudjotatmodjo Bedy Purnama Bobby Brillian Yerikho Dade Nurjanah Dodi Wisaksono Sudiharto Ekasanjaya , Ridho Bagus Ema Rachmawati Ema Rachmawati Ema Rachmawaty Ergandara Purba Setra Erwin Budi Setiawan Fat'hah Noor Prawira Fat’hah Noor Prawira Fat’hah Noor Prawira Fazmah Arif Yulianto Gia Septiana Gia Septiana Wulandari Hafiz Dewanto Hamad Fauzi Jessar Hani Nurrahmi Hilda Fahlena Hilmi Hidayat Arfisko Jayaperwira, Iklil Jody Jody Juni Permana, Armadhani Hiro Juni Permana Kadek Byan Prihandana Jati Kadek Byan Prihandana Jati M. Raihan Rafiiful Allaam Mahmud Dwi Sulistiyo Mohammad Ikhsan Syahputra Muhamad Elang Ramadhan Muhammad Arzaki Muhammad Bayu Samudra Siddik Muhammad Hanif Oktavianto Muhammad Khiyarus Syiam Niken Sylvia Puspitasari Nur Ghaniaviyanto Ramadhan Oddy Virgantara Putra Pradana, Muhammad Aryuska Pratama Juliantono Taufiq Prawita, Fat’hah Noor Putu Harry Gunawan Rajib , Ustami Rimba Whidiana Ciptasari Rita Rismala Rudy Prabowo Selly Meliana Trisnaningrum, Niken Untari Novia Wisesty Wahyu Rismawan Warih Maharani Yoan Amri Akbar Yuliarta, Chara Maria Emmanuel Yulisna Gita Hapsari ZK Abdurahman Baizal Zulkifli Zulkifli