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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 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 Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Almufi Jurnal Pengabdian kepada Masyarakat Indonesian Community Journal eProceedings of Engineering
Agung Toto Wibowo
Universitas Telkom
Religion 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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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

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

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
Pengenalan Dasar Kecerdasan Buatan dan Prinsip Etika Penggunaannya bagi Siswa SMA Telkom Bandung Jondri; Indwiarti; Agung Toto Wibowo
Almufi Jurnal Pengabdian Kepada Masyarakat Vol 6 No 1: Juni (2026)
Publisher : Yayasan Almubarak Fil Ilmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63821/ajpkm.v6i1.600

Abstract

Perkembangan teknologi pada era Revolusi Industri 4.0 menjadikan kecerdasan buatan (Artificial Intelligence/AI) sebagai salah satu kompetensi esensial yang perlu dimiliki peserta didik. Naskah Akademik Pembelajaran Koding dan Kecerdasan Artifisial pada Pendidikan Dasar dan Menengah yang dikeluarkan oleh Badan Standar, Kurikulum, dan Asesmen Pendidikan Kementerian Pendidikan Dasar dan Menengah Republik Indonesia menegaskan pentingnya integrasi literasi komputasi, pemrograman, dan AI secara sistematis dalam kurikulum, sekaligus menekankan dimensi etika, tanggung jawab, dan keamanan digital. Siswa Sekolah Menengah Atas berada pada posisi strategis sebagai calon talenta digital masa depan. Namun, pemahaman mereka terhadap konsep dasar AI dan implikasi etis penggunaannya masih terbatas. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan literasi digital siswa melalui pengenalan dasar-dasar kecerdasan buatan serta penanaman prinsip etika dalam pemanfaatan AI di konteks akademik maupun kehidupan sehari-hari. Metode pelaksanaan berupa pemaparan materi secara interaktif berbasis studi kasus, dan diskusi kelompok terarah mengenai konsep dasar AI dan masalah etika dalam penggunaan AI seperti privasi data, plagiarisme berbantuan AI,dan tanggung jawab pengguna teknologi. Kegiatan juga dilengkapi dengan demonstrasi sederhana tentang cara kerja AI untuk memperkuat pemahaman konseptual siswa. Hasil pengabdian masyarakat dapat dirasakan oleh peserta. Hal ini dapat dilihat dari 90% peserta yang menjawab setuju dan sangat setuju untuk pertanyaan kuesioner “materi kegiatan sesuai dengan kebutuhan mitra/peserta.
An Evaluation of Self-Attentive Sequential Recommendation (SASRec) Algorithm Using Hyperparameter Tuning Wibowo, Agung Toto; Hasmawati, Hasmawati; Nurrahmi, Hani; Salsabila, Imtitsal Ulya
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5158

Abstract

Sequential recommendation is a branch of Recommender Systems that aims to predict the next item a user will interact with based on their historical sequence of interactions. The main challenge in SR is to capture both short-term and long-term dependencies among items within a sequence. Self-Attentive Sequential Recommendation (SASRec) is a self-attention-based deep learning model designed to recognize sequential interaction patterns. Despite its effectiveness, the performance of SASRec is highly dependent on hyperparameter configurations, yet comprehensive evaluations remain limited. This research aims to evaluate the influence of SASRec's configuration through hyperparameter tuning on sequential recommendation performance. The hyperparameters used are hidden_size, inner_size, number of attention heads (num_heads), and number of layers (num_layers). The evaluation was conducted on two public datasets with different sparsity characteristics: MovieLens-1M (Sparsity ≈ 95.80%) and Amazon Musical Instruments (Sparsity ≈ 99.99%). In this study, Recall@k and MRR@k were used as performance metrics. The test results showed that hidden_size and inner_size had a significant positive impact on performance, especially on the dense dataset. The optimal hidden_size was obtained at hidden_size = 64 on the Amazon Musical Instrument dataset, and at hidden_size = 256 on the Movielens 1M dataset. The optimal inner_size was obtained at inner_size = 256 on both datasets. Meanwhile, the num_heads and num_layers hyperparameters did not provide a significant performance improvement. Furthermore, in the comparison between SASRec, GRU4Rec, and BERT4Rec, SASRec outperforms GRU4Rec and BERT4Rec in handling highly sparse datasets such as Amazon Musical Instruments obtained average recall@20 = 0.0678, and average MRR@20 = 0.0223.
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 H Hasmawati Hafiz Dewanto Hamad Fauzi Jessar Hani Nurrahmi Hilda Fahlena Hilmi Hidayat Arfisko Indwiarti Jayaperwira, Iklil Jody Jody Jondri 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 Salsabila, Imtitsal Ulya 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