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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JINAV: Journal of Information and Visualization Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Almufi Jurnal Pengabdian kepada Masyarakat eProceedings of Engineering Jurnal Matematika Integratif SmartComp CSRID LOGIC: Jurnal Penelitian Informatika Proceeding of Community Service and Engagement
Jondri Jondri
Universitas Telkom, Bandung
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Retweet Prediction Based on User-Based, Content-Based, and Time-Based Features Using ANN Optimized with GWO Irgi Aditya Rachman; Jondri Jondri; Kemas Muslim L
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1067

Abstract

Social media has emerged as immensely popular and favored platforms among the masses today. Twitter, being one of the most renowned social media platforms, allows users to express themselves through tweet postings. Retweeting is a crucial feature on Twitter, enabling users to disseminate tweets authored by others. In this context, this research aims to predict retweet behavior using User-Based, Content-Based, and Time-Based features, coupled with an Artificial Neural Network classifier optimized with Grey Wolf Optimization. One of the challenges in retweet prediction lies in class imbalance, where the number of retweets on certain tweets is significantly disproportionate compared to others. To address this issue, this study implements undersampling and oversampling techniques. Undersampling reduces the number of samples from the majority class, whereas oversampling involves duplicating or synthesizing samples from the minority class, thereby creating class balance. The research successfully achieves promising results in retweet prediction. After applying oversampling techniques, the classification process attains an accuracy of 85.58%, precision of 87.77%, recall of 83.92%, and F1-score of 85.80%. These results demonstrate the effectiveness of the proposed method in retweet prediction and handling class imbalance issues
Prediction Retweet Using User-Based and Content-Based with Artificial Neural Network-Harmony Search Rizky Ahmad Saputra; Jondri Jondri; Kemas Muslim Lhaksmana
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4079

Abstract

Online social networking services allow users to post content in the form of text, images or videos. Twitter is a microblogging social networking service that enables its users to send and read text-based messages of up to 140 characters. Retweet is one of the features in Twitter that is important in disseminating information, popular tweets reflect the latest trends on Twitter, the main mechanism that encourages information dissemination is the possibility for users to re-share content posted by their social connections, then it can flow throughout the system. Retweets happen when someone republishes or forwards a post to their homepage and personal profile. Most retweets are credited to the original author of the original post. The retweet prediction system uses an Artificial neural network optimized for Harmony search with tweets about the Jakarta-Bandung Fast Train, which shows the best results when the oversampling method has been carried out with an f1 score of 96.8%.
Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm Jondri Jondri; Indwiarti Indwiarti; Dyas Puspandari
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1193

Abstract

Retweets are a way to spread information on Twitter. A tweet is affected by several features which determine whether a tweet will be retweeted or not. In this research, we discuss the features that influence the spread of a tweet. These features are user-based, time-based and content-based. User-based features are related to the user who tweeted, time-based features are related to when the tweet was uploaded, while content-based features are features related to the content of the tweet. The classifier used to predict whether a tweet will be retweeted is Multi Layer Perceptron (MLP) and MLP which is optimized by the swarm intelligence algorithm. In this research, data from Indonesian Twitter users with the hashtag FIFA U-20 was used. The results of this research show that the most influential feature in determining whether a tweet will be retweeted or not is the content-based feature. Furthermore, it was found that the MLP optimized with the swarm intelligence algorithm had better performance compared to the MLP.
Retweet Prediction Using ANN Method and Artificial Bee Colony Jondri Jondri; Kamaludin Hanif Farisi; Kemas Muslim Lhaksmana
Computer Science Research and Its Development Journal Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

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

Abstract

In the ongoing modern era, the rapid dissemination of information takes place, utilizing various channels for data exchange. One such platform is the social media platform Twitter, renowned for its swift and extensive information propagation. A pivotal factor contributing to information distribution on Twitter is the retweet feature, whereby users can redistribute content to their audience. A study has been conducted to forecast this retweet activity by employing the Artificial Neural Network classification method in conjunction with the Artificial Bee Colony optimization approach. This study leverages diverse features, encompassing content-based feature, user-based feature, and time-based feature. The evaluation results from this study reveal that the proposed method achieves an accuracy value of around 83% with the highest accuracy value reaching 84%. These findings indicate that the fusion of the Artificial Neural Network classification method executed with optimization using the Artificial Bee Colony algorithm yields dependable and consistent performance in predicting retweet activities.
Twitter Sentiment Analysis of Kanjuruhan Disaster using Word2Vec and Support Vector Machine Rizky, Fariz Muhammad; Jondri, Jondri; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3612

Abstract

The Kanjuruhan disaster on 1 October 2022, gained the peoples attention. People share their thoughts on social media. Their posts contain a variety of perspectives. Sentiment analysis is possible to use on a dataset of people's posts. This final project applies the supervised learning Support Vector Machine (SVM) method with feature expansion using Word2Vec and TF-IDF as weighting. Three SVM kernels—rbf, linear, and polynomial—are applied. Three split data techniques and two different types of training data are used to train each kernel. Training data with oversampling and training data without oversampling are the two types of training data. The best result gained from using rbf kernel, split ratio 70:30, and oversampling. From it, oversampling trained model have relatively stable in every split rasio and kernel without having significant difference.
Retweet Prediction Based on User-Based, Content-Based, Time-Based Features Using ANN Classification Optimized with the Bat Algorithm Rahadian, Muhammad Rafi; Jondri, Jondri; L, Kemas Muslim
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12708

Abstract

Twitter is one of the most popular social media platforms today for information dissemination. It is favored by the public due to its real-time information sharing capabilities. Twitter provides two important features for information dissemination: Tweets and Retweets. Tweets allow users to write messages that can be instantly shared. Each tweet can contain text, media such as images, videos, or URLs. Retweets allow users to repost someone else's tweet and distribute it to their own followers. The Retweet feature is considered an effective way to spread information, as a high number of retweets indicates that the information in the tweet is spreading quickly and widely. This research aims to predict retweets based on several features: User-Based Feature, Content-Based Feature, and Time-Based Feature. The classification method used is Artificial Neural Network, which is optimized using a Nature-Inspired Algorithm called Bat Algorithm. The evaluation results of this study show an accuracy of 86%, precision of 87.8%, recall of 93.6%, and F1-score of 90.6% without imbalance class handling. Under Undersampling condition, the accuracy is 80.8%, precision is 91.0%, recall is 81.4%, and F1-score is 85.9%. Under Oversampling condition, the accuracy is 82.4%, precision is 89.6%, recall is 85.6%, and F1-score is 87.5%. These results indicate that using user-based, content-based, and time-based features, applying Artificial Neural Network classification method, and optimizing hyperparameters using Bat Algorithm are effective in predicting retweets.
Prediksi Retweet Berdasarkan Konten dan Berbasis Pengguna dengan Metode Seleksi Classifier Febiansyah, Muhamad; Jondri, Jondri; Indwiarti, Indwiarti
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7166

Abstract

Perkembangan media sosial telah mengubah cara penyebaran informasi secara drastis. Twitter, sebagai salah satu platform utama, memiliki peran penting dalam proses ini, dengan jutaan pengguna dan retweet yang terjadi setiap hari. Penelitian ini bertujuan untuk mengembangkan model prediksi retweet pada Twitter, memanfaatkan fitur content-based dan user-based. Metode classifier selection digunakan untuk memilih model terbaik, dengan eksplorasi berbagai teknik seperti oversampling. Hasil eksperimen menunjukkan bahwa penggunaan teknik-teknik tersebut dapat meningkatkan kinerja model dalam memprediksi retweet, terutama pada fitur user based. Meta learner dengan oversampling data pada fitur content based menunjukkan kinerja baik, penggunaan meta learner dan oversampling data memberikan dampak yang signifikan terhadap hasil penelitian
Retweet Prediction Using Artificial Neural Network Method Optimized with Firefly Algorithm Supriadi, Muhamad Rifqi; Jondri, Jondri; Indwiarti, Indwiarti
JINAV: Journal of Information and Visualization Vol. 4 No. 2 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1903

Abstract

Twitter is one of the social media platforms that has a large user base across various demographics. Users can use Twitter to search for information about celebrities, political issues, products, and trending topics of discussion. The information shared on Twitter can be referred to as tweets. Tweets can be further shared by other users using the retweet feature, which allows the tweet to reach a wider audience. This research aims to build a retweet prediction system and examine how tweets will spread. The method used in this research is Artificial Neural Network classification optimized with Firefly Algorithm, based on user-based and content-based features. This modeling approach demonstrated the best results after applying imbalanced class handling using oversampling with the SMOTE technique. The F1-Score obtained in this research is 88.07%.
Prediksi Retweet Berdasarkan Fitur UserBased, Content-Based, dan Time-Based Menggunakan Metode ANN-GSO Muhalani, Raisul; Jondri; indwiarti
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak - Media sosial menjadi salah satu platform yang banyak dipilih untuk sarana saling berbagi informasi, hiburan, serta dapat membuat mereka menghilangkan rasa penat dari aktifitas mereka sehari-hari. Media sosial sudah menjadi kebutuhan untuk sebagian besar masyarakat khususnya indonesia. Salah satu media sosial yang sering digunakan oleh masyarakat indonesia yaitu twitter. Twitter dapat membagikan sebuah postingan yang biasa disebut dengan tweet(kicauan) yang dapat digunakan oleh pengguna untuk membagikan tulisan, foto, video, maupun gif kepada publik. Salah satu fitur twitter yaitu retweet. Fitur retweet ini memiliki fungsi untuk membagikan kembali sebuah postingan, baik postingan mereka sendiri maupun postingan pengguna lain. Fitur ini sangat berperan penting dalam penyebaran informasi. Penelitian ini membahas mengenai prediksi retweet menggunakan fitur user-based, content-based, dan timebased dengan metode Jaringan Saraf Tiruan (Artificial Neural Network) untuk klasifikasinya, yang dioptimalkan dengan algoritma Glowworm Swarm Optimization (GSO) untuk mendapatkan tingkat akurasi yang lebih tinggi. Model ANN yang dioptimalkan dengan GSO menunjukkan hasil terbaik ketika dilakukan skenario oversampling, dengan akurasi sebesar 78% dan F1-Score 78%. Pada GSO terdapat peningkatan pada dataset model prediksi secara keseluruhan. Kata kunci - klasifikasi, Twitter, retweet, ANN, GSO
Prediksi Retweet Berdasarkan Konten Dan Pengguna Dengan Metode Classifier Selection Febiansyah, Muhamad; Jondri; indwiarti
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak - Perkembangan media sosial telah merubah cara penyebaran informasi, dengan Twitter memainkan peran utama. Penelitian ini bertujuan mengembangkan model prediksi retweet di Twitter menggunakan fitur content-based dan user-based, serta teknik oversampling untuk meningkatkan kinerja model. Hasil eksperimen menunjukkan bahwa meta learner tanpa oversampling pada fitur content-based memiliki macro average F1-score sebesar 0.52, namun dengan recall yang sangat rendah untuk kelas retweet (6%) dan F1-score 0.11. Sebaliknya, meta learner dengan oversampling pada fitur contentbased memperbaiki performa dengan presisi 0.86, recall 0.77, dan F1-score 0.80 untuk retweet, dengan nilai macro average F1-score sebesar 0.82 yang menunjukan kenaikan dibandingkan dengan meta learner tanpa oversampling. Untuk model user-based, tanpa oversampling, macro average F1-score memiliki nilai 0.75 dengan keseimbangan baik antara presisi dan recall pada kelas non retweet. Setelah oversampling, model user-based mempertahankan keseimbangan yang baik dengan presisi, recall, F1-score, dan macro average F1- score masing-masing sebesar 0.88 pada kelas retweet dan non retweet. Secara keseluruhan, oversampling meningkatkan kinerja model, terutama pada fitur content-based, dengan model user-based menunjukkan performa yang paling konsisten dan baik. Kata kunci - twitter, pemilihan pengklasifikasi, berbasis pengguna, berbasis konten
Co-Authors Achmad Hussein Sundawa Kartamihardja Achmad Rizal Achmad Salim Aiman Aditya Kusuma Setyanegara Adnan Hassal Falah Ahmad, Fathih Adawi Akbar, Muhammad Rizqi Al Azhar Al Azhar Alfredo Alfredo Ali Zainal Abidin Assajjad Anditya Arifianto Andrian Yoga Pratama Anggit Nourislam Anggit Nourislam Anggit Nourislam Aniq Atiqi Rohmawati Anisa Nur Aini Annisa Aditsania Arief Hutauruk Arifudin Achmad Artamira Rizqy Amartya Maden Arya Rafif Muhammad Fikri Astri Asroviana Putri Aswindo Putra Bambang Ari Wahyudi Bayu Prabawa Bintang Aryo Dharmawan Bramandyo Widyarto, Edgarsa Daffa Ulayya Suhendra Danang Triantoro M Danang Triantoro Murdiansyah Danu Ardiyanto Dea Taradipa Ardiagarianti Dede Tarwidi Deni Saepudin Denny Maulana Deny Sugiarto Wiradikusuma Devy Yendriani Dieka Nugraha Karyana Ditta Febriany Sutrisna Diwan Mukti Pambuko Diwan Mukti Pambuko, Diwan Mukti Dyas Puspandari E Handayani Echa Pangersa Sugianto Oeoen Edvan Tazul Arifin Eka Handayani Eka Handayani Ema Rachmawati Emha Ainun Erlina Febriani Ersa Christian Prakoso Fahrudin Julianto Faisal HAmdani Fakhrana Kurnia Sutrisno Fani Nuraini Farisi, Kamaludin Hanif Fauzan Azhim Umsohi Fazlur Rahman Amri Febiansyah, Muhamad Fery Kun Widi Yudantyo Firdaniza Firdaniza Fitriyani Fitriyani Fransisca Arvevia Intan Angelia Ghina Khoerunnisa Giali Ghazali Guntur Virgenius Hadi, Salman Farisi Setya Hafidz Firmansyah Hafidz Firmansyah Hafiz Denasputra Halprin Abhirawa Hendra Prasetyanwar Huda Sepriandi Ibrahim Husna Aydadenta Ida Bagus Gde Narinda Giriputra Ika Puspita Dewi Ilham Muhammad Iman Nur Fakhri Imannda Kusuma Putra Indwiarti indwiarti Iqbal Dwihanandrio Irgi Aditya Rachman Irma Palupi Irwan Ramadhana Kamaludin Hanif Farisi Karina Priscilia Karina Priscilia Kemas Muslim Lhaksmana Kukuh Rahingga Permadi Kurniawan Nur Ramadhani Ledya Novamizanti Mahmud Dwi Sulistiyo Mahmud Sulistiyo Megi Rahma Dony Moch. Bijaksana Muh. Arfan Arsyad Muhalani, Raisul Muhamad Febiansyah Muhammad Farhan Muzakki Muhammad Fikrie Abdillah Muhammad Ghazali Suwardi Muhammad Hasan Muhammad Hasbi Ashshiddieqy Muhammad Irfan Fathurrahman Muhammad Wildan Putra Aldi Muslim Lhaksmana, Kemas Naufal Dzaky Anwari Naufal Furqan Hardifa Nurseno Bayu Aji Nurseno Bayu Aji Patma Oktaviana Puspandari, Dyas Putri Haryati Rizki Putri Haryati Rizki Putu Harry Gunawan Rafi Hafizhni Anggia Rahadian, Muhammad Rafi Raisul Muhalani Ratih Puspita Furi Redha Arifan Juanda Redi Nurjamin Renette Ersti Reza Harun Rian F. Umbara Rian F. Umbara, Rian F. Rian Febrian Umbara Rica Ning Nurhasanah Rini Shintawati Rita Rismala Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky, Fariz Muhammad Roizal Manullang Siti Sa'adah Siti Saadah Sugondo Hadiyoso Supriadi, Muhamad Rifqi Syadzily , Muhammad Hasan Syifa Khairunnisa Salsabila Tedy Suwega Theo Andrew Tiara Laksmi Basuki Tifani Intan Solihati Tjokorda Agung Budi Wirayuda Ulky Parulian Wibowo Untari Novia Wisesty Untari Wisesty Varian Vianandha Vena Erla Candrika Vera Suryani Widi Astuti Widi Astuti Yahya Setiawan Yosua Marchel