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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Transformatika Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat JIEET (Journal of Information Engineering and Educational Technology) Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Angkasa: Jurnal Ilmiah Bidang Teknologi KACANEGARA Jurnal Pengabdian pada Masyarakat JISKa (Jurnal Informatika Sunan Kalijaga) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Innovation in Community Empowerment (JICE) Emerging Information Science and Technology Media Riset Bisnis & Manajemen Gotong Royong : Jurnal Pengabdian, Pemberdayaan Dan Penyuluhan Kepada Masyarakat (J-P3KM) Jurnal INFOTEL Indonesian Journal on Data Science Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Teknomatika: Jurnal Informatika dan Komputer Jurnal Komtika (Komputasi dan Informatika)
Ulfi Saidata Aesyi
Universitas Jenderal Achmad Yani Yogyakarta
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal INFOTEL

 1 
A User Recommendation Model for Answering Questions on Brainly Platform Puji Winar Cahyo; Kartikadyota Kusumaningtyas; Ulfi Saidata Aesyi
JURNAL INFOTEL Vol 13 No 1 (2021): February 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i1.548

Abstract

Brainly is a Community Question Answer (CQA) application that allows students or parents to ask questions related to their homework. The current mechanism is that users ask questions, then other users who are in the same subject interest can see and answer it. As a reward for answering questions, Brainly gives points. The number of points varies by question. The greater of total points users have, Brainly will automatically display them in the smartest user leaderboard on the site's front page. But sometimes, some users do not have good activity in answering questions. Thus, it is possible to have an urgent question that has not been answered by anyone. This study implements Fuzzy C-Means cluster method to improve Brainly's feature regarding the speed and accuracy of answers. The idea is to create student clusters by utilizing the smartest students' leaderboard, subjects interest, and answering activities. The stages applied in this research started with Data Extraction, Preprocessing, Cluster Process, and User Recommender. The optimal number of clusters in the answerer recommendation in the Brainly platform is 2 clusters. The value of the fuzzy partition coefficient for two clusters reached 0.97 for Mathematics and 0.93 for Indonesian. Meanwhile, the results of the recommendations were influenced by answer ratings. Many numbers of the answer are not given rating because the possibility of the answers are not appropriate or user's insensitivity in giving ratings.
Sentimen Topik Menggunakan Regresi Logistik dan Alokasi Dirichlet Laten sebagai Model Analisis Kepuasan Pelanggan Cahyo, Puji Winar; Aesyi, Ulfi Saidata; Santosa, Bagas Dwi
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1081

Abstract

Buying and selling goods now is more interesting through e-commerce or marketplaces because of the ease of carrying out online transactions. Each transaction usually generates a response from the customer. The transaction response on the Shopee platform is still in paragraph form and needs to be more specific. Therefore, this research aims to build a model analysis of customer satisfaction using the best algorithm between support vector machine (SVM), random forest, and logistic regression. This research method uses sentiment classification with logistic regression because the logistic regression algorithm has the best accuracy, with an accuracy of 90.5. Meanwhile, the SVM algorithm achieved an accuracy of 90.4, and random forest reached 90.2. The three algorithms were tested three times, splitting data train:test at 80:20, 70:30, and 60:40. The best results were obtained by splitting data at 60:40. The best model is used to predict data without labels. The prediction produces 12,844 positive sentiment comment data, 112 negative sentiment comment data, and 70 neutral sentiment comment data. The results of this research continued to topic modeling using latent dirichlet allocation (LDA) to generate a trending topic of customer satisfaction on sales products. Implications of discussing each trend topic can be used as a reference for improving products and services, especially in communicating with customers.
Co-Authors Ahmad Hanafi Aksan, Azzikra Ramadhanti Aldo Syahputra Anggraini, Deviana Dyah anggraini, devianadyah Ardilla Mega Agustin Arianti, Dennis Fitri Salsabilla Arif Himawan Arif Himawan, Arif Arumsari, Dewi Asnawi, Choerun Bagas Dwi Santosa Bagas Dwi Santosa Berlina, Laila Indah Cahayana, Nika Nur Dedy Hariyadi Dwijayanti, Irmma Faisal Dwi Nurwenda Herdiesel Santoso Khabani, Muhamad Zul Kharisma Kharisma Kharisma, Kharisma Kusumaningtyas, Kartikadyota Kusumaningtyas, Netania Indi Lahitani, Alfirna Rizqi Lintang Sumirat Maria Gratiana Dian Jatiningsih Maulana Ridwan, Muhamad Fikry Muhammad Rifqi Maarif, Muhammad Rifqi Netania Indi Kusumaningtyas Noviana Wulandari Nurul Fatimah Nurul Fatimah Puji Winar Cahyo Puji Winar Cahyo Putri Pusvitasari Retantyo Wardoyo Risky Firmansyach Riyanto Tri Kurniawan Riyanto Tri Kurniawan Rizqi Lahitani, Alfirna Santosa, Bagas Dwi Taufaldisatya Wijatama Diwangkara Taufaldisatya Wijatama Diwangkara