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All Journal BIOTIK: Jurnal Ilmiah Biologi Teknologi dan Kependidikan Jurnal Surya Medika Eralingua : Jurnal Pendidikan Bahasa Asing dan Sastra JURNAL PERTANIAN Jurnal Eduscience (JES) Paramasastra: Jurnal Ilmiah Bahasa Sastra dan Pembelajarannya Al Qisthas : Jurnal Hukum dan Politik Jurnal Teknik Informatika (JUTIF) J-Com (Journal of Computer) Sriwijaya Bioscientia BIOVALENTIA: Biological Research Journal JEPAL (Journal of English Pedagogy and Applied Linguistics) Ta'rim: Jurnal Pendidikan dan Anak Usia Dini eProceedings of Engineering OMEGA: Jurnal Keilmuan Pendidikan Matematika e-Jurnal Apresiasi Ekonomi Nusantara Community Empowerment Review Asian Journal of Islamic Studies and Da'wah Jurnal Ilmiah Al-Muttaqin Pragmatik : Jurnal Rumpun Ilmu Bahasa dan Pendidikan Jurnal Akademik Pengabdian Masyarakat Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Magister Ekonomi Syariah Jurnal Pendidikan Dasar Islam Aksi Nyata : Jurnal Pengabdian Sosial dan Kemanusiaan Kemas Journal:Jurnal Pengabdian masyarakat ILJ: Islamic Learning Journal Proceeding of the International Conference on Global Education and Learning JPT : Jurnal Pendidikan Tematik Jurnal Ilmiah Mahasiswa Pendidikan Geografi TECHNOLOGIC JI-KES (Jurnal Ilmu Kesehatan) Jurnal Pelita Raya (JPR)
Ayu Safitri
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Improving Sentiment Classification of Kredit Pintar Reviews Using IndoBERT, SMOTE, and Stacking Ensemble Safitri, Ayu; Risaldi, Muhammad; Alwi, Muh Naufal Ramadhani; Surianto, Dewi Fatmarani; Fadilah, Nur; Parenreng, Jumadi M
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5342

Abstract

Kredit Pintar is one of the most widely used fintech applications in Indonesia, generating millions of user reviews on the Google Play Store that reflect diverse user experiences. These reviews provide valuable insights into application performance; however, extracting sentiment from such unstructured and imbalanced textual data remains a challenging task. This study aims to improve sentiment classification of Kredit Pintar user reviews by proposing a hybrid approach that integrates IndoBERT, SMOTE (Synthetic Minority Over-Sampling Technique), and a stacking ensemble model. From 2020 to 2024, 2,278 user reviews were classified into positive, neutral, and negative categories based on star ratings. SMOTE was employed to rectify class imbalance, whereas IndoBERT gathered contextual representations of the Indonesian language. Furthermore, a stacking ensemble combining IndoBERT, Random Forest, and SVM (Support Vector Machine) was implemented to enhance classification performance. Experimental results show that IndoBERT without data balancing achieved an accuracy of 84%, whereas the proposed combination of IndoBERT, SMOTE, and stacking ensemble consistently produced superior performance, achieving 92% accuracy, precision, recall, and F1-score. The findings demonstrate that integrating language-specific transformer models with data balancing and ensemble techniques effectively improves sentiment classification. This study contributes to the advancement of Indonesian-language natural language processing in the fintech domain and provides practical insights for fintech developers in understanding user perceptions and improving digital financial services.
Classification of Watermelon Flavor Using Artificial Neural Network with Color, Texture, and Shape Features Musgamy, Muh Faqih S; Risaldi, Muhammad; Safitri, Ayu; Kaswar, Andi Baso; B, Muhammad Fajar; Parenreng, Jumadi M
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5457

Abstract

Watermelon (Citrullus vulgaris Schard) is a widely produced fruit due to its high nutritional value and health benefits. However, consumers often experience difficulty in distinguishing sweet and bland watermelons because quality assessment is generally conducted manually and subjectively. To address this issue, this study proposes a watermelon flavor classification system based on visual features, including color, texture, and shape, using an Artificial Neural Network approach with digital image processing. The dataset used in this study consists of 214 images collected from 55 watermelon samples, categorized into sweet and bland classes. The proposed method involves several stages, namely image acquisition, preprocessing, grayscale conversion, segmentation, morphological operations, feature extraction, and classification using a feedforward backpropagation learning algorithm. Various combinations of visual features were evaluated to determine the most effective configuration. Experimental results show that the proposed system achieves an accuracy of 93.67% on training data and 92.85% on testing data, with an average computation time of 0.319 seconds per image. The findings indicate that the integration of Hue Saturation Value color features, texture features derived from the Gray-Level Co-occurrence Matrix, and shape features significantly enhances the accuracy of watermelon flavor classification. This study contributes to the development of an objective, efficient, and non-destructive fruit quality assessment system and demonstrates potential applicability to other types of fruits using a similar approach.
Co-Authors . Hasmunir, . Adamu Abubakar Muhammad Afriyani, Asia Agustiansyah, Lucky Dita Ahmad Guntur Alfianto Al fathih Niska Adilah Alwi, Muh Naufal Ramadhani Amanda Roja Agustin Amsal Amri Ananda Rizky Fadillah Andi Baso Kaswar Andi Baso Kaswar Andriyani Puji Lestari Apriliawati Lahabu Arianto Arianto Arista Anindya Ramadhani Arya Sheva Satria Dewantara Asrori, Moh Syafaat Bima Guntara Dea Fadilla Chairunisa Dea Puspitasari Desi Riawanti Desty Endrawati Subroto Dewi Fatmarani Surianto Dewi Safitri Edy, Marwan Ramdhany Eka Damayanti, Fauziah Eka Prasetya Budi Mulyawan Elmania, Putri Eva Hudzaefah Fajar Hidayanto, Fajar Farras, M. Irsal Fartini, Ade Fathia Hanifa Syahida Fatima Az-zahra Wairooy Fatimah Aminati Fikri Adriyansah, Fikri Firdaus Firdaus Fitrianti Yakob Ibrahim Fratiwi, Ineke Halla, Maulana Ahsan Hanaa Azkia Hargianti, Mita Hari Hariadi, Hari Harman Hamidson Harvianti, Yuniar Hendrokumoro Hendrokumoro, Hendrokumoro Heni Yohandini Hikam, Moch Hikmah Puspasari Ibrahim, Irianto Ihsan Firmansyah Iqbar, Muhammad Iskandar, Yandra Jumadi Mabe Parenreng Khoirunnisa Khoirunnisa Krisnandi, Herry Laila Hanum Lailatul Zannah Lamazi, Lamazi Levy Olivia Nur Lubis, M. Ilyas Lubis, Nur Ainun M. Farhan Hidayatullah M. Fariz Fadillah Mardianto Maesaroh Maesaroh Martinus, Ali Mega, Zakria Ferisya Meilana Afif Mahmudi Mia Audina, Mia Mindo Mursalina Jen Mirza Desfandi Moch Alif Haqi Moch. Hoerul Gunawan Mochammad Reihan Rabbani Mubarok, Muhammad Syahrul Muhammad Agus Muhammad Agus Futuhul Ma'wa Muhammad Alif Rizky H Muhammad Andry Muhammad Dwiky Nugraha Muhammad Fajar B Muhammad Yassir namirudin Muhtadin Musgamy, Muh Faqih S Naila Joya Nazlah Azzahra Neng Dina Agustina Nisa, Asri Ainun Nisrina Alya Salsabila Nugraha, Irsyad NUR FADILAH Nur Hidayah Nur Jannah Nur Risal, Andi Akram Nuriessa Aputri, Farah Nurinda Rahim Doe Nurmina Nurmina, Nurmina Pamungkas, Barolym Tri Pragustiandi, Guntur Purwaningtyas, Esti Putra, Purniadi Putri, Cyntia Sabarani Rachmanita, Risse Entikaria Radial Anwar, Radial Rahman Ismail M.S, Rahman Ismail Rahmat Pratama, Rahmat Rahmat, Pratama Ramdhan, William Ratih Wijayanti, Ratih Rian Firmansyah Risaldi, Muhammad Riva Rivanti Rizki Rahmawati, Rizki Rosyita Nur Syafa'ah Rozi Rubi Awalia, Rubi Safriyah Safriyah SANTOSO SANTOSO Sendi Ibnu Hilmiawan Setia Budi Setyaningsih, Eka Wahyu Shafwan Aulia Rahman Siti Nurhalisa Sri Ayu Wandira Sugianti, Wiwik Wahyu Suhendra Suhendra Syahirani, Siti Thamrin Kamaruddin, Thamrin Untung Usada Vera Wati Viena Eka Yuadianti Wahyuni Ngabito Wulandari, Lucia Putri Yadi Oktariansyah Yuanita Windusari Zainal Arifin Zubaidah