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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Teknik dan Ilmu Komputer JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi) Jurnal Informatika dan Teknik Elektro Terapan Computatio : Journal of Computer Science and Information Systems Jurnal Sinergitas PkM & CSR MITRA: Jurnal Pemberdayaan Masyarakat MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Muara Ilmu Sosial, Humaniora, dan Seni Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan INTI Nusa Mandiri Jurnal Infortech Jurnal Bakti Masyarakat Indonesia Journal of Computer and Information Systems Ampera Prosiding Konferensi Nasional PKM-CSR Indonesian Journal of Multidisciplinary Science Prosiding Seri Seminar Nasional Communication and Information Journal International Journal of Application on Economics and Business Inovasi Teknologi Masyarakat Jurnal Serina Abdimas
Viny Christanti M
Program Studi Teknik Informatika Fakultas Teknologi Informasi Universitas Tarumanagara Jakarta - Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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Kinerja BART dalam Automatic Summarization Berita Otomotif CECILIANA TAKESHI; VINY CHRISTANTI MAWARDI; NOVARIO JAYA PERDANA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 1 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i1.61-72

Abstract

ABSTRAKIndustri otomotif menghadapi tantangan besar dalam mengelola dan menyajikan informasi yang relevan serta terstruktur di tengah meningkatnya volume data digital. Penelitian ini memperkenalkan sistem pencarian dan peringkasan berbasis teks menggunakan model BART (Bidirectional and Auto-Regressive Transformers) untuk meningkatkan efisiensi pencarian informasi dan peringkasan konten. Sistem ini mengintegrasikan web scraping, preprocessing teks, TF-IDF, dan teknik cosine similarity untuk mengekstrak dan memproses data, menghasilkan hasil yang ringkas dan akurat. Evaluasi menggunakan metrik ROUGE dan umpan balik pengguna menunjukkan kemampuan sistem dalam menghasilkan ringkasan yang informatif dan efisien dengan waktu pemrosesan yang lebih cepat. Sistem ini mencapai performa sebesar rata rata 78.5 berdasarkan evaluasi yang dilakukan. Temuan ini menyoroti efektivitas BART dalam menangani data otomotif yang kompleks dan memenuhi kebutuhan pengguna untuk mendapatkan ringkasan berita yang relevan, mendukung pengambilan keputusan berbasis data di sektor otomotif.Kata kunci: Pencarian Informasi, BART, NLP, Otomotif, Peringkasan Teks.ABSTRACTThe automotive industry faces significant challenges in managing and presenting relevant and structured information amidst the growing volume of digital data. This study introduces a text-based search and summarization system using the BART (Bidirectional and Auto-Regressive Transformers) model to enhance the efficiency of information retrieval and content summarization. The system integrates web scraping, text preprocessing, TF-IDF, and cosine similarity techniques to extract and process data, delivering concise and accurate results. Evaluations using ROUGE metrics and user feedback demonstrate the system’s ability to produce informative and efficient summaries with reduced processing time. The system achieved a performance average score of 78.5 based on the evaluation. The findings highlight the effectiveness of BART in handling complex automotive data and meeting user needs relevant news summaries, thereby supporting data-driven decision-making in the automotive sector.Keywords: Information Retrieval, BART, NLP, Automotive, Text Summarization.
ANALISA SENTIMEN ULASAN APLIKASI TRANSPORTASI MENGGUNAKAN METODE SVM DENGAN PENDEKATAN INSET LEXICON-BASED Eryca Dhamma Shanty; Brandon Alexander Jayadi; Marco; Viny Christanti Mawardi
Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Vol. 9 No. 1 (2025): Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan
Publisher : Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/19y15a17

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna terhadap tiga aplikasi transportasi publik digital di Indonesia, yaitu Access by KAI, MyMRTJ, dan MitraDarat. Pendekatan yang digunakan dalam penelitian ini adalah kombinasi metode klasifikasi Support Vector Machine (SVM) dan pendekatan leksikal menggunakan kamus INSET Lexicon-Based. Data ulasan dikumpulkan melalui proses scraping dari Google Play Store dan kemudian melalui tahapan pre-processing, pelabelan sentimen, serta proses klasifikasi. Hasil penelitian menunjukkan bahwa mayoritas ulasan pengguna didominasi oleh sentimen positif. Access by KAI memperoleh ulasan positif terbanyak (446 ulasan), diikuti oleh MitraDarat (322 ulasan), dan MyMRTJ (286 ulasan). Model SVM memberikan hasil klasifikasi yang baik dengan akurasi tertinggi dicapai oleh MyMRTJ (87%), diikuti Access by KAI (84%), dan MitraDarat (82%). Selain itu, visualisasi word cloud berhasil menampilkan kata-kata dominan yang sering muncul dalam ulasan seperti "bagus", "mudah", dan "jalan", yang menunjukkan kepuasan pengguna terhadap layanan aplikasi. Pendekatan INSET Lexicon-Based terbukti efektif dalam mengenali polaritas kata dalam konteks lokal berbahasa Indonesia, serta meningkatkan akurasi pelabelan sebelum proses klasifikasi dilakukan. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan aplikasi transportasi digital berbasis data ulasan pengguna.
PERBANDINGAN JAWABAN GURU DAN SISWA MENGGUNAKAN METODE LATENT SEMANTIC ANALYSIS UNTUK PENILAIAN ESAI OTOMATIS Nikolaus Nathaniel; Dhani Andika Maharsi; Viny Christanti Mawardi
Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Vol. 9 No. 2 (2025): Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan (IN PRESS)
Publisher : Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/n9zpj130

Abstract

Penilaian esai merupakan salah satu metode evaluasi yang penting dalam dunia pendidikan karena mampu menggambarkan kemampuan berpikir kritis, pemahaman konsep, serta kemampuan siswa dalam menyusun argumen secara tertulis. Namun, proses penilaian esai secara manual cenderung memakan waktu lama, tidak konsisten antar penilai, dan rentan terhadap bias subjektif. Untuk mengatasi permasalahan dalam penilaian esai secara manual, penelitian ini merancang sebuah sistem penilaian otomatis dengan menerapkan metode Latent Semantic Analysis (LSA). Sistem ini dirancang untuk mengukur tingkat kemiripan semantik antara jawaban siswa dan kunci jawaban dari guru. Proses dimulai dengan tahapan pra-pemrosesan teks, yang meliputi normalisasi huruf, pemisahan kata, penghilangan kata-kata umum (stopword), serta stemming. Setelah itu, teks yang telah dibersihkan diubah ke bentuk numerik melalui pendekatan Term Frequency-Inverse Document Frequency (TF-IDF). Representasi numerik tersebut kemudian diproses lebih lanjut dengan teknik Singular Value Decomposition (SVD) guna mereduksi dimensi dan mengekstrak makna laten dari setiap dokumen. Setelah memperoleh representasi vektor berdimensi rendah, perhitungan kemiripan dilakukan menggunakan metode cosine similarity. Hasil pengujian pada lima soal esai kimia tingkat SMA menunjukkan bahwa sistem dapat memberikan informasi kemiripan antara jawaban siswa dan guru secara kuantitatif, dengan nilai cosine similarity berkisar antara 0,76 hingga 0,88. Nilai tersebut dapat digunakan untuk membandingkan kesesuaian makna secara objektif. Meskipun demikian, sistem masih memiliki beberapa keterbatasan, seperti kesulitan dalam menangani kata majemuk dan ketergantungan terhadap pustaka pemrosesan bahasa lokal. Secara keseluruhan, sistem ini memiliki potensi besar untuk diterapkan sebagai alat bantu dalam penilaian esai secara efisien, konsisten, dan mendukung proses pembelajaran.
PERANCANGAN ANTARMUKA CHATBOT EDUKATIF UNTUK SISTEM TANYA JAWAB SDN KALIDERES 13 PETANG Eryca Dhamma Shanty; Georgia Sugisandhea; Viny Christanti Mawardi
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 8 (2025): Penguatan Ekonomi Masyarakat Berbasis Ekologis untuk Mencapai Keberlanjutan Menuju Ind
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v8i0.2651

Abstract

The development of artificial intelligence (AI) has the potential to enhance the effectiveness of learning; however, limitations in infrastructure and the understanding of teachers and students remain obstacles to its implementation in elementary schools. SDN Kalideres 13 Petang, with 314 students from various socio-economic backgrounds, faces challenges in improving effective interactions between students and teachers. The goal of this community service activity is to introduce and assist in the implementation of the educational chatbot EduBuddy as an interactive medium in the learning process. EduBuddy is designed based on a Large Language Model (LLM) and Command R technology, allowing the chatbot to respond to students' questions automatically, quickly, and relevantly. The activities were conducted through socialization, training, and technical assistance for teachers, as well as guidance in using the chatbot for students. Teachers gained skills in integrating the chatbot into teaching activities, while students were directed to utilize it as an interactive learning medium. The results of the activity showed an increase in student engagement, expanded access to information, and significant support for teachers in delivering content. Evaluation was conducted through questionnaires and classroom observations, which indicated positive acceptance from both teachers and students. It is hoped that the success of this activity can serve as a model for digital transformation in elementary education and encourage the broader application of AI technology in the world of education.
DETEKSI SPOILER PADA ULASAN BUKU BERBAHASA INDONESIA MENGGUNAKAN PENDEKATAN MACHINE LEARNING DAN DEEP LEARNING Natasya Agustine Sadhi; Hannah Larissa Halim; Jessica Winola; Viny Christanti Mawardi
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8587

Abstract

Spoiler detection in book reviews is a challenging text classification task because spoilers are not always identifiable from specific words but depend heavily on the narrative context in which information is revealed. This study compares six text classification models for detecting spoilers in Indonesian-language book reviews from Goodreads, namely Support Vector Machine (SVM), Random Forest, XGBoost, Bidirectional Long Short-Term Memory (BiLSTM), IndoBERT, and XLM-R (RoBERTa). Data were collected through Selenium-based web scraping and GraphQL API from 40 mystery and thriller book titles, resulting in 11,259 reviews with a class imbalance ratio of 1:10.4. All models were evaluated using AUC-ROC, PR-AUC, spoiler F1-score, and spoiler recall as primary metrics, with decision thresholds optimized through each model's validation set. XLM-R achieved the best overall performance with an AUC-ROC of 0.7017, a PR-AUC of 0.2263, and a spoiler F1-score of 0.2903, followed by IndoBERT, SVM, Random Forest, XGBoost, and BiLSTM. Overall, transformer-based models outperformed the traditional machine learning models and BiLSTM across most evaluation metrics. The results also indicate that each model exhibits different precision-recall characteristics, suggesting that model performance should not be evaluated using a single metric alone. These findings can serve as an initial reference for future research on Indonesian-language spoiler detection and support the development of automated spoiler detection systems for content moderation on digital book review platforms
Co-Authors Agus Budi Dharmawan Albert Jeremy Aleksander Nihcolson Andre Ertanto Andre Raymond Andreas Andreas Andreas Andreas Andreas Khosasi Anggreiny, Phoebe Cecilia Angkasa, Adhelia Anindita Septiarini, Anindita Antonius Sakti Wiradinata Ardianto Ardianto Arwi, Adelia Vannissa Augusfian, Fendy bagus Mulyawan Bagus Mulyawan Benedicta, Cheria Berlin Ong Karo Karo Billy Fernando Brandon Alexander Jayadi Bryan Filemon Buana, Salsabila Ayunda Martsa Calvin Calvin Carlene Lim Carlene Lim Caroline Wili Harto CECILIANA TAKESHI Chintia, Tiffany Dali S Naga Dali S. Naga Dali S. Naga Dali S. Naga Dali S. Naga, MMSI Dali S.Naga Dali Santun Naga Daniel Daniel Daniel Daniel Darius A Haris Darius Andana Haris Darryl Kresnadi Nugroho Davin Pratama Denis Kusbowo Desi Arisandi Desi Arisandi Desi Arisandi Dessy Yanti Destu Adiyanto Devi Ayu Permatasari Devin Abipraya Dewi Triani Dhani Andika Maharsi Didit Suprihanto, Didit Dinata, Fredickson Dyah Larasati, Annita Edward Darmaja Edy Susanto Endah Purnamasari Endah Setyaningsih Erikson T Erikson T Erwin Erwin Ery Dewayani Eryca Dhamma Shanty Eryca Dhamma Shanty Fat, Joni Fendy Augusfian Ferry Ruben Yudistira Ferry Ruben Yudistira Yudistira, Ferry Ruben Yudistira Freddy Kurniawan Fredickson Dinata Fundroo Orlando Georgia Sugisandhea Geraldine, Karmelia Gerry Geraldicky Gian Praista Gunadi, Alvin Nicolas Haikal M, Andrew Hamdani Hamdani Handoko Susanto Handoko Susanto, Handoko Handry Wardoyo Hannah Larissa Halim Hanven Pradana Hartanto, Jonathan Chris Helen, Helen Hendri Yukianto Hendri Yukianto, Hendri Hendryli, Janson Henry Hartono Herman, Sylvia Hetty Karunia Tunjungsari Husada, Yusianne Kasih Irvan Lewenusa Irvan Lewenusa, Irvan Ivanka, Risa James Eklie Janson Hendryli Janson Hendryli Janson Hendryli Janson Hendryli Janson Hendryli Januar Mansur Jeanny Pragantha Jeanny Pragantha Jeffri Alimin Jesica Jesica Jesica Kurniadi, Jesica Jessica Winola Jesslyn Jesslyn Jimmy Jimmy Joko Joko Jonathan Adrian Wibowo Joshua Octavianus Joshua Octavianus, Joshua Julius Evan Harya Chandra Kalyani, Khema Dwi Karo Karo, Berlin Ong Kenneth Hakim Kevin kevin Kevin Kurniawan H. Kevin Prasetio Kevin The Kuncoro Yoko Lavenia Lely Hiryanto Lie, Nadia Natha Livienia Livienia Manatap Dolok Lauro, Manatap Dolok Marco Maria Asinta Marpaung Maria Asinta Marpaung, Maria Asinta Marsel Dwiputra Marsel Dwiputra, Marsel Martsha Buana, Salsabilla Ayundha Marvellino Mei Ie Meiliansyah, Carens Berliyanti Meiriani Tjandra Meiriani Tjandra Meiske Yunitree Suparman Michael, Valentino Muhammad Farras Mutiara Ramadhani Sugiri Mutiara, Maitri Widya Nadia Natha Lie Naga, Dali S. Natasya Agustine Sadhi Natasya, Stephanie Niki Valentine Niki Valentine, Niki Nikolaus Nathaniel Novario Jaya Perdana Nurmadewi, Dita Orlando, Fundroo Pangandaheng, Grasella Aldonia Pharadya Ajeng Swari Sukmawati Phung, Mulan Prabu Alif Anggadiputra Prof. Dr. Ir. Dali S. Naga, MMSI Pusaka, Semerdanta Putra Lukita Putri, Aneesa Joenice rani puspitasari Rendi Kristyadi Ricky Cangniago Ricky Martin Rini, Cika Puspita Riwanda, Josephine Kayla Riyanto, Radika Yudha Rizqi Amelia, Aulya Robertus Budihalim Robertus Budihalim, Robertus Rudy Rudy Salsabila, Nur Maya Saskia Lavinsky Septiasari, Abellia Sharlene Solikhah, Nafia Stenly Tirta Wijaya stephanie stephanie Steven Steven Dharmawan Steven Muliadi Steven Muliadi, Steven Steven Steven Susilo, Andri Sylvia Wulandari, Sylvia Tania Rizgitta Tony Tony Tony Tony TRI SUTRISNO Utama, Didi Widya Vanesa Nellie Vincent Marcellino Wati, Masna Widi Santoso Wijaya, Dion Dwi Willyanto, Vinnie Wilson Gozal, Wilson Yagyu Munenori M.E. Yasser, Achmad Yohan Prasetyo Sugianto Yohanes Calvinus Yolanda, Aubrey Yosua Pandapotan Sianipar Yukianti, Chiara Rizka Yulianto Yulianto Yulianto Yulianto Zyad Rusdi Zyad Rusdi