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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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Fine-Tuning LLaMA-2-Chat untuk ChatBot Penerjemah Bahasa Gaul menggunakan LoRA dan QLoRA SUSILO, ANDRI; CHRISTANTI, VINY; LAURO, MANATAP DOLOK
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.248-260

Abstract

AbstrakBahasa gaul, yang berkembang pesat di kalangan generasi Z dan Alpha, sering kali sulit dipahami oleh generasi lain atau dalam konteks formal. Bahasa ini memiliki variasi yang tidak terstruktur dan terus berubah, memerlukan model bahasa yang adaptif untuk memahaminya. Penelitian ini bertujuan untuk mengukur kualitas hasil terjemahan fine-tuning model LLaMA-2 dalam menerjemahkan bahasa gaul ke bahasa formal, dengan menggunakan metrik evaluasi BLEU Score sebagai alat utama. Selain itu, pendekatan LoRA dan QLoRA digunakan untuk meningkatkan efisiensi fine-tuning dengan mengurangi kebutuhan komputasi dan memori. Dataset yang digunakan terdiri dari data media sosial dan data buatan yang diformat dalam bentuk percakapan untuk menangkap konteks secara lebih baik. Hasil evaluasi menunjukkan skor BLEU terbaik sebesar 0.0369, yang menegaskan bahwa model masih perlu disempurnakan untuk menghasilkan terjemahan bahasa gaul yang optimal.Kata kunci: bahasa gaul, LLaMA-2, LoRA, QLoRAAbstractSlang, which is growing rapidly among generations Z and Alpha, is often difficult for other generations to understand or in formal contexts. This language has unstructured variations and is constantly changing, requiring adaptive language models to understand it. This research aims to measure the quality of the translation results of fine-tuning the LLaMA-2 model in translating slang into formal language, using the BLEU Score evaluation metric as the main tool. Additionally, LoRA and QLoRA approaches are used to improve fine-tuning efficiency by reducing computing and memory requirements. The dataset used consists of social media data and artificial data formatted in conversational form to better capture context. The evaluation results show the best BLEU score of 0.0369, which confirms that the model still needs to be refined to produce optimal slang translations.Kata Kunci: slang language, LLaMA-2, LoRA, QloRA
Abstractive Text Summarization Berita Bahasa Indonesia Menggunakan Retrieval-Augmented Generation Antonius Sakti Wiradinata; Viny Christanti Mawardi
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v13i1.32861

Abstract

This research discusses the application of Abstractive Text Summarization (ATS) to Indonesian language news using the Retrieval-Augmented Generation (RAG) method. Increased access to news through various digital platforms often causes users to have difficulty identifying relevant information among the large amount of news available. RAG integrates retrieval and generation techniques to produce coherent and informative news summaries. In this research, news from the CNN and CNBC sites was collected via web scraping to form a dataset. The data is processed through several stages, including preprocessing, embedding, information retrieval, and summary generation. Summary quality evaluation was carried out using the ROUGE metric, where the test results show that this system has good performance in the precision aspect, with a ROUGE-1 Precision value of 0.7432 and ROUGE-2 Precision of 0.6174. However, a lower ROUGE Recall value indicates that there is important information that is not fully included in the summary. These results indicate that the RAG method in ATS is effective in helping users obtain core information concisely, but there needs to be improvement in capturing the entire news context
Virtual Assisten Dengan Metode Rule Base Untuk UMKM Latitaka Borneo Berbasis Telegram Devi Ayu Permatasari; Viny Christanti Mawardi; Manatap Dolok Lauro
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 13 No. 1 (2025): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v13i1.32866

Abstract

Latitaka Borneo MSMEs play a role in preserving local culture through typical Kalimantan herbal products. However, limitations in providing responsive customer service are a challenge amidst market competition. To overcome this problem, this research develops a virtual assistant based on a rule-based method that is integrated with the Telegram platform. This system is able to answer general questions, provide product information, and assist customers in the ordering process automatically. System testing involves evaluation using confusion matrices and cosine similarity to assess response accuracy and semantic relevance. The evaluation results show that the virtual assistant is able to increase operational efficiency and consistency of Latitaka Borneo services, so that it can better meet customer needs. It is hoped that this research can be a solution to increase the competitiveness of MSMEs through customer service automation.
CLUSTERING BERITA SEPAK BOLA DENGAN METODE K-MEANS Riyanto, Radika Yudha; Mawardi, Viny Christanti; Perdana, Novario Jaya
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24072

Abstract

Until now, many Indonesian people like soccer, both domestically and abroad. With so many football enthusiasts, people are becoming more active in finding news related to football. As time goes by, the amount of news circulating on the internet will also be more and more widespread. The large number of news makes the news need to be clustered or clustered to make it easier to access existing news. The website created is intended to group soccer news from several websites, namely: vivagoal.com, goal.com and bolasport.com. The method used on this sbobet is handicap to group news into clusters, then the method used to evaluate the quality of the clusters formed is the Silhouette coefficient method. The Silhouette coefficient value is 0.54, which means that the quality of the cluster formed is moderate.
Pemanfaatan Chatbot Retrieval-Based dan Analisis Sentimen untuk Meningkatkan Layanan Informasi Interaktif di Radio Untar Gian Praista; Viny Christanti Mawardi; Irvan Lewenusa
Comit: Communication, Information and Technology Journal Vol. 3 No. 2 (2025): Comit: Communication and Information Journal
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/comit.v3i2.8424

Abstract

This article discusses the implementation of a retrieval-based chatbot integrated with sentiment analysis to improve the efficiency of information services at Radio Untar. The chatbot developed uses the TF-IDF and cosine similarity methods to match user questions with FAQ data, and is able to handle requests for songs, articles, and podcasts. Sentiment analysis was performed on user interaction logs to assess satisfaction and effectiveness of answers. Based on the results of testing 150 interactions, the chatbot showed an increase in MRR scores from 0.468 to 0.91 and a satisfaction level from 50% to 92% after the fine-tuning process. These findings indicate that a lightweight chatbot retrieval-based system can be used effectively in a campus environment to improve digital interactions.
Chatbot Interaktif Berbasis Transformer untuk Penyediaan Informasi Dinamis di Industri Esports Calvin, Calvin; Mawardi, Viny Christanti; Lewenusa, Irvan
Comit: Communication, Information and Technology Journal Vol. 3 No. 2 (2025): Comit: Communication and Information Journal
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/comit.v3i2.8906

Abstract

The esports industry is experiencing rapid growth and has given rise to the need for an information system that is able to present news quickly and relevantly. This study develops an interactive chatbot based on the BART (Bidirectional and Auto-Regressive Transformers) model to answer user questions directly based on information collected through web scraping. The system is designed using Python and Flask, and uses MongoDB as data storage. Evaluation is carried out through F1-Score and Conversation-Based Evaluation to assess system performance in terms of relevance, coherence, and fluency of answers. The results show that the chatbot is able to provide accurate and coherent answers with fast response times, although there are still challenges in terms of recall and understanding ambiguous contexts.
APLIKASI ALGORITMA SARSA DALAM PENGENDALIAN MOTOR DC Wijaya, Dion Dwi; Fat, Joni; Mawardi, Viny Christanti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5770

Abstract

Motor DC telah menjadi komponen penting dalam berbagai aplikasi industri dan perangkat elektronik. Namun, kendala dalam kestabilan kecepatan akibat variasi tegangan dan beban membutuhkan solusi kontrol yang adaptif. Penelitian ini mengimplementasikan algoritma SARSA berbasis Reinforcement Learning untuk mengontrol parameter Proportional Integral Derivative secara dinamis. Algoritma ini dirancang untuk meningkatkan respons sistem dalam menghadapi perubahan kondisi operasional. Dataset diperoleh melalui simulasi MATLAB, kemudian digunakan untuk melatih tabel Q yang memandu keputusan algoritma SARSA. Hasil pengujian menunjukkan bahwa sistem mampu menyesuaikan parameter PID dengan adaptif, menjaga kecepatan motor mendekati nilai target meskipun terdapat fluktuasi pada fase awal dan transien. Rata-rata kesalahan sistem sebesar 12,96%, mengindikasikan ruang untuk optimasi lebih lanjut. Penelitian ini membuktikan efektivitas SARSA dalam meningkatkan kestabilan motor DC dan berpotensi diterapkan pada sistem kontrol lainnya yang memerlukan adaptasi real-time.
INTERNATIONAL BRAND IMAGE DEVELOPMENT FOR MSMES: CASE OF LEGIT CRACKERS Tunjungsari, Hetty Karunia; Ie, Mei; Utama, Didi Widya; Mawardi, Viny Christanti; Solikhah, Nafia; Yukianti, Chiara Rizka; Buana, Salsabila Ayunda Martsa
International Journal of Application on Economics and Business Vol. 1 No. 4 (2023): November 2023
Publisher : Graduate Program of Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/ijaeb.v1i4.2754-2761

Abstract

The growth of MSMEs drives Indonesia's economic growth in significant contribution. Businesses that are quite popular with MSMEs are businesses in the culinary sector. Legit Crackers is an MSME that produces snacks in the form of high quality and low-calorie mackerel fish crackers. Fish crackers are a popular snack among Indonesian people, with various types and various flavors. However, like many other MSME’s businesses, the fish cracker business also faces a number of problems that need to be overcome. One solution is to develop product formulas and brand images. This is important because good product quality can influence the awareness, trust and confidence of potential buyers. For Legit Crackers, a low-calorie snack business that was founded in 2017, building a brand image is a challenge. To increase marketing potential, good quality and unique product development, packaging and promotions are carried out. Development of a comprehensive strategic plan to overcome production efficiency problems, improve business image, and open access to international markets. This research aimed to innovate and develop market potential for low-calorie snack products. It is hoped that this will improve consumer brand image towards Legit Crackers, develop the low-calorie snack market potential and penetrate the global market.
SISTEM PENGOREKSIAN EJAAN TEKS BAHASA INDONESIA DENGAN DAMERAU LEVENSHTEIN DISTANCE DAN RECURRENT NEURA L NETWORK Augusfian, Fendy; Mawardi, Viny Christanti; Hendryli, Janson; Naga, Dali Santun
Computatio : Journal of Computer Science and Information Systems Vol. 3 No. 2 (2019): COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v3i2.6038

Abstract

This research was intended to create Indonesian Text Spelling Correction system with the capability to handle and make correction to both kind of spelling errors, non-word and real-word errors. Existing spelling correction system was analyzed and made some adjustment and modifications to boost its accuracy. The proposed spelling correction system is built with Damerau-Levenshtein Distance that used in existing spelling correction system along with the adjustment and modifications. The result that achieved by the system that uses by existing spelling correction with the word level accuracy of 40.6% and an average processing speed of 18.4 ms per sentence while the result that achieved by the system that uses Damerau-Levenshtein Distance and Recurrent Neural Network with the word level accuracy of 21.3% and an average processing speed of 29.21 ms per sentence. The result of retest text that achieved by the system that uses Damerau-Levenshtein Distance and Recurrent Neural Network with the word level accuracy of 74%. Tujuan dari penelitian ini adalah untuk membuat sistem pengoreksian ejaan teks Bahasa Indonesia, yang memiliki kemampuan untuk menangani dan memperbaiki kesalahan ejaan, baik kesalahan kata tidak sah maupun kesalahan kata sah. Sistem koreksi ejaan yang sudah ada dianalisis kembali dan dilakukan beberapa penyesuaian dan koreksi untuk meningkatkan akurasi. Sistem koreksi ejaan yang diusulkan dibuat dengan metode Damerau-Levenshtein, yang digunakan dengan penyesuaian dan koreksi dalam sistem koreksi ejaan yang sudah ada. Pencapaian yang dicapai oleh sistem koreksi ejaan yang sudah ada menghasilkan akurasi kata sebesar 40,6% dan kecepatan pemrosesan rata-rata 18,4 milidetik per kalimat dibandingkan hasil yang dicapai oleh sistem yang menggunakan Damerau-Levenshtein Distance dan Recurrent Neural Network Akurasi menghasilkan akurasi kata sebesar 21,3% dan kecepatan pemrosesan rata-rata adalah 29,21 milidetik per kalimat. Hasil pengujian ulang teks yang dicapai oleh sistem menggunakan Damerau-Levenshtein Distance dan Recurrent Neural Network menunjukkan akurasi kata sebesar dari 74%. 
Perancangan Aplikasi Pendeteksi Kemiripan Teks Dengan Menggunakan Metode Latent Semantic Analysis Karo Karo, Berlin Ong; Naga, Dali S.; Mawardi, Viny Christanti
Computatio : Journal of Computer Science and Information Systems Vol. 4 No. 1 (2020): COMPUTATIO : JOURNAL OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v4i1.7191

Abstract

Sistem aplikasi Pendeteksi Kemiripan Teks adalah aplikasi yang menggunakan Metode Latent Semantic Analysis (LSA). Aplikasi ini dapat memberikan informasi mengenai hubungan antar dokumen teks yang direpresentasikan menggunakan term dokumen matrix. Term dokumen matrix berguna untuk memberikan nilai kemiripan pada setiap kata dalam dokumen. Metode LSA tidak memperhatikan tata letak kata pada dokumen dengan kata lain makna yang terkandung di dalam teks tidak berpengaruh terhadap perhitungan kemiripan. Bila terdapat dua buah dokumen yang saling salin namum struktur kalimatnya telah diubah dan ketika dibandingkan menggunakan metode LSA dengan perhitungan kemiripan menggunakan metode cosine similarity maka akan didapat hasil presetase kemiripan yang sama. Aplikasi ini dibangun menggunakan bahasa pemrograman PHP dan juga bahasa pemrograman python. Aplikasi ini dilakukan pengujian menggunakan metode blackbox testing.
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