Article Per Year (5 Year)
p-Index From 2021 - 2026
9.995
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ELKHA : Jurnal Teknik Elektro Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) DoubleClick : Journal of Computer and Information Technology Informatik : Jurnal Ilmu Komputer Kurawal - Jurnal Teknologi, Informasi dan Industri JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pendidikan dan Teknologi Indonesia Jumat Informatika: Jurnal Pengabdian Masyarakat Bulletin of Computer Science Research Jurnal Abdi Masyarakat Indonesia Jurnal Pengabdian Masyarakat IPTEK Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Nasional Teknologi Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Informatika Progres The Indonesian Journal of Computer Science Research Mestaka: Jurnal Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Software Engineering and Computational Intelligence Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Applied Information Technology and Computer Science (AICOMS) Welfare: Jurnal Pengabdian Masyarakat Jurnal Nasional Teknologi Informasi dan Aplikasinya Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Rizky Pribadi
Universitas Multi Data Palembang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 87 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 8 Documents
Search
Journal : applied information technology and computer science aicoms

 1 
Analisis Sentimen Komentar Twitter Tentang Perfoma Manchester United Dengan Menggunakan Algoritma Support Vector Machine Dwi Cahyadi, Ambrosius; Rizvi Roshan, Muhamad; Rizky Pribadi, Muhamad
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1511

Abstract

Manchester United is one of the largest clubs in the English Premier League with an exceptional history in European and global football. In the 2023/2024 season, Manchester United experienced a very poor season, leading to various positive and negative sentiments from its fans, especially on social media. Sentiment data was gathered from Twitter, where Manchester United fans expressed their opinions regarding the team's performance in the Premier League. This study employs the Support Vector Machine (SVM) method to process and classify data collected from Twitter, aiming to analyze the sentiments of Manchester United fans based on their social media comments. The results indicate that the performance of the Support Vector Machine is relatively poor, achieving an accuracy of 58.73%. This is due to the dataset relying on a single keyword, which led to suboptimal and less complex data, resulting in the Support Vector Machine (SVM) producing relatively low accuracy.
Perbandingan Algoritma SVM dan Naïve Bayes Berbasis SMOTE dalam Analisis Sentimen Komentar Tiktok pada Produk Skincare Liem, Steven; Setiawan, Thomas; Pribadi , M. Rizky
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1523

Abstract

This research compares the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in sentiment analysis of TikTok comments about skincare products, using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The evaluation results indicate that SVM outperforms Naïve Bayes, achieving an accuracy of 59.43% compared to 47.65%. Additionally, SVM excels in the F1 Score metric (60.37% versus 54.74%), although Naïve Bayes demonstrates slightly higher precision (67.96% compared to 62.76%). Therefore, SVM proves to be more effective in classifying sentiment comments, making it the recommended algorithm for sentiment analysis tasks in the skincare product domain on TikTok.
Analisis Sentimen Terhadap Aplikasi Mitra Darat Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbor Wijaya, Ananda; Rivaldo, Mario; Rizky Pribadi, Muhammad
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1542

Abstract

The transportation industry is now an important element as the times develop, especially for today's young generation. Mitra Darat itself is also one of these industries. An application that allows users to easily find out the bus departure schedule that they will take anywhere and anytime on their mobile device. Reviews are definitely given for every app available both positive and negative. With this, we are trying to conduct sentiment analysis research for the Mitra Darat application through reviewing comments from the Google Play Store so that we can identify sentiments related to the use of the Mitra Darat application, as well as provide valuable insights to land transportation service providers to understand user views and improve user services. from the results of our sentiment analysis. The algorithms we use are KNN and NBC. These two algorithms are commonly used by many people because of their expertise in classifying sentiment analysis data and are also popular among researchers. Based on our test results, it can be concluded that our sentiment analysis model designed using the NB algorithm displays higher accuracy performance than KNN. The accuracy of the NB model reached 99.28%, while KNN achieved an accuracy of 80%. This shows that the naïve Bayes algorithm is more suitable to obtain maximum accuracy compared to using k-nearest neighbors.
Analisis Sentimen Fenomena “Brewek” Kartu Pokémon Pada Platform Reddit Menggunakan Arsitektur RoBERTa Siti Fatimah Az Zahrah; Klaudius Audie Irsansaputra; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/t3a91d20

Abstract

Social media platforms such as Reddit have long served as major discussion forums for various communities. This study aims to analyze public sentiment in order to understand community trends and perceptions toward Pokémon TCG. The research applies the RoBERTa (Robustly Optimized BERT Approach) Deep Learning architecture using the pre-trained model “cardiffnlp/twitter-roberta-base-sentiment” to perform sentiment analysis. The text data were cleaned, tokenized with a maximum limit of 512 tokens, and classified into positive, neutral, and negative sentiments, followed by word length distribution analysis and Top-N Words extraction. The model successfully classified sentiments objectively. The visualization results reveal the characteristics of word distribution after outlier handling and identify the top ten keywords representing the main discussion focus within each sentiment label. The findings indicate that the community sentiment is predominantly negative, providing a clear overview of the opinion dynamics within the Pokémon community on Reddit.
Analisis Sentimen Publik terhadap Isu Pembuatan CBDC di Indonesia Menggunakan IndoBERT Muhammad Radja Juang Jamemiko; Joseph Eduard Uly Loni; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/srtytf27

Abstract

Perkembangan teknologi finansial mendorong munculnya inovasi sistem pembayaran digital, salah satunya melalui pengembangan Central Bank Digital Currency (CBDC) atau Rupiah Digital oleh Bank Indonesia. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap isu pembuatan CBDC di Indonesia berdasarkan opini masyarakat pada platform media sosial X. Penelitian menerapkan pendekatan Natural Language Processing menggunakan model Deep Learning berbasis Transformer, yaitu IndoBERT, untuk melakukan klasifikasi sentimen secara otomatis. Data tweet yang telah dikumpulkan melalui proses crawling kemudian melalui tahapan pre-processing, tokenisasi, serta klasifikasi ke dalam tiga kategori sentimen, yaitu positif, netral, dan negatif. Selain itu, penelitian juga melakukan visualisasi distribusi sentimen dan pemetaan kata dominan menggunakan wordcloud untuk mengidentifikasi fokus pembahasan masyarakat terkait CBDC ataupun Rupiah Digital. Hasil penelitian menunjukkan bahwa sentimen netral mendominasi diskusi publik sebanyak 61,01%, diikuti oleh sentimen negatif 29,11% dan positif 9,87%. Temuan ini mengindikasikan bahwa masyarakat masih berada pada tahap pengamatan dan diskusi terhadap implementasi CBDC, namun tetap terdapat kekhawatiran terkait aspek keamanan, privasi, dan kontrol sistem keuangan digital.
Analisis Sentimen Komentar Youtube terhadap Kondisi Bursa Saham Indonesia akibat Isu Pengunduran Serempak Dewan BEI Menggunakan IndoBERT Daffa Yudha Musyaffa; Felix Gunawan; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/q27ea163

Abstract

Social media platforms such as YouTube have long served as a primary discussion space for retail investor communities in Indonesia. This study aims to analyze public sentiment in order to understand perception trends and the digital psychology of capital market participants regarding the issue of the simultaneous resignation of the Indonesia Stock Exchange (IDX) board members. The research applies the IndoBERT (Bidirectional Encoder Representations from Transformers for the Indonesian language) deep learning architecture through a fine-tuning process on a dataset of YouTube comments. The textual corpus was cleaned from noise, normalized from stock market slang vocabulary, tokenized, and automatically classified into three sentiment polarities: positive, neutral, and negative. The analysis stage was further continued with dominant keyword extraction using Word Cloud visualization and word frequency trend mapping to identify psychological variables driving market opinions. The model successfully classified the semantic complexity of informal language objectively. Visualization results indicate that communication dynamics were overwhelmingly dominated by negative sentiment (57.5%), reflecting widespread public concern and declining confidence in capital market stability due to the structural crisis. This study demonstrates the effectiveness of local transformer models as instruments for extracting digital market psychology to support real-time automated investment decision-making.
Analisis Sentimen Masyarakat terhadap Kenaikan Harga BBM Non-Subsidi Akibat Penutupan Selat Hormuz Menggunakan IndoBERT Jaysen Stephanus; Jonathan Tanujaya; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 5 No 1 (2026): AICOMS
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/kx4xgz78

Abstract

Public discussions regarding the potential increase in non-subsidized fuel prices resulting from the closure of the Strait of Hormuz on the X platform between January 1, 2026, and May 17, 2026, were highly intensive and generated diverse public responses to the global economic impacts triggered by the geopolitical conflict between Iran and Israel. The primary issue addressed in this study is the growing public concern over the possibility of rising non-subsidized fuel prices, which may affect transportation costs, logistics distribution, and daily living expenses. This study aims to analyze public sentiment toward this issue using the IndoBERT deep learning model to obtain a more accurate understanding of public opinion trends. Data were collected through a scraping process on the X platform using keywords related to non-subsidized fuel and the Strait of Hormuz. The collected data were then processed through several preprocessing stages, including case folding, noise removal, tokenization, stopword removal, and stemming, before being classified into positive, neutral, and negative sentiment categories. Out of 412 analyzed tweets, negative sentiment emerged as the dominant category at 49.8%, followed by neutral sentiment at 48.5%, while positive sentiment accounted for only 1.7%. The findings indicate that the majority of the public expressed concern regarding the potential increase in non-subsidized fuel prices and its impact on economic conditions and household expenditures.
Perancangan UI/UX Pada Aplikasi Elaruna Dengan Metode Design Thinking Fellycia Caroline; Steffanie Angelica; Muhammad Fajar Ariansyah; Serenity Devina Suryanto; Muhammad Rizky Pribadi
Applied Information Technology and Computer Science (AICOMS) Vol 4 No 2 (2025)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v4i2.1825

Abstract

Indonesia has great potential in the tourism sector due to its rich culture and natural beauty, but it still faces challenges in the form of limited access to integrated and reliable information for tourists. This study aims to design a mobile-based tourism information application user interface (UI/UX) that provides quick, accurate, and user-friendly access to information. The method used is Design Thinking, which consists of five stages: Empathize, Define, Ideate, Prototype, and Test. The design process was carried out using a user-centered approach to ensure the design meets the needs and preferences of tourists. Test results show that most users find the application interface easy to use, visually appealing, and clearly navigable, with 91.3% of respondents stating that the navigation is easy and 73.9% feeling that the application runs smoothly. This demonstrates that the Design Thinking approach is effective in producing design solutions that are responsive to user needs. This study is expected to contribute to the development of digital tourism applications in Indonesia and serve as a foundation for further research in the development of features and broader integration of information technology.
Co-Authors -, Felicia Adi Saputra Aditya Al Assad Aditya Ali Kusuma Adrian Chen Adrian Suparto Adrian Suparto Ahmad Dumyati Ahmad Zaky Nadimsyah Albert Cahayadi Alvin Hujaya Alwin Marcellino Amarullah, Rendy Ampu Syura Ananda Wijaya Andreas Andreas Andreas Danny Agus W Andreas Saputra Andrian Wijaya Angel Kelly Asyraq, Cerwyn Bakti Ananda Fernando Bautista, Christian Bebin Paula Bella Jenni Ourelia Boy Putra Brilliant Chandra Pratama Calvin Bertnas Valentino Calvin Saputra Carissa Maharani Chandra Chandra Saputra Christian Richie Wijaya Clara Meyhazlinda Putri Clement, Michael Joy Daffa Yudha Musyaffa Daniel Daniel Daniel Johan Daniel Udjulawa Daniel Wijaya Darwin Saputra David Sebastian Dedy Hermanto Desta Rahman Theja Desy Iba Ricoida Dicky Ryanto Fernandes Dina Lestari Putri Diva Putri Kynta Dwi Apriyanti Sastika Dwi Cahyadi, Ambrosius Effendi pratama, Samuel Egi Fransisco Saputra Eka Puji Widiyanto Evangs Mailoa Evi Maria Fadhel Muhammad Fadhil Sa'adat Farisi, Ahmad Farisi, Ahmad Fathimah Azzahra Felicia Felicia Felix Gunawan Fellyca Effendi Fellycia Caroline Femmy Johan Feriyanto Feriyanto Ferliansyah, Fernando Fernandi Indi Nizar G Fernando Feliansyah Fernando Fernando Fernando Namas Fionna Caroline Florence Renaldo Frans Bachtiar Fransiskus Daniel Chandra Frisky Wijaya Genisshanda Nabila Matari Geraldo Wilson Gerry Christian Pilipus Gunawan, Michael Hafidz Irsyad Hafiz Irsyad Hafizh Pebrian Hansen Hansen Hendrawan, Malvin Hendry Hindriyanto Dwi Purnomo Ilham Indra Hidayat Imelia Dwinora Cahyati Indi Nizar G, Fernandi Ivan Luthfi Laksono Ivan Luthfi Laksono Jackie Wijaya Jasen Jonathan Jaysen Stephanus Ja`Far Ja`Far Jelvin Krisna Putra Jennifer Verty Jerin, Nathaniel Jesen Ong Jonathan Jason Constantine Jonathan Tanujaya Jonathan Wijaya Joseph Eduard Uly Loni Jovansa Putra Laksana Kasanova, Sinyo Kelvin Dwi Wahyudi Kevin agustria zahri Kevin Andreas KGS M Ammar Yazid Klaudius Audie Irsansaputra Kurniawan, Ricky Arie Laurentius Ricardo Wijaya Leo Chandra Leonardo Yahya Liem, Steven Lin, Valen Julyo Armando Davincy Lipi Amanda Putra Lucretia, Jolyn M Lazuardi Ferdillian M. Dhafa Adjie Saputra Marcelino Marcelino Mario Rivaldo Michael Michael Joy Clement michael Wijaya Migel Orvin Febryan Millenia Mudita Chandra Muhammad Abdul Azizul Hakim Muhammad Alfa Rizi Muhammad Azril Fahrezi Muhammad Dafhi Mayrizkiy Muhammad Dody Muhammad Fadli Muhammad Fajar Ariansyah Muhammad Hamdandi Muhammad Naufal Anugrah Muhammad Radja Juang Jamemiko Muhammad Redho Saputra Muhammad Reyza Nirwana Muhammad Robi, Muhammad Muhammad Tri Setianto Nabila Syiva Altarisa Nabilah Dayanah Nathacia Lais Naufal Akbar Neilsen Nicholas Komah Nicolas Jacky Pratama Hasan Nova Ariansyah Opita Purwasih Pambudi, Readysna Krisna Peter Reynard Susanto Pibriana, Desi Prasetyo, Zavier Billy Pratama, Brilliant Chandra Putra Laksana, Jovansa Putri, Agnes Anastasia Raphael Lee Regian batistuta, Putra Reza Satria Rika Maulina Riki Chandra Rio Ferdynand Riska Fajriati Rivaldo Therino Elevan Rivaldo, Mario Riza Umami Rizky Kurniawan Rizvi Roshan, Muhamad Roby Julian Romi Laxi Ronaldo Putra Rusbandi rusbandi rusbandi, rusbandi Safeti Intan Pratiwi Salwa Fakhira Imletta San Gabriel Vanness Kenrick Erwi Sanila Maharani Santoso, Fian Julio Saputra Edika, Nelson Sardika, Ricky Putra Se, Abd Rosyiid Serenity Devina Suryanto Setiawan, Thomas Shela, Shela Sherdian Djunaidi Sinshevan Viswanatan Kravizt Erwi Siska Amelia Siti Fatimah Az Zahrah Sonia Sonia Sri Yulianto Joko Prasetyo Steffanie Angelica Stephanie Stephanie Stephen Setyawan Steven Tribethran Suparto, Adrian Suryasatria Trihadaru Sutarto Wijono Syahrani Nur Hakim Syalsabilla Valentisyesa Syifa Wahyuni Tad Gonsalves Tangguh Prana Welas Sukma Vannes Wijaya Vanness Bee Victoria Valensita Robert Vincent Vincent Virgiansyah, Muhammad Rifqi Wijang Widhiarso Wijang Widhiarso Wijaya, Ananda Wilcent, Wilcent William Wijaya Yennica Valentine Hagunawan Yohanes Andika Dharma Yohanes Fransisco Mardi Chandra Yohannes, Yohannes Yoko Saputra Dewa Yosefa Camilia Moniung Yunarto Yunarto, Yunarto `Adelia Anjelina