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All Journal Computatio : Journal of Computer Science and Information Systems JUTIK : Jurnal Teknologi Informasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas JUTIM (Jurnal Teknik Informatika Musirawas) Kurawal - Jurnal Teknologi, Informasi dan Industri Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Teknomatika (Jurnal Teknologi dan Informatika) Syntax: Journal of Software Engineering, Computer Science and Information Technology JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Ilmu Komputer dan Informatika Bulletin of Information Technology (BIT) Brilliance: Research of Artificial Intelligence Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi Jurnal Nasional Teknik Elektro dan Teknologi Informasi Arcitech: Journal of Computer Science and Artificial Intelligence Agrivet: Jurnal Ilmu-ilmu Pertanian dan Peternakan DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Insand Comtech : Information Science and Computer Technology Journal Buletin Ilmiah Informatika Teknologi JOINTECOMS (Journal of Information Technology and Computer Science) MDP Student Conference Software Development Digital Business Intelligence and Computer Engineering Journal Information & Computer (JICOM) Jurnal Software Engineering and Computational Intelligence Computing Insight: Journal of Computer Science Applied Information Technology and Computer Science (AICOMS) JISCOMP (Journal of Information System and Computer) Journal of Informatics and Computer Engineering Research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Hafiz Irsyad
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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ANALISIS PENCARIAN TEKS EMOSI DALAM TWEET BERBAHASA INDONESIA MENGGUNAKAN TF-IDF DAN COSINE SIMILARITY Saputra, Adi; Ferdilian, M Lazuardi; Irsyad, Hafiz; Rahman, Abdul
Journal of Informatics and Computer Engineering Research Vol. 2 No. 2 (2025)
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/jicer.v2i2.6012

Abstract

Social media platforms such as Twitter present a wide range of emotional expressions from users in short and informal texts, which pose significant challenges for automated analysis. This study develops a search system for Indonesian-language tweets that identifies user emotions based on semantic similarity to a given text query. The method employs Term Frequency-Inverse Document Frequency (TF-IDF) for feature weighting and Cosine Similarity to measure textual similarity. Preprocessing stages including normalization, tokenization, stopword removal, and stemming are applied to enhance text representation accuracy. The system is tested using emotion-based queries and returns relevant tweets with high semantic match scores. Experimental results show that 50% of the top retrieved tweets match the expected emotional context. This approach proves effective in detecting emotions in short texts and offers potential for further development in sentiment-driven opinion analysis and emotion-aware recommendation systems
KEYWORD EXTRACTION KOMENTAR TERHADAP KONFLIK INDIA-PAKISTAN PADA PLATFORM YOUTUBE MENGGUNAKAN TF-IDF DAN COSINE SIMILARITY Edison, Nicholas; Fernando, Kristian; Irsyad, Hafiz; Rahman, Abdul
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.6638

Abstract

Konflik antara India dan Pakistan merupakan isu geopolitik yang sering menjadi perhatian global dan menimbulkan diskusi luas di media sosial, termasuk platform YouTube. Penelitian ini bertujuan untuk mengekstraksi kata kunci dari komentar-komentar pengguna YouTube mengenai topik konflik India-Pakistan, serta menganalisis kemiripan makna seluruh komentar antar video menggunakan metode TF-IDF dan Cosine Similarity. Data diperoleh dari kolom komentar tiga video YouTube yang relevan dan diproses melalui tahapan pra-pemrosesan teks, perhitungan bobot kata menggunakan TF-IDF, serta pengukuran similaritas menggunakan Cosine Similarity. Hasil ekstraksi kata kunci menggunakan TF-IDF menunjukkan terdapat 20 kata kunci dengan frekuensi tertinggi, dengan 3 kata kunci tertinggi adalah “india”, “pakistan” dan “perang”. Hasil perhitungan Cosine Similarity menunjukkan bahwa tingkat kemiripan antar komentar video berkisar antara 0,544 hingga 0,695, dimana nilai similarity tertinggi terdapat pada perbandingan Video 1 dan Video 3 (0,695), Video 1 dan Video 2 (0,653), sementara Video 2 dan Video 3 (0,544). Hasil ini menunjukkan bahwa kombinasi metode ini efektif dalam mengidentifikasi topik dominan serta hubungan semantik antar komentar. Visualisasi kata kunci dengan WordCloud juga memperjelas representasi opini publik yang berkembang. Penelitian ini memberikan kontribusi dalam pemetaan diskursus digital secara kuantitatif dan efisien.
EKSTRAKSI BERITA HOAX PADA TURN BACK HOAX BERBASIS PENDEKATAN TF-IDF & COSINE SIMILARITY HIDAYAT, WILLIAM; Ong, Jesen; Muhdhor, Umar; Irsyad, Hafiz; Rahman, Abdul
Computing Insight : Journal of Computer Science Vol 7 No 2 (2025)
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/comp_insight.v7i2.26678

Abstract

Perkembangan teknologi telah membawa perubahan besar dalam kehidupan masyarakat. Salah satunya akses terhadap berita dan artikel yang semakin mudah, dan bebas. Namun, fenomena ini juga memunculkan permasalahan serius, yaitu penyebaran berita hoaks yang sangat cepat dan masif. Penelitian ini bertujuan untuk mengekstraksi informasi penting dari artikel hoaks yang dipublikasikan di situs TurnBackHoax.id menggunakan pendekatan text mining berbasis TF-IDF dan cosine similarity. Data artikel hoaks diperoleh melalui teknik web scraping dengan pustaka Python seperti requests dan BeautifulSoup, diikuti oleh tahap prapemrosesan teks yang meliputi case folding, penghapusan tanda baca, angka, serta stopwords, dan stemming. Teks yang telah diproses kemudian direpresentasikan dalam bentuk vektor numerik menggunakan metode TF-IDF untuk menentukan bobot kata berdasarkan frekuensi dan kelangkaannya dalam korpus. Selanjutnya, cosine similarity digunakan untuk mengukur tingkat kemiripan antar dokumen, sementara kata kunci diekstraksi berdasarkan bobot TF-IDF tertinggi. Visualisasi Word Cloud juga diterapkan untuk menggambarkan kata-kata dominan secara visual. Berdasarkan hasil evaluasi, metode yang digunakan dalam penelitian ini berhasil mencapai tingkat ketepatan sebesar 93,15%, menunjukkan efektivitas pendekatan TF-IDF dan Cosine Similarity dalam menganalisis dan mengelompokkan artikel hoaks. Hasil penelitian menunjukkan bahwa pendekatan ini efektif dalam mengidentifikasi kata kunci penting dan mengelompokkan artikel hoaks berdasarkan kemiripan konten. Kata kunci : Cosine Similarity,Hoaks,Text Mining, TF-IDF, kata kunci
Penerapan CNN Pada Klasifikasi Kepribadian Anak Sekolah Dasar Berdasarkan Citra Tulisan Tangan Muhammad Ishaq Maulana; Hafiz Irsyad
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2959

Abstract

Indonesia has a rich culture. This creates dynamics in personality formation. In schools, teachers' understanding of students' personalities is key. So far, conventional methods such as observation, interviews and graphology have been used to classify children's personalities, which are less efficient. This study uses the CNN algorithm with the Mobilenetv2 architecture. Dataset was taken from 5th grade students from 3 SDN Palembang with a total of 246 data divided into 2 classes, namely extrovert 101 data and introvert 145 data. Then grayscale preprocessing, normalization, and augmentation. Ratio of training, validation, and test data is 80:10:10. Model was trained with Adam optimizer, learning rate 0.0001, batch size 20, and epochs of 12. The result is a model accuracy of 69.2% with a tendency for the model to classify images as introverts. This study is expected to help teachers gain insight into the best teaching approach in the classroom.Keywords: CNN; Graphology; Elementary School AbstrakIndonesia memiliki budaya yang kaya. Ini menciptakan dinamika dalam pembentukan kepribadian. Di sekolah, pemahaman guru terhadap kepribadian siswa menjadi kunci. Selama ini untuk mengklasifikasi kepribadian anak, digunakan metode konvensional seperti observasi, wawancara dan ilmu grafologi yang kurang efisien. Penelitian ini menggunakan algoritma CNN dengan arsitektur Mobilenetv2. Dataset diambil dari siswa kelas 5 dari 3 SDN Palembang dengan total 246 data yang dibagi menjadi 2 kelas, yaitu extrovert 101 data dan introvert 145 data. Kemudian dilakukan preprocessing grayscale, normalisasi, dan augmentasi. Rasio data latih, validasi, dan uji adalah 80:10:10. Model dilatih dengan Adam optimizer, learning rate 0,0001, batch size 20, dan epoch sebanyak 12. Hasilnya akurasi model sebesar 69,2% dengan kecenderungan model mengklasifikasi citra sebagai introvert. Penelitian ini diharapkan dapat membantu guru mendapatkan pandangan tentang cara pendekatan mengajar yang terbaik di kelas.Kata kunci: CNN; Grafologi; Sekolah Dasar
OPINI PUBLIK TERHADAP ULASAN VIDEO RUU TNI MENGGUNAKAN TF-IDF, NAÏVE BAYES DAN SMOTE Patrisius Satria Hendrawan; Michael Gunawan; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3955

Abstract

The rapid development of digital technology has encouraged the public to actively express their opinions on public issues through social media platforms, including YouTube. The comment section on videos discussing the Draft Law on the Indonesian National Armed Forces (RUU TNI) has become a space for the public to convey support or rejection. This study aims to analyze public opinion regarding the RUU TNI by classifying YouTube comments into two sentiment categories: positive and negative. The methods employed include text preprocessing, feature extraction using TF-IDF, sentiment classification with the Naïve Bayes algorithm, and data balancing using the SMOTE technique to address class imbalance. The evaluation results show that the model achieved 80.7% accuracy before SMOTE; however, the recall and f1-score for the positive class were very low due to the imbalanced dataset. After applying SMOTE, the accuracy slightly decreased to 80.38%, but there was a significant improvement in the evaluation metrics for the positive class, with recall reaching 86.21% and f1-score 81.3%. WordCloud visualization also revealed dominant keywords that represent each sentiment. These findings indicate that the Naïve Bayes algorithm, when combined with SMOTE, is more effective in producing a balanced sentiment classification and is recommended for use in analyzing imbalanced textual data related to public opinion.
Co-Authors Abdul Rahman Abdul Rahman Adi Saputra Adrian Suparto Agnes Anastasia Putri Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Andreas Andreas Antony, Felix Arta Tri Narta Arta Tri Narta Aurelia, Reni Busdin, Rusdie Candra candra Chandra Wijaya Chandra, Kelvin William Christian Bautista Christy, Christy Cindy Meilani Daniel Wijaya Derry Alamsyah Devella, Siska dewa Dicko David K Dina Mariana Dwifa_Sophian, Muhammad Agus Edison, Nicholas Edward Pratama Eka Puji Widiyanto Fareza, Ivan Farisi, Ahmad Farisi, Ahmad Fariz Prasetya Ferdi Jiranda Sinaga Ferdilian, M Lazuardi Fernando Sugianto Putra Fernando, Kristian Franko, Billy Fujianto Graciela, Michelle Hansen, Hansen Hartati, Ery Hendra Nata Niko P Hidayat, Muhammad Syahrizal Hidayat, WIlliam Ibnusina, Fedri Ivander Destian Luis Jeason Lie Jocelyn, Jennifer Jolyn Lucretia jonathan stanly Jonathan Wijaya Juliana Nasution Julyo Armando Davincy Lin, Valen Kamilah, Nyimas Nisrinaa Kelly, Angel Kevin kevin Kevin Kevin Kotan, Jendraja Husein Kurniawan, Calvin Laksana, Jovansa Putra Leonardo Leonardo Lestari, Yehezekiel Gian levid, Jonathan Felix Lin, Jimmi M Ezar Al Rivan Meiriyama, Meiriyama Michael Gunawan Michael Joy Clement Michael Wijaya Molavi Arman Muhammad Bemby Putra Mansyah Muhammad Ishaq Maulana Muhammad Rizky Pribadi Muhdhor, Umar Mutia, Silvi Narta, Arta Tri Novan Wijaya Novan Wijaya Novan Wijaya Novan Wijaya Ong, Jesen Patrisius Satria Hendrawan Pribadi, M Rizky Putra Darmansius, Albertus Dwi Andhika Renaldo, Florence Reynald Dwika Prameswara Rikky, Rikky Rizki Ambarwati Roshan, Muhamad Rizvi RR. Ella Evrita Hestiandari Russel Wijaya Samuel Effendi pratama Santoti, Jennifer Velensia Sanu, Intan Saputra, M Reynaldi Setiawan, Christofer Evan Shela, Shela Silfia Suhartoyo, Rayvin Tanuwijaya, William Taqwiym, Akhsani Taqwiym, Akhsani Taqwiym, Akhsani Tinaliah, Tinaliah Triana Elizabeth, Triana Verrino Adityya Virginia, Callista Wati, Retiana Krisna Wati, Risha Ambar Wijang Widhiarso Wijaya, Christian Richie Willyanto, Aldo Wilyanto, Nicholas Wong, Jeovanni Yohannes, Yohannes Yunarto Yunarto, Yunarto