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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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Pelatihan Pemanfaatan Canva Dalam Mendesain Poster Wijaya, Novan; Irsyad, Hafiz; Taqwiym, Akhsani
FORDICATE Vol 1 No 2 (2022): April 2022
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1650.386 KB) | DOI: 10.35957/fordicate.v1i2.2418

Abstract

Pelatihan canva merupakan pelatihan mendesain gambar baik dalam berupa poster, persentasi, kartu ucapan, brosur, video dan dikerjakan secara online sehingga sangat mempermudah pengguna untuk mendesain. Aplikasi canva merupakan platform yang memudahkan seseorang membuat desain dan konten publikasi dengan mudah serta dapat dilakukan dimanapun. Adapun dalam pelatihan materi yang diperkenalkan ialah mengenai tutorial menggunakan menu dan implementasi pada desain yang akan dibuat. Peserta dalam pelatihan mendesain menggunakan canva merupakan siswa dari SMK Mandiri Palembang. Selama proses pelatihan semua perserta sangat interaktif dan bersemangat dalam mendesain. Desain yang diberikan pada saat pelatihan merupakan tutorial mendesain informasi yang akan di posting di media sosial. Kegiatan dilaksanakan secara langsung dan diharapkan setelah dengan adanya pelatihan dapat menjadi rangsangan kepada peserta dalam meningkatkan kemampuan dalam hal mendesain
OPINI PUBLIK TERHADAP ISU KEASLIAN IJAZAH PADA PLATFORM YOUTUBE DENGAN NAÏVE BAYES, KNN, DAN SMOTE Agnes Anastasia Putri; Christian Bautista; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3892

Abstract

In the digital era, public opinion spreads massively and instantly through various social media platforms. One issue that has sparked widespread attention and debate in the digital space is regarding the authenticity of President Joko Widodo's diploma. This issue has provoked various reactions, both support, criticism, and neutral attitudes from netizens, especially through the comments column on YouTube. This study analyzes public sentiment towards the issue using a machine learning approach with the Naïve Bayes and K-Nearest Neighbors (KNN) algorithms, as well as the SMOTE data balancing technique. A total of 1,000 comments were analyzed and classified into three sentiment categories, namely positive, negative, and neutral. Four test scenarios were carried out, namely: KNN, KNN with SMOTE, Naïve Bayes, and Naïve Bayes with SMOTE with a performance comparison tested to see the effectiveness of each in classifying digital opinion. The test results showed that the combination of Naïve Bayes and SMOTE provided the best performance with accuracy, precision, recall, and F1-score of 73%. In contrast, the worst performing model is KNN with SMOTE, which only achieves 27% accuracy, 53% precision, 34% recall, and 15% F1-score. This study emphasizes the importance of algorithm selection and data handling strategies in digital opinion classification, and can be the basis for developing a reliable sentiment analysis system in the future.
OPINI MASYARAKAT TERHADAP BONUS DEMOGRAFI PADA KANAL YOUTUBE DENGAN METODE TF-IDF, NAÏVE BAYES DAN SMOTE Samuel Effendi Pratama; Jolyn Lucretia; Hafiz Irsyad; Abdul Rahman
Jurnal Teknologi Informasi dan Komputer Vol. 11 No. 2 (2025): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2025
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v11i2.3902

Abstract

This study examines public opinion on the demographic bonus issue expressed through comments on YouTube channels using the TF-IDF, Naïve Bayes, and SMOTE methods. The data used consists of 870 comments that have been manually labeled into positive and negative sentiments. The research stages include data pre-processing in the form of case folding, removal of non-alphabetic characters, stopword removal, and stemming, then feature extraction using TF-IDF to convert text into numeric representations that can be processed by the algorithm. This study compares the performance of the Naïve Bayes sentiment classification model in two scenarios, namely without and with the application of SMOTE. The SMOTE technique is used to overcome data imbalance between sentiment classes so that the classification results are more balanced and unbiased. The evaluation results show that the model without SMOTE produces an accuracy of 70% but has a very low recall in the positive class. After applying SMOTE, the accuracy increased to 77%, with the highest precision of 0.89 in the negative class and the highest recall of 0.92 in the positive class. The word cloud visualization shows the dominant words that reflect the pattern of public opinion regarding the demographic bonus clearly and informatively. The results of this study can provide a quantitative picture of public perception and be a consideration for policy makers. In the future, this method can be further developed with other algorithms and data from various social media platforms to improve the accuracy and representativeness of sentiment analysis.
Analisis Sentimen terhadap Naturalisasi Pemain pada Youtube Menggunakan Decision tree dan Naive bayes Franko, Billy; Wilyanto, Nicholas; Irsyad, Hafiz
Software Development, Digital Business Intelligence, and Computer Engineering Vol. 3 No. 1 (2024): SESSION (SEPTEMBER)
Publisher : Politeknik Negeri Banyuwangi Jl. Raya Jember km. 13 Labanasem, Kabat, Banyuwangi, Jawa Timur (68461) Telp. (0333) 636780

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/session.v3i1.2024.8-16

Abstract

Penelitian ini dilakukan dengan tujuan untuk mengetahui sentimen masyarakat mengenai topik naturalisasi yang sedang ha­ngat belakangan ini mengenai pemain Timnas Sepakbola Indonesia. Peneliti memilih algoritma Decision Tree dan al­go­ritma Naive Bayes sebagai algoritma yang akan digunakan. Decision Tree dan Naive Bayes merupakan beberapa al­go­rit­ma yang umumnya dipakai dalam analisis sentimen karena memberikan hasil yang baik. Hasil pengujian menunjukan al­goritma Decision Tree dengan depth 3 memiliki akurasi sebesar 70%, depth 4 memiliki akurasi sebesar 71,8% dan depth 5 memiliki akurasi sebesar 70,9% dan algoritma Naive Bayes memiliki akurasi sebesar 85,4%. Hasil penelitian ini me­nun­jukkan bahwa algoritma Naive Bayes merupakan salah satu algoritma yang cukup efektif dalam membangun suatu mo­del klasifikasi dalam kasus analisis sentimen. Dengan memanfaatkan algoritma Decision Tree dan Naive Bayes dalam mem­bangun model guna analisis sentimen diharapkan mampu memberi gambaran yang lebih jelas mengenai pendapat ma­syarakat terhadap naturalisasi sehingga pemerintah dapat mengetahui apakah masyarakat mendukung atau justru me­no­lak adanya penambahan pemain naturalisasi dalam timnas sepakbola Indonesia.
Implementasi TF-IDF dan Cosine Similarity untuk Penyaringan Dokumen Berita Program Makan Siang Gratis Pemerintah Indonesia Tanuwijaya, William; Setiawan, Christofer Evan; Irsyad, Hafiz; Rahman, Abdul
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.6724

Abstract

Penelitian ini menerapkan metode Information Retrieval (IR) dalam menyaring berita yang relevan terkait program makan siang gratis yang diselenggarakan oleh pemerintah Indonesia, sebuah program yang ditujukan untuk meningkatkan gizi pelajar dan mencegah terjadinya stunting, namun juga menampilkan data berita dari berbagai media nasional, preprocessing data (termasuk case folding, tokenisasi, stopword removal dan stemming), pembobotan kata menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF), serta menggunakan pengukuran tingkat relevansi menggunakan Cosine Similarity. Dataset terdiri dari lima berita dengan topik terkait, yang IR mampu menyaring dokumen secara efektif. Dari lima Berita, empat di antaranya terdeteksi relevan dan satu tidak relevan. Evaluasi model menghasilkan akurasi sebesar 80%, precision 100%, recall 80% dan f1-score 89%. Nilai-nilai ini menunjukkan bahwa sistem dapat mengidentifikasi relevansi konten Berita terhadap topik yang terutama dalam kasus judul Berita yang bersifat clickbait. Penelitian ini juga memberikan kontribusi terhadap pengembangan sistem penyaringan informasi yang lebih efisien dan akurat dalam konteks isu publik.
Ekstraksi Kata Kunci Pada Portal Jurnal JATISI Menggunakan Metode TF-IDF dan Cosine Similarity Wijaya, Michael; Irsyad, Hafiz
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.6623

Abstract

Penelitian ini bertujuan untuk mengimplementasikan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan Cosine Similarity dalam mengekstraksi kata kunci dari abstrak artikel jurnal guna mengidentifikasi topik penelitian dalam periode tertentu. Harapannya, pengguna dapat menganalisis tren topik dan memperoleh gambaran komprehensif mengenai dinamika keilmuan di bidang Teknik Informatika dan Sistem Informasi. Penelitian ini menggunakan pendekatan eksperimental kuantitatif dengan tahapan: pengumpulan data dari 11 artikel jurnal JATISI Vol 6 No. 1 (2019), pre-processing data (case folding, penghapusan tanda baca dan angka, tokenisasi, stopword removal, dan stemming), pembobotan menggunakan TF-IDF, serta pengukuran relevansi antardokumen dengan Cosine Similarity. Hasil penelitian berhasil mengekstraksi kata kunci dari dokumen dan memberikan peringkat berdasarkan persentase kemunculannya, serta menghasilkan matriks cosine similarity untuk mengidentifikasi kemiripan antartulisan. Namun, nilai presisi 0.05, recall 0.04, dan F1-score 0.04 menunjukkan bahwa model ini belum mampu memberikan prediksi yang memadai untuk kasus ini. Temuan ini dapat dijadikan acuan bahwa model/metode tersebut tidak direkomendasikan tanpa modifikasi signifikan, sekaligus menjadi dasar untuk eksplorasi solusi alternatif di masa depan.
Keyword Extraction Abstrak Jurnal Ilmiah Menggunakan Metode TF-IDF dan KeyBERT Suhartoyo, Rayvin; Julyo Armando Davincy Lin, Valen; Irsyad, Hafiz; Rahman, Abdul
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.v4i2.1814

Abstract

Keyword extraction is a significant technique in natural language processing (NLP) that serves to summarize the essence of a document, such as a scientific journal summary. This study aims to analyze the effectiveness of two keyword extraction methods, namely Term Frequency-Inverse Document Frequency (TF-IDF) and KeyBERT, in finding significant keywords from a collection of scientific journal abstracts. The dataset used consists of several scientific journal abstracts accompanied by manual keywords as a basis for assessment. The TF-IDF method relies on the frequency of words in the document, while KeyBERT utilizes a cosine similarity approach based on the BERT transformer model to determine the most meaningful keywords. The research findings show that the KeyBERT method and the TF-IDF method have a moderate level of similarity with semantic similarity values ​​of 0.578 for the KeyBERT method and 0.469 for the TF-IDF method, respectively. These results show significant potential for the use of machine learning and deep learning-based models with both methods for topic classification systems, especially in the fields of information retrieval and text mining.
Klasifikasi Kerusakan Uang Rupiah Menggunakan CNN Dengan Arsitektur VGG16 Roshan, Muhamad Rizvi; Irsyad, Hafiz
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15125

Abstract

This study developed a deep learning model using a Convolutional Neural Network (CNN) architecture with VGG16 to classify the level of damage to rupiah banknotes. Previous studies have focused more on recognizing denominations and detecting counterfeit money using CNN and transfer learning, while the classification of physical damage to rupiah banknotes is still limited, both locally and internationally, and often relies on special acquisition devices or template registration. The dataset used consists of images of rupiah banknotes grouped into three damage categories: >20%, >40%, and >50%. This dataset is divided into 80% for training data (537 images) and 20% for test data (135 images). To enrich the data variety, this study applied on-the-fly data augmentation techniques with rotation, zoom, and flipping during the training process. The experimental results show that this model achieves an accuracy of 93.33%, with excellent precision, recall, and F1-score values, especially in the >50% damage category. The use of the ADAM optimizer with a learning rate of 1e-3 proved to provide more stable and efficient training. Overall, this study shows that the application of CNN with the VGG16 architecture is effective in classifying rupiah currency damage and can contribute to the development of image processing technology, particularly for evaluating currency feasibility in real-world scenarios.
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.
Co-Authors Abdul Rahman Abdul Rahman Adi Saputra Adrian Suparto Agnes Anastasia Putri Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Akhsani Taqwiym Andreas Andreas Angel Kelly 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 Jennifer Jocelyn Jennifer Velensia Santoti Jeremy Allegrato Hartono Jolyn Lucretia jonathan stanly Jonathan Wijaya Juliana Nasution Julyo Armando Davincy Lin, Valen Kamilah, Nyimas Nisrinaa 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 Nur Aisyah Wahyuni 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 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 Yeremia Agung Chandra Yohannes, Yohannes Yunarto Yunarto, Yunarto