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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKNOLOGIA Jurnal Linguistik Komputasional Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Journal of Data Science and Its Applications Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Prosiding Konferensi Nasional PKM-CSR Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Indonesia Sosial Teknologi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Dike Jurnal INFOTEL CSRID LOGIC: Jurnal Penelitian Informatika
Kemas Muslim Lhaksmana
Universitas Telkom, Indonesia
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Deep Learning and Imbalance Handling on Movie Review Sentiment Analysis Utami, Sri; Lhaksmana, Kemas Muslim; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12770

Abstract

Before watching a movie, people usually read reviews written by movie critics or regular audiences to gain insights about the movie’s quality and discover recommended films. However, analyzing movie reviews can be challenging due to several reasons. Firstly, popular movies can receive hundreds of reviews, each comprising several paragraphs, making it time-consuming and effort-intensive to read them all. Secondly, different reviews may express varying opinions about the movie, making it difficult to draw definitive conclusions. To address these challenges, sentiment analysis using CNN and LSTM models, known for their effectiveness in classifying text in various datasets, was performed on the movie reviews. Additionally, techniques such as TF-IDF, Word2Vec, and data balancing with SMOTEN were applied to enhance the analysis. The CNN achieved an impressive sentiment analysis accuracy of 98.56%, while the LSTM achieved a close 98.53%. Moreover, both classifiers performed well in terms of the F1-score, with CNN obtaining 77.87% and LSTM achieving 78.92%. These results demonstrate the effectiveness of the sentiment analysis approach in extracting valuable insights from movie reviews and helping people make informed decisions about which movies to watch.
Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment Harmandini, Keisha Priya; L, Kemas Muslim
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13376

Abstract

Sentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research.
Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter Damayanti, Lisyana; Lhaksmana, Kemas Muslim
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13379

Abstract

This analysis enables the identification and a deeper understanding of the positive and negative sentiments reflected in online conversations, providing a comprehensive view of the direction of public support and preferences regarding presidential candidates. Sentiment analysis through machine learning can manage extensive sentiment data, ensuring time efficiency, and enhancing accuracy in swiftly and comprehensively comprehending people's opinions and preferences. With these advantages, machine learning-based sentiment analysis has gained popularity as an effective choice for understanding people's perspectives, preferences, and responses to various issues and events. Therefore, this research focuses on sentiment analysis regarding public opinions on the 2024 presidential election. The method employed in this research is the SVM algorithm with Word2Vec feature extraction. The researcher is interested in conducting a study related to sentiment analysis of the 2024 Indonesian Presidential election using the Support Vector Machine algorithm because of its high accuracy compared to other algorithms. The use of feature extraction aims to improve the performance and effectiveness of the algorithm, and Word2Vec is chosen because it can represent contextual similarity between two words in the generated vectors, enabling concise and improved text classification based on context. The results of this research indicate the best performance at 80:20 ratio with a precision score of 88,94%, Recall 93.08%, F1-score 90,43% and accuracy of 90,75%. This study's results outperform prior research using the SVM method, which achieved an 82,3% accuracy.
PEMBUATAN SMART KOLAM LELE DALAM MENINGKATKAN EKONOMI MASYARAKAT KAMPUNG CYBER Lhaksmana, Kemas Muslim; Kusnayat, Agus; Widarmanti, Tri; Damayanti, Dida Diah; Doyo Yekti, Yusuf Nugroho; Rahayu, Mira; Sumargo, Seto
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.2190

Abstract

Desa Citeureup terletak di Kecamatan Dayeuhkolot, Kabupaten Bandung memiliki luas 205 ha yang ditempati sekitar 20.537 penduduk. Sebagai Perguruan Tinggi yang terletak di antara Desa Citeureup dan Desa Sukapura, Universitas Telkom mengadakan kegiatan pengabdian kepada masyarakat dengan tema Digital Community Service Engagement (Digital CSE) untuk pemberdayaan ekonomi masyarakat mandiri melalui implementasi teknologi di Desa Citeureup. Tema pengabdian tersebut merupakan amanat dari Pemerintah Provinsi Jawa Barat, yang telah menetapkan Desa Citeureup sebagai salah satu lokasi Kampung Cyber. Transformasi digital tidak hanya terkait dengan teknologi dan digitalisasi, namun yang terutama adalah memberikan solusi atas permasalahan utama masyarakat dengan berbasiskan teknologi. Oleh karena itu, kegiatan pengabdian kepada masyarakat yang dilakukan berupa pembuatan kolam “cerdas” budidaya lele untuk meningkatkan ekonomi warga sebagai kegiatan pengabdian yang berkelanjutan CSE hingga tiga tahun. Tujuan kegiatan pengabdian masyarakat adalah untuk meningkatkan ekonomi masyarakat Desa Citeureup melalui pembuatan kolam lele berbasis SMART (Specific, Measurable, Achievable, Relevant, Time-bound). Kolam lele merupakan salah satu potensi pengembangan ekonomi di wilayah tersebut, namun banyak warga yang menghadapi kendala dalam pengelolaan kolam lele yang efisien dan berkelanjutan. Metode SMART digunakan untuk merancang kolam lele yang tepat sasaran dan berdaya saing, sehingga dapat meningkatkan produktivitas dan pendapatan masyarakat setempat.
Multilabel Classification in Indonesian Translation of Religious Text using Word Centrality Term Weighting Dewantara, Muhammad Pascal; Lhaksmana, Kemas Muslim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7351

Abstract

This research focuses on enhancing the understanding of the Quran in the Indonesian translation dataset by employing a word centrality that feeds into a classifier model. The primary goal is to compare the hamming loss score from the TF-IDF and TW-IDF feature extraction methods in the Indonesia translation case study. The TF-IDF is commonly used in prior research. It has a higher hamming loss (which is worse in accuracy) than the TW-IDF incorporating centrality measurement more specifically in degree and closeness centrality. This research adds eigenvector centrality for a new compartment from the other methods. We used SVM, Random Forest (Bagging), and AdaBoost (Boosting) for the classifier model, with Mutual Information as the feature selection method. In evaluating the classifier, Hamming Loss is used given that the method is suitable for multilabel classification. Results indicate that the centrality measurement value for the term weighting method offers a significant improvement over regular TF-IDF. Each centrality method gives the best Hamming Loss score in each classifier model. Degree centrality gets 0.1275 in SVM, closeness centrality gets 0.1367 in AdaBoost, and eigenvector centrality gets 0.1204 in Random Forest. However, eigenvector centrality still can be a strong measurement method to lower the Hamming Loss score. Random Forest and AdaBoost give a significance better over SVM.
Sentiment Analysis of Lazada Product Reviews using Convolutional Neural Network and Naïve Bayes Models Siddiq, Ikhsan Maulana; Lhaksmana, Kemas Muslim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7834

Abstract

Lazada is one of the biggest marketplaces in Southeast Asia. One of the main features of Lazada is product reviews, any customer who has purchased and used a product from Lazada can provide reviews and ratings on the ones that have been purchased. Sentiment analysis on product reviews can help improve product and service quality, increase customer satisfaction, and improve purchasing decisions. Doing sentiment analysis of product reviews aims to help customers how to feel about the product, reading and analyzing each review manually is very not efficient. Sentiment analysis can automate handling large volumes of data quickly and accurately. In this research, using Lazada product review dataset to analyze sentiment by comparing Convolutional Neural Network (CNN) and Naive Bayes. CNN and Naive Bayes are two common methods used in text analysis and a comparison of their performance can provide the effectiveness of each in analyzing product sentiment. In this study, the authors propose to analyze the sentiment of product reviews using deep learning algorithm with CNN method. The results of this study explain that the CNN method can provide satisfactory results than Naive Bayes. Based on the overall evaluation, CNN gets an accuracy value of 99.31%, precision of 99.31%, recall 99.31%, and f1-score of 99.31%, while Naive Bayes gets the highest accuracy rate of 96.16%, precision 96.34%, recall 96.16%, and f1-score 96.16%.
Election Hoax Detection on X using CNN with TF-RF and TF-IDF Weighting Features Adelia, Dila; Astuti, Widi; Lhaksmana, Kemas Muslim
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5778

Abstract

X social media is a microblogging platform for sharing brief thoughts and trends. It has become a focal point for expressing political views. The increased political engagement on X social media has facilitated the swift and extensive sharing of ideas. Still, it also brings the risk of spreading false information and hoaxes that can manipulate public opinion. Preventing fake news on social media is crucial because it can influence election outcomes and social stability. For example, X social media has been used during elections to spread hoaxes, such as false claims of vote tampering or misleading information about candidate qualifications. This study implements a Convolutional Neural Network (CNN) due to its advantages in recognizing complex patterns and achieving high performance in tasks like classification. The dataset used in this study consists of 2,670 tweets. The dataset is divided into three subsets: 60% for training, 20% for testing, and 20% for validation. It also uses Term Frequency Relevance Frequency (TF-RF) and Term Frequency Inverse Document Frequency (TF-IDF) weighting features to improve accuracy in detecting fake news. This study compares the TF-RF and TF-IDF weighting features using the CNN classification method on the topic of the 2024 election. The testing results indicate that both TF-RF and TF-IDF achieved similar overall performance, with TF-RF slightly excelling in recall and F1-score. At the same time, TF-IDF showed a marginally higher precision.
Sentiment Analysis of Lazada Product Reviews using Convolutional Neural Network and Naïve Bayes Models Siddiq, Ikhsan Maulana; Lhaksmana, Kemas Muslim
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7834

Abstract

Lazada is one of the biggest marketplaces in Southeast Asia. One of the main features of Lazada is product reviews, any customer who has purchased and used a product from Lazada can provide reviews and ratings on the ones that have been purchased. Sentiment analysis on product reviews can help improve product and service quality, increase customer satisfaction, and improve purchasing decisions. Doing sentiment analysis of product reviews aims to help customers how to feel about the product, reading and analyzing each review manually is very not efficient. Sentiment analysis can automate handling large volumes of data quickly and accurately. In this research, using Lazada product review dataset to analyze sentiment by comparing Convolutional Neural Network (CNN) and Naive Bayes. CNN and Naive Bayes are two common methods used in text analysis and a comparison of their performance can provide the effectiveness of each in analyzing product sentiment. In this study, the authors propose to analyze the sentiment of product reviews using deep learning algorithm with CNN method. The results of this study explain that the CNN method can provide satisfactory results than Naive Bayes. Based on the overall evaluation, CNN gets an accuracy value of 99.31%, precision of 99.31%, recall 99.31%, and f1-score of 99.31%, while Naive Bayes gets the highest accuracy rate of 96.16%, precision 96.34%, recall 96.16%, and f1-score 96.16%.
Analisis Sentimen Tweet COVID-19 menggunakan K-Nearest Neighbors dengan TF-IDF dan Ekstraksi Fitur CountVectorizer Mahendra, Muhammad Hafizh; Murdiansyah, Danang Triantoro; Lhaksmana, Kemas Muslim
Dike Vol. 1 No. 2 (2023): Dike Edisi Agustus
Publisher : CV. Ro Bema

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69688/dike.v1i2.35

Abstract

Analisis sentimen tweet terkait COVID-19 telah menjadi topik penelitian yang menarik karena memberikan wawasan tentang pandangan dan perasaan pengguna media sosial terhadap situasi kesehatan global ini. Dalam penelitian ini, kami melakukan analisis sentimen tweet COVID-19 menggunakan metode K-Nearest Neighbors (K-NN) dengan dua metode ekstraksi fitur yang berbeda, yaitu Term Frequency-Inverse Document Frequency (TF-IDF) dan CountVectorizer. Langkah pertama dalam penelitian ini adalah mengumpulkan dataset tweet terkait COVID-19 dari sumber yang dapat dipercaya. Setelah itu, kami membersihkan dan melakukan pra-pemrosesan data untuk mengatasi masalah seperti tanda baca, stop words, dan tautan. Selanjutnya, kami menerapkan dua teknik ekstraksi fitur, yaitu TF-IDF dan CountVectorizer, untuk mengubah teks tweet menjadi representasi vektor yang dapat digunakan oleh algoritma K-Nearest Neighbors. Dalam implementasi K-NN, kami menentukan parameter K yang optimal melalui validasi silang untuk meningkatkan kinerja model. Kami juga membagi dataset menjadi subset pelatihan dan pengujian untuk mengukur akurasi dan kinerja model secara objektif. Hasil eksperimen menunjukkan bahwa K-Nearest Neighbors dengan ekstraksi fitur TF-IDF dan CountVectorizer keduanya memberikan hasil yang baik dalam analisis sentimen tweet COVID-19. Namun, kami menemukan bahwa satu metode mungkin memberikan performa yang lebih baik tergantung pada karakteristik dataset tertentu. Dalam kesimpulan, analisis sentimen tweet COVID-19 dengan menggunakan K-Nearest Neighbors dan dua metode ekstraksi fitur, TF-IDF dan CountVectorizer, dapat memberikan wawasan berharga tentang pandangan dan perasaan pengguna media sosial selama masa pandemi. Penelitian ini memberikan kontribusi untuk memahami persepsi publik tentang COVID-19 dan dapat berguna untuk menginformasikan kebijakan kesehatan dan strategi komunikasi yang lebih efektifPada studi ini digunakan KNN (K-Nearest Neighbor) yang memiliki kompleksitas komputasi rendah untuk mengklasifikasikan tweet. Kemudian ekstraksi fitur yang digunakan adalah TF-IDF (Term Frequency - Inverse Document Frequency) dan CountVectorizer. Hasil pengujian pada studi ini menghasilkan hasil akurasi terbaik 73,2% dengan menggunakan TF-IDF.
Hadith Text Classification Based on Topic Using Convolutional Neural Network (CNN) and TF-IDF Athallah, Muhammad Rafi; Lhaksmana, Kemas Muslim
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.20354

Abstract

Convolutional Neural Networks (CNN) will develop a hadith classification system to categorize texts based on specific topics or categories. This study compares two text representation techniques, namely Term Frequency- Inverse Document Frequency (TF-IDF) and Word2Vec, concerning the application of stemming and without stemming in the process. This study utilizes Category ID 0-5. About 2,845 data have been processed as required for testing. The data was divided into two parts, with a proportion of 80:20 for training and testing. Next, several models were evaluated, namely Word2Vec with stemming, TFIDFCNN without stemming, and TFIDFCNN with stemming. Accuracy, precision, recall, and F1 score metrics were used to assess the performance. The results show that the TFIDFCNN model without stemming performs best with 85% accuracy in topic-based text classification. This is due to the stability and efficiency of the model in processing data.
Co-Authors Abdurrahman, Azzam Abiyyu, Ahmad Syafiq Achmad Salim Aiman Adelia, Dila Adhyaksa Diffa Maulana Aditya Eka Wibowo Aditya Gifhari Soenarya Adiwijaya Aghi Wardani Agni Octavia Agus Kusnayat Ahmad Y, Rafly Ahmad Y Ahmad, Alif Faidhil Ahmad, Fathih Adawi Al Faraby, Said Alberi Meidharma Fadli Hulu Amalia Elma Sari Amien, Iqmal Lendra Faisal Andiani, Annisa Dwi Andini, Bilqiis Shahieza Angraini, Nadya Arda Anisa Herdiani Annisa Miranda Arini Rohmawati Athallah, Muhammad Rafi Aura Sukma Andini Bayu Muhammad Iqbal Bonar Panjaitan Brata Mas Pintoko Chandra Jaya Riadi Chlaudiah Julinar Soplero Lelywiary Choirulfikri, Muhammad Rizqi Damayanti, Lisyana Dana Sulitstyo Kusumo Danang Triantoro Murdiansyah David Winalda Delva, Dwina Sarah Deni Saepudin Denny Darlis Dewantara, Muhammad Pascal Dida Diah Damayanti Didit Adytia dina juni restina Dino Caesaron Donni Richasdy Donny Rhomanzah Dzidny, Dimitri Irfan Eki Rifaldi Eko Darwiyanto Ela Nadila Emrald Emrald Erwin Budi Setiawan Fakhrana Kurnia Sutrisno Farisi, Kamaludin Hanif Fatih, Muhammad Abdurrohman Al Ferdian Yulianto Fhira Nhita Guido Tamara Hadi, Salman Farisi Setya Haga Simada Ginting Haidar, Muhammad Dzakiyuddin Harahap, Rizki Nurhaliza Harmandini, Keisha Priya Haura Athaya Salka Herodion Simorangkir Hutama, Nanda Yonda Ika Puspita Dewi Intan Khairunnisa Fitriani Irgi Aditya Rachman Isman Kurniawan Jofardho Adlinnas Jondri Jondri Jordan, Brilliant Kacaribu, Isabella Vichita Kamaludin Hanif Farisi Kautsar Ramadhan Sugiharto Lukito Agung Waskito Luqman Bramantyo Rahmadi Luthfi, Muhammad Faris M. Mahfi Nurandi Karsana Mahendra Dwifebri Purbolaksono Mahendra, Muhammad Hafizh Marendra Septianta Marozi, Ericho Mehdi Mursalat Ismail Mira Rahayu Moch Arif Bijaksana Mohamad Reza Syahziar Muhammad Adzhar Amrullah Muhammad Arif Kurniawan Muhammad Yuslan Abu Bakar Muhammad Zaid Dzulfikar muhammad zaky ramadhan Muhammad Zidny Naf'an Murman Dwi Praseti Musyafa’noer Sandi Pratama Nanda Yonda Hutama Naufal Furqan Hardifa Naufal Hilmiaji Naufal Rasyad Nibras Syihabil Haq Octaryo Sakti Yudha Prakasa Okky Zoellanda A. Tane Pamungkas, Danit Hafiz Praja, Yudhistira Imam Purwita, Naila Iffah Putri, Arla Sifhana Putri, Meira Reynita Putrisia, Denada R. Fajrika hadnis Putra Rafi Hafizhni Anggia Rahadian, Muhammad Rafi Ramdhani, Muhammad Rifqi Fauzi Rastim Rastim Rayhan, Muhammad Aditya Resky Nadia Rizki Luthfan Azhari Rizky Ahmad Saputra Rizky Aria Mu’allim Rizky, Fariz Muhammad Seno Adi Putra Seto Sumargo Shabrina, Ghina Annisa Siddiq, Ikhsan Maulana Sindi Fatika Sari Sri Utami Sri Widowati Sukmawan Pradika Janusange Santoso Suwaldi Mardana Syadzily , Muhammad Hasan Tri Widarmanti Try Moloharto Try Moloharto Vitalis Emanuel Setiawan Wardhani, Fitri Herinda Widi Astuti Widi Astuti Youga Pratama Yuliant Sibaroni Yusuf Nugroho Doyo Yekti Zaena, Siffa Zaenal Abidin ZK Abdurahman Baizal Zulkarnaen, Imran