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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Dinamisia: Jurnal Pengabdian Kepada Masyarakat Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Jurnal Pengabdian Masyarakat Indonesia Journal La Multiapp eProceedings of Engineering Eduvest - Journal of Universal Studies Jurnal INFOTEL
Yuliant Sibaroni
Telkom University
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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A Optimizing Word2Vec Dimensions for Sentiment Analysis of Photomath Reviews using Random Forest and SVM Varissa Azis, Diva Azty; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6616

Abstract

Technology in the Industrial Revolution 4.0 era supports modern learning through apps like Photomath, simplifying math problem-solving for users. However, diverse user reviews highlight the need for sentiment analysis to evaluate app quality. This research analyzes 9,059 reviews of Photomath collected from the Google Play Store using Python. Word2Vec is used in the study to compare Random Forest and Support Vector Machine (SVM) classifiers for feature extraction. To ensure clean and consistent data, preprocessing techniques such as stemming, tokenization, and stopword removal were used. Text with rich semantic aspects was mathematically represented using Word2Vec. The findings show that SVM using an RBF kernel performed better than Random Forest, with an F1-score of 88.5%, 88.5% accuracy, 88.7% precision, and 88.5% recall. Performance was effectively improved by combining 300-dimensional Word2Vec with stemming algorithms. While Random Forest achieved slightly lower accuracy, it shows promise for specific use cases. This study offers practical insights for improving Photomath by tailoring updates based on user sentiment. The findings emphasize the importance of preprocessing, dimensional optimization, and classifier selection in developing accurate sentiment analysis models. Limitations include the dataset size and the use of classical machine learning models. Future research could address these by exploring larger datasets or deep learning techniques to further improve performance.
Classification of Multi-Label of Hate Speech on Twitter Indonesia using LSTM and BiLSTM Method Aurora Az Zahra, Elita; Sibaroni, Yuliant; Suryani Prasetyowati, Sri
JINAV: Journal of Information and Visualization Vol. 4 No. 2 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1864

Abstract

Social media is a communication tool that supports users to interact socially using technology. One of the most popular social media platforms is Twitter. However, its media platform has been considered by the virtual police as one of the main sources of spreading hate speech on social media. In this final project research, the authors conducted a study on the detection of hate speech in tweets on Twitter Indonesia. The method used in this research is multi-label classification by applying the LSTM and BiLSTM methods. The dataset used was 13,169 tweet data, and data labeling process was carried out into 12 classes. The results revealed that the LSTM and BiLSTM methods had good performance in classifying text data with 10 trials with an accuracy value of 78.67% for LSTM and 80.25% for BiLSTM. Based on the accuracy obtained, BiLSTM has higher accuracy than LSTM, so it can be concluded that BiLSTM is superior to LSTM.
Implementation of BiLSTM and IndoBERT for Sentiment Analysis of TikTok Reviews Farizi, Azziz Fachry Al; Sibaroni, Yuliant
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5815

Abstract

The significant increase in users on TikTok has led to a notable rise in the number of reviews in the form of opinions given to the application. The large number of opinions can be analyzed to identify the prevailing sentiment among the community towards the application. The sentiment analysis method employing machine learning is particularly well-suited to this problem due to its practicality and efficiency. The objective of this research is to develop a model that can be utilized as a sentiment analysis tool with a high degree of accuracy. In this research, the BiLSTM algorithm, combined with IndoBERT, a pre-trained model, is employed. The BiLSTM can comprehend the interrelationships between words within a sentence in a bidirectional manner. IndoBERT is pertinent to this research as it is a model that has been fine-tuned using Indonesian language datasets from various sources on the Internet. To support this research, a scenario was created by considering various aspects when adding methods as an optimization scheme until the optimal model was identified. The outcomes of experimentation demonstrate that sentiment analysis using the BiLSTM+IndoBERT method achieved the highest accuracy, reaching 81% in the classification report test and an average accuracy of 92.03% in cross-validation testing with a total of 10 folds.
Effectiveness of Word2Vec and TF-IDF in Sentiment Classification on Online Investment Platforms Using Support Vector Machine Rifaldy, Fadil; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6055

Abstract

Investing in Indonesia is increasingly popular, especially among the millennial generation. investments such as deposits, gold, stocks, and online investment applications are increasingly in demand. This research focuses on the sentiment classification of user reviews of the Nanovest online investment application on the Google Play Store using the Support Vector Machine (SVM) method. SVM is used because it can classify opinions into positive and negative sentiment classes with good accuracy, by evaluating how effective Word2Vec features extraction that can convert words in a text into numerical vectors and TF-IDF that is capable of high-dimensional word weighting and TF-IDF Weighted Word2Vec combination features to produce richer vector representations. Tests were conducted using four SVM kernels namely Linear, Polynomial, RBF, and Sigmoid. The results show that Word2Vec with RBF kernel and 300 vector size produces the highest accuracy of 95.46%, the combination of TF-IDF Weighted Word2Vec also gives good performance with 95.29% accuracy on RBF kernel. However, TF-IDF alone resulted in the lowest accuracy of 93.31% on the Sigmoid kernel. This research shows that Word2Vec and combined feature extraction methods are effective in improving sentiment classification performance compared to TF-IDF.
COMBINATION OF LOGISTIC REGRESSION AND NAÏVE BAYES IN SENTIMENT ANALYSIS OF ONLINE LENDING APPLICATION PLATFORMS BY UTILIZING THE LEXICONS FEATURE Zaenudin, Muhammad Faisal; Sibaroni, Yuliant
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6163

Abstract

In the digital age, online lending apps have become an important tool in facilitating financial transactions and supporting MSMEs. However, the existence of negative opinions related to violations such as theft of customer data raises concerns in the community. This research aims to analyze sentiment towards online loan applications, especially Kredivo, using a combination of Logistic Regression and Naïve Bayes which is optimized through the Lexicons feature. Data is taken from Google Play Store reviews, then labeling, preprocessing, and feature extraction are executed through TF-IDF technique. The classification models built are Naive Bayes (NB) and Logistic Regression (LR), where the results of the two models are combined with the ensemble voting method using lexicons features. The evaluation results show that the combination approach of the three methods can significantly improve classification accuracy compared to the use of a single method. The combined model achieved an accuracy of 89.62%, higher than Logistic Regression (86.19%) and Naive Bayes (83.54%).
Sentiment Classification in E-Commerce Using Naïve Bayes and Combined Lexicon - N-Gram Features Al Ghazali, Nabiel Muhammad; Sibaroni, Yuliant
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6157

Abstract

This study investigates sentiment classification in e-commerce using Naïve Bayes with lexicon-based, N-gram, and combined lexicon-N-gram features. While previous research has employed various e-commerce platforms and achieved varying degrees of accuracy using Naïve Bayes for sentiment analysis, the combination of lexicon and N-gram features with Naïve Bayes has not been extensively explored in e-commerce contexts. This study proposes to evaluate three models: Naïve Bayes with Lexicon Features, Naïve Bayes with N-Gram Features, and Naïve Bayes with Combined Lexicon-N-Gram Features. The research analyzes 10,000 customer reviews of the Shopee application from the Google Play Store. Results show that the Naïve Bayes model using combined lexicon-N-gram features achieved the highest performance among the three approaches. Using 10-fold cross-validation, the combined model achieved an average accuracy of 83.4%. The N-gram model showed strong performance with an average accuracy of 82.8%, while the lexicon-based model demonstrated lower performance with an average accuracy of 77%. These findings contribute to the field of sentiment analysis in e-commerce, highlighting the effectiveness of combining lexicon and N-gram features when used with Naïve Bayes classifiers. The study provides insights into optimizing sentiment classification techniques for e-commerce platforms, emphasizing the importance of leveraging both semantic and contextual information in sentiment analysis tasks.
Penerapan SEO dengan Teknik On Page Terhadap Website Studentaffair Telkom University untuk Meningkatkan Visibilitas Hanif, Ibrahim; Sibaroni, Yuliant
eProceedings of Engineering Vol. 12 No. 1 (2025): Februari 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak - Website studentaffair Telkom University adalah website Direktorat Kemahasiswaan Telkom University dibawah naungan wakil rektor III bidang admisi, Kemahasiswaan, Karir dan Alumni yang bertujuan agar mahasiswa Telkom University dan stakeholder dapat melihat dan mengetahui seluruh informasi dan layanan yang ada di Ditmawa. Website tersebut sudah informatif dan cukup lengkap namun masih terdapat kesulitan untuk menemukan layanan tersebut kecuali dengan mengetikkan kata kunci nama websitenya. Selain itu, isi kontennya masih sulit ditemukan mesin pencari Google. Terbukti hasil observasi dan analisis SEO website versi desktop menggunakan seprobot.com dengan pengumpulan kata kunci yang diambil dari kata kunci umum sampai spesifik ditemukan bahwa hasil pengumpulan data dari 32 kata kunci terdapat 2 halaman yang belum optimal di mesin pencari Google yaitu halaman layanan TAK dan sejarah. Oleh karena itu, perlu dilakukan implementasi on page SEO untuk meningkatkan visibilitas di mesin pencarian. Dengan mengumpulkan kata kunci dari google keyword planner kemudian diimplementasikan pada 8 indikator on page SEO pada satu halaman website studentaffair Telkom university. Implementasi tersebut digunakan karena merupakan suatu teknik yang difokuskan untuk mengoptimalkan factor internal website seperti bagaimana konten dapat tampil di halaman atas SERP. Hasil implementasi menunjukan peningkatan peringkat halaman website studentaffair Telkom university yang awalnya tidak ditemukan menjadi peringkat satu di mesin pencarian google. Kata kunci - on page, SEO, studentaffair, visibilitas, website
Deteksi Berita Hoaks Terkait Debat Capres Pemilu 2024 Pada Media Sosial Menggunakan Metode Bayesian Neural Network Maulida , Anandita Prakarsa; Sibaroni, Yuliant
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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Abstract

Berita Hoaks di media sosial semakin mengkhawatirkan,terutama pada saat pemilu, di mana informasi ini dapatmempengaruhi opini publik dan mengganggu integritaspemilu. Penelitian ini bertujuan untuk mengembangkansistem deteksi hoaks menggunakan metode Bayesian NeuralNetwork (BNN) yang dioptimalkan dengan teknik Termfrequency-Inverse Document frequency (TF-IDF). Hasilpengujian menunjukkan bahwa sistem ini berhasil mencapaiakurasi tinggi dalam mengklasifikasikan berita hoaks dannon-hoaks. Dibandingkan dengan penelitian lain, sepertimenggunakan metode K-Nearest Neighbor (K-NN) mencapaiakurasi 85%, Naïve Bayes dengan akurasi 82,6%, danpenelitian data mining menggunakan TF-IDF mencapaiakurasi rendah 57%. Dengan menggunakan metode inimengklasifikasi berita hoaks secara otomatis denganmemanfaatkan distribusi probabilistik untuk meningkatkanakurasi deteksi. Pengujian ini berhasil mendapatkan akurasidengan fitur TF-IDF mencapai 85,71%, fitur Word2Vecmencapai akurasi tinggi yaitu 90,24%, dan fitur BERTmendapatkan akurasi rendah 75,27%. Penelitian inidiharapkan dapat menjadi referensi bagi pengembanganlebih lanjut dalam sistem deteksi hoaks dan meningkatkankesadaran masyarakat akan pentingnya verifikasi informasi. Kata kunci: hoaks, bayesian neural network (BNN), pemilu 2024,media sosial, TF-IDF.
Pendeteksian berita palsu menggunakan RoBERTa dengan Optimalisasi Word Embedding Arminta, Adisaputra Nur; Sibaroni, Yuliant
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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Abstract

Penyebaran berita palsu (hoax) telah menjadi permasalahanserius yang mempengaruhi opini publik dan menciptakanpolarisasi di masyarakat. Penelitian ini bertujuan untukmendeteksi berita palsu menggunakan model RoBERTa yangdioptimalkan dengan tiga teknik word embedding. Wordembedding yang digunakan adalah RoBERTa, Word2Vec,dan GloVe. Dataset yang digunakan adalah "Indonesian factand hoax political news" yang diambil dari Kaggle, Datasetini memerlukan tahap pre-processing untuk membersihkanketidakkonsistenan data, seperti mengubah singkatanmenjadi kata lengkap dan menghapus tanda baca.Selanjutnya, dilakukan representasi teks menggunakan tigametode word embedding yaitu Word2Vec, GloVe, danRoBERTa. Proses pelatihan model dilakukan dengan validasisilang K-Fold untuk meningkatkan generalisasi model. Hasilpenelitian menunjukkan bahwa embedding RoBERTamencapai akurasi terbaik 96%, sedangkan word embeddingWord2Vec mendapatkan akurasi 94%. Word EmbeddingGlove menunjukkan performa paling rendah dengan akurasi51%. Penelitian ini membuktikan bahwa pemilihan teknikword embedding yang tidak tepat untuk model RoBERTadapat mengurangi akurasi dan efektivitas model dalammendeteksi berita palsu. Diharapkan bahwa temuan dalampenelitian ini dapat memberikan kontribusi terhadappeningkatan sistem deteksi berita palsu di masa mendatang. Kata kunci: hoax, RoBERTa, GloVe, Word2Vec
Land Price Distribution Prediction in Jakarta Using Support Vector Machine with Feature Expansion and Kriging Interpolation Pilar Gautama, Hadid; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2216

Abstract

Fluctuations in land prices over time are significant, especially in big cities, one of which is Jakarta. The increase in land prices is influenced by high demand, location-related needs, ease of access to various public facilities and population density. Uncontrolled prices and lack of information about the distribution of land prices cause buyers to acquire land that does not meet their needs. This study develops a land price distribution prediction system for Jakarta for 2025-2026 using Support Vector Machine (SVM) with time-based feature expansion and spatial interpolation. The SVM model with an RBF kernel demonstrated superior performance, achieving 93.14% accuracy for 2025 predictions using the t-1 model. For 2026 predictions, the t-2 model achieved 83.33% accuracy. This approach involves utilizing one to two years of historical data and systematically selected features, ensuring more accurate and relevant predictions. Ordinary kriging interpolation visualizations revealed a significant shift in land price distribution patterns, indicating a decline in affordable land availability and an increase in high-value properties across Jakarta. The integration of SVM and kriging interpolation, coupled with comprehensive evaluation metrics, provides a robust methodological framework for predicting urban land price distributions. This system offers practical implications for informed decision-making in Jakarta's dynamic land market, enabling stakeholders to make efficient, budget-based property decisions. The research contributes significantly to urban planning by providing a comprehensive tool for understanding and predicting land price trends, which can assist various stakeholders in making informed property investment decisions.
Co-Authors Abduh Salam Adhe Akram Azhari Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aditya Iftikar Riaddy Adiwijaya Agi Maulana Al Ghazali, Nabiel Muhammad Alfauzan, Muhammad Fikri Alya, Hasna Rafida Andrew Wilson Angger Saputra, Revelin Annisa Aditsania Apriani, Iklima Aqilla, Livia Naura Ardana, Aulia Riefqi Arista, Dufha Arminta, Adisaputra Nur Arya Pratama Anugerah Asramanggala, Muhammad Sulthon Atikah, Balqis Sayyidahtul Attala Rafid Abelard Aufa, Rizki Nabil Aulia Rayhan Syaifullah Aurora Az Zahra, Elita Azmi Aulia Rahman Bunga Sari Chamadani Faisal Amri Chindy Amalia Claudia Mei Serin Sitio Damar, Muhammad Damarsari Cahyo Wilogo Delvanita Sri Wahyuni Derwin Prabangkara Desianto Abdillah Devi Ayu Peramesti Dhina Nur Fitriana Dhina Nur Fitriana Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erwin Budi Setiawan Fadhilah Nadia Puteri Fadli Fauzi Zain Fairuz, Mitha Putrianty Faiza Aulia Rahma Putra Farizi, Azziz Fachry Al Fatha, Rizkialdy Fathin, Muhammad Ammar Fatihah Rahmadayana Fatri Nurul Inayah Fauzaan Rakan Tama Feby Ali Dzuhri Fery Ardiansyah Effendi Ferzi Samal Yerzi Fhira Nhita Fitriansyah, Alam Rizki Fitriyani Fitriyani F. Fitriyani Fitriyani Fitriyani Fitriyani Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hanif, Ibrahim Hanurogo, Tetuko Muhammad Hanvito Michael Lee Hawa, Iqlima Putri Haziq, Muhammad Raffif I Gusti Ayu Putu Sintha Deviya Yuliani I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Indra Kusuma Yoga Indwiarti irbah salsabila Irfani Adri Maulana Irma Palupi Islamanda, Muhammad Dinan Izzan Faikar Ramadhy Izzatul Ummah Janu Akrama Wardhana Jauzy, Muhammad Abdurrahman Al Kemas Muslim Lhaksmana Kinan Salaatsa, Titan Ku Muhammad Naim Ku Khalif Lanny Septiani Laura Imanuela Mustamu Lesmana, Aditya Lintang Aryasatya Lisbeth Evalina Siahaan Made Mita Wikantari Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mahmud Imrona Maulida , Anandita Prakarsa Mitha Putrianty Fairuz Muhamad Agung Nulhakim Muhammad Arif Kurniawan Muhammad Damar Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Kiko Aulia Reiki Muhammad Novario Ekaputra Muhammad Rajih Abiyyu Musa Muhammad Reza Adi Nugraha Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Ni Made Dwipadini Puspitarini Niken Dwi Wahyu Cahyani Novitasari, Ariqoh Nuraena Ramdani Okky Brillian Hibrianto Okky Brillian Hibrianto Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Prasetiyowati, Sri Prasetyo, Sri Suryani Prasetyowati, Sri Sulyani Prawiro Weninggalih Priyan Fadhil Supriyadi Purwanto, Brian Dimas Puspandari, Dyas Putra, Daffa Fadhilah Putra, Ihsanudin Pradana Putra, Maswan Pratama Putri, Dinda Rahma Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafik Khairul Amin Rafika Salis Rahmanda, Rayhan Fadhil Raisa Benaya Revi Chandra Riana Rian Febrian Umbara Rian Putra Mantovani Ridha Novia Ridho Isral Essa Ridho, Fahrul Raykhan Rifaldy, Fadil Rifki Alfian Abdi Malik Riski Hamonangan Simanjuntak Rizki Annas Sholehat Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Saniyah Nabila Fikriyah Saragih, Pujiaty Rezeki Satyananda, Karuna Dewa Septian Nugraha Kudrat Septian Nugraha Kudrat Serly Setyani Shyahrin, Mega Vebika Sinaga, Astria M P Siti Inayah Putri Siti Uswah Hasanah Sri Suryani Prasetiyowati Sri Suryani Prasetyowati Sri Suryani Sri Suryani Sri Utami Sujadi, Cika Carissa Suryani Prasetyowati, Sri Syarif, Rizky Ahsan Umulhoir, Nida Varissa Azis, Diva Azty Viny Gilang Ramadhan Vitria Anggraeni WAHYUDI, DIKI Widya Pratiwi Ali Winico Fazry Wira Abner Sigalingging Zaenudin, Muhammad Faisal Zaidan, Muhammad Naufal Zain, Fadli Fauzi ZK Abdurahman Baizal