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All Journal CALYPTRA : Jurnal Ilmiah Mahasiswa Universitas Surabaya Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Network Engineering Research Operation [NERO] Indonesian Journal of Information System Applied Information System and Management Keluwih: Jurnal Sains dan Teknologi Jurnal Teknik Informatika (JUTIF) Teknika
Vincentius Riandaru Prasetyo
Program Studi Teknik Informatika, Universitas Surabaya, Surabaya, Jawa Timur
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Sentiment Analysis for Sumber Gempong Rice Field-Based Tourism Destination using Long Short-Term Memory Benarkah, Njoto; Prasetyo, Vincentius Riandaru; Soetiyono, Jehuda Rivaldo
Keluwih: Jurnal Sains dan Teknologi Vol. 5 No. 2 (2024): Keluwih: Jurnal Sains dan Teknologi (August) - In Progress
Publisher : Direktorat Penerbitan dan Publikasi Ilmiah, Universitas Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24123/saintek.v5i2.6498

Abstract

Abstract—Sumber Gempong is a rice field-based tourist destination located in Ketapanrame Village, Trawas District, Mojokerto Regency, East Java Province. It is managed by a village-owned company (BUMDesa Mutiara Welirang). BUMDesa evaluates tourist satisfaction manually by reviewing online comments and it consumes time and labor works. Data used in this research automatically collected from Google Maps Review. Long Short-Term Memory (LSTM) method analyze data of two sentiment labels, positive or negative, based on four categories: facilities, services, culinary, and attractions. The collected dataset has 674 comments consist of 420 positive sentiments and 254 negative sentiments with 320 facilities, 61 services, 125 culinary, and 192 attractions comments. Five LSTM models were trained on each of four categories and an overall category. The trained models of overall, facilities, services, culinary, and attractions categories achieved, respectively, 91.2%, 86.8%, 94.1%, 89.7%, and 95.6% of accuracies. The average result accuracy is 91.48%. The manager of BUMDesa Mutiara Welirang satisfied with the results of the system and the sentiment results can be used as evaluation material for Sumber Gempong. Keywords: sentiment anaylsis, LSTM, deep learning, social media, tourism Abstrak—Wisata Sawah Sumber Gempong berada di Desa Ketapanrame, Kecamatan Trawas, Kabupaten Mojokerto dan merupakan tempat wisata alam yang dikelola oleh BUMDesa Mutiara Welirang. Evaluasi terhadap tempat wisata ini dilakukan dengan membaca secara manual ulasan-ulasan yang ditulis di media sosial dan pengamatan pribadi. Banyaknya jumlah ulasan yang ada menjadi kendala dalam melakukan evaluasi karena membutuhkan waktu yang cukup lama. Penelitian ini mengambil data ulasan secara otomatis dari media sosial yang diberi label positif atau negatif berdasarkan empat kategori, yaitu fasilitas, pelayanan, kuliner, dan wahana. Metode Long Short-Term Memory (LSTM) dipakai sebagai alat untuk melakukan analisis sentimen. Pengambilan data secara otomatis mendapatkan 674 ulasan yang dibagi menjadi 420 ulasan positif dan 254 ulasan negatif, dengan 320 ulasan fasilitas, 61 ulasan pelayanan, 125 ulasan kuliner , dan 192 ulasan wahana. Lima buah model dilatih berdasar tiap kategorinya dan kategori secara keseluruhan. Model yang telah dilatih mendapatkan nilai akurasi sebesar 91,2%, 86,8%, 94,1%, 89,7%, dan 95,6% berturut-turut untuk keseluruhan kategori, kategori fasilitas, layanan, kuliner, dan wahana. Rata-rata akurasi mencapai 91,48%. Hasil dari sistem telah diujicobakan kepada manajer BUMDesa Mutiara Welirang dan bisa dipakai sebagai bahan evalusi untuk peningkatan kualitas di Sumber Gempong. Kata kunci: analisis sentimen, LSTM, deep learning, media sosial, wisata
Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM Prasetyo, Vincentius Riandaru; Naufal, Mohammad Farid; Wijaya, Kevin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6334

Abstract

This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). Due to the complex linguistic structure of the Indonesian language, sentiment classification remains challenging, necessitating advanced methods to capture both local patterns and sequential dependencies. The primary objective of this research is to improve sentiment classification accuracy by leveraging a hybrid model that integrates CNN for feature extraction and Bi-LSTM for contextual understanding. The dataset consists of 800 manually labeled samples collected from social media platforms, preprocessed using case folding, stop word removal, and lemmatization. Word embeddings are generated using the Word2Vec CBOW model, and the classification model is trained using a hybrid architecture. The best performance was achieved with 32 Bi-LSTM units, a dropout rate 0.5, and L2 regularization, which was evaluated using Stratified K-Fold cross-validation. Experimental results demonstrate that the hybrid model outperforms conventional deep learning approaches, achieving 95.24% accuracy, 95.09% precision, 95.15% recall, and 95.99% F1 score. These findings highlight the effectiveness of hybrid architectures in sentiment analysis for low-resource languages. Future work may explore larger datasets or transfer learning to enhance generalizability.
Leveraging Deep Learning for Cultural Preservation: A Mobile Application for Padang Cuisine Benarkah, Njoto; Prasetyo, Vincentius Riandaru; Prakarsa, Andreas Bayu
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46680

Abstract

Padang cuisine, originating from West Sumatra, Indonesia, is recognized as one of the most widespread traditional food types due to its prevalence in restaurants across the country. Despite the increasing interest in classifying Indonesian food using artificial intelligence, there have been limited studies that have explicitly focused on classifying Padang dishes using deep learning approaches. This study aimed to develop an intelligent mobile application capable of identifying various Padang dishes from images using transfer learning-based convolutional neural networks (CNNs). Four pre-trained CNN architectures—EfficientNetV2M, MobileNetV2, VGG19, and ResNet152V2—were fine-tuned and evaluated on a dataset of Padang food images. This dataset comprised a total of 1,108 images, categorized into nine distinct Padang dishes, collected from both publicly available repositories and original photographs taken for this study. Among these models, ResNet152V2 achieved the best performance after optimization, with a validation loss of 0.4142 and a test accuracy of 91.33%. The optimized model was converted to TensorFlow Lite and deployed as a mobile application, enabling real-time recognition of Padang dishes. This study presented a deep-learning-based mobile solution for recognising nine traditional Padang dishes with high accuracy, demonstrating the potential of AI-driven applications to support culinary heritage preservation and promote cultural tourism in Indonesia.
HATE SPEECH DETECTION PADA VIDEO MENGGUNAKAN METODE KNN DAN NAIVE BAYES Christopher Kelvin Pintoro Kwan; Vincentius Riandaru Prasetyo; Fitri Dwi Kartikasari
CALYPTRA Vol. 13 No. 2 (2025): Calyptra : Jurnal Ilmiah Mahasiswa Universitas Surabaya (Mei)
Publisher : Perpustakaan Universitas Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract—Hate speech has had many negative impacts in Indonesia, such as riots, physical and verbal altercations, divisions in society, and many more. Social media is the place to spread hate speech most quickly. Not only through text posts, It is quite common to find hate speech in the form of videos. In this research, researchers will create a model that applies machine learning models to detect hate speech in videos, where currently most machine learning models are used to detect hate speech in text form only. In its application, the model will convert the input video into text using Google API. Then classification will be carried out using KNN to classify whether the video is hate speech or not, and Naive Bayes to classify the context of the video. In an unbalanced dataset, the classification results obtained for hate speech classification were 74% and for video context classification the accuracy was 45%. In a balanced dataset but overfitting occurs, the accuracy obtained in hate speech classification is 93% and in video context classification the accuracy is 55%. Based on the test results, it was found that the model used can have good accuracy if the dataset used is balanced between labels and there is no overfitting on the labels. Keywords: Hate Speech, Machine Learning, KNN, Naive Bayes Abstrak—Hate speech atau ujaran kebencian sudah memberikan banyak dampak yang negatif di Indonesia seperti kerusuhan, pertengkaran fisik maupun verbal, perpecahan di masyarakat, dan masih banyak lagi. Sosial media menjadi tempat untuk menyebarkan hate speech paling cepat. Tidak hanya melalui postingan teks, cukup sering juga ditemukan hate speech berbentuk video. Dalam penelitian ini, peneliti akan membuat model yang menerapkan model machine learning untuk mendeteksi adanya hate speech dalam video dimana saat ini kebanyakan model machine learning digunakan untuk mendeteksi hate speech dalam bentuk teks saja. Dalam penerapannya, model akan mengubah video yang diinput menjadi teks menggunakan Google API. Kemudian klasifikasi akan dilakukan menggunakan KNN untuk mengklasifikasikan apakah video hate speech atau bukan, dan naive bayes untuk mengklasifikasikan konteks dari video. Pada dataset yang tidak seimbang hasil klasifikasi yang didapatkan pada klasifikasi hate speech adalah 74% dan klasifikasi konteks video didapatkan akurasi sebesar 45%. Pada dataset yang seimbang namun terjadi overfitting akurasi yang didapatkan pada klasifikasi hate speech adalah 93% dan pada klasifikasi konteks video didapatkan akurasi 55%. Berdasarkan hasil uji coba didapatkan bahwa model yang digunakan dapat memiliki akurasi yang baik apabila dataset yang digunakan seimbang antar label dan tidak ada overfitting pada label. Kata kunci: hate speech, machine learning, knn, naive bayes
Improving the Performance of Machine Learning Classifiers in Sentiment Analysis of Jenius Application Using Latent Dirichlet Allocation in Text Preprocessing Prasetyo, Vincentius Riandaru; Benarkah, Njoto; Rahmad, Bayu Aji Hamengku
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5238

Abstract

Sentiment analysis aims to classify a person’s opinion into a specific sentiment, such as positive or negative. The choice of preprocessing used can influence the performance of a sentiment analysis model. The Latent Dirichlet Allocation (LDA) method, commonly used for topic modelling, can be employed as an additional preprocessing step to identify relevant words associated with a particular sentiment label. This study aims to assess whether the LDA method, implemented in the preprocessing stage, can enhance the performance of machine learning models, including Naïve Bayes, Decision Tree, KNN, Logistic Regression, and SVM. This study utilized a dataset comprising 1,800 reviews, with 900 labelled as positive and 900 as negative. Words with an LDA score of at least 0.15 were given additional weight in the TF-IDF stage before model training. After the model was developed, evaluation was carried out by calculating accuracy, precision, recall, and F1-score. The use of LDA in preprocessing improved the performance of all classification models by 1-3% across most evaluation metrics. Specifically, the Logistic Regression model achieved the best performance, followed by SVM and KNN. This performance improvement is aligned with the use of LDA to reduce semantic noise and improve feature representation. Furthermore, this research is also helpful for monitoring customer opinions in the digital banking sector, enabling the rapid and accurate identification of priority issues. Further research could explore the comparison of performance with other topic modelling and feature extraction methods, as well as expanding the dataset and utilizing multiclass models.
Analisis Sentimen untuk Identifikasi Bantuan Korban Bencana Alam berdasarkan Data di Twitter Menggunakan Metode K-Means dan Naive Bayes Prasetyo, Vincentius Riandaru; Erlangga, Gatum; Prima, Delta Ardy
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107077

Abstract

Media sosial telah menjadi sarana yang umum bagi orang untuk mengekspresikan diri dan meminta bantuan ketika mereka mengalami musibah. Banyak korban bencana alam di Indonesia menggunakan Twitter untuk meminta bantuan seperti makanan, air bersih, dan lainnya. Penelitian ini bertujuan untuk melakukan analisis sentimen dari data Twitter untuk menentukan bantuan bagi korban bencana alam di Indonesia. Pada penelitian ini, metode K-Means dan Naïve Bayes dikombinasikan untuk melakukan analisis sentimen. Dalam penelitian ini, bantuan yang akan ditemukan adalah pakaian, makanan, air bersih, dan obat. Metode K-Means dipilih karena mudah digunakan dan mudah diimplementasikan, sementara metode Naïve Bayes digunakan karena menghasilkan nilai akurasi yang baik dalam klasifikasi. Hasil uji coba memperlihatkan bahwa kombinasi K-Means dan Naïve Bayes menghasilkan akurasi sebesar 76,46%, di mana akurasi tersebut lebih tinggi daripada implementasi Naïve Bayes saja, dengan akurasi sebesar 74,65%. Berdasarkan validasi yang dilakukan dengan Kepala Badan Penanggulangan Bencana Daerah (BPBD) di Kota Tarakan, sistem ini dapat membantu BPBD Kota Tarakan dalam memberikan bantuan yang tepat ke lokasi bencana.   Abstract   Social media has become a common place for people to express themselves and ask for help when they are going through a calamity. Many victims of natural disasters in Indonesia use Twitter to request assistance such as food, clean water, and others. Therefore, this study aims to conduct sentiment analysis from Twitter data to determine aid for victims of natural disasters in Indonesia. In this research, K-Means and Naïve Bayes methods will be combined for sentiment analysis. In this study, the assistance that will be found is clothing, food, clean water, and medicine. The K-Means method was chosen because it is easy to use and easy to implement, while the Naïve Bayes method was chosen because it has a good level of accuracy in classification. The results showed that the combination of K-Means and Naïve Bayes had a higher accuracy rate of 76.46%, compared to the use of Naïve Bayes alone, which was 74.65%. Based on the validation conducted with the Head of the Regional Disaster Management Agency (BPBD) in Tarakan City, this system can assist the Tarakan City BPBD in providing appropriate assistance to disaster locations.
Co-Authors Aldo Adhi Mulyono Anasthasya Averina Budi Hartanto Budiarjo Budiarjo Christian Lauw Christopher Kelvin Pintoro Kwan David Sugiarto Erlangga, Gatum Fitri Dwi Kartikasari Hamzah Lazuardi Joko Siswantoro Juan Timothy Soebroto Kevin Wijaya, Kevin Lauren Sunyoto Michael Marly Angkiriwang Mirella Mercifia Mohammad Farid Naufal, Mohammad Farid Mohd Sanusi Bin Azmi Monica Widiasri, Monica Muhammad Alifian Fajar Pratama Njoto Benarkah Prakarsa, Andreas Bayu Prima, Delta Ardy Rahmad, Bayu Aji Hamengku Senradel Daniel Njudang Septian Ardi Winatan Sesilia Shania Setyawan, Christopher Soetiyono, Jehuda Rivaldo Stefan Axel Vioni Jannet Chrisintha