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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Infotekmesin JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research Jurnal Abdimas Bina Bangsa Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Atma Inovasia Jurnal INFOTEL Infact: Jurnal Sains dan Komputer
Sunneng Sandino Berutu
Universitas Kristen Immanuel
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Implementation of Latent Dirichlet Allocation Topic Modeling and VADER on Aspect-Based Sentiment Analysis Kevin, Kevin Miracle Satoko; Berutu, Sunneng Sandino; Jatmika; Palupi, Retno
Infact: International Journal of Computers Vol. 10 No. 01 (2026): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v10i01.708

Abstract

Aspect-Based Sentiment Analysis on a Product or Service is Crucial for Enhancing Customer Satisfaction. This Study Applies Latent Dirichlet Allocation (LDA) Topic Modeling to Identify Aspects. Then, the Valence Aware Dictionary and Sentiment Reasoner (VADER) Lexicon Method is Adopted to Determine Sentiment on These Aspects. The Data Source Comes from Customer Reviews of a Gelato Ice Cream Shop at Taman Siswa. Data was collected from Google Maps Using the Web Scraping Method via the Instant Data Scrapper Application. The Experimental Results Show that the LDA Method Identified 3 Aspects: Flavor, Place, and Service. Meanwhile, Sentiment Measurement Using VADER on the Flavor Aspect Revealed a Positive Sentiment of 213%, Negative Sentiment of 60%, and Neutral Sentiment of 218%. The Next Aspect, Place, Had a Positive Sentiment of 32%, Negative Sentiment of 4%, and Neutral Sentiment of 4%, while the Service Aspect Composed of 32% Positive Sentiment, 3% Negative Sentiment, and 3% Neutral Sentiment. Overall, the Positive Sentiment for the Flavor Aspect (Taste) Outweighed the Negative and Neutral Sentiments for the Place (Location) and Service (Service) Aspects.
Intelligent Service Quality Asse Aspect-Based Sentiment and Emotion Analysis on Online Reviews Using DistilBERT Method for Service Quality Evaluation: Aspect-Based Sentiment and Emotion Analysis on Online Reviews Using DistilBERT Mika, Jatmika; Zebua, Yuwinda Hartati; Berutu, Sunneng Sandino
Infact: International Journal of Computers Vol. 10 No. 01 (2026): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v10i01.807

Abstract

The growth of online reviews on digital platforms has made consumer opinions an important source for understanding perceptions of service quality in businesses. This study aims to analyze aspect-based sentiment and emotion from consumer reviews using the Distilled Bidirectional Encoder Representation from Transformers (DistilBERT) method. Data were collected from Google Reviews and processed through text preprocessing, aspect extraction, sentiment and emotion labeling, and fine-tuning of the DistilBERT model. Sentiment analysis was classified into three classes (positive, negative, and neutral), while emotion analysis included five categories (happy, angry, disappointed, sad, and neutral). The evaluation results show that the DistilBERT model achieved excellent performance in sentiment classification with an accuracy of 95.00%, precision of 93.60%, recall of 95.00%, and F1-score of 94.22%. For emotion classification, the model achieved an accuracy of 94.00%, precision of 88.36%, recall of 94.00%, and F1-score of 91.09%. These findings indicate that a Transformer-based approach is effective in understanding the contextual meaning of consumer reviews despite the use of a relatively limited dataset. This study concludes that DistilBERT is capable of providing accurate and efficient aspect-based sentiment and emotion analysis, which can be utilized as a foundation for evaluating and improving service quality and digital business reputation.
Penerapan Quantum Machine Learning Untuk Klasifikasi Ulasan Asli Dan Palsu Pada Amazon Krisna Putri Telaumbanua; Sunneng Sandino Berutu; Aninda Astuti
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3498

Abstract

Classifying product reviews on e-commerce platforms, both real and fake, requires a model that can effectively represent text data patterns. This study aims to compare the performance of several Quantum Machine Learning methods, namely QNN, QSVC, and Hybrid Quantum kernel and Classical SVM, in classifying Amazon product reviews. The study uses a quantitative approach with a computational experimental design. Review data is represented using TF-IDF, standardized, and reduced in dimension with Principal Component Analysis (PCA) before being transformed into quantum feature space. Performance evaluation is carried out using accuracy, precision, recall, F1-Score, and MCC metrics. The experimental results show that QNN achieved the best performance with an accuracy value of 85,63%, an F1-Score of 0.9130, and an MCC of 0.5608, while QSVC and the hybrid approach achieved an accuracy of 83.23% with an MCC of 0.4331. These results indicate that QNN has more balanced classification performance.Keywords: Quantum Neural Network; Fake Review Detection; Amazon Reviews; Natural Language Processing; Quantum Machine Learning. AbstrakKlasifikasi ulasan produk pada platform e-commerce, baik ulasan asli maupun palsu, memerlukan model yang mampu merepresentasikan pola data teks secara efektif. Penelitian ini bertujuan untuk membandingkan kinerja beberapa metode Quantum Machine Learning (QML), yaitu QNN, Quantum Support Vector (QSVC), dan Hybrid Quantum kernel and Classical SVM, dalam mengklasifikasikan ulasan produk Amazon. Penelitian menggunakan pendekatan kuantitatif dengan desain eksperimen komputasional. Data ulasan direpresentasikan menggunakan TF-IDF, distandardisasi, dan direduksi dimensinya dengan Principal Component Analysis (PCA) sebelum ditransformasikan ke ruang fitur kuantum. Evaluasi kinerja dilakukan menggunakan metrik accuracy, precision, recall, F1-Score, dan MCC. Hasil eksperimen menunjukkan bahwa QNN memperoleh kinerja terbaik dengan nilai accuracy sebesar 85,63%, F1-Score 0.9130, dan MCC 0.5043, sedangkan QSVC dan pendekatan hybrid mencapai accuracy 83,23% dengan MCC 0,4331. Hasil ini menunjukkan bahwa QNN memiliki performa klasifikasi yang lebih seimbang. 
Analisis Perbandingan Quantum Machine Learning Dalam Klasifikasi Berita Politik Fakta Dan Hoaks Linda Kristiani Zebua; Sunneng Sandino Berutu; Aninda Astuti
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3524

Abstract

This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news.Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification AbstrakPenelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pendekatan berbasis quantum kernel efektif dalam klasifikasi teks berdimensi sedang, sekaligus menunjukkan potensi Quantum Machine Learning sebagai alternatif metode klasifikasi berita politik fakta dan hoaks. 
Analisis Perbandingan ANN dan Hybrid QNN pada Klasifikasi Multikarakteristik Data Kharisteas Josan Sedi; Sunneng Sandino Berutu; Aninda Astuti
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3485

Abstract

The rapid advancement of machine learning has driven the exploration of various computational models to address classification problems across datasets with diverse characteristics. This study aimed to compare the performance of Artificial Neural Network (ANN) and Hybrid Quantum Neural Network (Hybrid QNN) under controlled experimental conditions. Three benchmark datasets, namely Red Wine Quality, Banknote Authentication, and Leukemia Gene Expression, were used with a training and test data split of 80:20. The evaluation was conducted using accuracy, precision, recall, F1-score, and computational time. The results showed that on the Gene Expression Leukemia dataset, Hybrid QNN achieved an accuracy of 0.9333, significantly outperforming ANN, which obtained 0.6111. Conversely, ANN demonstrated competitive performance and higher computational efficiency on low- and medium-dimensional datasets. These findings indicate that the advantages of Hybrid QNN are contextual and strongly dependent on data characteristics.Keywords: Artificial Neural Network; Hybrid Quantum Neural Network; classification; machine learningAbstrakPerkembangan pesat pembelajaran mesin telah mendorong eksplorasi berbagai model komputasi untuk menyelesaikan permasalahan klasifikasi pada data dengan karakteristik yang beragam. Penelitian ini bertujuan untuk membandingkan kinerja Artificial Neural Network (ANN) dan Hybrid Quantum Neural Network (Hybrid QNN) dalam kondisi eksperimen yang terkontrol. Tiga dataset acuan digunakan, yaitu Red Wine Quality, Banknote Authentication, dan Gene Expression Leukemia, dengan pembagian data latih dan uji sebesar 80:20. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, F1-score, serta waktu komputasi. Hasil eksperimen menunjukkan bahwa pada dataset Gene Expression Leukemia, Hybrid QNN mencapai akurasi 0,9333, jauh lebih tinggi dibandingkan ANN sebesar 0,6111. Sebaliknya, ANN menunjukkan performa yang kompetitif dan lebih efisien pada dataset berdimensi rendah hingga menengah. Temuan ini menunjukkan bahwa keunggulan Hybrid QNN bersifat kontekstual dan bergantung pada karakteristik data. 
Co-Authors Aninda Astuti Aninda Astuti Bobby Girsang Budiati, Heani Chen, Yeong-Chin Dachi, Christian Moris Dwi Dian Sari Nonibenia Hia Emerita Setyowati Febe Maedjaja Febe Maedjaja Fornieli Gulo gea, yuris mardayani Haeni Budiati Heri Wijayanto Indriyani Hulu Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika, Jatmika Jatmitka, Jatmitka Jimmy Richardo Chastelo B, Gabriel Juli Supriyanto Gea Kambayo, Andreas Noprianto Kambayo Kevin, Kevin Miracle Satoko Kharisteas Josan Sedi Krisna Putri Telaumbanua Kristian Juri Damai Lase Laia, Yardiana Lase, Anan Sosmita Linda Kristiani Zebua Marunduri, Tik Tanika mendrofa, victor crisman Mika, Jatmika Nauli, Sumitta Nehe, Pius Hermanto Nshimiyimana, Aristophane Rahman, Mosiur Reonardh Sibarani Retno Palupi Sispianygala, Aprilia Siswati, Susi Sumihar, Yo’el Pieter Tri Elsa, Maria Tri Yuli Yanto Veren, Yolanda Yuan, Anita Zakaria, Immanuel Richie De Harjo Zebua, Yuwinda Hartati Zendrato, Agus Dirgahayu