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All Journal J@TI (TEKNIK INDUSTRI) JURNAL SISTEM INFORMASI BISNIS Jurnal Rekayasa Sistem Industri Jurnal Teknologi dan Manajemen Informatika SPEKTRUM INDUSTRI PROSIDING SEMINAR NASIONAL CENDEKIAWAN JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sistem dan Manajemen Industri RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA SMARTICS Journal JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Artificial Intelligence and Data Mining JKTP: Jurnal Kajian Teknologi Pendidikan Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) Prosiding Seminar Nasional Pakar Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JUSIM (Jurnal Sistem Informasi Musirawas) ACADEMICS IN ACTION Journal of Community Empowerment Jurnal Sistem Teknik Industri Journal of Information Systems and Informatics Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Mulia International Journal in Science and Technical Journal of Industrial Engineering Zonasi: Jurnal Sistem Informasi International Journal of Industrial Research and Applied Engineering JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Decode: Jurnal Pendidikan Teknologi Informasi IDEAS: Journal of Management & Technology Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Widya Teknik
Anastasia Lidya Maukar
Universitas Presiden
Humanities 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 Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Sentiment Analysis of User Reviews for the PLN Mobile Application Using Naïve Bayes and Long Short-Term Memory Ayomi, Jose Mario; Vitianingsih, Anik Vega; Kristyawan, Yudi; Maukar, Anastasia Lidya; Widiartin, Tjatursari
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1342

Abstract

This study explores large-scale sentiment analysis of user reviews for the PLN Mobile application to better understand public perception and provide quantitative insights for improving digital electricity services in Indonesia. Addressing the lack of benchmarks for Indonesian public-service apps—where prior studies rely on smaller datasets and traditional machine learning—this research positions sentiment analysis as a tool for continuous user experience monitoring. A total of 50,000 Indonesian-language reviews from Google Play were collected and pre-processed using cleaning, case folding, tokenization, stopword removal, normalization, and stemming. Sentiments (positive, neutral, negative) were assigned using a domain-specific Indonesian sentiment lexicon, yielding approximately 40% positive, 35% neutral, and 25% negative labels. Two models were applied: Multinomial Naïve Bayes using TF-IDF features and a Long Short-Term Memory (LSTM) model with 100-dimensional word embeddings and a 128-unit LSTM layer. Naïve Bayes achieved 70.89% accuracy (F1-score: 0.6964), while LSTM outperformed it with 98.02% accuracy (F1-score: 0.9800). These results highlight the superiority of deep learning in sentiment monitoring and offer a scalable framework to help PLN and policymakers enhance digital public service delivery.
Sentiment Analysis E-Wallet Application Services Using the Support Vector Machine and Long Short-Term Memory Methods Mochammad Dzikri Arya Darmansyah; Anik Vega Vitianingsih; Anastasia Lidya Maukar; SY. Yuliani; Seftin Fitri Ana Wati
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apedaz75

Abstract

The rapid growth of financial technology services in Indonesia has increased the volume of user reviews, yet their utilization for sentiment-based insights remains limited in the e-wallet sector. This study compares the effectiveness of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) in classifying the sentiment of 3,185 DANA e-wallet reviews collected from the Google Play Store and Instagram. The research process includes text preprocessing, lexicon-based labeling, and feature extraction using TF-IDF for SVM and word embeddings for LSTM. Model evaluation is conducted using a confusion matrix based on accuracy, precision, and recall, without inferential statistical testing. The results show that LSTM outperforms SVM, achieving an accuracy of 86.66%, a recall of 81.86%, and a precision of 82.09%, while the best SVM variant with an RBF kernel attains an accuracy of 84.93%. This study contributes by identifying key service-related factors influencing user satisfaction and dissatisfaction and by providing practical, sentiment-based insights to support service quality improvement. The novelty lies in the multi-platform analysis of Indonesian e-wallet reviews and the direct comparison of classical machine learning and deep learning approaches without statistical hypothesis testing. These findings confirm the effectiveness of deep learning for sentiment analysis of unstructured Indonesian text.
Sentiment Analysis of Digital Korlantas Polri Apps Service Based on LSTM and SVM Methods Imanuel Soterius Prasetya Sunur; Anik Vega Vitianingsih; Achmad Muzakki; Anastasia Lidya Maukar; Seftin Fitri Ana Wati
JTERA (Jurnal Teknologi Rekayasa) Vol 11 No 1: Vol. 11 No. 1: Juni 2026
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v11.i1.2026.11-18

Abstract

Advancements in digital technology have encouraged numerous innovations in public services, one of which is the Digital Korlantas Polri app. This application makes it easier for the public to access traffic services such as driver’s license issuance and renewal, vehicle data checking, and accident reporting. However, despite the convenience it offers, there are still various user reviews that point to technical issues and dissatisfaction with the quality of service. This study applies sentiment analysis to understand public perception of the Digital Korlantas app, providing a basis for improving its quality. The collection of the dataset was achieved by web scraping 2,000 user reviews from the Google Play Store spanning the period from December 2023 to March 2025. The phases of the research encompass gathering data, pre-processing text, assigning sentiment labels based on lexicons, applying TF-IDF for word weighting, and performing classification using the Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms. The performance of the model was assessed through a confusion matrix, utilizing accuracy, precision, recall, and F1-score as evaluation metrics. The findings indicated that, out of 2,000 reviews, 1,402 were identified as positive, 538 were categorized as negative, and 60 were considered neutral. The SVM model demonstrated the highest performance, obtaining an accuracy of 96.8%, a precision of 65.6%, a recall of 50.0%, and an F1-score of 55.0%. At the same time, the LSTM model attained an accuracy of 94.5%, with a precision of 31.5%, a recall of 33.3%, and an F1-score of 32.4%. These results show that SVM is superior at handling high-dimensional data, while LSTM remains effective at capturing long-term context patterns in review texts.
SENTIMENT ANALYSIS OF BRIMO APPLICATION USER REVIEWS USING NAÏVE BAYES AND LONG SHORT-TERM MEMORY Muhammad Alif Ilmansyah; Anik Vega Vitianingsih; Anastasia Lidya Maukar; Seftin Fitri Ana Wati; Arizia Aulia Aziiza
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7341

Abstract

In the age of digital transformation, the development of digital banking platforms such as BRImo by Bank Rakyat Indonesia (BRI) continues to evolve to improve customer experience. However, many users still express dissatisfaction through online reviews, especially on platforms such as the Play Store and Twitter (X). This study conducts a systematic and fair comparison between a traditional machine learning approach (Naïve Bayes) and a deep learning approach (Long Short-Term Memory) for sentiment classification under identical dataset conditions. User reviews were collected using web scraping and crawling techniques, followed by text preprocessing, lexicon-based labeling, and appropriate feature representations for each model. The results indicate that both algorithms classify sentiments into three categories: positive, negative, and neutral. The Naïve Bayes model achieved an accuracy of 89%, with macro-average precision, recall, and F1-score of 0.88, 0.58, and 0.59, respectively. Meanwhile, the LSTM model achieved an accuracy of 85%, with macro-average precision, recall, and F1-score of 0.59, 0.63, and 0.60. The findings reveal that Naïve Bayes demonstrates more stable performance on short and highly imbalanced user review data, while LSTM shows limited improvement for minority classes despite its contextual modeling capability. These results highlight the importance of dataset characteristics and evaluation metrics beyond accuracy in sentiment analysis tasks. This research provides practical insights for BRImo development teams and contributes to the understanding of model behavior under real-world sentiment data imbalance.  
Sentiment Analysis Of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods Muh Nabil; Anik Vega Vitianingsih; Slamet Kacung; Anastasia Lidya Maukar; Seftin Fitri Ana Wati
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16311

Abstract

This research analyzed user sentiment toward the NTB Syariah application using Support Vector Machine (SVM) and Naïve Bayes classification methods. A dataset comprising 814 reviews was obtained via web scraping, with 245 allocated for testing. Preprocessing encompassed cleaning, case folding, tokenization, filtering, and stemming, while sentiment labeling employed a lexicon-based approach integrated with TF-IDF weighting, categorizing reviews as positive, neutral, or negative. Model performance was assessed through accuracy, precision, recall, and F1-score metrics. Results demonstrated SVM's superior performance (accuracy: 92.65%; precision: 0.9327; recall: 0.9265; F1-score: 0.9149) compared to Naïve Bayes (accuracy: 84.49%; precision: 0.8415; recall: 0.8449; F1-score: 0.8005). SVM exhibited greater robustness in managing high-dimensional, complex, and moderately imbalanced datasets, delivering consistent cross-class sentiment classification. Conversely, Naïve Bayes remained computationally efficient and suitable for rapid implementation scenarios. These findings underscore machine learning's efficacy in sentiment analysis for digital banking platforms.
Comparative Analysis of Naïve Bayes and K-Nearest Neighbor for Lexicon-Based Emotion Classification of Paxel App User Reviews Azka Salsabilah; Anik Vega Vitianingsih; Dwi Cahyono; Anastasia Lidya Maukar; Hewa Majeed Zangana
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16516

Abstract

The rapid growth of app-based delivery services has increased the importance of understanding user emotions as an indicator of service quality. User reviews on digital platforms provide valuable insights into customer perceptions, satisfaction levels, and service-related issues. This study aims to compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying user emotions related to the Paxel application. The dataset was collected from Google Play Store and X (Twitter) using web scraping techniques and subsequently processed through text pre-processing stages, including case folding, tokenization, and stopword removal. Emotion labels were assigned using the NRC Indonesian Emotion Lexicon, while feature extraction was performed using the TF-IDF method. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied prior to model training. Experimental results show that the Naïve Bayes model achieved the highest overall accuracy of 90.83% with a weighted F1-score of 0.90, while the KNN model obtained an accuracy of 81.21% and a weighted F1-score of 0.77. Both models performed well in identifying happy, sad, and neutral emotions, whereas anger remained the most challenging class to classify. Overall, Naïve Bayes demonstrated more consistent and reliable performance for sentiment analysis tasks..
Applying ADDIE Methodology and Vision Inspection to Reduce Missing Parts Defects in Toy Manufacturing Anastasia Lidya Maukar; Tiffany R Rianto
Jurnal Rekayasa Sistem Industri Vol. 15 No. 1 (2026): Jurnal Rekayasa Sistem Industri
Publisher : Universitas Katolik Parahyangan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26593/jrsi.v15i1.9668.76-92

Abstract

This research addresses missing parts defects in Advent Calendar toy production, a critical quality issue causing customer dissatisfaction and financial losses. The ADDIE methodology was applied as a systematic framework to analyze root causes, redesign ergonomic workstations with improved human-machine interactions, develop a vision inspection system, implement operator training, and evaluate outcomes. Results demonstrated significant improvements: defect reduction of 92% (from 53 to 4 cases), operator cycle time reduction of 75% (from 70.68 to 14.85 seconds), improved ergonomic conditions (RULA score decreased from 5 to 4), and labor cost savings of 75% (approximately IDR 1.2 billion annually). This study demonstrates that ADDIE provides a systematic, measurable approach for integrating human factors and technology to enhance inspection performance and support continuous improvement in manufacturing.
Diabetes Mellitus Disease Prediction Using Logistic Regression (LR) and Support Vector Machine (SVM) Methods Akbar Febrian Dwi Hastono; Anik Vega Vitianingsih; Pamudi Pamudi; Anastasia Lidya Maukar; Seftin Fitri Ana Wati
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 5 No. 1: MARET 2025
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v5i1.1039

Abstract

Diabetes Mellitus (DM), also known as diabetes or sugar disease, marked by high blood sugar levels and poses a major health issue in Indonesia with the number of cases increasing every year. Often referred to as the silent killer, DM often goes unnoticed due to its subtle symptoms, increasing the risk of severe complications if not treated promptly. The lack of information or awareness about the early symptoms of DM, limited time and cost in conducting health checks, and limited access to health services are challenges in detecting DM disease early. To overcome this problem, the development of a prediction model is essential to prevent serious complications. This study aims to create a predictive model using LR and SVM methods based on parameters such as pregnancy, glucose levels, blood pressure, skin thickness, insulin, BMI, diabetes pedigree, age, and outcome. The dataset used is DM disease risk data collected by Kaggle from the National Institute of Diabetes and Disgetive and Kidney Disease (NIDDK). Based on the research results, the LR method shows a better level of accuracy compared to the SVM method. The accuracy of the model using the Logistic Regression method is 79.31% while the SVM method has an accuracy value of 77.24%, with a difference in accuracy of 2.07%. This research applies hyperparameter tuning with Grid Search to find the best combination of hyperparameter.
Co-Authors Abdul Rezha Efrat Najaf Achmad Aziz Wahdana Achmad Choiron Achmad Muzakki Adi Saptari Agus Sasmito Agustinus Noertjahyana Ahmad Yanu Rokhim Akbar Febrian Dwi Hastono Ana Wati, Seftin Fitri Anang Aris Widodo Andira Andira Andira Andira Andira Andira Andira Taslim Andira, Andira ANGGI FIRMANSYAH Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Yuesti Apri Junaidi, Apri Arie Restu Wardhani Arizia Aulia Aziiza Arrosyadi, Laesa Qotrun Nada Arthur Silitonga Athina Sakina Ratum Avania Shinta Ayomi, Jose Mario Aziiza, Arizia Aulia Azka Salsabilah Azzahra, Morra Fatya Gisna Nourielda Bella Chelsea Berliana Burhan Primanintyo Cahyono, Cahyono Kaelan Cakranegara, Pandu Adi Carolena Setephany Christian Setiadi Ciswondo Ciswondo Dewa Anggara Kesuma Dian Retno Sari Dewi DWI CAHYONO Dwi Cahyono Efendi, Kacung Fauzan, Rizky Fawaidul Badri Firmansyah, Deden Fitri Marisa Fitri Marisa Fitri Marisa Fitri Marissa Fitri, Anindo Saka Gita Indah Marthasari Gunawan Hamidan, Rusdi Handini, Mia Hanum, Dinda Latifah Haryanto, Kurniawan Wahyu Hashim, Ummi Rabah Helmi Indra Purnomo Hermansyah, David Herwan Yusmira Hewa Majeed Zangana Hikmawati, Nina Kurnia Husri Sidi Imanuel Soterius Prasetya Sunur Ineu Widaningsig Sosodoro Ineu Widaningsih Ineu Widaningsih Sosodoro Ineu Widaningsih Sosodoro Ineu Widaningsih Sosodoro, Ineu Widaningsih Intan Puspita Pribadi Intan Yosa Pramisela Jack Febrian Rusdi Jazid Rizkon Jean Hillary P Korua Jenifer Cafriaty Johan Krisnanto Runtuk Johan Runtuk Julius Mulyono Kacung Hariyono Kamalrudin, Massila Kresna Arief Nugraha Krismantoro, Putu Gede Ari KRISTIAWAN KRISTIAWAN Luqman Hakim Ma'rifani Fitri Arisa Mardiana Andarwati Mashudi Mashudi Maulidiana, Putri Dwi Rahayu Maurits Walalayo Mieke Wijayanti Minggow, Lingua Franca Septha Mochammad Dzikri Arya Darmansyah Mochammad Syaiful Riza Mohamad Toha Mohd Syaiful Rizal Mucalinda Rupasari Mucalinda Rupasari Muh Nabil Muhammad Afra Irwansyah Muhammad Alif Ilmansyah Muzaki, Mochammad Rizki Niken Titi Pratitis Nurhaba Djiha Octa Wendy Tanurahardja Oktafamero, Yomara Oktavia Sunny Pamudi Pamudi Pamudi Pamudi, Pamudi Pangestu, Resza Adistya Pramisela, Intan Yosa Pramudita, Atanasia Pramudita, Krisna Eka Puspitarini*, Erri Wahyu Puspitarini, Erri Wahyu Putri, Jessica Ananda Putri, Natasya Kurnia Rachmad Ary Ramadhan Rahardiyanto, Panca Ramadhan, Rachmad Ary Rendy - Rhiza Adiprabowo Rhiza Adiprabowo, Rhiza Richki Hardi Rijal, Khaidar Ahsanur Rivaldo Tito Lamberto Da Silva Rusdi Hamidan Rusdi, Jack Febrian Salmanarrizqie, Ageng Seftin Fitri Ana Wati Seftin Fitri Ana Wati Seftin Fitri Ana Wati Seftin Fitri Ana Wati Seftin Fitri Ana Wati Shofa Ramadhina Sigit Sigalayan Siti Hajar Binti Mohtar Slamet Kacung Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Stefanus Setiady SUMARDI Susilo, Yunus Sutrisno Sutrisno SY. Yuliani Syahroni Wahyu Iriananda, Syahroni Wahyu Tantyo Edo Wicaksana TEGUH ARIFIANTO, TEGUH Tiffany R Rianto Tjatursari Widiartin Tubagus Mohammad Akhriza Uda, Tonich Ullum, Choirul Verdi Yasin Voni Anggraeni Suwito Putri Wati, Seftin Fitri Ana Widiya Nur Permata Wijiono, Aditya Kusuma Yana Hendriana Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Yunus Susilo Yustian Zandroto, Yosefin Yuniati Zangana, Hewa Majeed