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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) DINAMIKA DOTCOM Teknik dan Ilmu Komputer Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Teknik Informatika (JUTIF) SATIN - Sains dan Teknologi Informasi
Cynthia Hayat
Univeristas Kristen Krida Wacana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases Hayat, Cynthia; Soenandi, Iwan Aang
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.1-12

Abstract

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin   Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization
Membangun Platform Digital Desa Margamulya Tanjung Kait Untuk Menuju Masyarakat Society 5.0 Hayat, Cynthia; marcel, marcel; Putro, Endi; Windarto, Yudhi; Perangin Angin, Prasasti; Mudita, Damida Shu; Setiawan, Theodorous Stevanus; Christian, Juan; Putratama, Andhika; Christian, Neil
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 7 No. 2 (2024): April 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v7i2.2872

Abstract

The village of Margamulya Tj. Kait is one of the foster villages of LPPM Ukrida. From the results of interviews and direct observations, there are several main issues faced by partners, including: limited access and dissemination of financial information and village profiles to external parties as well as to the villagers themselves. Community service is conducted by utilizing technology to develop a digital village community ecosystem where social media-based platforms serve as everyday communication, socialization, and collaboration tools. The implementation method is carried out through: (1) development of social media platforms; (2) training on platform usage. With the developed social media platform program, the target output is achieved through the evaluation of activities, where the benefit of interaction between village officials and villagers, as well as the dissemination and access to important information, becomes easier. Keywords: digital platform; village digitalization; digital village; margamulya village
Evaluasi Layanan Fintech Shopeepay Melalui Klasifikasi Sentimen Berbasis Support Vector Machine Hayat, Cynthia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Pertumbuhan pesat layanan fintech di Indonesia mendorong perlunya evaluasi kualitas layanan yang objektif dan berbasis data. Penelitian ini bertujuan untuk mengevaluasi persepsi pengguna terhadap layanan ShopeePay dengan menggunakan pendekatan analisis sentimen berbasis algoritma Support Vector Machine (SVM). Data ulasan pengguna dikumpulkan dari platform digital dan melalui tahapan pra-pemrosesan teks serta ekstraksi fitur menggunakan metode Term Frequency–Inverse Document Frequency (TF–IDF). Dataset dibagi menjadi data latih (80%) dan data uji (20%) untuk membangun serta mengevaluasi model klasifikasi. Hasil pengujian menunjukkan bahwa model SVM mampu mengklasifikasikan sentimen pengguna dengan baik, dengan akurasi mencapai 84% dan F1-score tertinggi pada kategori sentimen positif. Analisis kata dominan mengungkap bahwa sentimen positif didominasi oleh kata seperti “mudah” dan “cepat”, sedangkan sentimen negatif terkait dengan “error” dan “saldo hilang”. Meskipun demikian, analisis kesalahan menunjukkan bahwa model masih menghadapi tantangan dalam menangani ulasan dengan unsur sarkasme, kritik implisit, dan opini campuran, yang dapat memengaruhi ketepatan klasifikasi. Secara praktis, hasil penelitian ini memberikan wawasan strategis bagi pengembang ShopeePay dalam meningkatkan kualitas layanan, terutama melalui optimalisasi fitur transaksi dan penanganan kendala teknis yang sering dikeluhkan pengguna. Dengan demikian, pendekatan analisis sentimen berbasis machine learning ini dapat menjadi alat evaluasi yang efektif untuk meningkatkan pengalaman pengguna dan daya saing layanan fintech di Indonesia.   Abstract The rapid growth of fintech services in Indonesia underscores the need for an objective and data-driven evaluation of service quality. This study aims to assess user perceptions of the ShopeePay service using a sentiment analysis approach based on the Support Vector Machine (SVM) algorithm. User review data were collected from digital platforms and underwent text preprocessing and feature extraction using the Term Frequency–Inverse Document Frequency (TF–IDF) method. The dataset was divided into training (80%) and testing (20%) subsets to build and evaluate the classification model. Experimental results indicate that the SVM model effectively classifies user sentiments, achieving an accuracy of 84% and the highest F1-score in the positive sentiment category. Dominant word analysis revealed that positive sentiments were associated with words such as “easy” and “fast,” while negative sentiments were related to “error” and “lost balance.” However, the error analysis showed that the model still faces challenges in handling reviews containing sarcasm, implicit criticism, and mixed opinions, which can affect classification accuracy. From a practical perspective, the findings provide strategic insights for ShopeePay developers to enhance service quality—particularly by optimizing transaction features and addressing common technical issues reported by users. Consequently, this machine learning-based sentiment analysis approach serves as an effective evaluation tool for improving user experience and strengthening the competitiveness of fintech services in Indonesia.
Sentiment Analysis of User Perceptions Toward the Gemini Application using the Naïve Bayes Algorithm Cynthia Hayat
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.5846

Abstract

Sentiment analysis, a pivotal technique in Natural Language Processing (NLP), facilitates the classification of textual data into positive or negative sentiment categories. This study applies a Naïve Bayes classification model to analyse 1,000 user reviews of the Gemini application, an AI-powered assistant developed by Google AI, collected from the Google Play Store. The research adopts the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, data mining, and evaluation stages. Reviews were pre-processed using text normalization techniques and transformed into numerical vectors using Term Frequency–Inverse Document Frequency (TF-IDF). The sentiment classifier achieved a high accuracy of 94.57%, with a precision of 95.7% and an F1-score of 96.7%, indicating effective classification performance. The results revealed a predominance of positive sentiment, with frequently occurring terms such as “assist,” “good,” and “application.” Conversely, negative sentiments were often associated with issues regarding language support and system limitations. These findings provide actionable insights into user satisfaction and areas for improvement in the Gemini application. While the Naïve Bayes algorithm proved efficient and interpretable, limitations include difficulty handling nuanced linguistic features such as sarcasm or ambiguity. Future research may enhance sentiment classification performance by incorporating larger datasets and advanced models such as BERT or deep neural networks. Overall, this study demonstrates the practical utility of machine learning-based sentiment analysis in guiding user-centric application development.
Co-Authors Afrido V. Hutapea Afrido Vadri Hutapea Angelina A Latuny Cancerita, Stefani Christian, Juan Christian, Neil David Panggeso Endi Putro Enggalwiguno Rahardja Frahselia Tandipuang Frans Pasamboan Fredicia Fredicia Gisela Nina Sevani Hansen Hansen Hutapea, Afrido V. Iwan Aang Soenandi Jenny Rosenta Ulina Purba Kurniawan, Velianny Limong, Samuel Marcel Marcel Marcella, Maria Mega Putri Oktaviani Meilisa Ratnasari Simanjuntak Mudita, Damida Shu Noviyanti Sagala Noviyanti Sagala Panggeso, David Perangin Angin, Prasasti Putratama, Andhika Sagala, Noviyanti Samuel Limong Setiawan, Theodorous Stevanus Tanimidjaja, Gereggi Septino Usen, Yolan Anjani Widjaja, Heru Yudhi Windarto