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All Journal JUKI : Jurnal Komputer dan Informatika SmartComp Infact: Jurnal Sains dan Komputer
Jatmika Jatmika
FMIPA UKRIM Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Pengembangan Aplikasi Pengelola Keuangan Berbasis Android Terintegrasi Kecerdasan Buatan Leong, Daniel Alexander; Jatmika, Jatmika
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 1 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i1.7151

Abstract

Mengelola keuangan pribadi seringkali sulit karena kurangnya alat yang efektif untuk melacak dan menganalisis pengeluaran. Penelitian ini bertujuan untuk mengembangkan aplikasi pengelolaan keuangan berbasis Android yang terintegrasi dengan kecerdasan buatan (AI) untuk membantu pengguna mengelola keuangannya dengan lebih efisien. Aplikasi ini melacak pendapatan dan pengeluaran, menganalisis pola pengeluaran, dan memberikan perkiraan keuangan serta rekomendasi anggaran yang disesuaikan dengan kebiasaan pengguna. Teknik pengembangan perangkat lunak yang digunakan meliputi analisis kebutuhan, perancangan sistem, implementasi, dan pengujian. Hasil pengujian menunjukkan bahwa aplikasi ini dapat meningkatkan keterampilan pengelolaan keuangan pribadi pengguna melalui antarmuka yang ramah pengguna dan fitur-fitur terkait. Dengan memanfaatkan AI, aplikasi ini memberikan saran yang lebih akurat dan personal. Kami berharap aplikasi ini menjadi solusi praktis bagi masyarakat untuk meningkatkan pengelolaan keuangan dan meningkatkan kesejahteraan finansial.
Aspect-Based Sentiment Analysis Using Latent Dirichlet Allocation (LDA) and DistilBERT on Threads App Reviews Kambayo, Andreas Noprianto Kambayo; Berutu, Sunneng Sandino; Jatmika, Jatmika; Nshimiyimana, Aristophane
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.707

Abstract

Threads is a social media application that offers news services and user interaction, integrated with Instagram. Unlike other platforms, Threads does not have features like direct messaging (DM), trending topics, or advertisements. To understand users' opinions about this app, a sentiment analysis based on aspects was conducted on Threads reviews. The steps involved include applying web scraping techniques to collect reviews data from the Play Store. Aspect categories were identified using the Latent Dirichlet Allocation (LDA) algorithm. Sentiment labeling was then performed for positive and negative categories using the DistilBERT method. The results show that the LDA algorithm identified three aspects: Usage and Integration (with 3.147 positive and 8.173 negative reviews), Features and Comparisons (with 1.108 positive and 1.709 negative reviews), and User Experience and Satisfaction (with 3.529 positive and 2.208 negative reviews). The sentiment analysis results indicated 7,784 positive reviews and 12,090 negative reviews. Model performance evaluation using the Confusion Matrix showed an accuracy of 96.71%, precision of 97.24%, recall of 94.48%, and F1-score of 95.84%. Evaluation was also conducted for each aspect, with an accuracy of 96.99%, precision of 96.60%, recall of 92.85%, and F1-score of 94.69% for the Usage and Integration aspect; accuracy of 95.74%, precision of 94.11%, recall of 95.23%, and F1-score of 94.67% for the Features and Comparisons aspect; and accuracy of 96.74%, precision of 95.83%, recall of 99.06%, and F1-score of 97.42% for the User Experience and Satisfaction aspect.
Analisis Sentimen Universitas Kristen Immanuel Menggunakan SVM, GAN dan SMOTE Jatmika, Jatmika; Jacobus, Liefson; Raffael, Devant Joe; Kuswanto, Edys
JUKI : Jurnal Komputer dan Informatika Vol. 7 No. 1 (2025): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2025
Publisher : Yayasan Kita Menulis

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

Abstract

Social media has become an important tool for people to express their opinions about various institutions, including universities. This research aims to analyze Universitas Kristen Immanuel (UKRIM) using the Support Vector Machine (SVM) classification method. Data collected from social media through web scraping was processed through several preprocessing stages such as data cleaning, tokenization, and stopword removal. One of the main challenges in this study is class imbalance, which was addressed using two techniques: Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Network (GAN). SMOTE was used to oversample the minority class, while GAN was utilized to generate artificial data that resembles real input. Feature extraction was carried out using the Bidirectional Encoder Representation from Transformers (BERT) model, and classification was performed using SVM. The evaluation results of the experiments show that the use of GAN to create artificial data and SMOTE to balance the classes in the data improves the classification model performance, as the SVM model can effectively distinguish negative, neutral, and positive sentiments. Keyword: SVM, SMOTE, GAN, sentiment analysis, social media
Co-Authors Kambayo, Andreas Noprianto Kambayo Kuswanto, Edys Leong, Daniel Alexander Liefson Jacobus, Liefson Nshimiyimana, Aristophane Raffael, Devant Joe Sunneng Sandino Berutu