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All Journal Jurnal Ilmu Komputer JAIS (Journal of Applied Intelligent System) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Manajemen Informatika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Infotekmesin Jurnal Manajemen Informatika dan Sistem Informasi Journal of Robotics and Control (JRC) Abdimasku : Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Inovasi Sosial: Jurnal Pengabdian Masyarakat
Winarsih, Nurul Anisa Sri
Universitas Dian Nuswantoro
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
Klasifikasi Kategori Produk untuk Manajemen Keuangan Remaja menggunakan Algoritma Long Short-Term Memory Sutrisno, Hendra; Winarsih, Nurul Anisa Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27959

Abstract

Generation Z often faces difficulties in managing their finances due to impulsive spending habits and a lack of financial planning, which can lead to long-term issues such as overspending and minimal savings. This research aims to develop a category classification model that can be integrated into a financial tracking application to help young people manage their money more effectively. The main feature of the application is an automated system that classifies product names into expense categories such as food, transportation, and shopping using a Long Short-Term Memory (LSTM) model. LSTM was chosen for its ability to understand word sequences and text context, which is essential in product grouping. The dataset used consists of 4,499 product entries divided into three categories: 1,488 for food, 1,682 for transportation, and 1,329 for shopping. The model was trained using a supervised learning approach, with data split for training and testing. The model achieved 86% accuracy on both validation and test data, with additional metrics such as precision, recall, and F1-score indicating good performance. This study contributes by applying innovative preprocessing techniques and oversampling to address data imbalance, which is expected to enhance the model's accuracy in classifying expenses.
Kinerja Naive Bayes dan SVM pada Data Survei Tidak Seimbang: Studi Klasifikasi Kepuasan Masyarakat Romadhoni, Mellynda Noor; Winarsih, Nurul Anisa Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.30185

Abstract

The utilization of Public Satisfaction Survey (SKM) data has not been optimal, highlighting the need for an effective classification method to determine the level of public satisfaction. This study aims to classify satisfaction levels using the 2024 SKM data from the Regional Civil Service and Training Agency (BKPPD) of Grobogan Regency, employing Naive Bayes and Support Vector Machine (SVM) algorithms. This quantitative research uses nine service elements rated on a scale of 1 to 4 as features, with satisfaction level as the target variable. The dataset consists of 303 entries: 156 “very satisfied,” 115 “satisfied,” and 32 “dissatisfied.” Random oversampling was applied to address class imbalance. Model performance was evaluated using accuracy, precision, recall, and F1-score, both before and after oversampling. Results showed Naive Bayes achieved 96.72% accuracy, while SVM scored 95.08%. After oversampling, SVM accuracy significantly improved to 98.36%, while Naive Bayes slightly decreased to 95.08%. Precision, recall, and F1-scores also demonstrated strong performance across all classes. This study is expected to support the improvement of public service delivery at BKPPD Grobogan and similar institutions.
Co-Authors Adhitya Nugraha Ahmad Zainul Fanani Akmaludin Akbar Alvin, Fris Astuti, Yani Parti Azzahra, Tarissa Aura Danny Oka Ratmana Dhani, Iqbal Didik Hermanto Djaka, Thesa Permatasari Djaka Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Epiphany Shavna Gracia Erlin Dolphina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi, Fakhrurrozi Filmada Ocky Saputra Filmada Ocky Saputra Guruh Fajar Shidik Hamid, Maulana As’an Ilmi, Nur Aqliah Imam Prayogo Pujiono Kartika, Gita Kusumawati, Yupie Mardiantara, Naya Alifiah az Azar Putri Miranti Alysha Zulia Larasati Muhammad Syaifur Rohman Muhammad Syaifur Rohman Mulyanto, Edy Naufal Fawwazi Pratama, Zudha Purwanto Purwanto Raihatuzzahra, Farah Ramadhani, Irfan Wahyu Ratmana, Danny Oka Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Romadhoni, Mellynda Noor Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Satria Andromeda, Rayhan Sekar Wangi, Sari Asih Septian Enggar Sukmana Sutrisno, Hendra Wibowo, Alrico Rizki Yani Parti Astuti Yani Parti Astuti Zainal Arifin Hasibuan