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All Journal JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal ULTIMATICS Journal on Education JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Prosiding Seminar Nasional Multidisiplin Jurnal Sistem informasi dan informatika (SIMIKA) At-Thullab : Jurnal Pendidikan Guru Madrasah Ibtidaiyah Jurnal Inovasi Penelitian Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Journal of Education Research Indonesian Community Journal Edu Research : Jurnal Penelitian Pendidikan Inspirasi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Media Bina Ilmiah Journal of Research Applications in Community Services AL-UMRON: Jurnal Pengabdian kepada Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat Almantiq NAWASENA BHAKTI
Ita Aristia Sa'ida
Universitas Nahdlatul Ulama Sunan Giri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Application of SMOTE-ENN Method in Data Balancing for Classification of Diabetes Health Indicators with C4.5 Algorithm Bakti Putra Pamungkas; Muhammad Jauhar Vikri; Ita Aristia Sa'ida
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2350

Abstract

Data imbalance in health datasets often leads to decreased performance of classification models, especially in detecting minority classes such as diabetics. This study evaluates the effect of the SMOTE-ENN method on improving the performance of the C4.5 algorithm in the classification of diabetes health indicators. The dataset used is the 2021 Diabetes Binary Health Indicators BRFSS from Kaggle, which consists of 236,378 respondent data with unbalanced class distribution: 85.80% non-diabetic and 14.20% diabetic. The SMOTE method was used to add synthetic data to the minority classes, while ENN was applied to remove data considered noise. After balancing, the C4.5 algorithm was used for classification. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the application of SMOTE-ENN improved accuracy from 79.49% to 80.33% and precision from 29% to 30%. Although the recall value did not increase, this method proved to be able to improve the overall stability of the prediction, especially in terms of the accuracy of the classification of the positive class. The novelty of this research lies in the specific application of the SMOTE-ENN method on large-scale health datasets with the C4.5 algorithm, which has not been widely explored before. Therefore, further exploration of other balancing techniques and algorithms is needed to obtain more optimal classification results on unbalanced data.
Optimizing Gated Recurrent Unit (GRU) for Gold Price Prediction: Hyperparameter Tuning and Model Evaluation on Historical XAU/USD Data Faqih, Abdul; Vikri, Muhammad Jauhar; Sa’ida, Ita Aristia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2352

Abstract

This study investigates the use of a Gated Recurrent Unit (GRU) model with a four-layer architecture for daily gold price closing prediction, motivated by the model's ability to effectively capture temporal dependencies in time series data. Gold price forecasting is highly challenging due to its volatility and external factors, making it an important area of research for investors and financial analysts. By systematically optimizing hyperparameters through 72 combinations of epochs, batch size, GRU layer units, and dropout rates, the study identifies the optimal configuration (100 epochs, batch size of 16, 256 units, dropout rate 0.1) based on MSE performance on validation data. The best model achieved MAE of 25.76, MSE of 954.97, and RMSE of 30.90, after inverse transformation on test data. These results highlight the potential of the GRU model in accurately forecasting gold prices, with implications for financial decision-making . However, the prediction error suggests that further improvements could be made by incorporating external factors or exploring advanced model architectures.
Co-Authors Abdul Faqih Aftha Harianto, Febby Refindha Agus Afriliyanto agustian, rifan Ainu Zuhriyah APRILLIA, YUSIFA Asfira, Eka Audytra, Hastie Ayu Mauliana Intahaya Bakti Putra Pamungkas Ceisa Santi, Nirma Devi Dwi Nuraliza Diah Anggraini, Diah Diah Fitriani, Diah Dirgantoro, Guruh Putro Dirgantoro, Guruh Putro Dirgantoro Dwi Issadari Hastuti Eli Widyawati, Reza Elsa Azia Ulhaq Fajar Nawulansih, Dewi Guruh Putro Dirgantoro Jauhar Vikri, Muhammad Laily, Amalia Nur M. Iqbal Tawakkal Mochamad Nizar Palefi Ma’ady Muarofah, Isna Ayu Muhammad Adib Asrori Muhammad Zaenal Abidin Mula Agung Barata Nawafila Februyani Niken Sukmawati Nurul Huda Nurul Maratus Sholihah Sahri Sahri Sahri Sahri Sahri Sahri Sanjaya, Ucta Pradema Santi, Nirma Ceisa Saputra, Agus Bima Siti Cholifah Siti Shoimah Sunu Wahyudhi Taufiq Hidayat Tawakkal, M. Iqbal Wulan, Diah Nawang Wulandari, Devi Ayu Zakki Alawi Zeti Novitasari