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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Engineering and Technology (INAJET) Journal of Informatics and Computer Science (JINACS) Jurnal Ilmiah Edutic : Pendidikan dan Informatika J-Icon : Jurnal Komputer dan Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ervin Yohannes
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering

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Penentuan Upah Minimum Kota Berdasarkan Tingkat Inflasi Menggunakan Backpropagation Neural Network (BPNN) Yohannes, Ervin; Mahmudy, Wayan Firdaus; Rahmi, Asyrofa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2 No 1: April 2015
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (913.176 KB) | DOI: 10.25126/jtiik.201521128

Abstract

Upah Minimum Kota (UMK) adalah sebuah standardisasi upah atau gaji karyawan atau pegawai untuk diterapkan diperusahaan baik itu BUMN, BUMS, maupun perusahaan lain yang berskala besar. Faktor yang mempengaruhi UMK sangat banyak dan beragam salah satunya adalah rata-rata inflasi pengeluaran dimana terdapat 8 kategori yang dipakai. Tulisan ini memaparkan penggunaan Backpropagation Neural Network (BPNN) untuk memprediksi besarnya UMK. Pada tahap uji coba data dibagi menjadi dua bagian yaitu data latih dan data uji, dimana data latih digunakan untuk mencari jumlah iterasi, jumlah hidden layer, dan nilai learning rate yang optimal. Pengujian data latih memberikan hasil yakni jumlah iterasi optimal diperoleh pada saat iterasi 80, sedangkan untuk jumlah hidden layer yang optimal adalah sebanyak satu hidden layer dan untuk nilai learning rate optimal yakni pada saat bernilai 0.8. Semua variabel yang diperoleh dikatakan optimal karena memiliki rata-rata MSE paling kecil dibandingkan dengan data lainnya. Hasil yang diperoleh saat data uji dengan menggunakan iterasi, jumlah hidden layer, dan nilai learning rate yang optimal didapatkan hasil MSE sebesar 0.07280534710552478.
A Performance Comparison of LSTM and GRU Architectures for Forecasting Daily Bitcoin Price Volatility Nafisah, Nurun; Yamasari, Yuni; Yohannes, Ervin
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n2.p156-167

Abstract

The highly volatile movement of Bitcoin prices necessitates the use of prediction methods capable of accurately capturing complex and rapidly changing patterns. This study aims to compare the performance of two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting Bitcoin prices based on historical time series data. The analysis was conducted using daily closing price data, with several parameter configurations applied, including dropout value, learning rate, and number of epochs at a window size of 30. The training process was carried out using a univariate approach to assess the fundamental ability of each model to learn temporal patterns without the influence of external variables. The results indicate that the GRU model consistently outperforms LSTM across most experimental settings. The best performance was achieved with 30 epochs, dropout 0.1, and a learning rate of 0.001, producing RMSE 1478.333, MAE 1000.900, R² 0.996081, and MAPE 1.973072. These metrics demonstrate a lower error level and a stronger fit to actual Bitcoin price movements. Moreover, a paired t-test confirmed that the performance gap between the two models is statistically significant. Overall, the findings suggest that the Gated Recurrent Unit architecture is more efficient in capturing nonlinear patterns and responding to the volatile dynamics of cryptocurrency price fluctuations, making it a promising approach for future predictive modeling in financial time series.
Optimizing UKT Prediction Based on Socio-Economic Features: A Multimodel Evaluation with Feature Selection Srategies Putri, Windy Chikita Cornia; Yustanti, Wiyli; Yohannes, Ervin
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31828

Abstract

Determining the tuition fee group (UKT) for new students in Indonesian public universities represents a complex challenge requiring an equitable, data-driven approach. This study introduces an integrative feature selection strategy that combines five popular techniques Chi-Square, Recursive Feature Elimination (RFE), LASSO Regression, Random Forest Importance, and Exploratory Factor Analysis (EFA) to extract the most relevant attributes from 53 socioeconomic variables of prospective students at Universitas Negeri Surabaya. As a novelty, the study identifies intersecting features consistently selected by all five methods and evaluates their impact on the performance of five classification algorithms: Support Vector Machine (SVM), Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Naïve Bayes. Experimental results demonstrate a significant improvement in accuracy, with SVM increasing from 0.7550 to 0.7810. These findings confirm that integrative feature selection can optimize model performance while reducing data complexity. This study provides a replicable methodological contribution for developing transparent and adaptive classification systems based on socioeconomic data in higher education contexts.
Co-Authors Ahmad Fikri Hanafi Asyrofa Rahmi, Asyrofa Esti, Esti Yogiyanti Mulyono, Alfito Nafisah, Nurun Putri, Windy Chikita Cornia Ramadhany, Sinta Dhea Shih, Timothy K. Utaminingrum, Fitri Wayan Firdaus Mahmudy Wiyli Yustanti Yogiyanti, Esti YUNI YAMASARI