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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Solikhun
STIKOM Tunas Bangsa
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Other

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Application of Neural Network Variations for Determining the Best Architecture for Data Prediction Mochamad Wahyudi; Firmansyah; Lise Pujiastuti; Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 5 (2022): Oktober 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (943.184 KB) | DOI: 10.29207/resti.v6i5.4356

Abstract

This study focuses on the application and comparison of the epoch, time, performance/MSE training, and performance/MSE testing of variations of the Backpropagation algorithm. The main problem in this study is that the Backpropagation algorithm tends to be slow to reach convergence in obtaining optimum accuracy, requires extensive training data, and the optimization used is less efficient and has performance/MSE which can still be improved to produce better performance/MSE in this research—data prediction process. Determination of the best model for data prediction is seen from the performance/MSE test. This data prediction uses five variations of the Backpropagation algorithm: standard Backpropagation, Resistant Backpropagation, Conjugate Gradient, Fletcher Reeves, and Powell Beale. The research stage begins with processing the avocado production dataset in Indonesia by province from 2016 to 2021. The dataset is first normalized to a value between 0 to 1. The test in this study was carried out using Matlab 2011a. The dataset is divided into two, namely training data and test data. This research's benefit is producing the best model of the Backpropagation algorithm in predicting data with five methods in the Backpropagation algorithm. The test results show that the Resilient Backpropagation method is the best model with a test performance of 0.00543829, training epochs of 1000, training time of 12 seconds, and training performance of 0.00012667.
OPTIMIZATION OF PREDICTION OF LUNG DISORDERS USING LSTM COMPARISON OF RMSPROP AND ADAM Egi Batubara; Solikhun; Agus Perdana Windarto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7767

Abstract

Accurate prediction of pulmonary disorders is essential to support early diagnosis and clinical decision-making. Medical time-series data are inherently nonlinear and temporally dependent, making conventional statistical approaches insufficient. This study formulates pulmonary disorder prediction as a regression problem and proposes an optimized Long Short-Term Memory (LSTM) model by comparing two widely used optimization algorithms, RMSProp and Adam. The dataset consists of 30,000 clinical records obtained from an open-source Kaggle repository, including demographic, behavioral, and health-related variables relevant to respiratory conditions. Data preprocessing involved categorical encoding and Min–Max normalization, followed by an 80:20 train–test split. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Experimental results demonstrate that the Adam optimizer achieves superior performance with lower prediction errors and more stable convergence compared to RMSProp and the baseline SGD optimizer. These findings highlight the critical role of optimizer selection in LSTM-based medical time-series modeling.
Optimasi Algoritma Logistic Regression untuk Memprediksi Risiko Dropout Mahasiswa dengan Teknik K-Fold Sandy Hardiansyah; Solikhun
Jurnal Manajemen, Pendidikan Dan Ilmu Komputer Vol. 3 No. 1 (2026): Volume 3 No 1 Januari 2026
Publisher : Yayasan Darus Soleh Parung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65309/tn4ka516

Abstract

Penelitian ini mengusulkan pengembangan model prediktif untuk mengidentifikasi potensi mahasiswa mengalami putus studi dengan memanfaatkan algoritma Logistic Regression yang dioptimalkan melalui penerapan teknik validasi silang K-Fold. Dataset yang digunakan mencakup berbagai atribut yang merepresentasikan aspek akademik dan kondisi sosial ekonomi mahasiswa, yang sebelumnya telah melalui tahapan pra-pemrosesan, meliputi penanganan data hilang, transformasi variabel kategorikal menjadi bentuk numerik, serta normalisasi fitur. Evaluasi kinerja model dilakukan dengan menggunakan metrik akurasi, presisi, recall, dan F1-score sebagai indikator utama. Berdasarkan hasil validasi, model menunjukkan performa yang konsisten dan unggul, dengan rata-rata akurasi sebesar 83,20% pada skema validasi K=5 dan meningkat menjadi 94,6% pada K=10. Temuan ini menegaskan bahwa pendekatan Logistic Regression layak diterapkan sebagai sistem deteksi dini (early warning system) yang efektif dalam lingkungan pendidikan tinggi. Selain itu, penelitian ini membuka peluang untuk pengembangan lebih lanjut melalui eksplorasi fitur tambahan, penerapan algoritma pembanding, serta pemanfaatan teknik ensemble learning dan penyetelan hiperparameter guna meningkatkan akurasi dan ketahanan model prediksi. 
Co-Authors Agus Perdana Windarto Egi Batubara Firmansyah Lise Pujiastuti Mochamad Wahyudi Sandy Hardiansyah