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All Journal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Bulletin of Computer Science Research Al-Muhtarifin: Islamic Banking and Islamic Economic Journal
Gunawan, Asrul
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Computer Science & IT Economics, Econometrics & Finance

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ANALISIS KUALITAS LABA PADA PERUSAHAAN KONTRUKSI DAN BANGUNAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) Rokan, Mustafa Kamal; Nurwani, Nurwani; Gunawan, Asrul
Al-Muhtarifin: Islamic Banking and Islamic Economic Journal Vol 4, No 1 (2025): JAN 2025
Publisher : Universitas Muhammadiyah Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/almuhtarifin.v1i1.17605

Abstract

This study aims to determine the effect of profitability, leverage, and liquidity on earnings quality in construction companies and buildings listed on the Indonesia Stock Exchange (IDX). In this research using quantitative methods with an associative approach. Population that used is the company's annual financial statements buildings and buildings listed on the Indonesia Stock Exchange (IDX) period 2016-2020 as many as 18 companies. Sampling technique using purposive sampling technique based on certain criteria. Results research shows that profitability significant effect on earnings quality. Leverage has no significant effect on earnings quality. Liquidity no significant effect on earnings quality. Simultaneously, all the independent variables are profitability, leverage, and liquidity simultaneously significantly influence earnings qualityKeyword: Profitability, leverage, Liquidity on earnings quality
Analisis Perbandingan Metode DES (Double Exponential Smoothing) dan WMA (Weighted Moving Average) dalam Peramalan Penjualan Laptop Gunawan, Asrul; Hermawan, Arief; Avianto, Donny
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15314

Abstract

Rapid technological developments increase demand for electronic devices, especially laptops. Fluctuations in monthly sales are a challenge for companies in determining the optimal amount of inventory. The inability to predict market demand can disrupt inventory management and customer satisfaction. Therefore, accurate sales forecasting is essential for planning marketing and procurement strategies. This study compares two sales forecasting methods, namely Double Exponential Smoothing (DES) and Weighted Moving Average (WMA), to analyze the accuracy of each method. The results showed that the DES method has a better level of accuracy with an average MAPE value of 16.72%, compared to WMA which reached 21.22%. This study provides practical insights for companies in choosing the right forecasting method, in order to improve inventory management, product procurement strategies, and customer satisfaction
Analisis Pengaruh Preprocessing Data dan Hyperparameter Tuning pada Backpropagation Neural Network dalam Klasifikasi Stroke Gunawan, Asrul; Hermawan, Arief; Avianto, Donny
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.956

Abstract

Data imbalance and scale differences between features are often the main factors that reduce the performance of neural network-based classification models. This study aims to analyze the effect of data preprocessing and hyperparameter tuning on the performance of Backpropagation Neural Network (BPNN) in stroke classification. This study used a stroke dataset from the Kaggle platform consisting of 5,110 patient data with 10 clinical features. The evaluation was conducted using five schemes and consisted of several data balancing techniques. These techniques include no balancing, SMOTE, and ADASYN. In addition, the evaluation also involved data normalization including no normalization, MinMaxScaler, and Z-Score. The BPNN model used has an architecture of 19 input neurons, 29 neurons in the hidden layer, and 1 output neuron. Hyperparameter tuning was performed by finding the best learning rate and number of epochs. The evaluation results showed that the model in scheme one has limitations. This limitation is most visible in identifying stroke classes. The application of SMOTE and MinMaxScaler in scheme two proved that the results were better and its performance increased significantly. On the other hand, the combination of ADASYN and Z-Score in scheme three showed more stable performance and was able to detect stroke cases more accurately. The hyperparameter tuning process in schemes four and five also proved to improve performance. The best results were obtained in scheme five, with an accuracy of 96.47%, a precision of 97.34%, a recall of 95.62%, and an F1-score of 96.47%. These findings indicate that the combination of adaptive balancing techniques, distribution-based normalization, and optimal parameter tuning is very effective in improving the accuracy and stability of BPNN for stroke classification.
Co-Authors Donny Avianto Nurwani Nurwani, Nurwani Rokan, Mustafa Kamal