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All Journal REKAYASA Academic Journal of Computer Science Research (AJCSR) TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora
Hudawi AS, Ahmad
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Physics Public Health Other

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Pengembangan Sistem Informasi Cuci Mobil Berbasiskan Website Rohmah, Maghfirotur; Syafiih, M.; Hudawi AS, Ahmad
Rekayasa Vol 14, No 3: Desember 2021
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v14i3.11384

Abstract

The car wash business is growing. A wide selection of car washes, spread across various regions. The increasing number of cars will provide more opportunities for entrepreneurs to open up in the car wash industry, while also providing entrepreneurs with potential for opportunities in the car market. With so many customers owned by Fuji Lestari, it is very risky for errors to update data every day, because the current transaction processes in the place of business still do not use computers, including in recording the number of consumers and this makes it difficult for Fuji Lestari to find data from files stored and archived by the company. From the problems that have been described, the researchers propose a website-based car wash information system. The waterfall method was chosen as the method in this research to achieve the expected goals. From the research that has been carried out by the author regarding the Web-Based Car Wash Information System at Fuji Lestari, several conclusions can be drawn, namely the Web-Based Car Wash Information System at Fuji Lestari has been produced, and after testing 5 users, from the results of calculating the total percentage obtained a percentage of 91%. Therefore, this information system is categorized as very good and suitable for use at Fuji Lestari car wash business.
Improve Metode Lightgbm untuk Prediksi Harga Mobil Bekas Menggunakan Hyper-Parameter Tuning Aulady, Moch. Aqil; Hudawi AS, Ahmad; Arifin, Zainal
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 5, No 3 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/trilogi.v5i3.9000

Abstract

This study aims to predict used car prices using the LightGBM method and hyperparameter tuning techniques in the context of data science. The analysis process includes collecting historical data on used cars, preprocessing the data to clean and encode variables, and splitting the data into training and testing sets. The LightGBM model was trained and optimized through hyperparameter tuning using GridSearchCV to improve model performance. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The results indicate that the well-optimized LightGBM model can accurately predict used car prices with high accuracy. The low MSE value (35207938112.028404) and high R-squared value (0.9462871489515565) demonstrate the model's excellent predictive quality. This research provides deeper insights into the factors influencing used car prices and contributes to the development of effective and reliable predictive models.
Optimasi Model CatBoost dengan Feature Selection dan Hyperparameter Tuning untuk Prediksi Nasabah Bank Potensial Eko Fitra Firmandani, Ahmad Muzakki; Hudawi AS, Ahmad; Tholib, Abu
Academic Journal of Computer Science Research Vol 6, No 2 (2024): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v6i2.15656

Abstract

Persaingan ketat di industri perbankan menuntut kemampuan memprediksi nasabah potensial deposito secara akurat dan efisien. Penelitian ini bertujuan meningkatkan akurasi prediksi nasabah potensial deposito dengan mengurangi kompleksitas komputasi dan dimensionalitas data, terutama pada penanganan fitur kategorik. Metode yang diusulkan menggunakan algoritma CatBoost yang mampu menangani data kategorik secara efisien tanpa memerlukan one-hot encoding. Feature selection berbasis feature importance diaplikasikan untuk memilih fitur paling relevan, sementara hyperparameter tuning dengan Hyperopt digunakan untuk mengoptimalkan parameter model CatBoost. Eksperimen pada dataset Bank Marketing dengan 45.211 baris data dan 16 fitur menunjukkan kombinasi CatBoost, feature selection, dan hyperparameter tuning mampu mencapai akurasi 92,8%, sensitivitas 91,0%, dan spesifisitas 94,8% dalam memprediksi nasabah potensial deposito. Pendekatan ini terbukti efektif mengurangi kompleksitas komputasi sekaligus meningkatkan akurasi prediksi nasabah potensial deposito.
Co-Authors Abu Tholib Aulady, Moch. Aqil Eko Fitra Firmandani, Ahmad Muzakki Rohmah, Maghfirotur Syafiih, M. Zainal Arifin