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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science
Ibrahim, Sang Adji
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Computer Science & IT

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Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation Rahmaddeni, Rahmaddeni; Wicaksono, M. Teguh; Wulandari, Denok; Agustriono, Agustriono; Ibrahim, Sang Adji
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4280

Abstract

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a stacking technique, with Random Forest Regressor and Gradient Booster Regressor as base models.The results show that the best model was achieved using the stacking ensemble technique with a 90:10 data split and polynomial features, yielding a Root Mean Square Error of 1.206, Mean Absolute Error of 0.990, and R2 of 0.670. This model has also met the assumptions of linear regression, such as residual normality, homoscedasticity, and no autocorrelation of residuals. This study concluded that the ensemble stacking technique and the addition of polynomial features could improve the model in estimating dissolved oxygen values and also contribute by providing an accessible user interface using the Gradio Framework, allowing users to estimate dissolved oxygen levels effectively.
Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation Rahmaddeni, Rahmaddeni; Wicaksono, M. Teguh; Wulandari, Denok; Agustriono, Agustriono; Ibrahim, Sang Adji
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4280

Abstract

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a stacking technique, with Random Forest Regressor and Gradient Booster Regressor as base models.The results show that the best model was achieved using the stacking ensemble technique with a 90:10 data split and polynomial features, yielding a Root Mean Square Error of 1.206, Mean Absolute Error of 0.990, and R2 of 0.670. This model has also met the assumptions of linear regression, such as residual normality, homoscedasticity, and no autocorrelation of residuals. This study concluded that the ensemble stacking technique and the addition of polynomial features could improve the model in estimating dissolved oxygen values and also contribute by providing an accessible user interface using the Gradio Framework, allowing users to estimate dissolved oxygen levels effectively.
Implementasi Algoritma Decision Tree untuk Rekomendasi Film dan Klasifikasi Rating pada Platform Netflix: Implementation of Decision Tree Algorithm for Movie Recommendation and Rating Classification on the Netflix Platform Mukhsinin, Dimas Aditya; Rafliansyah, M; Ibrahim, Sang Adji; Rahmaddeni, Rahmaddeni; Wulandari, Denok
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1255

Abstract

Sebagai salah satu platform video streaming terbesar di dunia, Netflix telah berkembang pesat sejak pendiriannya pada tahun 1997, awalnya berfokus pada penyewaan DVD, namun kemudian beralih ke layanan streaming online pada tahun 2007. Dengan jutaan pelanggan global, Netflix terus berinovasi dengan paket langganan, produksi konten eksklusif, dan teknologi analisis data serta machine learning untuk meningkatkan pengalaman pengguna. Penelitian ini menerapkan algoritma Decision Tree untuk meningkatkan sistem rekomendasi dan klasifikasi rating di Netflix. Menggunakan dua dataset utama, movies_df dan ratings_df, penelitian melibatkan langkah-langkah pengumpulan dan penggabungan data, penentuan fitur dan variabel target, pembagian data, pelatihan model, serta evaluasi. Hasilnya mencakup evaluasi model Decision Tree dengan metrik akurasi, precision, recall, dan F1-score untuk setiap kategori rating, serta visualisasi grafik batang tentang jumlah rating film dan presentase rating dari 1-5. Rekomendasi film berdasarkan model Decision Tree juga disajikan, memberikan wawasan tentang peningkatan sistem rekomendasi di Netflix. Kesimpulan menunjukkan bahwa implementasi algoritma Decision Tree dapat meningkatkan akurasi rekomendasi film dan klasifikasi rating di Netflix, berkontribusi pada pengalaman pengguna yang lebih personal di era layanan streaming online.
Co-Authors Agustriono, Agustriono Mukhsinin, Dimas Aditya Rafliansyah, M Rahmaddeni Rahmaddeni Wicaksono, M. Teguh Wulandari, Denok