Judul: HPO Bootstrapping to Handle Overfitting in Sentiment Classification for High-Dimensional Data from Local Mobile Indie Game Reviews
Abstrak: Penggunaan smartphone telah mendorong popularitas permainan seluler, memungkinkan perkembangan sub-sektor pengembang aplikasi dan game di Indonesia. Namun, indie game developers menghadapi kekurangan sumber daya promosi, yang mengakibatkan kurangnya data ulasan untuk membangun model klasifikasi yang baik. Mengklasifikasikan ulasan game dengan dimensi tinggi menimbulkan tantangan statistik yang signifikan, seperti overfitting. Penelitian ini bertujuan untuk mengevaluasi metode optimisasi hyperparameter terbaik dalam klasifikasi support vector machines (SVM) untuk menentukan model klasifikasi sentimen terbaik. Secara khusus, tujuannya adalah mengevaluasi metode optimisasi hyperparameter terbaik berdasarkan nilai akurasi hasil klasifikasi SVM pada data berdimensi tinggi dan menerapkan bootstrapping pada metode optimisasi hyperparameter terbaik untuk memperoleh nilai akurasi model terbaik. Hasil analisis menunjukkan bahwa optuna optimization dan genetic algorithm memiliki nilai akurasi dan F1 score yang lebih baik dibandingkan dengan metode optimisasi lainnya. Dari hasil evaluasi, optuna optimization dianggap sebagai metode terbaik dalam konteks ini tanpa menggunakan bootstrapping. Hasil analisis menunjukkan bahwa optuna optimization memiliki nilai akurasi yang stabil, dengan nilai F1 score yang menurun ketika menggunakan bootstrapping, The use of smartphones has driven the popularity of mobile games, enabling the development of the sub-sector of application and game developers in Indonesia. However, indie game developers face a lack of promotional resources, which results in a lack of review data to build a good classification model. Classifying game reviews with high dimensions poses significant statistical challenges, such as overfitting. This study aims to evaluate the best hyperparameter optimization method in support vector machines (SVM) classification to determine the best sentiment classification model. Specifically, the goal is to evaluate the best hyperparameter optimization method based on the accuracy value of SVM classification results on high-dimensional data and apply bootstrapping to the best hyperparameter optimization method to obtain the best model accuracy value. The analysis results show that optuna optimization and genetic algorithm have better accuracy values and F1 scores compared to other optimization methods. From the evaluation results, optuna optimization is considered the best method in this context without using bootstrapping. The analysis results show that optuna optimization has a stable accuracy value, with an F1 score value that decreases when using bootstrapping.
Keyword: Mobile Games, Bootstrapping, Sentiment Classification, Hyperparameter Optimization, Support Vector Machines (SVM)

Judul: Bootstrapping HPO untuk Menangani Overfitting pada Klasifikasi Sentimen untuk Data Berdimensi Tinggi dari Reviews Indie Game Mobile Lokal
Abstrak: Penggunaan smartphone telah mendorong popularitas permainan seluler, memungkinkan perkembangan sub-sektor pengembang aplikasi dan game di Indonesia. Namun, indie game developers menghadapi kekurangan sumber daya promosi, yang mengakibatkan kurangnya data ulasan untuk membangun model klasifikasi yang baik. Mengklasifikasikan ulasan game dengan dimensi tinggi menimbulkan tantangan statistik yang signifikan, seperti overfitting. Penelitian ini bertujuan untuk mengevaluasi metode optimisasi hyperparameter terbaik dalam klasifikasi support vector machines (SVM) untuk menentukan model klasifikasi sentimen terbaik. Secara khusus, tujuannya adalah mengevaluasi metode optimisasi hyperparameter terbaik berdasarkan nilai akurasi hasil klasifikasi SVM pada data berdimensi tinggi dan menerapkan bootstrapping pada metode optimisasi hyperparameter terbaik untuk memperoleh nilai akurasi model terbaik. Hasil analisis menunjukkan bahwa optuna optimization dan genetic algorithm memiliki nilai akurasi dan F1 score yang lebih baik dibandingkan dengan metode optimisasi lainnya. Dari hasil evaluasi, optuna optimization dianggap sebagai metode terbaik dalam konteks ini tanpa menggunakan bootstrapping. Hasil analisis menunjukkan bahwa optuna optimization memiliki nilai akurasi yang stabil, dengan nilai F1 score yang menurun ketika menggunakan bootstrapping, The use of smartphones has driven the popularity of mobile games, enabling the development of the sub-sector of application and game developers in Indonesia. However, indie game developers face a lack of promotional resources, which results in a lack of review data to build a good classification model. Classifying game reviews with high dimensions poses significant statistical challenges, such as overfitting. This study aims to evaluate the best hyperparameter optimization method in support vector machines (SVM) classification to determine the best sentiment classification model. Specifically, the goal is to evaluate the best hyperparameter optimization method based on the accuracy value of SVM classification results on high-dimensional data and apply bootstrapping to the best hyperparameter optimization method to obtain the best model accuracy value. The analysis results show that optuna optimization and genetic algorithm have better accuracy values and F1 scores compared to other optimization methods. From the evaluation results, optuna optimization is considered the best method in this context without using bootstrapping. The analysis results show that optuna optimization has a stable accuracy value, with an F1 score value that decreases when using bootstrapping
Keyword: Bootstrapping, Mobile Games, Sentiment Classification, Hyperparameter Optimization, Support Vector Machines (SVM)

Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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