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: Implementation of Support Vector Machine Algorithm for Sentiment Analysis of Flip Application Reviews on Google Play Store Site
Abstrak: Flip merupakan start-up kreasi pemuda Indonesia yang memiliki popularitas dan rating tinggi pada situs Google Play Store. Hal tersebut membuat tuntutan terhadap mutu pelayanan aplikasi juga semakin meningkat, sehingga muncul beragam komentar di kolom ulasan. Analisis sentimen merupakan salah satu cara untuk mengetahui polaritas opini yang memungkinkan klasifikasi ulasan ke dalam kelas bersentimen positif, netral, atau negatif. Penelitian ini bertujuan untuk mengidentifikasi perbedaan performa algoritma Support Vector Machine (SVM) menggunakan kernel linear, kernel polynomial, dan kernel Radial Basis Function (RBF) pada tiga fitur n-gram (unigram, bigram, dan trigram) serta melakukan analisis sentimen terhadap ulasan para pengguna aplikasi Flip. Algoritma Multi-Class SVM One Against All (OAA) diterapkan karena terdapat tiga kelas sentimen. Selain itu, algoritma Synthetic Minority Oversampling Technique (SMOTE) diterapkan untuk mengatasi ketidakseimbangan pada data. Pemodelan SVM menggunakan kernel RBF pada fitur unigram menghasilkan performa terbaik dalam mengklasifikasikan sentimen dengan nilai akurasi dan macro average F1-score yang didapat sebesar 89% dan 70%. Secara keseluruhan, kernel linear dan RBF pada ketiga fitur n-gram memberikan hasil klasifikasi terbaik. Sebaliknya, kernel polynomial memberikan hasil klasifikasi terendah. Fitur unigram memberikan hasil klasifikasi terbaik dan fitur trigram memberikan hasil klasifikasi terendah untuk ketiga kernel., Flip is a start-up created by Indonesian youth that has high popularity and ratings on the Google Play Store site. This makes the demands on the quality of application services also increase, thus various comments appear in the review column. Sentiment analysis is one way to find out the polarity of opinions that allows the classification of reviews into classes that have positive, neutral or negative sentiments. This study aims to identify differences in the performance of the Support Vector Machine (SVM) algorithm using a linear kernel, a polynomial kernel, and a Radial Basis Function (RBF) kernel on three n-gram features (unigram, bigram, and trigram) and to conduct sentiment analysis on the reviews of Flip application users. The Multi-Class SVM One Against All (OAA) algorithm is applied because there are three sentiment classes. In addition, the Synthetic Minority Oversampling Technique (SMOTE) algorithm was applied to overcome the data imbalance. SVM modeling using the RBF kernel on the unigram feature produces the best performance in classifying sentiment with an accuracy value and a macro average F1-score obtained at 89% and 70%. Overall, the linear kernel and RBF kernel on the three n-gram features give the best classification results. On the other hand, the polynomial kernel gives the lowest classification result. The unigram feature gives the best classification result and the trigram feature gives the lowest classification result for the three kernels.
Keyword: flip, kernel, n-gram, sentiment analysis, support vector machine

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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