Judul: Perbandingan Analisis Sentimen Menggunakan Naive Bayes,  Lexicon-Based, dan Hybrid pada Ulasan Pengguna Aplikasi Livin’ by Mandiri
Abstrak: Perkembangan teknologi mengalami peningkatan yang sangat pesat hampir di seluruh sektor kehidupan manusia, salah satunya peningkatan inovasi perbankan dalam menghadirkan akses layanan transaksi digital. Analisis sentimen digunakan untuk menganalisis serta mengklasifikasikan ulasan pengguna aplikasi yang bertujuan menentukan kebijakan atau evaluasi pada pengembangan aplikasi. Penelitian ini membandingkan performa analisis sentimen berbasis Naive Bayes, Lexicon-based, dan Hybrid dalam mengklasifikasikan sentimen ulasan pengguna aplikasi Livin’ by Mandiri pada Google Play Store. Hasil pada penelitian ini menunjukkan bahwa metode Naive Bayes menghasilkan model dengan performa terbaik dengan nilai akurasi dan macro avg F1-score sebesar 0,96. Model Naive Bayes menggunakan information gain sebagai seleksi kata dan stratified k-fold cross validation sebagai ukuran validasi. Mayoritas ulasan pada rentang waktu yang diamati pada penelitian ini memiliki sentimen positif. Keluhan utama dari pengguna aplikasi Livin’ by mandiri adalah frekuensi pembaruan yang terlalu sering serta memiliki rentang waktu yang berdekatan. Meskipun pada penelitian ini model Hybrid bukan merupakan model terbaik, namun model ini dapat menjadi model alternatif yang mudah dan cepat untuk menganalisis sentimen., Development of technology has advanced very rapidly in almost all sectors of human life, and one of them is the advancement of banking innovation, which provides access to digital transaction services. Sentiment analysis can be used to analyze and classify user reviews of applications, aiming to determine policies or evaluate application development. This study compared the performance of Naive Bayes-based, Lexicon-based, and Hybrid-based sentiment analysis to classify the sentiment of user reviews of the Livin' by Mandiri application on the Google Play Store. The results of this study showed that the Naive Bayes method produced the model with the best performance, achieving an accuracy and macro avg F1-score of 0.96. The Naive Bayes model used information gain as the basis for word selection and stratified k-fold cross validation as a validation measure. The majority of reviews during the study period showed positive sentiments. The main complaint from users of the Livin' by Mandiri application was the frequency of updates, which were considered too frequent and had a short time frame. Although the Hybrid model was not the best in this study, it could serve as an easy and fast alternative for analyzing sentiment.
Keyword: hybrid, lexicon-based, livin' by mandiri, naive bayes, sentiment analysis, analisis sentimen

Judul: Analisis Sentimen Pengguna Aplikasi Female Daily Menggunakan Support Vector Machine: Evaluasi Leksikon Sentistrenghtid dan InSet
Abstrak: Female Daily merupakan sebuah aplikasi kecantikan yang menyediakan wadah bagi sesama penggunanya untuk berdiskusi tentang produk kecantikan. Aplikasi ini dapat diunduh salah satunya melalui Google Play dimana penggunanya dapat memberikan ulasan yang berupa komentar positif, netral, maupun negatif. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan pengguna aplikasi Female Daily, serta mengidentifikasi perbedaan performa penggunaan kamus dari leksikon sentistrenghtid dan InSet untuk proses pelabelan data pada algoritma Support Vector Machine. Data yang digunakan merupakan data ulasan hasil scrapping dari Google Play dalam rentang waktu Januari 2019 hingga Desember 2023. Grid search digunakan untuk mencari kombinasi fungsi kernel dan hyperparameter (C, ?, P) yang optimal. Pemodelan dilakukan menggunakan metode multi-class Support Vector Machine (SVM) dengan menerapkan algoritma One Against All (OAA). Model SVM pertama dibentuk menggunakan data hasil pelabelan sentistrenghtid dengan fungsi kernel RBF serta nilai C = 5 dan gamma = ’scale’. Selanjutnya, model SVM kedua dibentuk menggunakan data hasil pelabelan InSet dengan fungsi kernel linear serta nilai C = 1. Hasil evaluasi kedua model menggunakan data uji memperlihatkan nilai akurasi model sebesar 78% dan 76%. Selanjutnya, kedua model diterapkan ke seluruh data lalu dievaluasi menggunakan sentimen nilai rating. Akurasi yang didapatkan yaitu sebesar 65% dan 57%., Female Daily is a beauty application that provides a forum for fellow users to discuss beauty products. This application can be downloaded via Google Play, where users can provide reviews in the form of positive, neutral, or negative comments. This research aims to conduct sentiment analysis on the reviews of Female Daily application users and to identify the differences between the performance of using the dictionary from sentistrenghtid and InSet lexicon for data labeling process in the Support Vector Machine algorithm. The data used is review data scraped from Google Play from January 2019 to December 2023. Grid search is used to find the optimal combination of kernel function and hyperparameters (C, ?, P). Modeling was done using the multi-class Support Vector Machine (SVM) method by applying the One Against All (OAA) algorithm. The first SVM model was formed using data from the sentristrenghtid labeling with the RBF kernel function and the values C = 5 and gamma = 'scale'. Then the second SVM model was formed using data from the InSet labeling with the Linear kernel function and the value C = 1. Evaluation results of the two models using test data showed model accuracy values of 78% and 76%. Furthermore, when the two models were applied to all data and then evaluated using the sentiment of rating value, the accuracy obtained was 65% and 57%.
Keyword: analisis sentimen, SVM, grid search, InSet, sentistrenghtid

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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