Judul: Perbandingan antara Multinomial Naïve Bayes dan Regresi Logistik pada Analisis Sentimen Ulasan Aplikasi Sayurbox
Abstrak: Sayurbox needs to know the satisfaction of users regarding the applications they make to keep users' loyalty. One way to find out users satisfaction is to look at the users' reviews through the Google Play Store. Sentiment analysis is useful for managing these reviews and allows reviews to have certain categories such as positive or negative. The classification process can be done manually, but it will take a long time if the number of reviews that need to be classified is large. Therefore, a model is required to classify reviews automatically for saving time. The algorithms to be used in this study are Multinomial Naïve Bayes and logistic regression, and these algorithms will be combined with different kinds of n-gram (unigram, bigram, trigram, unibigram, unitrigram, bitrigram). The study was conducted to compare both algorithms and the six types of n- gram, select the best model combinations, and predict review sentiment from June 2022 to July 2022. The results have shown that the use of two different algorithms and several types of n-gram had a significant effect on the accuracy value, AUC, and f1-score. Interaction between algorithms and n-gram also had significant influence. The result shows that Multinomial Naïve Bayes with unibigram has been the best algorithm because it has shown the highest average of accuracy (91,02%), AUC (83,66%), and f1-score (94,38%). Multinomial Naïve Bayes with unibigram was used to predict reviews from June 2022 – July 2022. It has been predicted that there were 153 positive reviews and 37 negative reviews.
Keyword: logistic regression, Multinomial Naïve Bayes, n-gram, Sayurbox, sentiment analysis

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: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4