Judul: Implementation of Support Vector Machine for Sentiment Analysis of LinkedIn App Reviews
Abstrak: LinkedIn adalah aplikasi yang digunakan untuk membantu individu membangun jaringan dan menemukan peluang karir dengan lebih dari 722 juta pengguna di seluruh dunia. Popularitas aplikasi LinkedIn yang tinggi juga diiringi oleh tuntutan akan layanan aplikasi yang berkualitas tinggi. Hal tersebut ditunjukkan pada ulasan pengguna di Google Play Store. Penelitian ini mengidentifikasi sentimen ulasan pengguna aplikasi di Google Play Store yang diklasifikasikan menjadi opini positif dan negatif menggunakan algoritma Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah data ulasan aplikasi LinkedIn berbahasa Inggris di Google Play Store sejak tanggal 22 Februari 2023 hingga 22 Maret 2023. Mayoritas ulasan sentimen positif menunjukkan bahwa aplikasi LinkedIn memberikan layanan sesuai yang dijanjikan, yaitu membantu memperluas jaringan professional. Mayoritas ulasan sentimen negatif menyebutkan keluhan pada aplikasi, seperti gagal membayar premi, tidak bisa login, dan kendala pembaruan aplikasi. Pemodelan SVM menggunakan kernel linier menghasilkan kinerja yang cukup baik dalam mengklasifikasikan sentimen, dengan nilai rata-rata akurasi, sensitivitas, dan spesifisitas dengan 10-fold cross-validation sebesar 95,6%, 96,7%, dan 92%. Nilai akurasi menyatakan bahwa 95,6% prediksi klasifikasi kelas sentimen sesuai dengan kelas sentimen sebenarnya., LinkedIn is an application used to help individuals build networks and find career opportunities. LinkedIn is the world's largest professional network, with over 722 million global users. The high popularity of the LinkedIn app is also accompanied by a demand for high quality app services. This is reflected in user reviews on the Google Play Store. This research identifies the sentiment of application user reviews on the Google Play Store which are classified into positive and negative opinions using the Support Vector Machine (SVM) algorithm. The data used in this study is the English-language LinkedIn application review data on the Google Play Store from February 22, 2023 to March 22, 2023. The majority of positive sentiment reviews indicate that the LinkedIn application provides the promised service, which helps expand the professional network. The majority of negative sentiment reviews mentioned complaints about the application, such as failing to pay premiums, not being able to log in, and application update problems. SVM modeling using a linear kernel resulted in a fairly good performance in classifying sentiment, with average values of accuracy, sensitivity, and specificity with 10-fold cross-validation of 95.6%, 96.7%, and 92%. The accuracy value states that 95.6% of the predicted sentiment class classification matches the actual sentiment class.
Keyword: linkedin, sentiment analysis, review, support vector machine

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: Studi Taksonomi Katak Subgenus Papurana (Lesson, 1831) dari Halmahera (Anura, Ranidae, Hylarana), Studi Taksonomi Katak Subgenus Papurana (Lesson, 1831) dari Halmahera (Anura, Ranidae, Hylarana
Abstrak: I reevaluated taxonomic of subgenus Papurana position from Halmahera based on morphological and molecular analyses. My results show that two separate species occur on the Halmahera Island, each nested within two distinct clades (hereafter papua clade and celebensis clade), one of which corresponds to H. moluccana (celebensis clade) and the other to an unidentified species (papua clade). By p-distance ranging from 6.9 to 11.5% on the 16S rRNA gene, the unidentified species (Hylarana sp.) is genetically different from all congeners of the papua clade. The Hylarana sp. can be distinguished from all congeners by following combination of charactes: vomer teeth in two oblique rows with narrow inter-vomer distance; snout rounded dorsally; dorsum with few scattered cone-shaped tubercles that are black with white tips; distinct skin folds (ridges) on the dorsal side of the thigh coinciding with dark brown cross bars; a marbled pattern on the ventral side of the thigh and yellowish groin. I also present new data for H. daemeli, H. volkerjane, and H. arfaki from the western region of Papua. Furthermore, I found that H. celebensis has numerous mtDNA lineages, indicating cryptic diversity within the celebensis clade. The discovery of two separate species on Halmahera necessitates more research into the biogeographic history of Hylarana in Northern Moluccas.
Keyword: frog, Halmahera, Hylarana, morphology, Papurana, phylogenetic