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: Support Vector Machine Algorithm for Aspect Based Sentiment Analysis on Online Game Reviews Mobile Legends: Bang-Bang
Abstrak: Kehadiran era teknologi digital dipermudah dengan adanya koneksi internet yang mudah diakses serta menyediakan banyak fitur dan hiburan salah satunya game online. Mobile Legends: Bang-Bang merupakan game online berjenis Multiplayer Online Battle Arena (MOBA) yang populer sejak diluncurkan pada tahun 2016. Kepopuleran tersebut tidak terlepas dari ulasan pengguna yang memberikan informasi dan sentimen berbeda. Penelitian ini akan mengidentifikasi sentimen ulasan pengguna aplikasi berdasarkan aspek gameplay, performa, visualisasi, dan player. Metode klasifikasi yang digunakan dalam penelitian ini adalah Support Vector Machine (SVM). Aplikasi game online Mobile Legends: Bang-Bang cenderung memiliki sentimen negatif berdasarkan aspek gameplay, performa, dan player. Akan tetapi, dari aspek visualisasi ulasan cenderung memiliki sentimen positif. Hasil evaluasi model dilakukan berdasarkan nilai akurasi, F1-score, dan AUC didapatkan bahwa aspek gameplay, performa, dan player memberikan tingkat klasifikasi yang lebih baik dibandingkan aspek visualisasi., The presence of the digital technology era is facilitated by an internet connection that is easily accessible and provides many features and entertainment, one of which is online games. Mobile Legends: Bang-Bang is a Multiplayer Online Battle Arena (MOBA)-type online game that has been popular since its launch in 2016. Currently, Mobile Legends: Bang-Bang is still the top free game on the Google Play Store. This popularity is inseparable from user reviews that provide different information and sentiment. This research will identify the sentiment of application user reviews based on aspects of gameplay, performance, visualization, and player. The classification method used in this study is the Support Vector Machine (SVM). The online game application Mobile Legends: Bang-Bang tends to have negative sentiment from aspects of gameplay, performance, and player. However, from the visualization aspect, they tend to have positive sentiment. The results of the evaluation of the model based on the value of accuracy, F1-score, and AUC, it was found that the gameplay, performance, and player aspects gave better classification results than the visualization aspect.
Keyword: aspect based sentiment analysis, game online, mobile legends: bang-bang, SVM

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