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"ABSTRAKPerusahaan e-marketplace perlu menjaga dan meningkatkan kualitas aplikasi mobile dan layanan melalui evaluasi berdasarkan opini pelanggan untuk mengembangkan perusahan dan memenangkan kompetisi antar perusahaan sejenis. Salah satu bentuk opini pelanggan terdapat di toko penyedia aplikasi, seperti Google Play Store dan App Store. Ulasan online ini dapat dimanfaatkan oleh perusahaan e-marketplace, yaitu dengan melakukan analisis opini pelanggan opinion mining terhadap aplikasi dan layanan e-marketplace berdasarkan aspek pendukungnya. Penelitian ini menggunakan ulasan berbahasa Inggris dan Indonesia yang ada pada Google Play Store dan App Store guna mengetahui penilaian pelanggan terhadap enam perusahan e-marketplace di Indonesia, yaitu BliBli, Bukalapak, Lazada, OLX, Shopee dan Tokopedia. Ulasan berbaasa Inggris diolah berdasarkan prinsip Recursive Neural Tensor Network RNTN dengan dua macam pengolahan yaitu dengan lemmatization dan tanpa lemmatization. Ulasan berbahasa Indonesia diolah berdasarkan dictionary-based approach dengan dua macam pengolahan yaitu dengan stemming dan tanpa stemming. Uji akurasi dari luaran opinion mining menunjukkan bahwa ulasan berbahasa Inggris lebih baik diolah dengan melakukan lemmatization, sedangkan ulasan berbahasa Indonesia lebih baik diolah tanpa melakukan stemming . Hasil penelitian dapat digunakan untuk meningkatkan kualitas aplikasi dan layanan tiap perusahaan e-marketplace kedepannya.

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ABSTRACTE marketplace companies need to maintain and improve the quality of mobile application and services through an evaluation based on customer opinions to grow the company and win competition among similar companies. One form of customer opinion is found in app store stores, such as Google Play Store and App Store. This online review can be utilized by e marketplace company, by conducting customer rsquo s opinion analysis opinion mining of e marketplace application and services based on its supporting aspects. This study use English and Indonesian reviews available on Google Play Store and App Store platforms to determine customer ratings for six e marketplace companies in Indonesia, namely BliBli, Bukalapak, Lazada, OLX, Shopee and Tokopedia. English based reviews are processed based on the principle of Recursive Neural Tensor Network RNTN with two kinds of processing, with lemmatization and without lemmatization. Indonesian language reviews are processed based on dictionary based approach with two kinds of processing, with stemming and without stemming. The accuracy test from the results of the opinion mining shows that the English reviews are better processed with lemmatization, while Indonesian reviews are better processed without stemming. The results of the research can be used to improve applications and services quality of each e marketplace company in the future."

"Penelitian ini menyelidiki dampak kebijakan relaksasi restrukturisasi kredit yang baru diterapkan di Indonesia sebagai variabel moderasi terhadap kinerja sektor perbanlan. Dengan menggunakan analisis regresi data panel pada sampel 105 bank dari tahun 2017 hingga 2022, penelitian ini menguji hubungan antma non-performing loans (NPL), cadangan kerugian penumnan nilai (CKPN), beban operasional terhadap pendapatan operasional (BOPO), dan return on assets (ROA) dengan moderasi kebijakan relaksasi restrukturisasi kedit. Penelitian menunjukkan bahwa ROA cenderung menurun selama penerapan kebijakan relaksasi restrukturisasi kredit. Selain itu terdapat penurunan NPL selama penerapan kebijakan restrukturisasi kredit yang menunjukkan perbaikan kualitas kredit, namun disisi lain CKPN meningkat menunjukkan bank lebih berhati-hati dan mempersiapkan diri terhadap potensi kerugian. Kebijakan relaksasi restrukturisasi kredit ditemukan memperkuat dampak negatif CKPN terhadap ROA, menunjukkan penundaan pengakuan kerugian memperburuk profitabilitas bank. Terakhir, kebijakan relaksasi restrukturisasi kredit ditemukan dapat membantu meredam dampak negatif BOPO terhadap ROA, menunjukkan adanya perbaikan dalam efisiensi operasional bank selama penerapan kebijakan relaksasi restrukturisasi kredit. Penelitian ini memberikan kontribusi dalam pemahaman dinamika regulasi keuangan dan memberikan wawasan bagi pembuat kebijakan dan lembaga perbankan dalam menghadapi kompleksitas pasar.

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This study investigates the impact of newly implemented credit restructuring relaxation policies in Indonesia as a moderating variable on the performance of the banking sector. Using panel data regression analysis on a sample of 105 banks from 2017 to 2022, this study examines the relationships between non-performing loans (NPL), loan loss provisions (LLP), operating expenses to operating income ratio, and return on assets (ROA) moderated by the credit restructuring relaxation policy. The findings indicate that ROA tends to decrease during the implementation of the credit restructuring relaxation policy. Furthermore, there is a decrease in NPL during the implementation of the restructuring policy, indicating an improvement in credit quality; however, LLP increases, suggesting banks are more cautious and prepared for potential losses. The policy of credit restructuring relaxation is found to strengthen the negative impact of LLP on ROA, indicating that delaying the recognition of losses worsens bank profitability. Lastly, the credit restructuring relaxation policy is found to help mitigate the negative impact of operating expenses to operating income ratio on ROA, indicating improvements in bank operational efficiency during the policy implementation. This study contributes to understanding the dynamics of financial regulation and provides insights for policymakers and banking institutions in addressing market complexities."