Judul: Dampak Penerbitan Central Bank Digital Currency terhadap Indikator-Indikator Makroekonomi
Abstrak: Bank Indonesia merilis “Blueprint Sistem Pembayaran Indonesia 2025” termasuk di dalamnya rencana penerbitan Digital Rupiah. Indonesia menjadi salah satu negara yang berada pada tahap penelitian sebelum menerbitkan Central Bank Digital Currency (CBDC). Sebelum secara resmi menerbitkan CBDC, dampak penerbitan CBDC terhadap indikator makroekonomi negara yang telah menerbitkan CBDC dapat dijadikan pertimbangan. Sampel pada penelitian ini adalah sembilan negara yang telah menerbitkan CBDC dan enam negara potensial yang belum menerbitkan CBDC dalam periode waktu 2018 hingga 2022. Metode yang digunakan dalam penelitian ini adalah analisis deskriptif, regresi data panel dengan dummy interaksi, serta difference-in-differences (DID). Peningkatan PDB riil negara-negara yang telah menerbitkan CBDC belum dapat dipastikan terjadi karena adanya penerbitan CBDC sebab adanya perbedaan antara hasil regresi data panel dengan dummy dan dengan tren yang ditampilkan pada grafik. Hasil DID menunjukkan bahwa tingkat suku bunga riil di negara yang telah menerbitkan CBDC mengalami peningkatan sebagai respon terhadap penerbitan CBDC jika dibandingkan dengan negara yang belum menerbitkan CBDC. Penelitian ini hanya menampilkan efek pengenalan dari hadirnya CBDC, penelitian selanjutnya dapat memasukkan berapa besar jumlah CBDC dalam total uang yang beredar agar dapat dilihat pengaruh sebenarnya dari penerbitan CBDC., Bank Indonesia released the "Indonesian Payment System Blueprint 2025," including the plan to issue Digital Rupiah. Indonesia is in the research stage before issuing a Central Bank Digital Currency (CBDC). Before officially issuing CBDCs, the impact of CBDC issuance on the macroeconomic indicators of countries that have issued CBDCs can be considered. The samples in this study are nine countries that have launched CBDCs and six potential countries that have not launched CBDCs from 2018 to 2022. The method used in this research is descriptive analysis, panel data regression with dummy interactions, and differences-in-differences ( did). The increase in the real GDP of countries that have issued CBDCs cannot be ascertained due to the issuance of CBDCs because there are differences between the results of the panel data regression with the dummy and the trends shown on the graph. DID results show that real interest rates in countries that have launched CBDCs have increased in response to CBDC issuance compared to countries that have not issued CBDCs. This study only shows the recognition effect of the presence of CBDCs, further research can include how many CBDCs are in the total money supply so that the actual effect of CBDC issuance can be seen.
Keyword: CBDC, DID, macroeconomic indicators, panel data regression

Judul: The Impact of Electronic Money on Financial Inclusion in Indonesia: Implication for Central Bank Digital Currency
Abstrak: Tren digitalisasi transaksi pembayaran dalam satu dekade terakhir telah merevolusi perilaku pelaku ekonomi. Perilaku konsumsi belanja masyarakat semakin beralih secara online bersamaan dengan kemudahan sistem pembayaran yang serba mobile, cepat, dan pada saat yang sama tetap aman. Hal ini meningkatkan penggunaan uang elektronik sebagai alat pembayaran di Indonesia. Dampak penggunaan uang elektronik memungkinkan meningkatkan inklusi keuangan karena kemudahan akses layanan keuangan digital dapat meningkatkan partisipasi kelompok masyarakat yang tidak dapat dijangkau oleh layanan keuangan tradisional. Penelitian ini bertujuan untuk menganalisis dampak uang elektronik terhadap inklusi keuangan di Indonesia dan implikasinya pada Central Bank Digital Currency. Metode yang digunakan adalah analisis deskriptif dan regresi data panel dengan variabel dummy. Objek yang diteliti adalah provinsi-provinsi di Indonesia pada periode tahun 2017-2021. Hasil penelitian ini menunjukkan bahwa variabel uang elektronik, Indeks Pembangunan Teknologi Informasi dan Komunikasi, dummy Pulau Jawa, PDRB per kapita, dan Pengeluaran Pemerintah Daerah berpengaruh positif signifikan terhadap inklusi keuangan. Sedangkan variabel dummy COVID-19 berpengaruh negatif signifikan terhadap inklusi keuangan., The trend of digitizing payment transactions over the last decade has revolutionized the behavior of economic actors. People's consumption behavior has been gradually shifting towards online shopping, along with the change in payment systems that are more mobile, fast, while at the same time maintain its security. This has increased the use of electronic money as a means of payment in Indonesia. The impact of the use of electronic money allows increasing financial inclusion because the ease of access to digital financial services can increase the participation of community groups that cannot be reached by traditional financial services. This study aims to analyze the impact of electronic money on financial inclusion in Indonesia and its implications for Central Bank Digital Currency. The methods used are descriptive analysis and panel data regression with dummy variables. The objects studied were provinces in Indonesia from the 2017–2021 period. The results of this study indicate that the variables of electronic money, Information and Communication Technology Development Index, Java Island dummy, GRDP per capita, and Local Government Expenditure have a significant positive effect on financial inclusion. Meanwhile, the COVID-19 dummy variable has a significant negative effect on financial inclusion.
Keyword: inklusi keuangan, regresi data panel, uang elektronik, variabel dummy

Judul: Klasifikasi Fragmen Metagenome Menggunakan Metode Support Vector Machine (SVM)
Abstrak: Metagenome analysis is one of the most important bioinformatics field. This field is related to genome which is taken directly from the environment. The purpose of this research is to classify metagenome fragment into some taxonomic levels using support vector machine (SVM) method. Feature extraction is performed using spaced k-mers. Classification process is conducted by creating model using the training data from 381 organisms. The evaluation results show that the accuracies for short fragments (400 bp) are 65.3% and 82.1% at genus level and phylum level, respectively. Meanwhile, the accuracies increase significantly for long fragments (10 kbp), with a value of 95.4% at genus level and 97.6% at phylum level. It can be stated that the accuracy will be increased with the increasing of fragments length and higher taxonomic levels. In addition, the results of the study also conclude that the feature extraction methods used was very good and produce data with linearly separable conditions.
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