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"The existing attendance system still has drawbacks, namely the queue in front of the finger scanner, the attendance data are not integrated with Human Resources Systems, and also the employees who work outside the office cannot get in the attendance system to roll presence. In the other hand, everyone has the mobile devices and all the mobile devices will be embedded a finger scanner in the future. In this paper, it is proposed the absence system using one own device. The finger scanner and coordinate Global Position System (GPS) are used as inputs for the attendance system that integrated with payroll system and human resource management tools. Application base on android platform is developed because the android is the most platforms that have been using in the most mobile devices. Using our proposed methodology, the employee can roll presence using their mobile devices and the do not need to be in queue and the employees who work outside the office also can roll presence. Research showed that proposed methodology can be used for the next generation attendance system."

"Pencurian identitas menjadi ancaman kejahatan di dunia maya pada masa kini, khususnya transaksi online. Untuk mengatasi masalah tersebut, voice biometrics dikembangkan untuk keamanan identitas. Penelitian ini mengusulkan skema voice biometrics pada algoritma deep learning Convolutional Neural Network (CNN) Residual dan CNN Depthwise Separable Convolution (DSC) dengan fitur ekstraksi \hybrid Discrete Wavelet Transform (DWT) dan Mel Frequency Cepstral Coefficients (MFCC) serta mengembangkan pembuatan data suara untuk pengguna ber-Bahasa Indonesia dalam waktu 25 menit. Skema tersebut ditargetkan untuk meningkatkan kinerja akurasi. Penelitian ini mengembangkan 2 model simulasi yang terpisah, yaitu model CNN Residual dan CNN DSC. Untuk setiap pengujian model, hasilnya dibandingkan dengan CNN Standard. Hasil pengujian pertama menunjukkan kinerja terbaik, model CNN Residual ini mampu meningkatkan kinerja validasi akurasi training voice biometrics 98.6345%, presisi 99,91% dan akurasi 99,47% pada speaker recognition (siapa yang bicara?), serta akurasi speech recognition (apa yang diucapkan?) 100%. Hasil pengujian kedua menunjukkan kinerja terbaik, model CNN DSC ini mampu mengurangi kinerja training parameter dan mampu mempercepat kinerja waktu proses training voice biometrics menjadi 5,12 detik. Sehingga hasil kinerja tersebut dapat mengurangi beban komputasi dan lebih baik dalam kinerja akurasinya. Dapat disimpulkan bahwa CNN Residual dan CNN DSC telah mengungguli CNN Standard. Sehingga pengembangan skema voice biometrics dapat diaplikasikan untuk identifikasi dan verifikasi/autentikasi suara user secara akurat, efisien dan cepat untuk aplikasi keamanan identitas dalam transaksi perbankan.

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Theft of identity is a threat to cybercrime today, especially online transactions. To overcome this problem, voice biometrics was developed for identity security. This research proposes a voice biometrics scheme on deep learning algorithms the CNN Residual and CNN Depthwise Separable Convolution (DSC) with Hybrid of Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC) Feature Extraction and develops voice data establishment for Indonesian users within a short period of time 25 minutes. The scheme is targeted to improve accuracy performance. This research developed 2 separate models, i.e. CNN Residual and CNN DSC model. For each model testing, the results are compared with the CNN Standard. The results of the first testing show the best performance, the CNN Residual model is able to improve the performance of training accuracy validation on voice biometrics of 98.6345%, precision of 99.91% and accuracy of 99.47% on speaker recognition (who is speaking?), and accuracy on speech recognition (What is uttered?) of 100%. The results of the second testing show the best performance, the CNN DSC model is able to reduce the performance of training parameters and is able to accelerate the performance of the voice biometrics training process time to 5.12 seconds. So that the performance results can reduce the computational load and and better in its accuracy performance. It can be concluded that CNN Residual and CNN DSC have outperformed CNN Standard. So that the development of voice biometrics schemes can be applied for identification and verification/authentication of the user's voice accurately, efficiently and quickly for identity security applications in banking transactions."

"Kemampuan artificial intellegence (AI) dalam bertindak secara mandiri menimbulkan ancaman tersendiri terhadap perlindungan data pribadi, salah satunya pengumpulan data biometrik oleh AI tanpa persetujuan dari Pemilik data terkait. Sementara bilamana terjadi pengumpulan data biometrik oleh AI tanpa persetujuan Pemilik merupakan tindakan yang melanggar hukum, sehingga diperlukan pertanggungjawaban atas tindakan AI terkait. Atas hal tersebut, dalam tulisan ini akan menganalisis mengenai (1) perlindungan data biometrik sebagai bentuk perlindungan data pribadi di Indonesia, (2) kedudukan hukum AI berdasarkan hukum Indonesia, Yunani, dan Inggris, serta (3) pertanggungjawaban hukum atas pengumpulan data biometrik melalui AI tanpa persetujuan Pemilik data di Indonesia. Karya ilmiah ini dibentuk melalui metode penelitian yuridis normatif dengan pendekatan perundang-undangan, konseptual, komparatif, dan kasus. Kesimpulan dari penelitian adalah (1) perlindugan data biometrik di Indonesia dapat ditemukan pada ketentuan Undang-Undang tentang Informasi dan Transfer Elektronik serta Undang-Undang Nomor 27 Tahun 2022 tentang Perlindungan Data Pribadi, yang mana bentuk perlindungan data pribadi dapat berbentuk hak dari Pemilik data dan persetujuan terkait pemrosesan data, (2) kedudukan hukum AI berdasarkan hukum Indonesia, Yunani, dan Inggris adalah objek hukum, (3) berdasarkan ketentuan yang berlaku di Indonesia, pertanggungjawaban hukum terhadap tindakan AI dalam pengumpulan data biometrik tanpa persetujuan Pemilik data jatuh di bawah pertanggungjawaban Penyelenggara AI terkait.

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The ability of artificial intelligence (AI) to act autonomously and access data in big data poses a separate threat to the protection of personal data, one of which is gathering the biometric data in usage of AI without the consent of the Data Owner. Meanwhile, the AI action of gathering the biometric data without consent of the Data Owner is considered to be illegal by law, thus requiring legal liability for the AI actions. Therefore, in this thesis will analyze (1) the protection of biometric data as a form of personal data protection in Indonesia, (2) AI legal standing based on Indonesia, Greece, and the United Kingdom legal systems, and (3) the legal liability of gathering the biometric data through AI without the consent of the Data Owner in Indonesia. This thesis is conducted through a normative juridical research method with multiple legal approach. The conclusion of the research is (1) Provisions of biometric data protection can be found in the Indonesia Information and Electronic Transactions Act as well as the Indonesian Personal Data Protection Act, such as form of Data Owner rights and data processing consent, (2) according to Indonesian, Greek, and United Kingdom legal system, these three legal systems view AI as a legal object, (3) based on the applicable provisions in Indonesia, legal liability for AI’s actions in gathering the biometric data without the consent of the Data Owner falls on the AI Operator."

"Biometrics is a method used to recognize humans based on one or a few characteristicsphysical or behavioral traits that are unique such as DNA, face, fingerprints, gait, iris, palm, retina,signature and sound. Although the facts that ear prints are found in 15% of crime scenes, ear printsresearch has been very limited since the success of fingerprints modality. The advantage of the useof ear prints, as forensic evidence, are it relatively unchanged due to increased age and have fewervariations than faces with expression variation and orientation. In this research, complex Gaborfilters is used to extract the ear prints feature based on texture segmentation. Principal componentanalysis (PCA) is then used for dimensionality-reduction where variation in the dataset ispreserved. The classification is done in a lower dimension space defined by principal componentsbased on Euclidean distance. In experiments, it is used left and right ear prints of ten respondentsand in average, the successful recognition rate is 78%. Based on the experiment results, it isconcluded that ear prints is suitable as forensic evidence mainly when combined with otherbiometric modalities."

"Layanan e-wallet telah mengalami pertumbuhan yang signifikan di Indonesia selama sembilan tahun terakhir dan menjadi tren yang berkembang pesat. E-Wallet memungkinkan pengguna untuk melakukan transaksi online tanpa perlu menggunakan uang tunai atau kartu fisik dengan hanya melakukan autentikasi pembayaran menggunakan PIN. Sebagai sistem yang paling banyak digunakan oleh masyarakat saat ini untuk memenuhi kebutuhan transaksi dan pembayaran secara digital, e-wallet menghadapi isu keamanan dan privasi data dari PIN dengan membuat metode autentikasi biometrik. Meskipun begitu, belum banyak pengguna yang beralih menggunakan biometrik. Penelitian ini bertujuan untuk menganalisis faktor yang menjadi pengaruh atas niat pengguna dalam beralih menggunakan biometrik (switching intention) di dalam autentikasi pembayaran e-wallet. Pengumpulan data dilakukan dengan melalui wawancara terhadap 15 narasumber dan survei terhadap 441 responden. Pengolahan data kualitatif menggunakan metode grounded theory. Sementara itu, pengolahan data kuantitatif menggunakan metode partial least squares structural equation modeling (PLS-SEM). Hasil dari penelitian menunjukkan bahwa system reliability, secure comfortability dari faktor pull berpengaruh signifikan terhadap persepsi pengguna terkait manfaat biometrik (perceived usefulness). Sementara itu, perceived inconvenience dan perceived vulnerability dari faktor push tidak berpengaruh. Selanjutnya, variabel facilitating conditions, habit, social influence, dan perceived usefulness berpengaruh signifikan terhadap switching intention sementara personal innovativeness tidak. Terakhir, niat beralih pengguna berpengaruh terhadap sikap berpindah pengguna. Temuan ini diharapkan dapat membantu platform e-wallet dalam merancang strategi yang efektif untuk meningkatkan fitur biometrik dalam autentikasi pembayaran e-wallet.

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E-Wallet services have experienced significant growth in Indonesia over the past nine years, becoming a rapidly growing trend. E-Wallets allow users to perform online transactions without the need for cash or physical cards by simply authenticating payments using a PIN. As the most widely used system by the public today for fulfilling digital transaction and payment needs, e-wallets face data security and privacy issues related to pins by implementing biometric authentication methods. However, not many users have switched to using biometrics. This study aims to analyze the factors influencing users' intentions to switch to biometric authentication (switching intention) in e-wallet payment authentication. Data collection was conducted through interviews with 15 informants and surveys of 441 respondents. Qualitative data analysis was performed using grounded theory, while quantitative data analysis utilized partial least squares structural equation modeling (PLS-SEM). The results of the study indicate that system reliability and secure comfortability, from the pull factors, significantly affect users' perceptions of the usefulness of biometrics (perceived usefulness). Meanwhile, perceived inconvenience and perceived vulnerability, from the push factors, have no significant effect. Furthermore, the variables facilitating conditions, habit, social influence, and perceived usefulness significantly influence switching intention, while personal innovativeness does not. Finally, users' switching intention influences their switching behavior. These findings are expected to assist e-wallet platforms in designing effective strategies to enhance biometric features in e-wallet payment authentication."

"This book presents an approach to postmortem human identification using dental image processing based on dental features and characteristics, and provides information on various identification systems based on dental features using image processing operations. The book also provides information on a novel human identification approach that uses Infinite Symmetric Exponential Filter (ISEF) based edge detection and contouring algorithms."