A Train Control and Monitoring System (TCMS) is a vital part of monitoring sensors in a train. The data output of sensors is sent wirelessly to the data server for monitoring. However, as the wireless channel used to send the data is a shared public network, the transmitted data are prone to hackers and attacks. This paper proposes the Securebox architecture to manage secure data transfer from the onboard Vehicle Control Unit (VCU) to the data server in TCMS. The architecture is comprised of four main functions: network management, buffer management, data management, and security management. The architecture has been successfully developed in an HSM (Hardware Security Modul) and verified using alpha and beta software testing to form a secure TCMS. From the real-time testing phase in an electric-diesel train, the average performance of the AES-based HSM showed 55% faster time processing with unnoticed 0.1% added memory usage compared to the 3DES. The secure TCMS also withstands MITM attack and provides end-to-end data security compared to the (Mobile Station) MS to Base Station (BS) only in GSM-R.

The establishment of green villages has been a key initiative of the Citarum Harum program since 2015 in order to address pollution in the Citarum watershed, particularly in the Cirasea sub-watershed. This program’s success depends largely on community involvement in planning and implementation. Thie aim of this study was to identify the extent of community participation in developing environmentally conscious hamlets in Cirasea. Using a mixed-methods approach—quantitative surveys followed by qualitative interviews and observations—data were collected from 10 villages. The findings show varying participation levels, influenced by socioeconomic conditions, program characteristics, environmental issues, and institutional support. Community involvement was assessed across the planning, implementation, benefit-taking, and evaluation stages. Participation was very extensive in decision-making and implementation, as well as in evaluation, but it was less satisfactory in benefit-taking. This research underscores the critical role of inclusive community engagement in ensuring the success and sustainability of environmental development programs.

The renewable Energy Certificate (REC) schemes play a crucial role in combating climate change and promoting sustainable living. However, issues such as traceability, lack of transparency, and user awareness limit its effectiveness. The current study aims to address such limitation by proposing a blockchain-based solution for Indonesia's electricity grid which can enhance end-to-end traceability, real-time data access, and tamper-proof records. The methodology encompasses interview with energy stakeholders, blockchain model development, prototyping, and evaluation of its efficacy. Results show that blockchain technology can revolutionize Indonesian REC market by improving security, traceability, and promote stakeholders' trust. The proposed framework addresses current system limitation and introduces a semi-fungible REC token for secure issuance, trading, and verification. Our solution offers a scalable model that can support Indonesia and global green energy adoption and sustainable living.

Human online activities leave digital traces that provide a perfect opportunity to understand their behavior better. Social media is an excellent place to spark conversations or state opinions. Thus, it generates large-scale textual data. In this paper, we harness those data to support the effort of personality measurement. Our first contribution is to develop the Big Five personality trait-based model to detect human personalities from their textual data in the Indonesian language. The model uses an ontology approach instead of the more famous machine learning model. The former better captures the meaning and intention of phrases and words in the domain of human personality. The legacy and more thorough ways to assess nature are by doing interviews or by giving questionnaires. Still, there are many real-life applications where we need to possess an alternative method, which is cheaper and faster than the legacy methodology to select individuals based on their personality. The second contribution is to support the model implementation by building a personality measurement platform. We use two distinct features for the model: an n-gram sorting algorithm to parse the textual data and a crowdsourcing mechanism that facilitates public involvement contributing to the ontology corpus addition and filtering.

Akhir tahun 2019 dunia digemparkan dengan kemunculan virus baru yaituCorona Virus yang diakibatkan dari pathogen SARS Cov-2 atau dikenal dengan COVID-19. Usaha preventif yang dilakukan pemerintah untuk menanggulangi penyebaran virus ini adalah dengan menerapkan protokol kesehatan yang ketat. Strategi lain yang juga dilakukan pemerintaha adalah melakukan vaksinasi agar terbentuk herd-immunity (kekebalan kelompok) secara cepat. Kendala yang dihadapi dalam program vaksinasi adalah munculnya kalangan yang menolak vaksin. Kejadian tersebut menimbulkan keresahan dimasyarakat. Respon kekhawatiran masyarakat diekspresikan dalam media sosial, salah satu media sosial yang digunakan sebagai pilihan untuk menyampaikan respon dan opini tersebut adalah twitter. Tujuan dari penelitian ini adalah melakukan analisis sentimen terhadap opini terkait vaksin yang beredar di twitter serta melakukan analisis jaringan sosial (SNA) yang terbentuk untuk mengetahui aktor yang paling berperan dalam penyebaran informasi mengenai vaksin COVID-19. Penelitian menggunakan metode klasifikasi dengan algoritma naïve bayes dan metode Social Network Analysis (SNA). Hasil dari penelitian ini menunjukan bahwa 92% sentimen pengguna twitter adalah positif terhadap vaksin COVID-19 dan aktor yang paling berperan dalam penyebaran informasi adalah akun @jokowi. Keywords: COVID-19, vaksinasi, analisis sentimen, SNA, twitter

Financial technology (Fintech) mengalami pertumbuhan yang cukup pesat sejak awal kehadirannya di Indonesia. Fintech merupakan industri jasa finansial yang memanfaatkan teknologi sehingga memungkinkan penggunanya melakukan berbagai transaksi keuangan secara digital. Saat ini banyak fintech baru yang bermunculan di Indonesia, sehingga dibutuhkan strategi yg tepat untuk bisa bersaing dgn kompetitor. Analisis interaksi pengguna media sosial, biasa disebut dengan Electronic Word of Mouth (EWOM) dapat memberikan informasi yang dapat mendukung berbagai keputusan bisnis, salah satunya adalah terkait customer engagement. Tujuan dari penelitian ini adalah mengidentifikasi customer engagement yang terbentuk dari hasil implementasi Social Customer Relationship Management (SCRM) yang dilakukan oleh perusahaan. Data yang digunakan dalam penelitian ini adalah data sekunder yang merupakan data tweets berisi interaksi pengguna twitter mengenai 3 fintech di Indonesia yaitu GoPay, OVO, dan LinkAja. Analisis data dilakukan peneliti mengunakan metode social network analysis dengan menghitung properti jaringan dari ketiga objek penelitian. Hasil menunjukkan bahwa LinkAja mebentuk customer engagement lebih optimal lewat implementasi SCRM yang dilakukan perusahaan.