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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
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Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol 6 No 1 (2025): June 2025" : 5 Documents clear
Ekonomi Aset Digital: Analisis Sentimen Masyarakat Berbasis Leksikon Terhadap Kebijakan Kripto di Indonesia Hasyyati, Zata; Qausar, Haves; Yumni, Hazhiyah; Jannah, Miftahul
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7508

Abstract

This study analyzes the shift in public sentiment toward cryptocurrency regulation in Indonesia following policy implementation and identifies the role of cognitive biases in shaping public responses. The primary contribution of this research lies in the development of an integrative analytical framework that connects real-time social media data from X (formerly Twitter) with behavioral economics theory, alongside the formulation of evidence-based strategic recommendations. By applying text mining and lexicon-based sentiment analysis (InSet Lexicon), the study examines sentiment dynamics across two periods: pre-regulation (2009–2018), dominated by neutral sentiment (81.7%), and post-regulation (2019–2024), which exhibits a significant increase in negative sentiment (64.8%) alongside a fivefold growth in positive sentiment (31.7%). The findings reveal that cognitive mechanisms, such as loss aversion and status quo bias, amplify public resistance to new regulations. Policy implications include three innovative strategies: (1) redesigning communication using loss-avoidance framing, (2) enhancing financial literacy among younger generations, and (3) developing a more participatory regulatory sandbox. This study underscores the importance of adopting regulatory approaches that are responsive to public psychological dynamics and the prevailing low levels of financial literacy.
Clustering Data Penduduk Desa Menggunakan Algoritma Mean Shift Maulani, Tedi; Haerani, Elin; Wulandari, Fitri; Oktavia, Lola
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7550

Abstract

Social welfare remains a serious challenge in Indonesia, including in Riau Province, which, despite its abundant natural resources, still struggles with unequal distribution of welfare. One of the government’s efforts to address this issue is through social assistance programs. However, identifying the right beneficiaries remains problematic. This study aims to cluster residents of Desa Bina Baru using the Mean Shift algorithm to support more targeted social aid distribution. The clustering results were evaluated using the Silhouette Score to measure their quality. The optimal clustering was achieved at a quantile of 0.9, with the highest Silhouette Score of 0.5747, producing nine clusters with varying socioeconomic characteristics. Based on the analysis, clusters 2, 1, 5, and 6/7 were identified as the most eligible groups to receive government aid due to economic pressure, high number of dependents, and inadequate housing conditions. This prioritization is crucial for more accurate, data-driven distribution of aid and provides valuable insights to support sustainable poverty alleviation strategies in Desa Bina Baru.
Analisis Perilaku Kesadaran Privasi Data Pengguna Sistem Informasi Akademik dengan Pendekatan Technology Threat Avoidance Theory Hamdi, Yusrizal; Gunawan, Catur Eri; Putra, Imamulhakim Syahid
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7590

Abstract

The implementation of the Sistem Informasi Akademik (SIMAK) has raised concerns regarding data security, particularly the privacy of personal data, academic grades, and Kartu Rencana Studi (KRS) information. Threats to user data privacy often originate from internal actors, posing risks to the confidentiality, integrity, and availability of information. This study aims to explore the factors influencing SIMAK users’ privacy awareness behavior in response to such threats, using the Technology Threat Avoidance Theory (TTAT) as a theoretical framework. A quantitative research approach was employed, with data collected through questionnaires distributed to 393 respondents, comprising active SIMAK users from both student and faculty groups. The collected data were analyzed using the Covariance-Based Structural Equation Modeling (CB-SEM) method with the SmartPLS 4 software. The results reveal that perceptions of vulnerability and severity, as well as their interaction, positively influence threat perception. This perceived threat significantly enhances the motivation to avoid risks. However, the interaction between threat perception and safeguard effectiveness shows a negative impact. On the other hand, self-efficacy positively contributes to avoidance motivation, which in turn influences users’ awareness behavior in protecting data privacy. These findings emphasize the importance of enhancing individual awareness and capability in maintaining information security within academic information systems.
Penerapan Metode GA-TOPSIS untuk Sistem Seleksi Karakter Game dengan Pembobotan Dinamis Berbasis Sensor Suhu Prakasa, Aji Bagas; Nugroho, Fresy; Faisal, Muhammad; Lestari, Tri Mukti; N, Alfina Nurrahma; Taufiqulhakim, Adnan Muhammad
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7646

Abstract

This study aims to develop a decision support system for optimal character selection by implementing a hybrid Genetic Algorithm and TOPSIS (GA-TOPSIS) method that considers temporal variations in criterion weighting. The approach combines the optimization capability of Genetic Algorithms for automatic weight determination with the multi-criteria decision-making technique of TOPSIS. The research results demonstrate that GA optimization produces significant variations in weighting according to time scenarios: morning conditions dominated by Movement (82%), daytime emphasizing Height (52%) and Health (38%), and nighttime dominated by Defense (85%).Evaluation using TOPSIS yields different alternative rankings for each scenario. In morning conditions, alternative A4 achieves the highest score (0.83) due to its superiority in Movement criteria. The daytime scenario ranks A2 as optimal ( =0.90) because of its performance in Height and Health, while at night, A3 excels ( =0.89) with the best Defense. Result consistency is shown by A1 consistently ranking lowest due to minimal criterion values. This research makes important contributions to the development of adaptive decision support systems, particularly those requiring dynamic weight adjustments based on environmental changes. The potential integration with IoT technology for real-time weight updates adds value to the method's application.
Comparative Study of Mobilenet and Resnet for Watermelon Leaf Disease Classification Using Deep Learning Ahmad, Abdullah; Wanto, Anjar; Windarto, Agus Perdana; Poningsih, Poningsih
TIN: Terapan Informatika Nusantara Vol 6 No 1 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i1.7543

Abstract

Watermelon leaf diseases, caused by various factors such as fungi, viruses, and bacteria, can have a significant impact on agricultural yields. To increase the amount and quality of watermelon produced, early diagnosis of this disease is essential. This study aims to compare the performance of two Convolutional Neural Networks (CNN) architectures included in Deep Learning, namely MobileNet and ResNet, in classifying watermelon leaf diseases using a dataset taken from Kaggle. This dataset consists of 1000 watermelon leaf images with three conditions, namely Downy Mildew (380 images), Healthy (205 images), and Mosaic Virus (415 images). ). 95% accuracy, 96% precision, 94% recall, and 95% f1-score are the results of the MobileNet model. In contrast, the ResNet model performs better, with 97% accuracy, 96% precision, 97% recall, and 97% f1-score. The study's findings show that ResNet outperforms MobileNet in the classification of watermelon leaf illnesses, despite both models' excellent and effective performance for automatic plant disease detection applications.

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