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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
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Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other
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 582 Documents
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Penerapan Business Intelligence dalam Analisis dan Visualisasi Tren Bencana Alam Menggunakan Tableau Murti, Aqmal Bastian; Irwansyah, Irwansyah
TIN: Terapan Informatika Nusantara Vol 5 No 12 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i12.7427

Abstract

DKI Jakarta is highly vulnerable to natural disasters due to geographical conditions, high population density, and the impact of climate change. Recurring disasters such as floods, fires, landslides, fallen trees, and tornadoes demand an effective data management and visualization system. This study applies Business Intelligence (BI) using the Tableau platform to analyze and visualize disaster trends in DKI Jakarta from 2018 to 2024. A total of 413 incident records were collected from Satu Data Jakarta and Satu Data Indonesia. The research involved ETL (Extract, Transform, Load) processes, data validation, and the development of interactive dashboards. The visualization presents key information such as disaster types, number of victims, incident locations, and yearly trends. The analysis revealed that South Jakarta recorded the highest fatalities (40,325), followed by East Jakarta (38,550). Tableau’s interactive features enable clear and accessible data presentation through charts, maps, and diagrams, supporting evidence-based decision-making. The implementation of BI successfully simplifies complex data and assists stakeholders—both government and the public—in comprehending disaster patterns. This approach offers a strategic solution for enhancing disaster preparedness, formulating effective mitigation policies, and increasing public awareness of environmental risks.
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.
Penerapan Faster RCNN + ResNet 50 untuk Mengidentifikasi Spesies dan Stadium Parasit Plasmodium Malaria Prananda, Alifia Revan; Novichasari, Suamanda Ika; Fatkhurrozi, Bagus; Abdillah, Muhammad Nurkholis; Frannita, Eka Legya; Majidah, Zharifa Nur; Wibowo, Fadhila Syahida
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7187

Abstract

Malaria is one of the epidemic health diseases and is well-known as a serious infectious disease. The malaria examination process had occurred by analyzing the digital microscopic images using a microscope. Those examination procedures were conducted manually, which lead to some hurdles such as misinterpretation, misdiagnosis and may produce subjective results. This research aims to develop a method for detecting the Plasmodium parasite and identifying the species and stage of Plasmodium parasite. The proposed method was performed into 488 raw data comprising of 538 parasites. The proposed method was started by conducting a data augmentation process for balancing the number of data, training model, testing model, evaluation. In this study, both the training and testing processes were performed by applying Faster RCNN + ResNet-50. The result of the testing process shows that Faster RCNN + ResNet-50 successfully achieved mAP of 0,603. It also achieved accuracy of 93.91%, sensitivity of 66.20%, specificity of 96.10%, PPV of 60.14% and NPV of 97.30%. This result indicates that the proposed method is powerful for detecting Plasmodium parasites and identifying all species and stadiums.
Analisis Komparatif Metode MOORA dan MAUT untuk Rekomendasi Pengangkatan Tenaga Pendidik Oktavia, Petricia; Frindo, Muhamad Meky
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7337

Abstract

The objective and data-driven recruitment of educators is a strategic step toward improving the quality of education. This study aims to develop a recommendation system for selecting prospective educators by comparing two multi-criteria decision-making methods: MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) and MAUT (Multi-Attribute Utility Theory). Five key criteria were used in the evaluation: Grade Point Average (GPA), mastery of didactic and methodological knowledge, teaching experience, age, and distance from home to school. Primary data were collected directly from educational institutions and prospective educators, involving six alternatives that were analyzed through normalization, weighting, and final score calculation. The analysis showed that the MOORA method identified candidate A2 as the best with a score of 0.3143, while the MAUT method ranked candidate A6 highest with a score of 0.644. The difference in rankings stems from the distinct evaluation principles of the two methods: MOORA relies on normalized ratios relative to the maximum value, while MAUT applies an aggregated utility approach. Despite this variation, both methods consistently identified the top three candidates. The developed recommendation system not only enhances transparency and accountability but also outperforms conventional intuition-based approaches by providing a structured, measurable, and replicable framework. This system has the potential to be adopted by educational institutions to ensure a fairer and more objective educator recruitment process.
Seleksi Wiraniaga Terbaik dengan Pendekatan Multi-Kriteria Metode ARAS dan SMART Cahyono, Yono; Ikasari, Ines Heidiani; Khoirudin, Khoirudin
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7404

Abstract

The selection of the best salesperson is a critical component in enhancing sales performance and maintaining a company’s competitiveness. However, this process is often hindered by subjective assessments and difficulties in objectively comparing candidates. This study aims to apply two Multi-Criteria Decision Making (MCDM) methods, ARAS (Additive Ratio Assessment System) and SMART (Simple Multi-Attribute Rating Technique) to evaluate and rank 15 sales candidates based on six criteria: Work Quality, Creativity, Initiative, Teamwork, Expertise, and Cost Efficiency. Qualitative assessment data were converted into quantitative values and analyzed using both methods to obtain final scores and rankings. The results indicate that the top-ranked candidates using the ARAS method are Lia Husna (A7), Eriza (A13), and Dodi (A3), while the SMART method identifies Septian (A5), Zainal (A2), and Lia Husna (A7) as the top performers. The difference in rankings is attributed to the weight distribution and the methods’ sensitivity to attribute values—SMART emphasizes high values in heavily weighted criteria, whereas ARAS evaluates the relative ratio against the ideal solution. Correlation analysis between both methods reveals partial alignment, suggesting that integrating multiple approaches strengthens result validation. Overall, the dual-method approach enhances selection objectivity and provides a more robust foundation for strategic decision-making in salesperson performance management.
Pengembangan Intelligent Leather Inspection Method Berbasis Interpretable Artificial Intelligence Frannita, Eka Legya; Wulandari, Dwi; Putri, Naimah; Rahmawati, Atiqa; Prananda, Alifia Revan
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7425

Abstract

The Industry 4.0 revolution, characterized by the widespread adoption of artificial intelligence and automation, has fundamentally transformed quality inspection processes in manufacturing sectors. Nevertheless, the leather tanning industry continues to rely on conventional visual inspection methods conducted by human operators, which are inherently susceptible to subjectivity, inter-operator variability, and inconsistent outcomes. This study proposes an integrated deep learning framework utilizing the NasNet-Large architecture combined with Local Interpretable Model-Agnostic Explanations (LIME) to automate objective defect detection and quality classification of pickled leather. The research employs a digital image dataset comprising four distinct leather grade categories, each annotated with expert-validated ground truth labels and professional interpretations. Experimental results demonstrate consistent model performance with 75% accuracy in both training and validation phases while achieving improved testing accuracy of 79%. LIME-based interpretability analysis reveals significant spatial convergence between model-identified defect regions and expert-annotated ground truth references. These findings indicate that the developed model exhibits remarkable competence in replicating professional leather quality inspection capabilities. The proposed approach not only enhances inspection efficiency by reducing human-dependent errors but also provides transparent decision-making interpretability - a critical requirement for reliable AI implementation in industrial applications. This research contributes to the advancement of explainable AI systems in material quality assessment, offering methodological innovation and practical implementation value for the leather manufacturing sector.
Digitalisasi Manajemen Kegiatan Kelompok Kerja Guru Menggunakan Teknologi Web Ramlah, Ramlah; Utami, Listia; Sulehu, Marwa; Ratnawati, Ratnawati; Idris, Nur Idil Fitri; Mashud, Mashud
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7659

Abstract

Teacher Working Group (KKG) is a professional forum for teachers to improve their competence and collaboration through routine activities such as training, preparation of teaching materials, and discussions on education policies. However, these activities are often hampered by manual management resulting in information delays, data duplication, and unstructured documentation. This study aims to design and implement an integrated and efficient web-based KKG activity management information system. The study was conducted in Cluster 3, Mamajang District, Makassar City. The method used is the Waterfall software development model, with data collection techniques through observation, interviews, and literature studies. System testing was carried out using the black box method to ensure that the system's functionality runs according to plan. The implementation results show that the system can manage membership data, activity schedules, documentation reports, and internal notifications effectively. The test results show that all system functions run well. This digitalization has been proven to support the efficiency of KKG activity management and has the potential to be adopted more widely in the context of improving the quality of basic education.

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