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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
Core Subject : Economy, Health, Science, Education, Art, Social, Engineering,
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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Optimizing Contextual Features for Instagram Engagement Prediction using Long Short-Term Memory (LSTM) Aswad, Hazrul; Mulyana, Dadang Iskandar; Kastum, Kastum
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8166

Abstract

Instagram has become an important communication medium for academic institutions, enabling the dissemination of information, promotion of activities, and engagement with the campus community. At STIKOM CKI Jakarta, the official Instagram account plays a key role in academic communication, making it essential to optimize content strategies for higher audience interaction. This study analyzes 311 publicly available posts collected from July 2023 to July 2025 from the institution’s official account. Although relatively small for deep learning, the dataset provides representative patterns for the case study while highlighting the model’s capability under limited data conditions. A predictive framework based on Long Short-Term Memory (LSTM) was developed by integrating textual features from captions with contextual features such as posting time, content type, hashtag count, and interaction metrics. The aim is to accurately estimate engagement scores and provide actionable posting recommendations. The evaluation achieved an R² of 88.00%, MAE of 0.0450, and RMSE of 0.0720, indicating strong predictive performance. The contribution of this research lies in demonstrating that optimizing contextual features can significantly enhance academic social media engagement and in providing an adaptable methodology for institutions with limited historical data.
Kajian Literatur Sistematis Terhadap Aplikasi Mobile Verifikasi Produk Halal: Analisis Metode, Fitur, dan Potensi Inovasi Rasyadi, M Javier; Kurniawardhani, Arrie
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8174

Abstract

The rapid growth of the global halal industry has intensified the demand for efficient, transparent, and easily accessible halal product verification methods. This research aims to identify and analyze the commonly used verification methods in halal mobile applications and to evaluate their core and additional features available publicly on the Google Play Store. Using a systematic literature review combined with content analysis of eleven selected mobile applications, this study provides a comprehensive overview of the current digital halal application ecosystem. The findings of this research indicate that barcode scanning is the most dominant method due to its efficiency and ease of use. However, this method has limitations in providing transparent product ingredient details, necessitating support from Optical Character Recognition (OCR) technology for deeper ingredient analysis. A hybrid approach combining barcode, OCR, and manual input was found to be the most optimal, providing application flexibility and resilience across various usage scenarios. Furthermore, regarding application features, this study discovered that additional functionalities such as scan history, fatwa references, user reporting, and ingredient translation remain underutilized, despite being crucial for enhancing user trust and halal literacy. This research provides essential implications for application developers, users, and regulators toward establishing an inclusive and trustworthy halal digital ecosystem.
Peran Kecerdasan Buatan dalam Inovasi Instrumen Keuangan Hijau untuk Pembangunan Berkelanjutan Giovanni, Axel; Dewantara, Ghiyats Furqan; Musthafa, Afif; Hartono, Budi; Putri, Namira Rahma; Fadlilah, 'Atikah Nur; Witantri, Galuh; Kurniasari, Erika
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8176

Abstract

Climate change is driving the urgency of transitioning to a low-carbon economy. In this regard, green finance is an important instrument in mitigating climate change. However, the implementation of green finance faces challenges such as data complexity, information asymmetry, and the risk of greenwashing. This study aims to systematically examine the role of artificial intelligence (AI) in expanding the adoption, effectiveness, and innovation of green finance instruments among stakeholders. The method used is a Systematic Literature Review (SLR) of 61 articles from the Scopus database, analyzed using the PICo framework and CASP quality assessment. The results of the study indicate that AI can fundamentally support 1) stakeholder needs and challenges, 2) accuracy, transparency, and efficiency, and 3) mitigating the risk of greenwashing. Stakeholder needs and challenges can be addressed by AI through improved accuracy in risk prediction and market analysis, as well as optimizing green portfolios. AI mechanisms have proven capable of improving accuracy through advanced predictive models, strengthening transparency with Explainable AI (XAI) and blockchain, and driving efficiency through automation and resource optimization. Significantly, AI integration strengthens the positive impact of sustainable investments and serves as a powerful mitigation tool against greenwashing risks by objectively verifying environmental claims and enhancing accountability. AI emerges as a transformative technology to accelerate an effective and credible green financial ecosystem.
Hubungan antara OHIS dengan Kebutuhan Perawatan Periodontal Pasien Skizofrenia di RS Jiwa Dirman, Rezki; Asnuddin, Asnuddin; Sakinah, Sri; Laiya, Nova; Zulkaidah, Utari
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8182

Abstract

It is important to prioritize oral health as an important component of overall medical care. Patients with mental illnesses are particularly susceptible to oral diseases, especially when hospitalized. Factors such as the type of psychiatric illness, length of treatment, and side effects of treatment can affect their oral health. Additionally, behaviors associated with mental illness, such as alcoholism, drug abuse, and smoking, can further exacerbate oral health problems in this population. The purpose of the study was to determine the relationship between oral hygiene and periodontal care needs in schizophrenia patients in the male inpatient room of the Prof. DR. V.L Ratumbuysang Mental Hospital, North Sulawesi Province. This type of research uses an analytical observational design with a cross-sectional approach. This research was conducted in the male inpatient room of RSJ Prof. Dr. V.L. Ratumbuysang in June-July 2024. The study population was all male schizophrenia patients who were treated in the inpatient room of Prof. Dr. V.L. Ratumbuysang Hospital which amounted to 85 patients. Samples were taken using the Total Sampling Technique with the criteria that the patient was calm and cooperative when the examination was carried out. It was found that 60 samples met the inclusion and exclusion criteria. Then the results of the study found a relationship between oral hygiene and the need for periodontal care of schizophrenia patients in the male inpatient room of Prof. Dr. V. L. Ratumbuysang Manado Hospital. The correlation coefficient value obtained was 0.896, meaning that the hygiene strength of the oral cavity and the need for periodontal care of schizophrenia patients were strong.
Sentiment Analysis of Public Opinion on Facebook Monetization in Social Media Using the SVM Algorithm Nurmaiyah, Nurmaiyah; Lubis, Aidil Halim
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8210

Abstract

Sentiment analysis on Facebook’s monetization policy has become a significant topic in the era of rapid digital transformation. This study examines public opinion on the policy by analyzing TikTok user comments that specifically discuss Facebook monetization. TikTok was chosen as the data source because it reflects spontaneous and real-time public reactions, including discussions about other platform policies. A total of 5,000 TikTok comments were collected using web scraping techniques. The data underwent several preprocessing stages, including text cleaning, tokenization, normalization, stopword removal, and stemming. Sentiment labeling was carried out using the Indonesian Sentiment Lexicon (InSet), while feature extraction employed the Term Frequency–Inverse Document Frequency (TF-IDF) method. The classification process was conducted using the Support Vector Machine (SVM) algorithm with a linear kernel. The dataset was split into training and testing sets with an 80:20 ratio. The classification achieved an accuracy of 80%, with a precision of 80% for both positive and negative sentiments, recall scores of 81% and 79%, and F1-scores of 81% and 79%, respectively. These findings demonstrate that integrating TF-IDF weighting with the SVM algorithm is effective for automatically classifying public sentiment toward social media monetization policies. Furthermore, this study provides insights into public reactions to Facebook monetization from the perspective of TikTok users, thereby contributing to an understanding of how monetization policies influence user sentiment on social media platforms.
Forecasting Data Time Series Menggunakan MLP dan LSTM untuk Memprediksi Jumlah Produksi Bir Rachmatullah, Muhammad Ibnu Choldun
TIN: Terapan Informatika Nusantara Vol 6 No 4 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i4.7755

Abstract

Time series data forecasting is an important approach in various sectors such as finance, energy, and healthcare. As technology advances, deep learning methods such as Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) are increasingly being used to improve prediction accuracy. This study compares the performance of these two methods in forecasting a time series dataset of monthly beer production in Australia. The model was trained and tested using a 70% training and 30% testing data split. Performance evaluation was based on the Root Mean Square Error (RMSE) value after 10 experimental repetitions. The results show that MLP has a lower RMSE value and a smaller standard deviation than LSTM, both on the training and testing data. This indicates that MLP is more stable and efficient in handling datasets with simple patterns and low complexity, while LSTM tends to require more intensive tuning and has a higher risk of overfitting. Therefore, MLP is recommended as a lighter and more consistent alternative forecasting method for similar data scenarios.
Perencanaan Strategis Sistem Informasi Menggunakan Metode Tozer Nurhayati, Nurhayati; Purnasari, Manja; Karman, Zulfi; Hartiwi, Yessi
TIN: Terapan Informatika Nusantara Vol 6 No 4 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i4.7927

Abstract

This study aims to design a strategic information system plan for the Nipah Panjang Sub-District Office using the TOZER method. The main problem identified is the absence of an integrated information system, which results in data duplication and difficulties in managing public service data. The TOZER methodology is employed to analyze the strategic needs of the information system through five phases: defining the scope and context, identifying business information and support needs, evaluating the existing systems, formulating strategic solutions, and planning for implementation. The analysis produced an information system portfolio that includes proposed applications such as the Public Service Information System (SIPMAS), Public Complaints Information System (SIDUMAS), Personnel Information System (SIKEP), and a web-based information system. This strategy is expected to enhance the efficiency and effectiveness of public services.
Evaluasi Usabilitas Aplikasi “SIGERAK” Sebagai Media Pembelajaran Interaktif Menggunakan System Usability Scale (SUS) Naiya, Rara Weinita; Azwardi, Azwardi; Saputra, Ariansyah
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.7951

Abstract

Digital learning media has become one of the alternative solutions to address the challenges of 21st-century education, especially in delivering abstract material such as the human movement system at the elementary school level. This study was conducted to design and assess the feasibility of the SIGERAK application as an interactive digital learning medium. The application is equipped with instructional content, quizzes, and educational mini games designed to enhance students understanding of the human movement concept. The trial was carried out at SD Negeri 85 Palembang using a descriptive quantitative approach. A total of 30 students and 2 sixth-grade teachers participated in the learning sessions using the application. The evaluation process employed the System Usability Scale (SUS) instrument, which consists of 10 Likert scale statements to assess aspects of user comfort and ease of use. The collected data were analyzed through score conversion and interpreted based on three dimensions Acceptability Ranges, Grade Scale, and Adjective Ratings. The evaluation results showed that SIGERAK obtained an average score of 80.63. This score falls into the Acceptable category, corresponds to Grade B, and is classified as Good. This study indicates that SIGERAK has a high level of usability and is considered suitable as a game-based thematic learning medium at the elementary school level.
Immature Platelet Fraction Pada Pasien Sepsis: Hubungannya dengan Derajat Keparahan Sepsis Indrawati, Yeti; Hartono, Benny
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8187

Abstract

Sepsis and septic shock are major health problems that contribute to increased mortality rates. Early detection and assessment of sepsis severity are critical for appropriate management. This study aims to evaluate the role of Immature Platelet Fraction (IPF) as a biomarker for detecting the development and severity of sepsis in patients, compared to Procalcitonin (PCT). This cross-sectional study involved 42 patients admitted to the Emergency Department of Dr. Saiful Anwar Hospital, Jambi, from May to June 2017. Data collected included clinical parameters, laboratory tests, and the SOFA score. The results showed that IPF levels were significantly higher in patients with severe sepsis compared to those with uncomplicated sepsis (p=0.007) and had a significant positive correlation with the SOFA score (r=0.502, p=0.005). IPF also effectively differentiated septic patients from those with SIRS (p=0.003) and demonstrated diagnostic performance comparable to PCT in distinguishing severe sepsis from uncomplicated sepsis (AUC 0.811 for IPF and 0.868 for PCT). This study concludes that IPF can be a useful biomarker for detecting and assessing the severity of sepsis, with potential widespread application in healthcare settings at a low cost.
Healthcare Access : A Hybrid Systematic Literature Review and Bibliomertic Analisis Annur, Wa Ode Fifin; Saflia, Ismiliani; Habriani, Habriani
TIN: Terapan Informatika Nusantara Vol 6 No 3 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i3.8286

Abstract

Healthcare Access has been a central focus of research over the past few decades; nevertheless, in-depth investigations of this topic remain limited. This study conducts a Systematic Literature Review (SLR) and a bibliometric analysis of Healthcare Access using articles indexed in the Scopus database from 1990 to 2025, yielding 91,938 publications. The SLR evaluation was carried out on July 30, 2025. Bibliometric analysis with VOSviewer was employed to uncover emerging trends and patterns in the literature. The results indicate that Healthcare Access attracts attention not only in countries with advanced health systems but also in developing countries that face challenges in providing equitable and affordable services. Future research should broaden its scope to countries with developing health systems, such as those in Southeast Asia and Africa. In addition, community participation emerged as a critical factor, with six core attributes: affordability, availability, geographic accessibility, information quality, awareness, and community participation. The study also identifies the need for infrastructure development and deeper community engagement to achieve fair and equitable health outcomes.

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