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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 656 Documents
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Analisis Sentimen Bull dan Bear Market Bitcoin Pada Komentar YouTube Menggunakan Algoritma Support Vector Machine Hardi Wirkan; Andri Firmansyah
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9952

Abstract

The development of the cryptocurrency market, particularly Bitcoin, significantly influences public opinion expressed through social media platforms such as YouTube. This study aims to analyze bull and bear market sentiments in YouTube comments about Bitcoin using the Support Vector Machine (SVM) algorithm. A total of 25,000 comments were collected using the YouTube Data API. After preprocessing stages including case folding, cleaning, tokenizing, word normalization, and stopword removal, 8,991 valid data were obtained. The dataset was divided into 7,192 training data (80%) and 1,799 testing data (20%). TF-IDF weighting was applied before classification using the SVM algorithm. The evaluation results show that the model achieved an accuracy of 98%, with a macro F1-score of 0.91 and a weighted F1-score of 0.98. Sentiment distribution in the testing data indicates 50.42% neutral (907 comments), 47.08% positive (847 comments), and 2.50% negative (45 comments). The dominance of neutral and positive sentiments reflects relatively stable and optimistic public opinion, consistent with the upward trend of the cryptocurrency market in 2024. This study contributes by providing a YouTube comment-based sentiment analysis approach to describe public opinion tendencies toward Bitcoin bull and bear market conditions, while also offering empirical evidence regarding the effectiveness of the Support Vector Machine algorithm in classifying sentiment within high-dimensional text-based social media data. Furthermore, the findings provide insights into the relationship between public opinion tendencies and cryptocurrency market conditions, which may serve as a reference for understanding market psychology through social media-based sentiment analysis. This study demonstrates that the SVM algorithm is effective in classifying YouTube comment sentiments related to Bitcoin market conditions.
Implementasi Sistem Rekomendasi Hybrid untuk Penentuan Reviewer dan Rekomendasi Anggota Tim Peneliti Fify Mustika Wondal; Graciella Eunike Bawiling; Anritsu Steven Christian Polii; Robby Tangkudung
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9953

Abstract

The Center for Research and Community Service (P3M) of Manado State Polytechnic manages a growing volume of research activities. Consequently, a digital transformation in administrative governance is required to ensure temporal efficiency, precise expertise mapping, and compliance with administrative regulations. Beyond objective reviewer assignments, researchers needs a support platform to identify cross-disciplinary collaborators based on publication track records. This study aims to implement a Hybrid Recommender System as an automated solution for reviewer selection and research team formation. The system integrates Content-Based Filtering (TF-IDF and Cosine Similarity algorithms) for text analysis with Constraint-Based Filtering for automated business rule validation. The research methodology follows a Research and Development (R&D) approach using a modified Waterfall model. The processed dataset comprises 516 research titles and profiles of 317 lecturers with SINTA IDs. Black Box testing results confirm the system's effectiveness in validating academic degree qualifications, SINTA score thresholds, and the transparent prevention of conflicts of interest. A comparative evaluation of 44 actual assignment cases demonstrates that the system provides highly relevant recommendations, achieving an accuracy rate of 79.5% compared to manual expert decisions. This research contributes a Microservice-based backend infrastructure that accelerates research coordination while strengthening the transparency and accountability of institutional research management.
Pengembangan Media Pembelajaran Neuroanatomi Berbasis WebXR (Website Extended Reality) dengan Pendekatan Aksesibilitas dan Optimalisasi Gede Bramanda; I Gede Partha Sindu; Putu Hendra Suputra
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9954

Abstract

This study aims to develop and evaluate a WebXR (Website Extended Reality)-based learning media specifically designed for medical students at Universitas Pendidikan Ganesha. Neuroanatomy learning is a crucial yet challenging component of medical education, often difficult to comprehend due to limited learning media, the scarcity of cadavers, and the abstract nature of the SOBOTA anatomical atlas. The primary contribution of this research lies in the integration of an affordable and easily accessible web-based immersive platform, which simultaneously resolves technical constraints such as motion sickness and stuttering on standalone devices. Behind a well-structured and designed foundation, this media operates on a server-based infrastructure and runs on HTML5 technology architecture to deliver flexible, real-time data accessibility directly through the browser. Technical optimization was implemented in the form of FPS stabilization (FPS Locking) and dynamic resolution scaling to maximize the computing capacity of the Snapdragon XR2 chipset on Meta Quest 2. This application was built using the Multimedia Development Life Cycle (MDLC) framework. The assessment results show that the application has very high validity from both material and media experts, achieving an overall score of 1.00 based on the Gregory matrix. Furthermore, user experience testing using the User Experience Questionnaire (UEQ) with medical students yielded an "Excellent" rating across all dimensions, with the Stimulation dimension receiving the highest score (2.46), placing this application within the top 10% of products globally. The findings indicate that the developed WebXR media effectively offers immersive appeal and can serve as a high-quality, interactive, and cross-platform alternative practical tool for neuroanatomy introduction.
Mitigasi Bias Feedback Loops dalam Rekomendasi Buku Menggunakan Pendekatan Causal Adjustment Yohanes Andika Dharma; Daniel Udjulawa
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9955

Abstract

The digital publishing industry has experienced exponential growth over the past decade, with platforms such as Goodreads and Amazon cataloging over 50 million book titles available online (Ricci et al., 2022). This abundance of choices paradoxically creates difficulty for users to discover books that genuinely match their preferences. The book domain was specifically chosen due to its unique characteristics: a highly asymmetric consumption distribution (bestsellers dominate 80% of sales despite representing only 5% of titles), extreme genre and language diversity, and readers' need for intellectual exploration beyond mere popularity. Collaborative filtering-based recommender systems address this challenge but are vulnerable to feedback loops that reinforce popularity bias, causing popular books to receive excessive exposure while long-tail items are neglected. This problem is exacerbated by Missing Not at Random (MNAR) data. This study proposes CAFL-SVD, a Matrix Factorization model based on SVD integrated with the CAFL algorithm through IPS and K-Means cluster regularization. Evaluated on Book-Crossing dataset with 585,579 ratings, CAFL-SVD reduces Gini coefficient by 37.7% (0.5783 to 0.3601), achieves peak NDCG@5 of 0.6207, maintains 100% coverage, and average Novelty Score of 14.0, demonstrating that causal approaches can simultaneously improve recommendation fairness and relevance without significant accuracy sacrifice.
Penerapan Saliency Maps dalam Explainable AI Untuk Deteksi Penyakit Paru-Paru pada Citra X-Ray Dada dengan Deep Learning Wahyu Reinaldy; Benny Sukma Negara; Muhammad Irsyad; Muhammad Affandes; Surya Agustian
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9962

Abstract

Early identification of lung diseases is very important so that medical personnel can quickly provide first aid and further study the patient's condition. In this study, a model was developed to classify chest X-ray images of the lungs using the VGG16 architecture. These chest X-ray images were categorized into three groups: COVID-19, normal lungs, and pneumonia. A combination of hyperparameters, including a learning rate of 0.001, 50 epochs, and a batch size of 16, was used to train the model, achieved an accuracy of 96%. Several evaluation metrics, including precision, recall, f1-score, and confusion matrix, were used to assess the model. In addition, saliency map methods were used to visually interpret the model's prediction output and display the areas of the chest X-ray images that most influenced the model's decision-making. The saliency map visualization findings show that the model focuses its predictions on regions of the lungs associated with the disease, which helps in understanding the algorithm's decision-making process.
Analisis Validitas dan Keandalan Tanda Tangan Digital pada Dokumen PDF Chriscel Novian; Hanif Al Fatta
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9963

Abstract

This research aims to analyze the level of validity, measure consistency as a benchmark, and identify the technical metadata factors that influence the validation results of digital signatures. The research scope includes PDF documents with certificates from official authorities or Certificate Authorities (CA) as well as self-signed ones, tested in a Windows 11 environment using Adobe Reader, Foxit Reader, Google Chrome, and Microsoft Edge. An experimental quantitative method was used through a technical approach with automated data processing based on Python scripts for metadata extraction. The research results show a non-uniform level of validation consistency across applications; Foxit Reader achieved the highest accuracy (96.67%), followed by Adobe Reader (91.67%) and Microsoft Edge (90.00%), while Google Chrome (0%) proved to fail completely due to the absence of a cryptographic computation module. The inconsistencies were identified to stem from differences in parser architectures, local security tolerance limits, and software cipher suite library support. Further diagnostic analysis found vulnerabilities due to the absence of third parties such as a Timestamp Authority, as well as an injection security flaw in Microsoft Edge, which failed to detect directory injection attacks. It can be concluded that the absence of standardised PDF parsing can lead to conflicting cross-platform validation results, the reliability of which is entirely dictated by the integrity of the hash algorithm, the cipher suite, the publisher’s status, and the completeness of the timestamp. The contributions of this research include the development of an interoperability benchmark for PDF digital signature validation, the creation of a consistency matrix for cross-platform evaluation, and the identification of technical metadata factors that are the primary determinants of successful validation. The research findings are expected to serve as a reference for application developers, electronic certificate authorities, and organisations in enhancing the security and consistency of digital document validation.
Pemilihan Parameter Crossover Moving Average Adaptif pada BTC/USDT Menggunakan Proximal Policy Optimization Anandava Eka Buana Baskara; Joko Aryanto
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9968

Abstract

Cryptocurrency markets exhibit high volatility, making it challenging to determine the optimal combination of Moving Averages (MA Short and MA Long) for technical indicator-based trading strategies. This study aims to develop an adaptive crossover Moving Average strategy using the Proximal Policy Optimization (PPO) algorithm to evaluate and recommend the most effective MA combinations. Daily cryptocurrency price data from January 1, 2021, to May 15, 2026, comprising a total of 1960 candles, were obtained through an exchange platform API. The data were processed through preprocessing to form market states that include price, volume, and volatility indicators, which were then used as input for the PPO agent during training and strategy evaluation. Test results indicate that the MA 3/50 combination was most frequently selected by PPO based on average probability, while the MA 25/40 combination produced the best financial performance in terms of profit factor, net profit, and win rate. Visualizations of the equity curve, drawdown, and entry and exit points confirm the strategy’s ability to adaptively adjust decisions, capture market trends, and balance risk and profitability. These findings provide practical guidance for selecting adaptive crossover Moving Average parameters, enabling technical indicator-based trading strategies to navigate the complex and rapidly changing dynamics of cryptocurrency markets.
Implementasi Sistem Informasi Manufaktur Berbasis Web dengan Pendekatan Hybrid Rule-Based dan Machine Learning untuk Evaluasi Kinerja Pemasok pada Industri Otomotif Dody Mulyadi; Cahyono Santoso
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9977

Abstract

Supplier performance evaluation in the automotive manufacturing industry is a critical activity that determines production line continuity. However, this process remains predominantly manual, resulting in administrative inefficiency, data inconsistency, and slow decision-making. This study aims to design and implement a web-based manufacturing information system that integrates a hybrid rule-based and machine learning approach to optimize supplier performance evaluation at PT ABC. The dataset comprises 1,008 transaction records from 28 suppliers over three years (2022–2024) with seven evaluation criteria: Accident, Incident, Line Stop, Off Line, Kanban Delay, Delivery Problem Report (LMD), and Delay Delivery. The research methodology employs Research and Development (R&D) with the Waterfall SDLC model enriched by the CRISP-DM methodology for the analytical component. Feature engineering produced 22 input variables through lag-1, trend analysis, and rolling average techniques, while class imbalance was addressed using SMOTE. Three ensemble algorithms (Random Forest, XGBoost, and Gradient Boosting) were evaluated through 5-Fold Stratified Cross Validation. XGBoost was selected as the best model with 88.82% accuracy and 88.80% Macro F1-Score. The hybrid fusion layer successfully generated tiered action recommendations across five urgency categories, with prediction accuracy on actual operational data reaching 93.16%. The contribution of this research to the development of scientific knowledge is the integration of an AI-based decision support system concept with an operational manufacturing information system platform, while providing a replicable hybrid framework for other manufacturing industry contexts in Indonesia.
Rancang Bangun Website Member Cuci Sepatu Menerapkan Metode Waterfall Naufal Ariesta Zildjian; Rauhulloh Ayatulloh Khomeini Noor Bintang
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9985

Abstract

The current development of the shoe cleaning service industry demands efficiency and transparency in service, but operations at Alwayscleanhoes Shoes in Karanganyar are still carried out manually. The recording process via instant messaging risks data loss and irregular transaction history. This research intends to devise and develop a member-backed footwear laundering operation intelligence system utilizing the Laravel infrastructure and MySQL data repository. The platform evolution technique applied constitutes the Waterfall paradigm, which encompasses the phases of necessity evaluation, architectural planning, execution (building), alongside examination. This system is designed to centralize customer data management, order automation, and real-time tracking features for work status. System testing was conducted through the User Acceptance Testing (UAT) method with 10 respondents consisting of admins, regular customers (members), the general public, and technical experts. The findings indicated that the deployment of this online platform effectively streamlined the functional workflow and facilitated consumers to approach utilities autonomously. Based on a Likert scale analysis, the UAT test resulted in a score percentage of 88.67%, indicating that the system is in the "Very Good" category and is worthy of full implementation. This research provides a practical contribution to improving service quality and data management accuracy at Alwayscleanhoes Shoes.
Rekomendasi Aktivitas Pembelajaran Anak Usia Dini Berbasis Q-Learning dan Profil Perkembangan Faiz Ahmad Fauzan; Joko Aryanto
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9986

Abstract

This study designs a Q-Learning-based recommendation system for early childhood learning activities in a Raudhatul Athfal setting. The system supports teachers in selecting activities aligned with children's developmental profiles, especially physical-motor and cognitive aspects. The recommendation problem is formulated as a Markov Decision Process. The state consists of children's age in months, physical-motor level, cognitive level, previous activity, and previous participation score. The action space contains twelve learning activities, while the reward combines participation, developmental fit, and activity variation. Testing was conducted using scenario-based learning data with five experimental seeds. The results show that Q-Learning achieved an average evaluation reward of 24.667 with a standard deviation of 0.222 from a theoretical scenario bound of 30 points. Ranking evaluation produced Precision@1 of 0.645, Recall@5 of 0.448, and NDCG@5 of 0.641. These results support Q-Learning as a transparent tabular baseline, but they do not prove pedagogical impact. Q-Learning was selected because the state is discrete, the action space is limited, and Q-values are traceable. Since operational data have not been used, the claims are limited to system design, recommendation traceability, and computational evaluation.

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