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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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Sistem Informasi Rental PlayStation Berbasis Client-Server: Evaluasi Penerimaan Pengguna Menggunakan Technology Acceptance Model Setyadi, Dian Febry; Wibowo, Adityo Permana
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8743

Abstract

Effective operational management is a critical aspect in the PlayStation rental business as it plays a role in ensuring service smoothness and supporting comprehensive business oversight. At Bossman PlayStation, operational processes are still managed manually using conventional recording methods. This condition results in slow customer service (average 4 minutes per transaction), vulnerability to transaction and inventory recording errors (3-5 errors per week) that potentially cause financial losses, and limited owner capability to monitor business development in real-time. This study aims to design an integrated information system with a client-server architecture whose solution includes two main platforms: Mobile Application to accelerate daily employee operations, and Web Application focused on monitoring and approval functions by the owner. The system design uses a UML-based approach to model requirements and workflows, utilizing WebSocket technology to provide real-time unit status updates and database triggers to automate business rules such as stock updates. The achieved result is a mature and detailed system design, where all main workflows including two rental models, sales transactions, to approval processes have been clearly defined and are ready for implementation. Functional testing showed 100% success in 10 main scenarios, and user acceptance testing (TAM) obtained a score of 4.525/5.0. The system successfully reduced transaction time to 1 minute and eliminated recording errors.
Calligraphy Style Personalization in Serious Games Using User-Based Collaborative Filtering with Cosine Similarity N, Alfina Nurrahma; Nugroho, Fresy; Arif, Yunifa Miftahul
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8744

Abstract

This study aims to develop the Try Calligraphy serious game equipped with a personalized recommendation system to assist players in selecting the most suitable Arabic calligraphy style (khat) based on their performance. The primary objective of this research is to optimize learning personalization by implementing a User-Based Collaborative Filtering approach that predicts the most appropriate handwriting styles for new players based on similarity to prior users. Performance data consisting of final scores generated from decoration, neatness, and completion time are recorded and compared to construct player similarity profiles. The system calculates predicted scores for untested calligraphy styles using cosine similarity and subsequently recommends the top three styles with the highest estimated performance potential. Two experimental scenarios were conducted to assess predictive performance. The results show Mean Absolute Error (MAE) values of 16.08 and 13.92, indicating a moderate level of accuracy. These findings suggest that while the system is capable of providing relevant and targeted recommendations, additional training data and improved similarity parameter design can further enhance predictive precision. Usability evaluation using the GUESS-18 instrument involved ten respondents and produced average scores above 3.7 across all constructs, reflecting positive user perceptions in terms of usability, aesthetics, enjoyment, and personal engagement. Overall, the system demonstrates that the integration of User-Based Collaborative Filtering in a serious game environment can enhance personalized learning, increase user involvement, and support the digital preservation and education of Islamic calligraphy art.
Aplikasi Manajemen Data Siswa dan Nilai Berbasis Android Menggunakan Flutter dan Firebase dengan Metode Waterfall Setiawan, Chandra Bagus; Rusliyawati, Rusliyawati
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8747

Abstract

The development of information technology has brought significant changes to the education sector, particularly in the management of academic data. Manual processing of student data and grades often leads to various issues, such as delayed input, recording errors, and a lack of transparency for students. To address these problems, this study aims to design and develop an Android-based Student and Grade Management Application that improves efficiency and accuracy in academic data management within schools. The application is built using the Flutter framework and utilizes Firebase Realtime Database as a cloud-based storage system. The development process follows the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance. System testing is carried out using the Black Box method to ensure that all features function as intended. The testing results show a success rate of 98%, indicating that the application performs well according to the design specifications. The implementation of this application helps teachers manage grades more quickly and accurately, while allowing students to access academic information in real time. Overall, the application supports the digital transformation of schools and promotes the adoption of paperless school practices.
Analisis Sentimen Kinerja Lembaga Legislatif di Indonesia Menggunakan Algoritma Random Forest Berbasis Data Media Sosial X Saputra, Imam; Rahim, Robbi
TIN: Terapan Informatika Nusantara Vol 5 No 10 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i10.7133

Abstract

Legislative institutions such as the DPR RI are often the center of public attention and criticism on social media, particularly the X platform (formerly Twitter). The high volume of public opinion necessitates an automated classification system to monitor public perception efficiently. This study aims to analyze public sentiment towards the DPR RI in January 2025 using the Random Forest algorithm. A total of 699 tweets were collected via crawling techniques and processed through preprocessing stages including cleansing, folding, normalization, filtering, and stemming. Text features were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method. Distribution results show a dominance of the neutral class (73.5%), followed by negative (22.2%) and positive (4.3%) sentiments. Model testing using a confusion matrix demonstrates that the Random Forest algorithm achieves high performance with an accuracy rate of 96.43%. Feature importance analysis reveals that profanity and integrity issues such as "corruption" are the primary indicators of negative sentiment. This study concludes that Random Forest is highly reliable in classifying public opinions with strong emotional polarity on social media.
Aplikasi Tes Minat Bakat dan Perencanaan Karier Menggunakan Adaptasi Teori RIASEC Berbasis Forward Chaining Rianto, Veshaka Yessananta; Wulandari, Sri
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8544

Abstract

Identifying interests, talents, and determining career plans is a common challenge faced by adolescents, especially at the upper secondary education level. This study aims to develop a mobile application to identify interests, talents, and career planning using the forward chaining method to analyze user responses. The system’s knowledge base was constructed by adapting Holland’s RIASEC theory, refined through interviews with psychology experts. Instrument validation used the percentage-of-agreement parameter, which, after revisions based on expert feedback, achieved a 100% agreement rate. These validation results were transformed into 38 IF–THEN inference rules that map 30 questionnaire items to six interest categories. The application was developed as a cross-platform mobile system integrated with cloud services for authentication and data storage. The forward chaining inference engine operates through four stages: receiving user response facts, matching patterns against the rule base, executing satisfied rules, and drawing conclusions by calculating the accumulation of rules in each category to determine three dominant categories as the identification result. The prototype implementation includes a job-trend feature based on data from the Ministry of Manpower (Kemnaker) for 2022–2024 as additional support for interest and talent identification. Functional testing showed a 100% success rate across all features. System accuracy evaluation using 20 test data points and a confusion matrix produced an Accuracy of 61.9%, Precision of 23.1%, and Recall of 33.3%. The dominance of True Negative values (23 cases) indicates that the system’s main strength lies in its filtering capability to eliminate irrelevant career options. The low precision value reflects the system’s inclusive design, intended to detect multipotentiality that may not yet be recognized through users’ subjective perceptions. These findings indicate that the application of the forward chaining method in a mobile-based system has the potential to assist users in recognizing their interests and talents more systematically; however, the limitations of binary logic in capturing gradients of interest intensity suggest the need for developing a hybrid model by integrating uncertainty-based methods such as the Certainty Factor or Fuzzy Logic to improve diagnostic sensitivity in future research.
Pengaruh Foreign Direct Invetment, Ekspor dan Konsumsi Energi Terhadap Pertumbuhan Ekonomi Indonesia Sari, Firanda Puspita; Muslinawati, Retno; Adianita, Happy
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8592

Abstract

This study was conducted to examine and provide empirical evidence regarding the influence of Foreign Direct Investment (FDI), exports, energy consumption on GDP in 2009-2023. This study uses secondary data from the World Bank, an associative quantitative approach. The data were analyzed using multiple linear regression of time series data through the Eviews program version 12. The variables are Foreign Direct Investment (FDI) X1 exports X2 energy consumption X3 and economic growth (Y). The results of the study show that Foreign Direct Investment (FDI) has no influence on economic growth with a probability value of 0,8561 (<0,05), a coefficient of -0,022959, while exports have a positive influence on economic growth with a probability value of 0,0014 (<0,05), a coefficient of 0,144058, and energy consumption has a negative effect on economic growth with a probability of 0,0088 (<0,05), a coefficient of -0,161483. The coefficient of determination is 57,92% and the rest is influenced by variables outside the study.
Optimizing Decision Making in MSMEs through Business Intelligence Dashboards using Python and Power BI Subagio, Azka Raisa
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.8634

Abstract

Micro, Small, and Medium Enterprises in Indonesia play a vital role in national economic growth; however, many continue to rely on manual spreadsheet-based reporting and intuitive judgment, limiting the effectiveness and timeliness of data-driven decision making. This study aims to examine how Business Intelligence dashboards integrating Python and Power BI can enhance operational decision-making performance in Indonesian retail-sector micro, small, and medium enterprises. Using a quantitative descriptive approach, the study analyzes secondary data from the Grocery Store Sales Dataset (2025) obtained from the Kaggle open-source platform. A total of 1,980 transaction records were processed to simulate typical operational decision-making scenarios commonly faced by retail enterprises. In the baseline condition, decision making was conducted using conventional spreadsheet summaries without automated analytics or real-time visualization. Python was employed for data preprocessing, transformation, and key performance indicator computation, while Power BI was used to develop an interactive Business Intelligence dashboard. Descriptive statistical analysis and scenario-based simulations were conducted to compare decision-making efficiency and accuracy before and after dashboard implementation. The results indicate that the proposed Business Intelligence approach reduced average decision-making time by 36.36 percent, improved information accuracy by 41.18 percent, and accelerated strategic planning speed by 40 percent. These findings demonstrate that integrating Python-based analytics with Business Intelligence dashboards offers a low-cost, scalable, and effective solution to support data-driven managerial practices and strengthen the digital readiness of Indonesian micro, small, and medium enterprises.
Klasifikasi Citra Biji Kopi Sangrai Arabika dan Robusta Menggunakan Convolutional Neural Network Al Firdaus, Muhammad Rafi; Mardhiyyah, Rodhiyah; Sanjaya, Fadil Indra
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8695

Abstract

Coffee is one of Indonesia's leading commodities, with two main varieties: Arabica and Robusta. The differences in characteristics between these two types of coffee, such as bean shape, color, and texture, are often difficult to distinguish visually, especially for the general public. This study aims to develop an automatic classification system capable of distinguishing Arabica and Robusta coffee beans using the Convolutional Neural Network (CNN) method with the application of transfer learning based on the MobileNetV2 architecture. The dataset used consists of 210 images of coffee beans taken using a smartphone camera with various positions and lighting, which were then divided into training data (60%), validation data (20%) and test data (20%). Before the training process, data augmentation such as rotation, zoom, flip, and brightness adjustment was performed to enrich image variation and reduce the risk of overfitting. Training was conducted with a learning rate of 0.0001, a batch size of 32, and an Adam optimizer. The results showed that the CNN model with MobileNetV2 transfer learning was able to achieve a training accuracy of 99.21% and a testing accuracy of 97.62%, with relatively low loss values of 0.0682 for training data and 0.1333 for validation data. The application of transfer learning contributes to improving the stability of the training process by utilizing the pre-trained weights from the ImageNet model. Based on these results, it can be concluded that the MobileN-based CNN method.
Prototype Sistem Otomasi Jemuran Cerdas (Smart Clothesline) Berbasis NodeMCU ESP8266 dan Aplikasi Blynk Pamungkas, Rully Panji Mustiko; Pristi, Anisa Nur; Hellyana, Corie Mei
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8700

Abstract

Indonesia experiences two seasons, the rainy season and the dry season, which often result in unpredictable weather and hinder household activities such as drying clothes. This study aims to design and construct an Internet of Things (IoT)-based automatic clothesline system capable of detecting weather conditions using an LDR (Light Dependent Resistor) sensor and a rain sensor, controlled via NodeMCU ESP8266. The system enables the clothesline to close automatically during rain or when sunlight intensity is insufficient. The research employs the prototype method, which involves the stages of design, development, testing, and evaluation. This study contributes to the development of logic that integrates rapid rain protection and light-based drying efficiency into a single system monitorable in real-time. Data were collected through observation, interviews, and literature review. Test results indicate that the device operates automatically based on weather conditions and can be remotely controlled using the Blynk application. Although the device has limitations, such as not closing at night in the absence of rain and limited manual control within the application, the system has proven effective in reducing user reliance on manual monitoring and offers a practical solution for modern IoT-based households.
Implementasi Recommender System Menggunakan Algoritma K-Means pada Aplikasi Inventory untuk Manajemen Pengeluaran Barang Fadiah, Khalisha Dwi; Widodo, Tri
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8702

Abstract

Inventory management in retail businesses has not yet fully optimized the process of goods issuance. The primary problem in managing goods issuance is the risk of loss due to products approaching their expiration dates. This research aims to design and implement a goods issuance recommendation system. The application is developed by implementing the K-Means algorithm to analyze product data. The goods issuance process can be carried out manually or based on recommendations generated through clustering. The recommendation system is integrated into a web-based inventory application. The research methodology encompasses data collection, data preprocessing using aggregation techniques, one-hot encoding for categorical features (type and nature of goods), and feature engineering on expiration dates. The K-Means algorithm is applied to group goods based on similarity. The number of clusters (K) is determined dynamically based on the amount of available data. The system automatically identifies the cluster with the nearest average expiration date as the recommendation target. Clustering results are visualized using Principal Component Analysis (PCA). This system provides end-to-end functionality, ranging from a dashboard and K-Means analysis to the execution of goods issuance. The research results conclude that the system is effective in providing actionable decisions. By prioritizing the issuance of high-risk goods, the system supports operational efficiency and minimizes losses due to expired products.

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