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TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
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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
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Articles 756 Documents
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Analysis of Student GitHub Repository Activity Patterns in Web Framework Programming Courses using K-Means Clustering Yori Adi Atma; Andrew Kurniawan Vadreas
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10084

Abstract

Instructors in project-based web framework courses frequently lack objective mechanisms to monitor individual student development progress, particularly when engagement occurs asynchronously across multiple weeks and repository activity is not systematically analyzed. This study aims to identify distinct behavioral engagement profiles among students using GitHub repository activity data, and to demonstrate the utility of unsupervised machine learning as a scalable progress monitoring tool for project-based programming courses. The K-Means Clustering algorithm was applied to analyze repository activity patterns of 73 students enrolled in a Web Framework Programming course using Laravel at a vocational higher education institution. Five behavioral features were extracted from each student's GitHub repository, namely total_commit, active_days, avg_commit_per_day, weekend_commit, and last_commit_gap. Following data normalization using StandardScaler, the optimal number of clusters was identified as k=3 using the Elbow Method. The clustering analysis revealed three distinct behavioral profiles: Cluster 0 (51 students, 69.86%) as Passive Learners characterized by low commit activity and a high last_commit_gap indicating deadline-driven development behavior; Cluster 2 (20 students, 27.40%) as Productive Learners demonstrating substantially higher commit intensity and broader repository engagement; and Cluster 1 (2 students, 2.74%) as Highly Consistent Learners exhibiting stable, multi-session repository interaction throughout the project period. As an initial validation of clustering quality, the Silhouette Score of 0.4041 confirms a moderate yet meaningful partition structure within the dataset. The primary contribution of this study lies in demonstrating that mandatory GitHub repository submissions, already required in most project-based programming courses, can be repurposed into an objective, low-cost behavioral monitoring instrument without additional data collection burden. This contributes a replicable, repository-based learning analytics framework that enables instructors to objectively classify student project engagement, supporting early instructional intervention and more process-oriented assessment strategies in software engineering education.
Pengembangan dan Validasi Sistem Pemantauan Mikroklimat Real-Time Berbasis IoT sebagai Pendukung Evaluasi Kondisi Pengeringan Pascapanen pada Solar Dome Dryer Joko Riyanto; Anton Yudhana; Abdul Fadlil
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10165

Abstract

Continuous monitoring of temperature and humidity is necessary to evaluate the microclimate conditions that affect the drying performance of agricultural products in the Solar Dome Dryer. However, manual monitoring has not been able to provide real-time data, while previous research has largely focused on the implementation of devices without validating the measurements against standard measuring instruments under operational drying conditions. This research aims to develop a real-time microclimate monitoring system based on the Internet of Things to support the evaluation of post-harvest drying performance on the Solar Dome Dryer. The system was developed using the DHT22 sensor, ESP32 microcontroller, Wi-Fi connection, and cloud platform to display temperature and humidity data through a web-based and smartphone dashboard. Testing was conducted on the functions of acquisition, transmission, data visualisation, and sensor accuracy by comparing the measurement results with the standard measuring instrument UNI-T UT333. During the testing, the system was able to record and transmit microclimate data stably within the temperature range of 29.1–65.6 °C and relative humidity of 13.7–79.5% RH. In a limited test of 100 transmission cycles with a 10-minute interval, all data were successfully received by the server under network conditions during the testing. Validation using 50 pairs of data resulted in an MAE of 0.150 °C and an RMSE of 0.159 °C for temperature, as well as an MAE of 0.286% RH and an RMSE of 0.293% RH for humidity. The MAPE values were 0.34% and 0.92%, respectively. The contribution of this research is the development of a low-cost microclimate monitoring framework that integrates acquisition, transmission, storage, real-time visualisation, and statistical sensor validation under the operational conditions of the Solar Dome Dryer. The system can be used as a data source to support drying condition evaluations, with the limitation that accuracy validation above 60 °C has not yet been conducted using appropriate reference instruments.
Evaluasi Efektivitas Strategi Promosi Perguruan Tinggi Menggunakan Algoritma DBSCAN dan Sistem Informasi Geografis Sania Darista; Rozali Toyib
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10284

Abstract

Campus promotions that lack a clear direction often lead to inefficient budget use and imbalanced differences in the number of applicants between regions. This study aims to examine how effective the promotional methods used by the University of Muhammadiyah Bengkulu are, using a combination of the DBSCAN algorithm and Geographic Information Systems. The DBSCAN algorithm is used to group data from prospective students based on the density of applicants and identify areas that are unusual or have irrelevant data, while GIS helps to visually map the distribution patterns of these data. The evaluation results show that applicants tend to gather in certain areas that support the success of the promotion, while also identifying potential areas that have not been optimally reached, the model evaluation produces a Silhouette Score of 0.159, which indicates spatial overlap, GIS visualization successfully provides a clear picture of the distribution of students, The contribution of this study is to integrate the DBSCAN algorithm with Geographic Information Systems to evaluate the effectiveness of promotional strategies based on the spatial distribution of prospective students so that it can provide recommendations for more targeted and data-based promotional areas. These insights provide a strategic foundation for designing more efficient, targeted, and data-driven marketing campaigns in high-potential regions.
Rancang Bangun Alat Pemilihan Suara Berbasis Internet of Things Menggunakan Blockchain Ahmad Saiful Mutaqi Azis; Fara Triadi; Irwansyah Irwansyah
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.v7i1.10318

Abstract

Manual election processes still face various challenges regarding accuracy, efficiency, and transparency, including the potential for data manipulation and delayed vote recapitulation. This study aims to design and build a prototype Internet of Things (IoT)-based e-voting system integrated with the Hyperledger Fabric permissioned blockchain network, as a solution to improve recording accuracy, process transparency, and recapitulation efficiency. As the problem-solving method, the system architecture comprises an ESP32-S3-based Electronic Voting Machine (EVM), a Node.js Backend API Gateway, and a VotingContract chaincode that enforces election phases, prevents double voting via a voterHash derived from the transient map, and provides real-time result query interfaces. Development followed an Agile Scrum methodology across four sprints: Fabric infrastructure setup, EVM assembly and firmware development, data-schema design with API-ledger integration, and web dashboard development. Testing results demonstrate successful execution of authentication, candidate selection, vote recording with Transaction ID (TxID), tally increments, and rejection of double voting across a 74-transaction simulation without a single failure, without storing voters' personal data on the ledger. The main contribution of this research lies in the direct implementation of the Hyperledger Fabric network on a standalone, resource-efficient ESP32-S3-based EVM hardware terminal, an approach that remains largely unexplored in prior IoT-blockchain e-voting research.
Implementasi E-Commerce Berbasis Laravel untuk UMKM Lissiger dengan Desain Mobile-Responsive Bagas Tri Panji Susilo; Fitria Fitria
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10597

Abstract

The rapid advancement of digital technology has encouraged Micro, Small, and Medium Enterprises (MSMEs) to adopt electronic commerce in order to improve their business competitiveness. Lissiger MSME, located in Bandar Lampung and engaged in the production of traditional Lampung siger crowns, still relies on conventional marketing and manual transaction recording, resulting in limited market reach and inefficient administrative processes. This study aims to design and develop a mobile-responsive ecommerce platform to expand digital marketing reach and automate transaction management for Lissiger MSME. The system was developed using the Agile Scrum methodology through three structured sprint cycles. Laravel framework, Bootstrap 5, and MySQL were utilized to develop the application System evaluation was conducted using Black Box Testing to verify system functionality and User Acceptance Testing (UAT) involving 10 respondents, consisting of one MSME owner and nine prospective customers, to evaluate user acceptance.The developed platform provides a 50% Down Payment (DP) transaction mechanism and an interactive visualization feature presenting nine stages of siger production as a cultural educational medium. The Black Bo Testing results showed a 100% success rate across 11 functional test scenarios, while the UAT achieved an average score of 88.70%, indicating that the system is highly acceptable for users. These findings demonstrate that the proposed platform effectively supports digital marketing expansion and transaction management for Lissiger MSME. The main contribution of this study is the development of a mobile-responsive e-commerce platform that integrates a 50% Down Payment (DP) payment mechanism aligned with MSME business processes and an interactive visualization of siger production as a medium for cultural education and preservation within a single system.
Rancang Bangun Asisten Virtual untuk Layanan Bengkel Berbasis Retrieval-Augmented Generation dan Tool Calling Patrick Ardian Yoga Purnomo; Dwi Budi Santoso
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10640

Abstract

The development of Artificial Intelligence (AI) has created new opportunities for Small and Medium Enterprises (SMEs) to improve customer service quality through communication automation. C Maestro Workshop in Semarang still handles customer inquiries manually through telephone and messaging applications, resulting in delayed responses to service cost inquiries, service reservations, and vehicle repair status updates. These services also depend on staff availability and cannot be provided consistently on a 24-hour basis, leading to inefficient customer service and potentially reducing customer satisfaction. This study aims to design and develop a Retrieval-Augmented Generation (RAG)-based virtual assistant utilizing the OpenRouter Application Programming Interface (API) to address these issues. The system was developed using a TypeScript-based client-server architecture running on the Node.js runtime environment. The RAG approach was implemented through a context injection mechanism by integrating the workshop's internal knowledge base, which includes service information, estimated service costs, Frequently Asked Questions (FAQ), initial diagnostic guidance, and other supporting information, into the system prompt of the Large Language Model (LLM). In addition, the system implements a tool-calling mechanism that enables the virtual assistant to perform business functions such as service reservations, vehicle repair status inquiries, and complaint escalation to mechanics. Operational data are stored in an SQLite database, while the user interface is provided through a web-based chat application with Server-Sent Events (SSE) support to deliver real-time responses. The evaluation results indicate that the system is capable of generating relevant responses based on the knowledge base, automating the service reservation process, and escalating requests beyond its capabilities. This study contributes by developing a virtual assistant architecture that integrates Retrieval-Augmented Generation (RAG) based on context injection with a tool calling mechanism using the OpenRouter API to support both information services and workshop operational processes. Therefore, the developed virtual assistant offers an effective, practical, and cost-efficient digital solution for supporting the digital transformation of customer services in small and medium-sized automotive workshops.
Perbandingan Gated Recurrent Unit dan Time Series Transformer untuk Prediksi Kabut Menggunakan Sliding Window Chandra Dwi Pratomo; Agung Budi Susanto; Arya Adhyaksa Waskita
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10687

Abstract

Fog is one of the most hazardous weather phenomena for aviation operations. Dense fog can reduce visibility to below 1,000 meters, potentially causing flight delays, cancellations, and even aviation incidents. To date, fog prediction, particularly at Budiarto Airport, still relies on manual analysis by weather forecasters, making it prone to subjectivity and delays in information delivery. This study proposes and compares two deep learning architectures: the Gated Recurrent Unit (GRU) as an efficient recurrent model, and Time Series Transformer (TST) based on self-attention as a state-of-the-art model for METAR (Meteorological Aerodrome Report) data-based fog event prediction. The METAR data is initially processed using a sliding window technique before becoming a ready-to-use dataset. The dataset comprises 153,838 METAR records from the Budiarto–Curug Meteorological Station spanning from September 2015 to February 2026, which were processed through a METAR code parsing pipeline, BMKG rule-based median imputation, Min-Max normalization, and the construction of a 9-1 sliding window dataset. Experimental results on the test data demonstrate that TST 9-1 delivers the best performance with a Root Mean Squared Error (RMSE) of 0.452427 and a three class classification accuracy (No Fog, Light Fog, Dense Fog) of 88.21%, significantly outperforming GRU 9-1, which achieved an RMSE of 0.883981 and an accuracy of 72.97%. The main novelty of this research lies in the comparative study of GRU and TST architectures for METAR based fog prediction at airports, combined with a sliding window technique and the conversion of visibility regression into a multi class classification of fog events. This research contributes a fog prediction modeling framework capable of processing time-series data sequentially and more effectively, which can serve as a foundation for the development of an accurate, automated early warning system for fog events in airport environments.
Penerapan Sistem Pakar untuk Diagnosis Kerusakan Smartphone Menggunakan Metode Forward Chaining Berbasis Android Muhammad Syahuda Hasibuan; Abdul Halim Hasugian
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10699

Abstract

This study aims to design and develop an Android-based expert system application used to diagnose smartphone malfunctions using the Forward Chaining method. The main issue in this study is the limited knowledge of lay users in understanding the symptoms and types of smartphone malfunctions before seeking repairs. Research data was obtained through interviews with expert smartphone technicians at CarlCare Mobile Phone Repair Service in Medan, which is an official service center for Transsion vendors, namely Infinix, Itel, and Tecno. The data collected consists of 45 symptoms, 37 types of damage, 44 diagnostic rules, and 37 brief repair solutions. This application was developed using Flutter and Dart, with Firebase as the primary database and SQLite as the local database to ensure the application remains usable offline after synchronization is complete. The Forward Chaining method was applied by matching symptoms selected by the user with IF-THEN rules in the knowledge base to generate a diagnostic conclusion. The implementation results show that the app can display fault categories, symptom lists, diagnostic results, brief solutions, diagnostic history, as well as database synchronization and reset features. Black-box testing results indicate that all features function as expected. Thus, this app can help users obtain an initial diagnosis of Transsion smartphone faults in a simple, fast, and targeted manner.
Evaluasi Arsitektur Long Short-Term Memory untuk Klasifikasi Gestur Tangan Dinamis Berbasis MediaPipe Sebagai Kendali Presentasi Putu Gede Dimas Witjaksana; I Nyoman Saputra Wahyu Wijaya; Putu Hendra Suputra
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10711

Abstract

Advances in Human-Computer Interaction (HCI) are driving the use of more natural interaction methods through hand gesture recognition technology. Dynamic gestures have an advantage over static gestures because they contain temporal information such as direction and movement patterns hat is more representative in conveying a command. However, the development of dynamic gesture recognition models still faces challenges in maintaining generalization capabilities for new users, necessitating an evaluation scheme capable of measuring model performance more representatively. This study aims to develop a dynamic hand gesture classification model for controlling PowerPoint presentations based on MediaPipe Hands, MediaPipe Pose, and Long Short-Term Memory (LSTM). The research stages included video data collection, hand and shoulder landmark extraction, data normalization using the midpoint of both shoulders as a reference point, training of eight variations of the LSTM architecture, and evaluation using the Leave-One-Subject-Out Cross-Validation (LOSO-CV) scheme, such that each participant took turns serving as test data to evaluate the model’s generalization ability toward new users. Test results show that the Baseline 1 (B1) architecture delivers the best performance with an average accuracy of 95.77%, precision of 96.03%, recall of 95.90%, and an F1-score of 95.84%, Analysis of the confusion matrix shows that most gestures were correctly recognized, while misclassifications occurred primarily in the “idle” class and for some gestures with similar hand poses. The results of the study indicate that the combination of MediaPipe and LSTM is capable of building a dynamic hand gesture classification model that maintains consistent classification performance in cross-subject testing.
Penerapan Naive Bayes untuk Klasifikasi Opini Fans Manchester United pada Media Sosial Muhammad Luthfi Lubis; Aidil Halim Lubis
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10724

Abstract

This study examines the application of the Naïve Bayes algorithm to classify the opinions of Manchester United fans in Indonesian-language comments on YouTube. The diverse linguistic forms found in the comments such as slang, abbreviations, jokes, and sarcasm make manual analysis inefficient and potentially subjective. Data was collected from eight YouTube videos using the YouTube Data API v3 and stored in a MySQL database. Of the 4,886 comments obtained, 1,962 were identified as being in Indonesian. A total of 1,000 comments were used as ground truth, consisting of 400 positive, 300 negative, and 300 neutral comments. The data was divided into 80% training data and 20% test data using a stratified split. The processing stages included text preprocessing, TF-IDF weighting, Naïve Bayes classification, and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The test results yielded an accuracy of 92.50%, a macro precision of 92.81%, a macro recall of 92.36%, and a macro F1-score of 92.57%. Of the 1,944 comments successfully classified, positive sentiment dominated at 47.58%, followed by negative at 28.34% and neutral at 24.07%. The web-based system, built using Laravel, PHP, and MySQL, is capable of integrating the processes of data extraction, labeling, classification, evaluation, and result visualization.

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