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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 582 Documents
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Aplikasi Pemantauan Akademik dan Non-Akademik Siswa Sekolah Dasar Berbasis Web dan Mobile Hidayat, Rizki; Mardhiyyah, Rodhiyah
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8455

Abstract

Student learning assessment is an essential component of education as it measures competency achievement and supports the continuous development of learning strategies. At SDN 2 Bungko Lor, Cirebon, the management of academic and non-academic data is still carried out manually using printed report cards and Excel spreadsheets. This condition limits parents’ ability to monitor their children's learning progress, as results are only accessible at the end of each semester. This study aims to design and develop a Web- and Android-based Academic and Non-Academic Monitoring Application using the Waterfall method to simplify data management, improve information accessibility, and strengthen communication among schools, teachers, and parents. The system was developed using Vue.js for the web application, Flutter for the Android application, PHP as the back-end, and MySQL as the integrated database. The system design applied UML (Use Case, Activity, and Class Diagrams) to model workflows and data structures. The main features include multi-user login, management of grades and attendance as academic data, recording of student behavior as non-academic data, class schedules, and a two-way chat feature to support coordination between schools and parents. Testing using the Black Box method confirmed that all core functionalities operated properly. The implementation of this system provides a more structured presentation of information and enhances collaboration between schools and parents in monitoring students’ academic and non-academic development.
Pengelompokkan Tingkat Stres Akademik Pada Mahasiswa Menggunakan Algoritma Fuzzy C-Means Alfaiza, Raihan Zia; Budianita, Elvia; Gusti, Siska Kurnia; Afrianty, Iis
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8460

Abstract

Academic stress is a common problem experienced by students due to the burden of assignments, exams, and social pressures. If not managed properly, it can impact achievement and psychological well-being. This study aims to classify the academic stress levels of students at the Faculty of Science and Technology, Sultan Syarif Kasim State Islamic University, Riau, using the Fuzzy C-Means (FCM) algorithm, which allows flexibility in the degree of data membership in more than one cluster. Data were obtained from a modified Perception of Academic Stress Scale (PASS) questionnaire, with 587 respondents from the 2021–2024 intake. The research stages included data selection, cleaning, and transformation, application of the FCM algorithm, and evaluation using three validation metrics: the Partition Coefficient Index (PCI), the Fuzzy Silhouette Index (FSI) and the Silhouette Coefficient. The test results showed the optimal number of clusters at C = 2, with the highest PCI value of 0.5663, FSI and ilhouette Coefficient score of 0.3056, resulting in two groups of students: 313 with high stress levels and 274 with low stress levels. The decrease in PCI, FSI and Silhouette scores across a larger number of clusters indicates that dividing two clusters provides the clearest grouping structure. These findings demonstrate that the FCM algorithm is effective in mapping students' academic stress patterns and can be used as a basis for designing more targeted academic mentoring strategies, counseling services, and psychological intervention programs services.
Hyperparameter Optimization of Naive Bayes for Supervisor Recommendation in Computer Science Sinaga, Muhammad Nabil; Kurniawan R, Rakhmat
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8478

Abstract

The increasing number of students in the Department of Computer Science at UIN Sumatera Utara has made the process of selecting thesis supervisors more complex and time-consuming. This study aims to develop a system that automatically recommends the most suitable supervisor based on the similarity between thesis titles and lecturers’ areas of expertise. The proposed model applies text preprocessing techniques such as case folding, tokenization, stopword removal, and keyword extraction to transform thesis titles into meaningful features. These features are then classified using the Naive Bayes algorithm to predict the probability of each lecturer being the most relevant supervisor. The dataset consists of 794 thesis titles and 25 lecturers collected from 2019–2024. The model was evaluated using an 80:20 data split, achieving an accuracy of 87.3% with stable precision and recall scores, demonstrating reliable performance in supervisor recommendations. This enhanced Naive Bayes model can assist academic departments in ensuring a fairer and more efficient supervisor assignment process.
Pengembangan Aplikasi Mobile Berbasis Location-Based Service dalam Mendukung Efisiensi Distribusi Pertanian Padi Yakti, Ikhwan Kuncoro; Mardhiyyah, Rodhiyah
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8545

Abstract

Rice harvest distribution is a critical aspect of maintaining price stability and national food security. However, the traditional distribution system still faces numerous challenges, such as long supply chains, lengthy delivery times, and limited information regarding prices and rice mill locations. These conditions lead to distribution inefficiencies, price fluctuations detrimental to farmers, and a decrease in their welfare. This research focuses on developing a Location-Based Service (LBS) mobile application integrated with a monitoring website to support the efficiency of rice distribution. The developed system is designed to connect farmers, rice mills, and distributors on a single digital platform, enabling a more monitored and integrated distribution process. The mobile application provides features for harvest recording, searching for nearby rice mill locations, real-time market price information, product sales and ordering, transaction logging, agricultural news, and digital payment integration. The monitoring website is intended for relevant agencies to display production and distribution data, progress graphs, and distribution maps of farmers and rice mills. The system was implemented using Flutter for the mobile application, Vue.js for the website, and Firebase Realtime Database as the integrated database, using data from the Cilamaya Wetan Agricultural Technical Service Unit (UPTD). Black Box Testing results indicate that all main system functions such as authentication, product management, ordering, location services, and payment integration are functionally sound and operate according to user requirements. Nevertheless, this testing was limited to technical functionality and did not include usability evaluation or user acceptance in the field. While the traditional distribution process involves 3-4 intermediaries (e.g., farmers, brokers, collectors, mills, large distributors, retailers, end distributors), the developed system offers a design that can shorten this process to only 1-2 intermediaries via a direct channel from farmer to mill, and then to the distributor. Potential analysis indicates that the system could enhance distribution efficiency by reducing intermediaries, improving price transparency, and facilitating easier monitoring by relevant agencies. The mobile application can display rice mill location information on a digital map, accessible to farmers in real-time. This research, therefore, yields a system developed to digitally support rice distribution efficiency. It can serve as a foundation for future research to test the system's implementation in the field and assess its real-world impact on farmer welfare and distribution effectiveness.
Comparative Study of Mobilenet and Resnet for Watermelon Leaf Disease Classification Using Deep Learning Ahmad, Abdullah; Wanto, Anjar; Windarto, Agus Perdana; Poningsih, Poningsih
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.v6i1.7543

Abstract

Watermelon leaf diseases, caused by various factors such as fungi, viruses, and bacteria, can have a significant impact on agricultural yields. To increase the amount and quality of watermelon produced, early diagnosis of this disease is essential. This study aims to compare the performance of two Convolutional Neural Networks (CNN) architectures included in Deep Learning, namely MobileNet and ResNet, in classifying watermelon leaf diseases using a dataset taken from Kaggle. This dataset consists of 1000 watermelon leaf images with three conditions, namely Downy Mildew (380 images), Healthy (205 images), and Mosaic Virus (415 images). ). 95% accuracy, 96% precision, 94% recall, and 95% f1-score are the results of the MobileNet model. In contrast, the ResNet model performs better, with 97% accuracy, 96% precision, 97% recall, and 97% f1-score. The study's findings show that ResNet outperforms MobileNet in the classification of watermelon leaf illnesses, despite both models' excellent and effective performance for automatic plant disease detection applications.
Pengembangan Sistem Rekomendasi Program Studi Multikelas Menggunakan Algoritma Random Forest Astri, Renita; Kamal, Ahmad; Zulfahmi, Zulfahmi; Faradika, Faradika
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8369

Abstract

Choosing a major is a crucial decision for prospective students entering higher education. An inappropriate choice may lead to low learning motivation, poor academic performance, and career mismatches. This study aims to develop a multiclass majors recommendation system based on machine learning using the Random Forest algorithm. The dataset consists of 855 student and alumni records from 10 majors at Dharma Andalas University (UNIDHA), including academic attributes (subject grades, GPA, entrance test results) and non-academic attributes (gender, high school major, interest, and alumni career field). The model was trained using an 80:20 train-test ratio and evaluated using accuracy, precision, recall, F1-score, and macro-average AUC. The results show that the Random Forest outperforms Decision Tree, K-Nearest Neighbor, and Naive Bayes, achieving an accuracy of 0.920 and AUC of 0.972. These findings demonstrate that ensemble-based algorithms are highly effective for multiclass recommendation problems and can serve as a foundation for academic and career guidance systems in universities.
Pengaruh Kompensasi dan Motivasi terhadap Kinerja Karyawan pada Perusahaan Jasa Olahraga Harniati, Titik; Lasiyono, Untung; Afkar, Taudlikhul
TIN: Terapan Informatika Nusantara Vol 6 No 5 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i5.8542

Abstract

This study employed an explanatory method with a quantitative approach. Data were collected through questionnaires from 30 employees and analyzed using multiple linear regression with t-test, F-test, and coefficient of determination (R²). The results indicate that compensation and motivation have a positive and significant effect on job satisfaction, both partially and simultaneously. Motivation has the dominant influence, with a regression coefficient of 0.746 and a significance level of 0.000, indicating that higher motivation greatly contributes to higher employee job satisfaction. The findings imply that the company should balance financial compensation and employee motivation enhancement to achieve optimal performance and better retention. Theoretically, the results reinforce the view that motivation is a key internal factor influencing job satisfaction. The novelty of this study lies in its application to the sports service sector, particularly the driving range facility, which has rarely been explored in the context of human resource management.
Analisis Forensik Ransomware Pada Sistem Berbasis Linux dengan Pendekatan Perbandingan Disk Dirgantoro, Revandho Vianuara; Luthfi, Ahmad
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8341

Abstract

This study aims to analyze the impact of Monti ransomware infection on Linux operating systems through a digital forensic approach based on artefacts and metadata. The investigation was conducted in an isolated laboratory environment using physical hardware, employing RAM acquisition and disk imaging methods on two system states: before and after infection. The ransomware execution was triggered by the monti.elf binary located in the temporary /tmp direktori, initiating encryption of operational files within the /Documents direktori. The analysis utilized Sleuthkit tools, focusing on file system structures, inode metadata, timestamps, and artefact distribution. Findings indicate that Monti employs an in-place encryption technique, replacing file contents without altering inode or block location. Key artefacts identified include encrypted files (.puuuk, .monti), ransom notes (readme.txt), execution logs (result.txt), and the ransomware binary (monti.elf). All artefacts share identical timestamps, suggesting automated execution within a single session. Validation was performed through comparative analysis of clean and infected systems, entropy measurements, and examination of TOR-based communication structures embedded in the ransom notes. These findings confirm that Monti operates as part of a Ransomware-as-a-Service (RaaS) ecosystem, with a structured and efficient infection pattern. This research contributes to the mapping of Monti ransomware artefacts and the development of forensic investigation methodologies tailored for Linux environments.
Implementation of the Preference Selection Index Method in a Decision Support System for Determining Customer Loan Eligibility Ilham, Safarul
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.v6i7.8357

Abstract

This study aims to apply the Preferential Selection Index (PSI) method in evaluating the eligibility of loan applicants for the PNM Mekaar program. The research investigates the effectiveness of PSI in selecting the most eligible borrowers based on multiple criteria, such as Age, Owns a Business, Loan Capital, Income, Business Permit, Business Permit Level and Customer History. The analysis is conducted using a decision support system (DSS), where each alternative is evaluated against these criteria and weighted accordingly. The study finds that the alternative with the highest value, C8 (0.222403657), is followed closely by alternative A7 (0.207720657). The results of this study demonstrate that PSI offers a more structured, objective, and efficient approach to loan eligibility assessment compared to traditional methods. The integration of PSI within the DSS allows for faster decision-making, improved consistency, and a reduction in the risk of loan defaults. These findings contribute to enhancing the decision-making process in microfinance institutions, particularly in improving financial inclusion and supporting the growth of micro, small, and medium enterprises (MSMEs). The research concludes that PSI is a valuable tool for financial institutions seeking to adopt data-driven, transparent, and reliable loan approval procedures.
Combination of MOORA and ITARA Methods in Decision Support Systems for Measuring the Performance of Quality Control Teams Hendrastuty, Nirwana; Wang, Junhai; Sulistiyawati, Ari; Darwis, Dedi; Setiawansyah, Setiawansyah; Jumaryadi, Yuwan; Sumanto, Sumanto
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8382

Abstract

The problems that often arise in evaluating the performance of the Quality Control team are the subjectivity in determining the weight of criteria and the limitations of traditional methods in producing objective and consistent rankings. To address this issue, this research integrates the Indifference Threshold-based Attribute Ratio Analysis (ITARA) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods within a decision support system. The ITARA method is used to determine the weights of criteria based on data variation, making them more representative of real conditions, with the result that Accuracy of Product Defect Identification becomes the most dominant criterion with a weight of 0.3999, followed by Response Speed to Issues at 0.1877, while other criteria have lower weights. Furthermore, the MOORA method is used to calculate the preference of alternatives, resulting in a final ranking. The analysis results indicate that the Quality Assurance Team ranks first, followed by the Quality Improvement Team in second place, while the Quality Inspection Team is in the last position. To test the reliability of the model, a sensitivity analysis was conducted by varying the weights of the main criteria. The results show that the ranking structure is relatively stable, with changes only occurring in the positions of the first and second ranks when the accuracy weight is reduced by 0.2. In conclusion, the combination of ITARA-MOORA proves to be capable of producing objective, robust, and reliable performance evaluations as a basis for strategic decision-making in enhancing the quality of the quality control teams.

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