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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 6 Documents
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Search results for , issue "Vol 6 No 4 (2025): September 2025" : 6 Documents clear
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.
Pemahaman dan Pengalaman Pengguna terhadap Penerapan PACS (Picture Archiving and Communication System) pada Layanan Radiologi Samsi, Farah Aulia Rahma; Nugroho, Anshor; Mayani, Anita Nur
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.v6i4.8311

Abstract

Picture Archiving and Communication System (PACS) is a computer-based technology designed to manage the acquisition, transmission, storage, distribution, display, and interpretation of medical images across institutions. This system is tightly integrated with imaging processes in the Radiology Department and clinical practices that depend on radiological examinations. At the Radiology Installation of RSUD Panembahan Senopati Bantul, the delivery of radiology results has become more efficient with PACS. For internal hospital patients, results are distributed electronically to radiologists and clinical specialists without printing. However, for referred patients from external hospitals or those being referred out, printed film remains necessary. This study applied a descriptive quantitative design, conducted in the Radiology Installation of RSUD Panembahan Senopati Bantul from May 5th to May 10th, 2025. Respondents consisted of 20 radiographers, and data collection was carried out using a Guttman scale questionnaire. The data were analyzed descriptively, with validity and reliability testing. Findings indicate that most respondents possess a strong understanding and acceptance of PACS, demonstrated by the predominance of “Yes” answers, some reaching 100%. This highlights the system’s optimal utilization and its positive contribution to service speed, patient data security, and workflow efficiency. The majority of respondents confirmed that PACS significantly improves radiology services, though some indicators showed varied responses, suggesting areas for further development.
Perbandingan Algoritma Naïve Bayes, Support Vector Machine, dan Random Forest Untuk Analisis Sentimen Komentar Politik Youtube Santoso, Bayu Aji; Nugroho, Bangkit Indarmawan; Asyfiya, Dzurrotu Umi
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.v6i4.8326

Abstract

Sentiment analysis is an important field in natural language processing that is widely used to understand public opinion on social media. This study compares the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine (SVM), and Random Forest, in analyzing YouTube comment sentiment. The dataset consists of 15,257 comments obtained from the Indonesia Lawyers Club (ILC) and Rakyat Bersuara channels. The research process includes preprocessing stages (cleaning, case folding, tokenizing, normalization with a slang dictionary, stopword removal, and stemming), data labeling with a Lexicon-based approach using InSet Lexicon, data division with a ratio of 80% training data and 20% test data, and evaluation using accuracy, precision, recall, and F1-score metrics, complemented by K-fold cross validation tests. The results of the sentiment distribution show a dominance of negative sentiment at 43.2%, followed by neutral at 34.9%, and positive at 21.9%. Model evaluation showed that SVM excelled with 83.52% accuracy, 83.55% precision, 83.52% recall, and 83.52% F1-score, followed by Random Forest with 77.20% accuracy, while Naïve Bayes achieved the lowest result at 64.71%. The K-Fold test further strengthened these results, with the best accuracy of 84.14% for SVM. Thus, SVM can be concluded as the most effective algorithm for analyzing political comment sentiment on YouTube.
TikTok dan Kesehatan Mental: Studi Pengaruh Media Sosial pada Remaja Carolan, Farrin; Lawwin, Jay; Jovin, Jovin; Ronald, Ronald; Novisari, Winny
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.v6i4.8157

Abstract

This study aims to analyze how the use of TikTok affects the mental health of adolescents in Batam. The method used in this study is a quantitative method using a survey, involving 405 participants aged between 10 and 24 years who actively use TikTok. Based on data from 405 respondents, 86.9% (352 respondents) actively use TikTok, covering all levels of adolescent education. The data collection tool was a questionnaire with a Likert scale designed to measure independent variables (TikTok use) and dependent variables (mental health, including depression, anxiety, stress, and bipolar disorder). Data analysis was performed using the Structural Equation Modeling (SEM) method with AMOS software. The results of this study indicate that TikTok usage significantly contributes to an increase in symptoms of depression, anxiety, stress, and bipolar disorder among adolescents. The reliability test results show that the Cronbach's Alpha values for the variables TikTok Usage (TU) are 0.768, Depression (D) are 0.801, Stress (S) are 0.872, Anxiety (A) are 0.868, and Bipolar Disorder (BD) are 0.874. Based on standard interpretation, a Cronbach's Alpha value greater than 0.70 is considered to have adequate reliability. Therefore, all variables indicate that the items in the questionnaire have a good level of internal consistency. These findings highlight the importance of improving digital literacy and time management in social media use to maintain adolescent mental health. This study provides valuable insights into understanding the complex relationship between social media and mental health, as well as providing a foundation for more effective interventions in the future.
Implementasi Model Extreme Programming dalam Pengembangan Sistem Simulasi Tes TOEFL Rahman, Reyvi; Khaira, Ulfa; Abidin, Zainil
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.v6i4.8272

Abstract

English proficiency is a key requirement in both academic and professional settings, with TOEFL serving as a standardized measure of competency. However, many candidates are unprepared for this exam due to a lack of understanding and practice that simulates actual test conditions. Yanto Tanjung English Academy, a course provider offering online TOEFL instruction, currently lacks a comprehensive test simulation facility. Therefore, this research aims to develop a web-based TOEFL test simulation system to provide a realistic exam experience, enabling participants to be better prepared and more confident. The system was developed using the Extreme Programming (XP) method, which consists of planning, design, coding, and testing phases. Testing was conducted using a Test-Driven Development (TDD) approach to ensure the system's functionality and alignment with user requirements. The results show that all main features are accessible, as evidenced by an efficient average execution time of 2.126 seconds across all tested features. This performance data confirms that the TDD approach successfully yielded a system that is not only functional but also responsive for its users. Furthermore, the system received a high acceptance rate from both stakeholders and course participants. This system is expected to enhance the effectiveness of independent and flexible TOEFL learning and to serve as a beneficial digital solution for course participants and the general public wishing to practice TOEFL online

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