cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
tin.journal@fkpt.org
Editorial Address
Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
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 31 Documents
 1   2   3   4 
Search results for , issue "Vol 6 No 8 (2026): January 2026" : 31 Documents clear
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.
Analisis Sentimen Ulasan Wisata Alun-Alun Brebes pada Google Maps Menggunakan Support Vector Machine Maulida, Azkiyatul; Irawan, Bambang; Ramdhan, Nur Ariesanto
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.v6i8.8883

Abstract

The rapid development of information technology has encouraged the use of digital platforms as media for sharing opinions, including tourism reviews on Google Maps. Alun-Alun Brebes, as one of the most frequently visited public spaces, has generated thousands of reviews with diverse textual characteristics, making manual analysis inefficient and impractical. This study aims to analyze visitor sentiment toward Alun-Alun Brebes by applying a text mining approach using the Support Vector Machine algorithm. The dataset consists of 1,000 Google Maps reviews, including 327 reviews manually labeled as positive and negative sentiments and 673 unlabeled reviews. The research stages include data collection, text preprocessing, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, Support Vector Machine model training and testing, and automatic labeling of unlabeled data. Model performance was evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics based on the manually labeled data. The results show that the Support Vector Machine model with a linear kernel achieved an accuracy of 100% with an F1-score of 1.00, indicating excellent sentiment classification performance. Furthermore, word cloud visualization reveals that positive sentiment is dominated by aspects related to comfort and facilities, while negative sentiment is associated with cleanliness, crowd density, and environmental management. These findings provide data-driven insights into key aspects that should be maintained and improved in managing Alun-Alun Brebes as a public space.
Monitoring Tekanan Freon dan Beban Arus Kompresor Untuk Menjaga Efisiensi Kinerja Pendingin Ruangan (AC) Berbasis Internet of Things (IOT) Nuryasa, IMade Fredy; Hayatalfalah, Agus; Sulistiyowati, Indah; Ahfas, Ahmad
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.v6i8.8936

Abstract

This study aims to develop an IoT-based monitoring system for Split AC units to track refrigerant pressure, compressor current load, and room temperature in real-time, enhancing operational efficiency and supporting preventive maintenance. The system is designed using an Arduino Nano as the main processing unit and an ESP8266 module for wireless communication integrated with the Blynk platform. A pressure transmitter measures refrigerant pressure, an ACS712 5A current sensor monitors the compressor load, and a DHT22 sensor records room temperature. Test results show that the pressure sensor has a maximum deviation of less than 0.1 psi compared to a manifold gauge, the ACS712 current sensor shows an average difference of 0.03 A compared to a clamp meter, and the DHT22 temperature sensor has a maximum deviation of 0.4 °C compared to a reference thermometer. All sensor data were consistently displayed on the LCD and Blynk application, confirming reliable data transmission. The system can detect anomalies in pressure and current based on manufacturer-specified thresholds, potentially reducing early failure risks, improving energy efficiency, and extending the lifespan of AC units.
Perancangan Sistem Informasi Rapor Digital Siswa Berbasis Mobile Menerapkan RAD Alda, Muhamad; Safitri, Nabilah; Alisya, Dwi; Akbar, Ahnaf Chaisar; Wardana, Dimas Arya
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.v6i8.8948

Abstract

The development of information and communication technology has encouraged educational institutions to adopt digital systems for managing academic data, including the preparation and distribution of student report cards. This study aims to design and develop a mobile-based Digital Report application using the Kodular platform as a solution to the inefficiency and errors of manual systems. The development method used is Rapid Application Development (RAD), focusing on quick iterations through stages of requirement analysis, interface design, and functional testing. Data were collected through literature study, observation, and interviews with schools to ensure that the application features met user needs. The results show that the application provides login, dashboard, student data input, score management, and real-time report access. Implementation of the Kodular no-code platform in the development of a mobile-based digital report card system that is easy to implement in the school environment, as well as providing an efficient and adaptive academic system development model for schools with limited technical resources. Kodular proved effective in speeding up the development process without complex coding, supporting school digitalization, and improving transparency and efficiency in managing academic data.
Analisis Komparatif Respons Insiden DDoS: Efisiensi MTTR pada Penanganan Manual Versus Otomatis Berbasis SIEM dan SOAR Darmayoga, I Nyoman; Mardhiyyah, Rodhiyah
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.v6i8.8972

Abstract

The increasing use of digital services in Indonesia has been accompanied by a growing number of cybersecurity threats, particularly DDoS attacks that target service availability. One real-world incident occurred on the news website Suara.com, which experienced a large-scale DDoS attack that was handled manually by the technical team. The manual handling of this incident revealed limitations in terms of the speed and measurability of the initial response, as not all response stages were systematically documented. This study aims to compare the mechanisms and speed of initial responses between manual handling of the DDoS incident on Suara.com and automated responses using the SYRA system. SYRA is a web-based security system developed to support automated detection and response to cyber incidents through the integration of SIEM and SOAR. The research method used is a comparative study that utilizes public data from the chronology of the Suara.com incident as a representation of manual response, as well as data from DDoS attack testing on the SYRA system conducted in a controlled environment as a representation of automated response. The main parameter used in the analysis is MTTR as an indicator of initial response speed. The results show that the SYRA system is able to execute initial responses consistently with an average MTTR value of 42.97 seconds, allowing initial mitigation actions to be carried out in less than one minute after the attack is detected. These findings indicate that the implementation of automated response plays an important role in maintaining the continuity of digital services, particularly in the media and public service sectors that are highly dependent on system availability.
Kualitas Produk Brand Ambassador Content Marketing Terhadap Keputusan Pembelian Sunscreen Gloowandbe di TikTok Shop Fasha, Widitha; Karamang, Ezra
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.v6i8.8986

Abstract

This study aims to analyze the influence of product quality, brand ambassadors, and content marketing on the decision to purchase Gloowandbe sunscreen products on TikTok Shop. The research method used is quantitative descriptive with quota sampling technique. Data collection was conducted through an online survey (questionnaire) to 100 respondents who were users or former users of Gloowandbe sunscreen aged 12-34 years on the TikTok Shop platform. The analyses used were validity-reliability tests, multiple linear regression, t-tests, f-tests, and R2 determination tests, with the help of SPSS 27 statistical tools. The results showed that simultaneously, product quality, brand ambassador, and content marketing had a positive and significant effect on purchasing decisions with an F value of 60,277 (Sig. 0.001 < 0.05). Partially, product quality is the most dominant variable influencing purchasing decisions with a value of t=5,351, followed by content marketing (t=3,572), and brand ambassador (t=2,702). The R2 value (coefficient of determination) shows that these three variables contribute 64,2% to purchasing decisions, while the remaining 35,8% is influenced by factors outside this study. Thus, it can be concluded that marketing strategies on TikTok Shop need to integrate consistent product quality with creative content and the selection of the right brand ambassadors to increase sales.
Pengembangan Sistem Prediksi Saham Menggunakan Model Hybrid Gated Recurrent Unit–Long Short-Term Memory Berbasis Integrasi Indikator Teknikal Konvensional Aryanto, Fajar Hanggoro Dwi; Sejati, Rr. Hajar Puji; Sanjaya, Fadil Indra
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.v6i8.8988

Abstract

Stock price prediction is a crucial aspect of investment decision-making in the Indonesian capital market. This study aims to design a hybrid Gated Recurrent Unit–Long Short-Term Memory (GRU–LSTM) model architecture integrated with technical indicators such as Moving Average Convergence Divergence, Moving Average, Exponential Moving Average, and Relative Strength Index to improve the accuracy and objectivity of predictions. Additionally, this study aims to optimize model performance through grid search and implement it into a Flask-based web application as a decision support system for investors. The system was developed using a research and development approach at the Yogyakarta University of Technology. Historical data on PT Bank Rakyat Indonesia (Persero) Tbk. (BBRI.JK) shares for the period from January 2, 2020, to October 17, 2025, was obtained through the Yahoo Finance API as the main dataset. The model was optimized to determine the best combination of hyperparameters. Evaluation was performed using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The test results show that the model achieved MAE 0.0241, MSE 0.0012, RMSE 0.0346, and MAPE 2.7%, indicating a high level of accuracy. The web application provides interactive visualization dashboard features, model development, and educational documentation. These findings confirm that the integration of deep learning with technical indicators is an effective solution for more measurable and systematic stock analysis.
The Design and Evaluation of a Decentralized E-Voting System Using Ethereum Smart Contracts Hurit, Ludgerdus Pati; Sumihar, Yo'el Pieter; Budiati, Haeni
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.v6i8.8997

Abstract

The widespread implementation of electronic voting systems poses ongoing challenges related to data integrity, transparency, and centralized control, which can increase the risk of vote manipulation and reduce traceability. To address these issues, this study designs and evaluates a decentralized electronic voting system implemented using Ethereum smart contracts. The objective of this research is to test the ability of blockchain technology to support a secure, transparent, and tamper-resistant voting process in a decentralized environment. The research methodology includes requirements analysis, system design, system implementation, and functional testing. Black-box testing was conducted to verify the system's functionality throughout the voting process. The proposed system permanently records voting transactions on the blockchain, preventing unauthorized modifications while allowing transaction verification by network participants. Voter privacy is maintained by separating voter identity data from voting records and implementing blockchain address abstraction, ensuring that individual votes cannot be directly linked to voter identities. System evaluation focuses on transaction costs and confirmation times. Performance testing was conducted using six test transactions on the Sepolia blockchain network. The total transaction cost recorded was 0.006076 ETH, with an average cost of 0.001013 ETH per transaction. The minimum transaction cost of 0.000091 ETH occurred during voting operations, while the maximum cost of 0.005596 ETH was associated with smart contract deployment and higher network base fees. The average transaction confirmation time was approximately 12 seconds. Although the evaluation was based on a limited number of transactions, the results indicate that the proposed system demonstrates reliable transaction execution, acceptable gas usage, and high transparency. Further large-scale testing is recommended for future work.
Pengaruh Kualitas Produk, Harga, dan Konten TikTok Terhadap Minat Beli Moisturizer Pada Generasi Z Patricia, Putri; Karamang, Ezra
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.v6i8.9008

Abstract

The rapid growth of the skincare sector and the widespread use of social media, particularly TikTok, have prompted businesses to understand what drives Generation Z's purchasing intentions. This study aims to examine how product quality, cost, and TikTok content influence purchase intentions for Labore products. This study uses quantitative methods with an explanatory focus. To collect data, an online survey was conducted involving 120 Generation Z participants (17-26 years old) who actively use TikTok and have been exposed to Labore content in urban Indonesia. Multiple linear regression analysis was conducted after validity, reliability, and classical assumptions (normality, multicollinearity, heteroscedasticity) were met. The results show that all three variables have a significant positive effect on purchase intentions (p < 0.05) with product quality as the strongest predictor (β = 0.612; t = 18.117), followed by TikTok content (β = 0.455; t = 14.737) and price (β = 0.092; t = 3.143). The research model explained 91.8% of the variance in purchase intention (R² = 0.918; F = 445.259; p = 0.000). The findings confirm that an integrated strategy combining superior product quality, competitive pricing, and authentic-educational TikTok content can significantly increase Generation Z consumers' purchase intention towards local skincare products.
Penerapan Metode Asosiasi Menggunakan Algoritma Apriori pada Penjualan Produk Tenun Abthol, Muhammad Rijalul; Wibowo, Gentur Wahyu Nyipto; Maori, Nadia Annisa
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.v6i8.9044

Abstract

The development of information technology has led to a significant increase in the volume of sales transaction data stored in business information systems. Such data possess substantial potential to generate strategic insights when properly analyzed. However, in many small and medium-sized enterprises (SMEs), transaction data have not yet been optimally utilized. This study aims to apply association analysis using the Apriori algorithm to sales transaction data of woven products at Sientong Tenun in order to identify consumer purchasing patterns based on support and confidence values. The research adopts a quantitative approach employing data mining methods on sales transaction data that have undergone a data preprocessing stage. The final dataset used in this study consists of 120 sales transactions. The parameters applied in the analysis include a minimum support threshold of 20% and a minimum confidence threshold of 60%. The results indicate that all main products meet the criteria for frequent 1-itemsets, with Woven Fabric and Shawl exhibiting the highest support values, at 65.00% and 58.33%, respectively. The strongest association rule identified is Woven Fabric → Shawl with a confidence value of 70.51%, followed by Woven Fabric and Shawl → Woven Sarong with a confidence value of 63.64%. These findings demonstrate a significant purchasing relationship among woven products. The results of this study can be utilized by business practitioners to support marketing strategies, sales bundle development, product arrangement, and data-driven inventory management. Furthermore, this research contributes academically to the application of the Apriori algorithm within the culturally based creative industry sector.

Page 1 of 4 | Total Record : 31

 1   2   3   4 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 6 No 9 (2026): February 2026 Vol 6 No 8 (2026): January 2026 Vol 5 No 12 (2025): May 2025 Vol 5 No 11 (2025): April 2025 Vol 5 No 10 (2025): March 2025 Vol 6 No 7 (2025): December 2025 Vol 6 No 6 (2025): November 2025 Vol 6 No 5 (2025): October 2025 Vol 6 No 4 (2025): September 2025 Vol 6 No 3 (2025): August 2025 Vol 6 No 2 (2025): July 2025 Vol 6 No 1 (2025): June 2025 Vol 5 No 9 (2025): February 2025 Vol 5 No 8 (2025): January 2025 Vol 4 No 12 (2024): May 2024 Vol 4 No 11 (2024): April 2024 Vol 4 No 10 (2024): March 2024 Vol 5 No 7 (2024): December 2024 Vol 5 No 6 (2024): November 2024 Vol 5 No 5 (2024): October 2024 Vol 5 No 4 (2024): September 2024 Vol 5 No 3 (2024): August 2024 Vol 5 No 2 (2024): July 2024 Vol 5 No 1 (2024): June 2024 Vol 4 No 9 (2024): February 2024 Vol 4 No 8 (2024): January 2023 Vol 3 No 12 (2023): May 2023 Vol 3 No 11 (2023): April 2023 Vol 3 No 10 (2023): March 2023 Vol 4 No 7 (2023): December 2023 Vol 4 No 6 (2023): November 2023 Vol 4 No 5 (2023): October 2023 Vol 4 No 4 (2023): September 2023 Vol 4 No 3 (2023): August 2023 Vol 4 No 2 (2023): July 2023 Vol 4 No 1 (2023): June 2023 Vol 3 No 9 (2023): February 2023 Vol 3 No 8 (2023): January 2023 Vol 2 No 10 (2022): Maret 2022 Vol 3 No 7 (2022): December 2022 Vol 3 No 6 (2022): November 2022 Vol 3 No 5 (2022): October 2022 Vol 3 No 4 (2022): September 2022 Vol 3 No 3 (2022): August 2022 Vol 3 No 2 (2022): July 2022 Vol 3 No 1 (2022): June 2022 Vol 2 No 9 (2022): Februari 2022 Vol 2 No 8 (2022): Januari 2022 Vol 1 No 12 (2021): Mei 2021 Vol 1 No 11 (2021): April 2021 Vol 1 No 10 (2021): Maret 2021 Vol 2 No 7 (2021): Desember 2021 Vol 2 No 6 (2021): November 2021 Vol 2 No 5 (2021): Oktober 2021 Vol 2 No 4 (2021): September 2021 Vol 2 No 3 (2021): Agustus 2021 (in press) Vol 2 No 3 (2021): Agustus 2021 Vol 2 No 2 (2021): Juli 2021 Vol 2 No 1 (2021): Juni 2021 Vol 1 No 9 (2021): Februari 2021 Vol 1 No 8 (2020): Januari 2021 Vol 1 No 7 (2020): Desember 2020 Vol 1 No 6 (2020): November 2020 Vol 1 No 5 (2020): Oktober 2020 Vol 1 No 4 (2020): TIN: September 2020 Vol 1 No 3 (2020): TIN: Agustus 2020 Vol 1 No 2 (2020): TIN: Juli 2020 Vol 1 No 1 (2020): Juni 2020 More Issue