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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 5 Documents
 1 
Search results for , issue "Vol 6 No 12 (2026): May 2026" : 5 Documents clear
Penerapan Engineering Design Process dalam Pengembangan Tong Sampah Berbasis QR Code Terintegrasi Website untuk Pengelolaan Sampah Berbasis Masyarakat Frandis, Albertus Nyam; Herikson, Rifaldi; Slam, Berta Erwin; Dio, Rafi
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.8889

Abstract

Community-based waste management remains a challenge in residential environments, particularly due to low community participation in reporting waste bin conditions and the lack of information media for waste management activities. This condition highlights the need for technological innovation that can increase community involvement in waste management more effectively. This study aims to develop a technology-based innovation grounded in real community needs through the implementation of the Engineering Design Process (EDP). The research stages began with field observations to identify problems, followed by recording and grouping findings, formulating problems and solutions, creating initial sketches, comparing ideas with similar innovations, and selecting a solution in the form of a QR Code-based waste bin integrated with a website. Furthermore, a 3D design of the waste bin and a website interface were developed, followed by the construction of a simple prototype of the QR Code-based waste bin and a localhost-based website. The website provides features including a homepage, community profile, activity schedule, reporting, educational content, suggestion forms, and an admin page for data management. System testing was conducted through user testing involving community residents as system users. The results indicate that the majority of respondents considered the system easy to use, useful, and aligned with environmental needs, as reflected by positive feedback from all participants during interview-based testing. The study demonstrates that applying the Engineering Design Process through direct field observation can produce technological innovations that are relevant and provide tangible benefits for community-based waste management.
A Comparative Analysis of XGBoost and Random Forest for Time Series Based Stock Price Prediction with Directional Movement Evaluation Fahmi, Andri; Rofiq, Nur
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9197

Abstract

Stock price prediction remains a complex task due to the dynamic nature of financial time series and the difficulty of extracting informative patterns from historical price movements. This study addresses the need to better understand whether the choice of model or the design of time series features plays a more dominant role in prediction performance. The objective of this research is to comparatively evaluate Extreme Gradient Boosting (XGBoost) and Random Forest for stock price prediction using engineered time series features, while also assessing their ability to capture directional price movements. The proposed approach applies a structured pipeline involving data preprocessing, extraction of time series features (lag, moving average, and volatility), and evaluation using a time-aware data split to preserve temporal order. Unlike conventional studies that focus solely on prediction accuracy, this research integrates both regression-based evaluation (RMSE, MAE, and R²) and directional movement analysis using confusion matrix, along with feature importance interpretation to understand model behavior. The experimental results, based on 1,258 daily stock price records, show that XGBoost achieved an RMSE of 457.97, MAE of 345.28, and R² of 0.884, while Random Forest obtained an RMSE of 462.01, MAE of 351.02, and R² of 0.882. The difference in R² (0.002 or 0.2%) indicates that both models perform comparably, with no substantial performance gap. Directional evaluation further reveals that both models are more accurate in predicting upward trends than downward movements. These findings suggest that feature engineering plays a more critical role than model selection in this context, providing a practical contribution to the development of stock prediction systems.
Sistem Monitoring Ketersediaan Slot Parkir Berbasis Internet of Things (IoT) Menggunakan ESP32 dan Sensor Ultrasonik Saenong, Andi; Hanapi, Khaerunnisa; Fitriani, Fitriani; Sartika, Sartika; Fettyana, Fettyana
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9504

Abstract

Limited real-time information on parking slot availability is one of the factors that causes vehicle queues, long parking search times, and potential congestion in parking areas of buildings, shops, and shopping centers. This study aims to develop an Internet of Things (IoT)-based smart parking system that is able to automatically detect parking slot availability and display information via a website in real-time. The system was developed using an ultrasonic sensor to detect the presence of vehicles in parking slots, an ESP32 microcontroller as a data processor and sender, a database as a slot status storage, a website as a monitoring medium, an LCD as a local information medium, and a servo motor as a parking barrier controller. The research method used is Research and Development (R&D) with a Prototyping model, which includes the stages of needs analysis, system design, prototype creation, implementation, testing, and evaluation. The test results show that the system is able to detect the status of empty and occupied parking slots, send data from the ESP32 to the database, and update the website display in an average of 2–3 seconds under stable network conditions. In addition, the system is able to automatically control the barrier based on parking slot availability. Based on the test results, the system achieved an average accuracy of 94.16%. Therefore, the developed smart parking system can be a more effective, efficient, automated, and easily accessible parking monitoring and management solution via the internet.
Klasifikasi Teks Komentar Penggunaan Listrik Gratis di Youtube Menggunakan Metode Naïve Bayes Harahap, Mikho Alfatih; Hasugian, Abdul Halim
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9731

Abstract

The growth of social media has made YouTube one of the platforms used by the public to express opinions regarding government policies, including the free electricity program. The large number of comments makes manual analysis difficult; therefore, a text classification method is needed to automatically categorize comments. This study aims to classify YouTube user comments related to the free electricity program using the Naïve Bayes algorithm. The research data were obtained through a crawling process from ten YouTube videos discussing the free electricity policy, resulting in 910 comments, which were reduced to 906 comments after data cleaning. The data processing stages included cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and term weighting using TF-IDF. Furthermore, the data were classified into four categories: Public Discussion and Information, Policy Support and Appreciation, Complaints and Technical Issues, and Non-Electricity. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the Naïve Bayes algorithm provided fairly good classification performance with an accuracy of 70.9%, precision of 0.62, recall of 0.80, and F1-score of 0.70. The Non-Electricity category achieved the best performance with precision of 0.77, recall of 0.90, and F1-score of 0.83. Based on these findings, the Naïve Bayes method is considered effective for classifying public opinion from social media comment data.
Implementasi Algoritma K-Means Clustering untuk Pengelompokan Produk E-Commerce Berdasarkan Harga, Diskon, dan Total Revenue Pasaribu, Rinaldi; Siregar, Saidi Ramadan
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9872

Abstract

The rapid growth of e-commerce has generated a large volume of transactional data; however, this data has not been fully utilized to support strategic decision-making, particularly in product segmentation. The main problem addressed in this study is the absence of a systematic product grouping approach based on key attributes such as price, discount, and revenue, which leads to less effective pricing and promotional strategies. Therefore, this study aims to analyze product sales patterns and cluster e-commerce products based on the characteristics of price, discount_percent, and total_revenue. The dataset used is an Amazon-style e-commerce dataset consisting of 50,000 transaction records and 13 attributes, with the analysis focusing on the three main attributes as the basis for clustering. The method applied in this research is K-Means Clustering, which involves data preprocessing, normalization using Min-Max Scaling, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The results indicate that the optimal number of clusters is three clusters, supported by the highest Silhouette Score of 0.354 and a clear elbow pattern in the Elbow graph. Additional evaluation using the Davies-Bouldin Index of 0.9335 indicates that the clustering quality is fairly good, although not yet optimal. The clustering results produce three main groups: premium product cluster (high price, low discount, high revenue), discount product cluster (moderate price, high discount, moderate revenue), and low-performance product cluster (low price, low discount, low revenue). In conclusion, the K-Means algorithm is capable of effectively clustering e-commerce products based on relevant numerical attributes and generating insights that can support business strategies such as pricing and promotional decisions.

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