cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Priyo Wibowo,
Contact Email
garuda@apji.org
Phone
+6285885852706
Journal Mail Official
febri@apji.org
Editorial Address
Perum Cluster G11 Nomor 17 Jl. Plamongan Indah, Kadungwringin, Pedurungan, Semarang, Provinsi Jawa Tengah, 50195
Location
Kota semarang,
Jawa tengah
INDONESIA
Router : Jurnal Teknik Informatika dan Terapan
Published by Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia
ISSN : 30263611     EISSN : 30323312     DOI : 10.62951
Core Subject : Science,
Computer Science & IT
Jurnal ini fokus mempublikasikan berbagai hasil penelitian dari berbagai disiplin ilmu di bidang Teknik Informatika dan ilmu terapan. Router
Arjuna Subject : Teknik (semua kategori) - Komputasi Mekanik
Articles 4 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan" : 4 Documents clear
Identification of Flower Type Images Using KNN Algorithm With HSV Color Extraction and GLCM Texture Edhy Poerwandono; M. Endang Taufik
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.385

Abstract

Due to the variety of types of flowers that exist and having and tracking each variety, making plant lovers and cultivators difficult to distinguish in determining the type of flower, it takes a very long time to find out the type of flower if you only rely on the five senses. With the application of the K-Nearest Neighbor algorithm and feature extraction of color and texture, it is very helpful in image processing to identify flowers more easily and shorten the time, with the greatest accuracy of 71% using the K-7 value, the flower was successfully carried out.
Penerapan Algoritma Machine Learning dalam Prediksi Prestasi Akademik Mahasiswa Riska Rismaya; Dwi Yuniarto; David Setiadi
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.389

Abstract

This study explores the application of machine learning algorithms, specifically Linear Regression and Decision Tree Regressor, for predicting student academic performance using academic grade data from Kaggle. The analyzed factors include attendance, assignment grades, midterm exam grades, and final exam grades. The research methodology encompasses data collection, preprocessing, model development, training, and validation. This study contributes to the field of educational data analytics by demonstrating how machine learning can provide actionable insights into students' learning patterns and academic outcomes. The findings emphasize the effectiveness of Linear Regression for linearly distributed data and Decision Tree Regressor for capturing complex, non-linear relationships. The implications of this research suggest that machine learning models can assist educators in identifying key factors influencing student performance, enabling targeted interventions to enhance learning outcomes. Future research should explore larger, more diverse datasets and incorporate ensemble methods, such as Random Forest or Gradient Boosting, to improve model generalization and prediction accuracy. Additionally, integrating socio-economic and psychological factors could provide a more holistic perspective on academic achievement.
Analisis Sentimen Review Film Avatar 2 pada Platform IMDb Menggunakan LSTM dan GRU Rani Saputri; Anna Baita
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.395

Abstract

This research uses a deep learning-based sentiment analysis approach with several main stages, namely data collection, preprocessing, model preparation, and model building. In addition, this research also evaluates the impact of data splitting techniques on the model's performance during the training process.The evaluation results show that the LSTM-GRU model achieved the best performance on the character aspect, with an F1-score of 0.72 in the 90:10 splitting scenario. Meanwhile, the lowest F1-score was found in the editing aspect, with a value of 0.51 in the 80:20 splitting scenario. These findings indicate that the model is more effective in recognizing sentiment in narrative aspects compared to technical aspects. Furthermore, the data splitting technique significantly influences model performance, both in determining accuracy levels and in optimizing the model's effectiveness in identifying sentiment patterns more accurately.
Predicting Hotel Booking Cancellations Using Machine Learning for Revenue Optimization Andy Hermawan; Aji Saputra; Nabila Lailinajma; Reska Julianti; Timothy Hartanto; Troy Kornelius Daniel
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i1.400

Abstract

Hotel booking cancellations pose significant challenges to the hospitality industry, affecting revenue management, demand forecasting, and operational efficiency. This study explores the application of machine learning techniques to predict hotel booking cancellations, leveraging structured data derived from hotel management systems. Various classification algorithms, including Random Forest, XGBoost, and LightGBM were evaluated to identify the most effective predictive model. The findings reveal that XGBoost model outperforms other models, achieving F2-score of 0.7897. Key influencing factors include deposit type, total number of special requests, and marketing segment. The results underscore the potential of predictive modeling in optimizing hotel revenue strategies by enabling proactive measures such as dynamic pricing, targeted customer engagement, and improved overbooking policies. This study contributes to the ongoing advancements in data-driven decision-making within the hospitality industry, offering insights into how machine learning can mitigate financial risks associated with booking cancellations.

Page 1 of 1 | Total Record : 4

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 3 (2025): Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 2 (2025): Juni: Router : Jurnal Teknik Informatika dan Terapan  Vol. 3 No. 1 (2025): Maret: Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 4 (2024): Desember: Router: Jurnal Teknik Informatika dan Terapan Vol. 2 No. 3 (2024): September : Router : Jurnal Teknik Informatika dan Terapan Vol. 2 No. 2 (2024): Juni : Router: Jurnal Teknik Informatika dan Terapan Vol. 2 No. 1 (2024): Maret : Router: Jurnal Teknik Informatika dan Terapan Vol. 1 No. 2 (2023): Juni: Router: Jurnal Teknik Informatika dan Terapan