cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Eko Fajar Cahyadi
Contact Email
ekofajarcahyadi@ittelkom-pwt.ac.id
Phone
+6285384848666
Journal Mail Official
infotel@ittelkom-pwt.ac.id
Editorial Address
Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Institut Teknologi Telkom Purwokerto Jl. D. I. Panjaitan, No. 128, Purwokerto 53147, Indonesia
Location
Kota bandung,
Jawa barat
INDONESIA
Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 14 Documents
 1   2 
Search results for , issue "Vol 17 No 4 (2025): November" : 14 Documents clear
Forecasting the Stock Price of PT Unilever Indonesia Using the ARCH-GARCH Model with the Application of Kalman Filter Sausan Sausan; Atika Ratna Dewi; Aminatus Sa’adah
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1408

Abstract

PT Unilever Indonesia experiences significant stock price volatility driven by both internal and external factors. This volatility underscores the need for accurate forecasting methods to support investment decision-making and risk management. This study aims to forecast the company’s stock prices using ARCH-GARCH models, enhanced with the Kalman Filter to improve predictive performance. Daily historical stock price data were obtained from the yfinance library. The research methodology consists of several stages, including literature review, data collection, exploratory data analysis (EDA), data preprocessing, forecast modelling, and evaluation. Among the evaluated models, the GARCH(1,2) with a skewed Student’s t error distribution was identified as the best-fitting model, achieving an AIC value of -5.476981. The initial forecast using the GARCH model produced a MAPE of 49.47%, RMSE of 45.56%, and MAE of 37.16%. After applying the Kalman Filter, the model’s forecasting performance improved substantially, with MAPE decreasing to 6.04%, RMSE to 6.01%, and MAE to 5.02%. These results demonstrate the effectiveness of the Kalman Filter in reducing noise, dynamically updating predictions, and enhancing the model’s responsiveness to market fluctuations.
Identifying the Learning Style of Students Using Reinforcement Learning Techniques NESI SYAFITRI; Suzani Mohamad Samuri; Yudhi Arta
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1409

Abstract

Understanding how students learn is key to making education more effective. This research presents an innovative, automated method for identifying students’ learning styles using artificial intelligence. This research employed the Felder–Silverman Learning Style Model (FSLSM), which examines how students process information, prefer input (visual or verbal), and comprehend concepts. Instead of asking students to fill out long forms every time, this study trained a Q-Learning agent, a type of reinforcement learning, to recognize learning patterns directly from questionnaire data. This study tested this approach using data from 799 students from various universities in Indonesia. The results showed that the model could accurately predict learning styles in almost every case, particularly in how students process and understand information. In a follow-up test with 50 students, the model achieved 100% accuracy, matching traditional FSLSM assessments perfectly. This demonstrates that Q-Learning can be a powerful tool for automatically identifying learning styles. It opens up new possibilities for creating personalized and adaptive learning systems that adjust materials and methods based on each student’s unique style. Moving forward, the system can be improved to better handle cases where certain learning styles are underrepresented
Machine Learning-Based Malware Detection: A Critical Comparative Analysis of Random Forest, Naive Bayes, and Neural Network on Imbalanced Datasets Muhamad Hanif Rafiq Sulaeman; Rakhmad Maulidi
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1413

Abstract

Malware detection remains a major challenge in cybersecurity as threats become increasingly complex. This study critically compares three machine learning algorithms Random Forest, Naive Bayes, and Neural Network for automated malware detection using a large, imbalanced dataset (131,574 samples, 57 features). Class imbalance is addressed with SMOTE (Synthetic Minority Oversampling Technique), and preprocessing includes feature selection (SelectKBest), normalization (StandardScaler), and outlier handling. Evaluation metrics include accuracy, Precision, recall, F1-score, and AUC-ROC, using 5-fold cross-validation. Results show Random Forest achieves the highest accuracy (98%, AUC-ROC 0.998), followed by Neural Network (95%, AUC-ROC 0.95), and Naive Bayes (93%, minority class recall 0.80). Feature analysis identifies ImageBase and ResourcesMinSize as key contributors. This study highlights the effectiveness of ensemble methods and the critical importance of addressing class imbalance for robust malware detection. Limitations and implications for real-world deployment are discussed.
IoT-Based: Smart Hydroponic Farming with SSD MobileNet and Fuzzy Logic Arnisa Stefanie; Lela Nurpulaela; Yuliarman Saragih; Safrian Andromeda; Muhammad Fachri Azizi; Muhammad Rifqi Setyanto; Selfi Arfianti; Sandi Sandi
JURNAL INFOTEL Vol 17 No 4 (2025): November
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i4.1424

Abstract

Traditional hydroponic systems largely rely on manual observation and regulation of essential environmental variables, such as pH, nutrient concentration, temperature, and humidity. This dependence often causes inefficiency, inconsistent crop quality, and greater labor requirements. To overcome these limitations, this study proposes an IoT-based Smart Hydroponic System that integrates fuzzy logic control with computer vision using the SSD MobileNet architecture. The objective of this research is to design and implement an intelligent automation framework capable of improving hydroponic cultivation through continuous data monitoring, analytical decision-making, and autonomous environmental adjustment. Within this framework, fuzzy logic dynamically stabilizes nutrient and pH levels, while the SSD MobileNet model analyzes plant images to classify growth stages and determine harvest readiness. Experimental testing produced an average classification loss of 0.1283, demonstrating reliable detection accuracy. Compared with conventional methods, the proposed integration enhances adaptability, precision, and computational efficiency for edge-level IoT applications. This system introduces a novel and scalable approach to precision agriculture, enabling more effective automation and decision making in hydroponic farming. Future studies are encouraged to expand their implementation to various plant species and adaptive learning models for broader applicability.

Page 2 of 2 | Total Record : 14

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 18 No 1 (2026): February Vol 17 No 1 (2025): February 2025 Vol 17 No 4 (2025): November Vol 17 No 3 (2025): August Vol 17 No 2 (2025): May Vol 16 No 4 (2024): November 2024 Vol 16 No 3 (2024): August 2024 Vol 16 No 2 (2024): May 2024 Vol 16 No 1 (2024): February 2024 Vol 15 No 4 (2023): November 2023 Vol 15 No 3 (2023): August 2023 Vol 15 No 2 (2023): May 2023 Vol 15 No 1 (2023): February 2023 Vol 14 No 4 (2022): November 2022 Vol 14 No 3 (2022): August 2022 Vol 14 No 2 (2022): May 2022 Vol 14 No 1 (2022): February 2022 Vol 13 No 4 (2021): November 2021 Vol 13 No 3 (2021): August 2021 Vol 13 No 2 (2021): May 2021 Vol 13 No 1 (2021): February 2021 Vol 12 No 4 (2020): November 2020 Vol 12 No 3 (2020): August 2020 Vol 12 No 2 (2020): May 2020 Vol 12 No 1 (2020): February 2020 Vol 11 No 4 (2019): November 2019 Vol 11 No 3 (2019): August 2019 Vol 11 No 2 (2019): May 2019 Vol 11 No 1 (2019): February 2019 Vol 10 No 4 (2018): November 2018 Vol 10 No 3 (2018): August 2018 Vol 10 No 2 (2018): May 2018 Vol 10 No 1 (2018): February 2018 Vol 9 No 4 (2017): November 2017 Vol 9 No 3 (2017): August 2017 Vol 9 No 2 (2017): May 2017 Vol 9 No 1 (2017): February 2017 Vol 8 No 2 (2016): November 2016 Vol 8 No 1 (2016): May 2016 Vol 7 No 2 (2015): November 2015 Vol 7 No 1 (2015): May 2015 Vol 6 No 2 (2014): November 2014 Vol 6 No 1 (2014): May 2014 Vol 5 No 2 (2013): November 2013 Vol 5 No 1 (2013): May 2013 Vol 4 No 2 (2012): November 2012 Vol 4 No 1 (2012): May 2012 Vol 3 No 2 (2011): November 2011 Vol 3 No 1 (2011): May 2011 Vol 2 No 2 (2010): November 2010 Vol 2 No 1 (2010): May 2010 Vol 1 No 2 (2009): November 2009 Vol 1 No 1 (2009): May 2009 More Issue