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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 488 Documents
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Privacy-Preserving Automated QA Dataset Generation for Fine-Tuning LLMs with Local Models and Information Retrieval Ary Suryadi; Dedi Dwi Saputra; Windu Gata; Riza Fahlapi; Angge Firizkiansah; Nuryani Mawar Putri
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.v17i4.1388

Abstract

This paper introduces a novel framework for automated question-answering (QA) dataset construction, integrating information retrieval (IR) with a lightweight local large language model (LLM), SmolLM2- 360M-Instruct, to ensure privacy and scalability for domain-specific applications. Addressing the limitations of manual dataset creation and cloud-based LLMs, our approach leverages PyPDF2 for robust PDF text extraction and a novel sentence segmentation algorithm to generate concise, contextually relevant QA pairs from domain-specific corpora. The framework employs IR techniques to align questions with precise answers, enhancing dataset quality while maintaining data privacy through localized processing. Rigorous evaluation using automated metrics and manual expert review confirms the high quality and semantic alignment of the generated QA pairs. This approach offers significant benefits for fine-tuning LLMs in niche domains, such as education and technical support, by providing scalable, privacy-preserving datasets that improve contextual understanding and adaptability. Our work contributes to efficient NLP dataset generation, offering a robust solution for advancing LLM performance in specialized real-world applications.
IOT-Based Electrical Energy Consumption Monitoring Application on Machine Tools Putra Bismantolo; Kurnia Anggriani; Nurul Iman Supardi; Gusta Gunawan; Emilio Oktori
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.v17i4.1395

Abstract

Deep Learning for Periodontitis Diagnosis on Two Dimensional Dental Radiograph: A Systematic Review Jordan Valentino Lomanto; Monica Widiasri
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.v17i4.1401

Abstract

Periodontitis is an inflammatory disease that affects the supporting structures of the teeth and is a major contributor to tooth loss. Traditional diagnosis through clinical examination and manual interpretation of two-dimensional (2D) dental radiographs is prone to variability and subjectivity. The emergence of deep learning (DL) offers a powerful tool in medical image analysis, including dental radiography. This study aims to systematically review the existing literature on the use of DL approaches for diagnosing periodontitis using two-dimensional (2D) dental radiographic images, and to assess their diagnostic effectiveness in comparison to conventional clinician-based evaluation. A systematic literature review (SLR) was conducted following the PRISMA 2020 protocol and guided by the PICO framework. Five major databases (Scopus, PubMed, Semantic Scholar, Web of Science, and ScienceDirect) were searched for relevant studies published between 2016 and 2025. A total of 27 studies (across 29 reports) were included based on eligibility criteria, covering classification, segmentation, or detection tasks using panoramic, periapical, or bitewing radiographs. The results indicate that DL models show high diagnostic potential, with classification accuracies often exceeding 80% and segmentation models achieving Dice coefficients above 0.88. Although some models outperformed clinicians, external validation and real-world deployment remain limited. This review highlights both the diagnostic potentials and present limitations of DL in 2D dental radiographs. In conclusion, DL shows substantial promise for automated periodontitis diagnosis using 2D radiographs, though challenges still remain in standardization, external validation, and integration into clinical workflows.
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.
Evaluating The Impact of Social Media Sentiment on University Enrollment Decisions Using Machine Learning Classifier Painem, Painem; Soetanto, Hari; Solichin, Achmad; Nair, Anju A
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1390

Abstract

Public sentiment expressed through social media is increasingly recognized as a potential factor influencing higher education enrollment decisions. This study investigates whether sentiments on Twitter regarding Universitas Budi Luhur correlate with the number of new student admissions. To achieve this, tweet data were collected and analyzed using four supervised machine learning algorithms—Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbor (KNN), and Logistic Regression (LR)—combined with two lexicon-based sentiment dictionaries: SentiWord and InSet. Experimental results demonstrate that the SentiWord-based approach consistently outperformed the InSet-based approach across all models, with the SVC-SentiWord combination achieving the highest F1-score of 0.86. Despite the strong performance of these models in classifying sentiment, correlation analysis reveals no statistically significant relationship between Twitter sentiment and actual student enrollment trends. These findings underscore the effectiveness of lexicon-enhanced machine learning in sentiment analysis while raising important questions about the real-world impact of online sentiment on university admissions.

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