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Jurnal Teknologi dan Manajemen Informatika
Published by Universitas Merdeka Malang
ISSN : 16936604     EISSN : 25808044     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Jurnal Teknologi dan Manajemen Informatika (JTMI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Merdeka Malang. JTMI terbit 2 edisi per tahun pada Januari - Juni dan Juli - Desember dengan scope ilmu komputer yang mencakup teknologi informasi, sistem informasi, dan manajemen informatika.
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Articles 10 Documents
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Search results for , issue "Vol. 11 No. 2 (2025): Desember 2025" : 10 Documents clear
Development of a Web-Based and Mobile Psychological Consultation Queue Service at DP3AKB Balikpapan with API Ihsan; Sari, Danar Retno; Armin; Aditya, Angga Wahyu; Zulkarnain; Irawan, Candra
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16081

Abstract

The increasing demand for effective public services necessitates a more efficient queue management system. This study aims to design and implement a real-time online queue application for web and mobile platforms, using the Django and Flutter frameworks, and integrating Application Programming Interfaces (APIs) as a bridge between the two platforms. The methodology applied is software engineering with a case study approach at Puspaga Harapan, Balikpapan City. The system design includes digitizing the registration, validation, consultation scheduling, and psychological reporting stages. The developed application enables users to register independently and provides administrators and psychologists with real-time access to manage client data through a responsive and secure interface. The results of the study, based on 19 respondents who assessed 15 questions via a Likert scale questionnaire, with an average percentage of 94.73%, indicate very satisfactory results. This suggests that the system improves service efficiency, speeds up the registration and consultation process, and produces more accurate report data. The development of this system can overcome various obstacles from manual systems and support digital transformation in psychological consultation services.
Ashabul Kahfi Serious Game with Adaptive Recommendation System Based on Knowledge-Based Filtering and MULTIMOORA for Islamic Education Yuniar Setyo Marandy; Fresy Nugroho; Ririen Kusumawati
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16082

Abstract

This study aims to develop a serious game based on the story of Ashabul Kahfi using a difficulty level recommendation system based on Knowledge-Based Filtering (KBF) and the MULTIMOORA method. This game is designed for elementary school students in the context of Islamic religious education, instilling moral and spiritual values through the narrative of the story of Ashabul Kahfi. The difficulty level in the game is adjusted to the player's profile based on age, experience, and preferences obtained through a questionnaire. The MULTIMOORA method is applied to process questionnaire data and provide adaptive and personalized difficulty level recommendations. The results of the study show that the application of this recommendation system is able to increase student learning motivation and learning effectiveness by providing challenges that are appropriate to each player's abilities. Thus, this study contributes to the development of adaptive and effective game-based learning media, particularly in improving the understanding of religious values among students.
Application of YOLO11 and Long Short-Term Memory Architecture for Exercise Form Evaluation in Weightlifting Dylan Lienardi , Nicholas; Evan Tanuwijaya
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16112

Abstract

Exercise provides significant benefits for physical health, and weightlifting has become increasingly popular among fitness enthusiasts. However, improper lifting techniques often lead to injuries, discouraging beginners and affecting long-term training consistency. To address this issue, this study proposes a deep learning approach that automatically evaluates weightlifting form through movement classification. The proposed method integrates the YOLO11n-pose algorithm for detecting keypoints from exercise video recordings and the Long Short-Term Memory (LSTM) network for classifying movement types and determining the correctness of form execution. The model achieved a mean average precision of 88.8% using side-view recordings of single- repetition weightlifting exercises. YOLO11n-pose extracts the coordinates of body keypoints, which are converted into joint angle data and analyzed over time using LSTM to identify movement quality based on expert-validated training data. The trained model was implemented into an iOS application called KorForm, developed using FastAPI, to provide real-time feedback for users. The results demonstrate that combining YOLO11n-pose and LSTM effectively supports weightlifting form evaluation and offers a practical solution for promoting safer and more consistent exercise habits.
New Approach: Customer Segmentation using RFM Model and Demand Classification Fewie Rusly; Ronsen Purba; Muhammad Fermi Pasha
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16208

Abstract

This research introduces an integrated data mining framework that combines RFM (Recency, Frequency, Monetary) analysis with demand pattern classification—encompassing Smooth, Erratic, Intermittent, and Lumpy categories—to refine customer segmentation strategies. While RFM effectively captures transactional behavior, its scope remains insufficient as it overlooks demand variability and intermittency, which critically influence purchasing dynamics and inventory planning. By incorporating demand classification, this model addresses behavioral dimensions beyond conventional transactional metrics, thereby enhancing segmentation precision and strategic relevance. Customer clustering employs the K-Means algorithm, with cluster optimization validated through Elbow Method and Silhouette Index analyses, yielding five distinct segments: Ideal, Interest, Improve, Inconsistent, and Inactive. Subsequently, Customer Lifetime Value (CLV) is computed by weighting RFM and demand parameters via Analytic Hierarchy Process (AHP), with Consistency Index and Consistency Ratio assessments ensuring methodological rigor. Results are synthesized within an interactive dashboard, facilitating data-driven decision-making in retention strategies, inventory optimization, profitability enhancement, and sustainable business development.
Implementation of the CNN-LSTM Hybrid Model in Predicting Bitcoin Price Fluctuations Candra Wibowo; Ronsen Purba; Muhammad Fermi Pasha
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16239

Abstract

Digital financial systems of today face formidable obstacles from the extreme price volatility and unpredictability of Bitcoin. Data cleaning, Min-Max normalization, and sequence creation with a sliding window were performed on the daily BTC-USD historical data received from Yahoo Finance from 2020 to 2024 before implementing a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model in this study. The CNN layers are responsible for extracting local patterns with a limited time horizon, whereas the LSTM layers are responsible for capturing the time series' long-term relationships. The experimental findings show that the CNN-LSTM model outperforms the CNN and LSTM in terms of predictive ability, with an RMSE of 2,202.717, an MAE of 1,553.202, and a MAPE of 2.244%, which translates to an accuracy of about 97.756%. These results provide useful information for adaptive trading techniques and digital asset risk management based on artificial intelligence, and they prove that the hybrid method is successful in dealing with complicated, non-linear, and unpredictable trends in the cryptocurrency market.
Comparative Performance of Machine Learning Algorithms for Detecting Online Gambling Promotional Comments on Youtube Michael Angelo; Robet; Hendrik, Jackri
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16286

Abstract

Online-gambling promoters increasingly exploit YouTube comment sections, using text obfuscation, Unicode characters, emojis, irregular spacing, and symbols to evade automated moderation. This study aims to identify the most effective machine-learning algorithm for detecting such promotional comments by comparing models on standard metrics (precision, recall, F1-score, accuracy). We employ semi-supervised pseudo-labelling to expand the labelled set from 1,648 to 9,111 comments without additional manual annotation, admitting only high-confidence predictions. The pipeline includes customised character normalization, selective cleaning, tokenization, stopword removal, and Nazief–Adriani stemming, followed by TF–IDF feature extraction. Four algorithms are evaluated: Multinomial Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine, with hyperparameter optimization and class balancing via SMOTE. On a 1,823-sample test set, all models achieve over 98% accuracy; SVM yields the most balanced performance, resulting in the highest F1-score for the promotion class (0.9908). Confusion matrices and learning curves indicate stable behavior without overfitting or underfitting. We therefore recommend SVM for operational deployment in automated moderation of gambling-promotion comments on YouTube. These findings provide practical guidance for platform safety teams and suggest methodological baselines for similar NLP moderation tasks. Future work should explore ensemble and deep learning approaches, incorporate character and subword-level features, and further evaluate robustness under adversarial obfuscation and domain shift.
Rule-Based Pitch Inference in Optical Music Recognition on Polyphonic Scores using YOLOv12 Derend Marvel Hanson Prionggo; Tanuwijaya, Evan
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16291

Abstract

Optical Music Recognition (OMR) faces significant challenges when applied to polyphonic music scores, due to the high symbol density and the overlapping of notes. This study proposes a hybrid method of combining the detection of noteheads using YOLOv12 with rule-based pitch inference, which converts the spatial position of the detected noteheads into accurate pitch information. The dataset used in this study is DeepScoresV2-Dense, which is processed through annotation conversion, image normalization, and staff extraction as a reference to infer the pitch of a note. The YOLOv12 model was trained for 30 epochs using a transfer learning approach, resulting in an mAP50 value of 0.75, a precision of 0.85, and a recall of 0.58 on the validation data. The implementation of rule-based pitch inference successfully achieved a pitch accuracy of 0.87 with an F1 score of 0.87, demonstrating a balance between accuracy and completeness of prediction. This result shows that the integration of YOLOv12 and rule-based pitch inference can be an effective solution for pitch extraction in polyphonic music scores, with potential applications in music information retrieval, digital music score conversion, and an artificial intelligence-based music learning system.
Sentiment Analysis Of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods Nabil, Muh; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16311

Abstract

This research analyzed user sentiment toward the NTB Syariah application using Support Vector Machine (SVM) and Naïve Bayes classification methods. A dataset comprising 814 reviews was obtained via web scraping, with 245 allocated for testing. Preprocessing encompassed cleaning, case folding, tokenization, filtering, and stemming, while sentiment labeling employed a lexicon-based approach integrated with TF-IDF weighting, categorizing reviews as positive, neutral, or negative. Model performance was assessed through accuracy, precision, recall, and F1-score metrics. Results demonstrated SVM's superior performance (accuracy: 92.65%; precision: 0.9327; recall: 0.9265; F1-score: 0.9149) compared to Naïve Bayes (accuracy: 84.49%; precision: 0.8415; recall: 0.8449; F1-score: 0.8005). SVM exhibited greater robustness in managing high-dimensional, complex, and moderately imbalanced datasets, delivering consistent cross-class sentiment classification. Conversely, Naïve Bayes remained computationally efficient and suitable for rapid implementation scenarios. These findings underscore machine learning's efficacy in sentiment analysis for digital banking platforms.
Optimized LightGBM Model for Predicting Total Cup Points of Arabica Coffee using Sensory Cupping Data Arya Rezagama Sudrajat; Ricardus Anggi Pramunendar; Mohammad Syaifur Rohman
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16348

Abstract

Evaluating coffee quality through sensory cupping is essential but inherently subjective, as scoring depends on the consistency and expertise of professional panelists. To improve objectivity, this study applies the Light Gradient Boosting Machine (LightGBM) algorithm to predict the Total Cup Points of Arabica coffee using sensory evaluation data. The dataset, obtained from the Coffee Quality Institute Arabica Reviews (May 2023), contains 1,509 cupping records assessed according to the Specialty Coffee Association (SCA) protocol. Nine sensory attributes aroma, flavor, aftertaste, acidity, body, balance, uniformity, clean cup, and sweetness were used as predictors. The modeling process included data preprocessing, feature selection, hyperparameter tuning using RandomizedSearchCV, and performance evaluation through 5-Fold and 10 Fold Cross-Validation. The tuned LightGBM model achieved an R² of 0.9634 and an RMSE of 0.4673 under the 10-Fold scheme. Comparative analysis showed that LightGBM produced lower prediction error than XGBoost, Random Forest, and Support Vector Regression (SVR) when evaluated under identical default parameter settings. Feature importance indicated that flavor, balance, clean cup, and aftertaste were the most influential contributors to total cup points. The findings provide a reliable computational framework to support more objective, consistent, and efficient coffee cupping assessments
Market Value Tier Classification of Indonesian Football Players using Ensemble Machine Learning and SHAP Analysis Paramita, Cinantya; Wildan Akhya, Malfino; Nurtantio Andono, Pulung
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16399

Abstract

The persistent discrepancy between actual transfer fees and the theoretical market values of football players highlights the need for a more objective and data-driven framework for player valuation. This study aims to classify the market value tiers of Indonesian Liga 1 players in the 2024/2025 season using an ensemble-based machine learning approach integrated with SHAP interpretability analysis. The dataset comprises 226 players with 27 attributes encompassing demographic, career, performance, physiological, and socio-economic dimensions. The research process involved secondary data collection, preprocessing, feature engineering, and percentile-based label construction, followed by model training using Random Forest, XGBoost, CatBoost, and a Stacking Ensemble. Experimental results show that the CatBoost model achieved the best performance, attaining an accuracy of 89%, a Macro-F1 score of 0.85, and an F1(High-Tier) of 0.78, demonstrating its robustness in handling heterogeneous and imbalanced data. SHAP analysis identified minutes played, age, and social media exposure as the most influential variables determining market value tiers. These findings demonstrate that combining ensemble learning with model interpretability can yield a transparent, adaptive, and practical framework for data-driven player valuation. The proposed approach provides actionable insights for football clubs and analysts in optimising player recruitment and developing fairer, evidence-based transfer strategies.

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