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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Elektro Tadris: Jurnal keguruan dan Ilmu Tarbiyah Sinergi Jurnal Penelitian dan Pembelajaran IPA Economic Education Analysis Journal IJIE (Indonesian Journal of Informatics Education) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Diglosia: Jurnal Kajian Bahasa, Sastra, dan Pengajarannya Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Community Empowerment Journal of Soft Computing Exploration TIERS Information Technology Journal MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Pendidikan Indonesia (Japendi) Information Technology Education Journal Journal of Mandalika Literature Jurnal Bina Desa Jurnal Abdi Negeri
Yahya Nur Ifriza
Information System Study Program, Universitas Negeri Semarang, Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Optimization CatBoost using GridSearchCV for Sentiment Analysis Customer Reviews in Digital Transportation Industry Ifriza, Yahya Nur; Sanusi, Ratna Nur Mustika; Febriyanto, Hendra; Kamaruddin, Azlina
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7201

Abstract

The rapid expansion of ride-hailing services has generated a massive volume of user feedback, making automated sentiment analysis essential for understanding customer satisfaction. This study aims to classify public sentiment towards the Uber application into positive, neutral, and negative categories using the CatBoost algorithm, a gradient boosting method prioritized for its Ordered Boosting mechanism, which effectively prevents overfitting and enhances the model's generalization capabilities. Despite the use of TF-IDF for numerical text representation, CatBoost is selected for its superior performance on heterogeneous datasets compared to other boosting frameworks like XGBoost and LightGBM. The dataset comprises customer reviews collected 12.000 from the Google Play Store between January and March 2024 using web scraping techniques upload in Kaggle. The data underwent rigorous preprocessing, including lemmatization and TF-IDF vectorization, to structure the textual features, to maximize model performance, hyperparameter optimization was conducted using GridSearchCV. The experimental results demonstrate that the optimization process successfully improved the model's generalization capabilities, raising the Accuracy from 0.907 to 0.910 and the F1-Score from 0.893 to 0.897. Most significantly, the AUC score increased from 0.949 to 0.957, indicating a superior ability to distinguish between sentiment classes. However, while the model exhibited high precision in identifying positive and negative polarities, analysis of the confusion matrix revealed limitations in correctly predicting the neutral class, suggesting challenges related to class imbalance. These findings confirm that an optimized CatBoost model is a robust tool for sentiment classification, though future work is recommended to address minority class detection.
Enhanced Wind Turbine Power Forecasting via Hyperparameter-Optimized XGBoost Dimas Ramadhani; Yahya Nur Ifriza
Information Technology Education Journal Vol. 5, No. 2, May (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i2.278

Abstract

Purpose – This study aims to evaluate the accuracy and computational efficiency of XGBoost in forecasting wind turbine output using a dataset aligned at hourly timestamps. This topic is important because wind turbine output exhibits fluctuating and non-linear patterns, requiring a model capable of capturing the relationship between meteorological conditions, historical turbine patterns, and the generated Energy values. Design – This study uses the 2016 Sotavento Galicia data, which consist of hourly Numerical Weather Prediction (NWP) data and historical turbine operational data originally recorded every 10 minutes. Temporal alignment was performed by retaining only turbine operational records located exactly at hourly timestamps and then merging them with the NWP data at the same timestamps. The final dataset was modeled as an hourly aligned time series dataset. The Energy variable was used as the prediction target. Since the dataset does not explicitly state the unit of Energy, RMSE and MAE were reported in the original scale of the Energy variable and cautiously interpreted as kWh per retained 10-minute record based on the variable label, recording resolution, and value range. Three model scenarios were compared, namely the XGBoost baseline, XGBoost with GridSearchCV, and XGBoost with RandomizedSearchCV. Internal validation was performed using TimeSeriesSplit, while final testing was conducted using monthly holdout on months 10, 11, and 12. Findings – The results show that XGBoost with RandomizedSearchCV produced the lowest average prediction error, with an RMSE of 135.591, MAE of 87.710, and R² of 0.907. This model reduced RMSE by 5.86% compared to the XGBoost baseline and reduced computation time by 69.51% compared to GridSearchCV. Research implications – These findings are limited to a single wind farm dataset, one observation period, and a constrained hyperparameter search space. Originality – This study demonstrates that RandomizedSearchCV can serve as an efficient tuning strategy for XGBoost-based wind power forecasting.
Analysis of Relationship Between Self Efficacy and Resilience on Kip-Kuliah Students Learning Outcomes Yahya Nur Ifriza; Istijabah Ifti Mufsiroh; Amalina Shabrina; Nurul Faizah; Sri Murti Retnoningrum
Jurnal Pendidikan Indonesia Vol. 6 No. 5 (2025): Jurnal Pendidikan Indonesia (Japendi)
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i5.7796

Abstract

The KIP-Kuliah scholarship program aims to support underprivileged Indonesian students in higher education, yet the psychological factors influencing their success remain underexplored. This study investigates the interplay between self-efficacy and resilience in shaping academic and non-academic outcomes among KIP-Kuliah recipients. The research aims to (1) analyze the relationship between self-efficacy and resilience, and (2) assess their combined impact on learning achievements. A mixed-methods approach was employed, combining quantitative surveys (n=100) and qualitative interviews (n=10) with KIP-Kuliah students at Universitas Negeri Semarang. Statistical analyses (correlation, regression) and thematic interviews were conducted. Results revealed a strong positive correlation (r=0.678, p<0.01) between self-efficacy and resilience. Qualitative data underscored the role of financial aid, social support, and organizational involvement in enhancing these traits. The study highlights the need for integrated support programs that address both financial and psychological barriers. Recommendations include tailored mentoring and policy enhancements to maximize the KIP-Kuliah program’s impact.
Modelling of Laboratory Information Systems in Higher Education Based on Enterprise Architecture Planning for Optimizing Monitoring and Equipment Maintenance Ifriza, Yahya Nur; Veronika, Trisni Wulandari; Suryarini, Trisni; Supriyadi, Antonius
IJIE (Indonesian Journal of Informatics Education) Vol 6, No 2 (2022): IJIE (Indonesian Journal of Informatics Education)
Publisher : Universitas Sebelas Maret

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Abstract

Retracted on author's request 
Optimizing Javanese script recognition using fine-tuned ResNet-18 and transfer learning Nur Fateah; Subhan Subhan; Yahya Nur Ifriza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp443-453

Abstract

Javanese script, known as Aksara Jawa, is an ancient script used in historical and cultural texts. However, its complex character structure poses challenges for accurate recognition in modern digital applications. This study proposes an optimized classification approach for Aksara Jawa using a fine-tuned ResNet-18 model combined with the Adam optimization algorithm and transfer learning on the Hanacaraka image dataset. By leveraging the residual learning framework of ResNet-18, the model effectively captures deep spatial features of the script while reducing vanishing gradient issues. Fine-tuning is applied to enhance model adaptability, ensuring robust feature extraction specific to Javanese characters. Experimental results demonstrate that the fine-tuned ResNet-18 outperforms conventional deep learning architectures in recognizing Aksara Jawa characters, achieving 93% precision, 91% recall, 91% F1-score, and 91% accuracy. The study further explores the impact of hyperparameter tuning, data augmentation, and dropout regularization on model performance. The findings highlight the effectiveness of transfer learning in resource-limited scenarios, making it a feasible solution for optical character recognition (OCR) applications in Javanese script digitization. This research contributes to the preservation of cultural heritage through advancements in deep learning-based script recognition.
The modeling of laboratory information systems in higher education based on enterprise architecture planning (EAP) for optimizing monitoring and equipment maintenance Yahya Nur Ifriza; Trisni Wulandari Veronika; Trisni Suryarini; Antonius Supriyadi
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 13 No. 1 (2023): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v13i1.1-11

Abstract

The laboratory is a place to conduct scientific research, experiments, measurements, or scientific training. FMIPA UNNES has several laboratories distributed in each department to support student lectures. Through the implementation of practicum in the laboratory, students are expected to be able to find a concept, foster scientific attitudes, and critical thinking skills. Good laboratory management is expected to be able to utilize laboratory resources effectively and efficiently. Laboratory equipment must be ensured to function properly and be ready to be used for practicum. To support this, it is necessary to monitor the condition of the equipment and immediately repair the equipment if any damage is found. The current obstacle is monitoring tool repairs manually, so there are shortcomings such as poor documentation, and equipment conditions that cannot be monitored online. In this study, an information system for monitoring the maintenance of laboratory equipment in the departments in the FMIPA UNNES environment will be built. The research method begins with a literature study, initial data collection and observation, EAP-based system design, system testing, system analysis, and system evaluation. This study uses the SDLC (System Development Life Cycle) approach which is used to develop a product for the Monitoring Information System for the Maintenance of Laboratory Equipment. Testing is done using black box testing. From the results of development and testing, it can be concluded that the system can be used to simplify the process of managing laboratory equipment with a UAT value of 88% suitable for use.
Gradient Boosting Models with Optuna Hyperparameter Optimization for Contemporaneous Wind Turbine Active Power Estimation at Esenkoy Wind Farm Muhammad Naufal Rustiawan; Yahya Nur Ifriza
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.13674

Abstract

Purpose – Wind power estimation is critical for grid stability. This study tests whether Bayesian-tuned gradient boosting, using a leakage-safe pipeline, can estimate turbine power without the Theoretical Power Curve (TPC), benchmarked against LSTM. Design/methods/approach – The study uses Esenkoy SCADA and 2018 MERRA-2 weather data (8,760 hourly observations), split before any transformation; outlier bounds are fitted on training data only, and this dataset needed no imputation. TPC is excluded as a redundant, deterministic function of wind speed. Four gradient boosting models are tuned via Optuna-TPE with 5-fold CV; an LSTM uses identical features and evaluation. Findings – LightGBM has the lowest full-test RMSE (364.17 kW), but CatBoost (385.88 kW) has significantly lower median error (p<0.0001); metrics disagree on the best model, and LightGBM shows a markedly larger train-test gap, consistent with overfitting. CatBoost and GBM outperform LSTM on normal-operation data (p<0.05), while AdaBoost and LightGBM do not. Permutation importance shows wind speed drives over 87% of predictive signal despite differing built-in measures. CatBoost beats LSTM by 18.3% NRMSE on normal-operation data, narrowing to 2.3-9.6% on full data. A seven-seed check confirms these full-test and normal-operation advantages, though intervals overlap. Research implications/limitations – Data cover 2018 at one Turkish site, limiting generalizability; a random split misses temporal shifts, and 50 trials may not fully explore hyperparameter space. Originality/value – This leakage-safe SCADA pipeline shows gradient boosting modestly but significantly outperforms LSTM, with gains depending on abnormal-condition inclusion. Future work should apply temporal cross-validation, test more sites, and tune LSTM more rigorously.
Co-Authors Abdul Jabbar Agung Kuswantoro Alamsyah - Amalina Shabrina Antonius Supriyadi Azkiya, M. Akiyasul Budi Prasetiyo, Budi Budiman, Kholiq Damayanti, Dela Rista Decky Avrilianda Dhela Septian Anggaretta Dimas Ramadhani Djuniadi Djuniadi Efrilianda, Devi Ajeng Erna Noor Savitri, Erna Noor Fateah, Nur Febriyanto, Hendra Hakim, M Faris Al Hakim, M. Faris Al Istijabah Ifti Mufsiroh Jumanto Jumanto Jumanto, Jumanto Kamaruddin, Azlina Masfufati Azizah Mu'arifuddin, Mu'arifuddin Muarifuddin Muarifuddin, Muarifuddin Much Aziz Muslim Muhammad Naufal Rustiawan Muhammad Sam&#039;an Muhammad Sam’an Niam Wahzudik Nina Oktarina Nur Fateah Nur Fateah Nurul Faizah Parmin - Parmin Parmin Parmin, Parmin Prabowo Yudo Jayanto Purasani, Hana Netti Raditya Ahmad Rifandi Rahayu, Endah Fitriani Rakainsa, Senda Kartika Riza Arifudin Sam'an, Muhammad Sam’an, Muhammad Sanusi, Ratna Nur Mustika Satrio Ardiansyah, Adi Siminto Siminto Siminto Siminto, Siminto Sri Murti Retnoningrum Subhan Subhan Subhan Subhan Subhan Sudarno Sudarno Supriyadi, Antonius Syarif, Deny Lukman Trisni Suryarini Trisni Wulandari Veronika Veronika, Trisni Wulandari Wicaksono, Suntoro Widhihastuti, Endah Widhiya Ninsiana Wisudani Rahmaningtyas Yosza Dasril Yusuf Wisnu Mandaya