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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sistem Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Seminar Nasional Teknologi Informasi Komunikasi dan Industri JSiI (Jurnal Sistem Informasi) Jurnal Teknologi dan Informasi Jurnal Mantik Ihsan: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Applied Science eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa Jurnal INFOTEL Biosaintifika: Journal of Biology & Biology Education Proceeding of Community Service and Engagement Altifani Journal: International Journal of Community Engagement
Alfian Akbar Gozali
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Power Station Engine Failure Early Warning System Using Thermal Camera Alfian Akbar Gozali
Jurnal Penelitian Pendidikan IPA Vol 9 No 8 (2023): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i8.4598

Abstract

Power station engines are critical infrastructure components that require constant monitoring to prevent failures and ensure an uninterrupted power supply. This paper proposes a failure early warning system based on a thermal camera using a computer vision approach. The system uses a thermal camera to generate thermal images in a video format, which is then processed by an automated fire detection engine and temperature detection engine. The results of these two subsystems are then used as input for an anomaly detection engine, which predicts the likelihood of engine failure. Based on the results of the experiments, it can be concluded that the YOLOv7 model outperforms Faster R-CNN in detecting fires, achieving a higher mAP score on the one-class dataset. The proposed temperature and anomaly detection system also accurately detected temperature levels and anomalies in thermal images. Furthermore, in the failure time prediction experiment, the Holt-Winters additive method with additive errors, additive trend, and additive seasonality model was identified as the best fit among the models evaluated. In contrast, the Decision Tree model showed good performance and a short training time, making it a good choice for applications where training time is critical. These results highlight the importance of selecting the most suitable method for a given application. Moreover, it demonstrates the effectiveness of different models and approaches for engine failure early warning systems in a power station using a thermal camera.
Pelatihan Optimalisasi AI untuk Peningkatan Kompetensi Digital Guru SMK IT Baitul Aziz Alfian Akbar Gozali; Elvira Lailatuth Thohiroh; Villy Satria Praditha; Anranur Uwaisy Marchiningrum; Ario Harry Prayogo
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 3 (2026): Mei
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i3.4262

Abstract

Kegiatan pengabdian mayarakat ini bertujuan untuk meningkatkan kompetensi digital guru SMK IT Baitul Aziz melalui pelatihan optimalisasi kecerdasan buatan (AI) dalam pembelajaran. Kegiatan dilakukan dengan lima tahapan, yaitu: persiapan, pelaksanaan pelatihan, monitoring dan pendampingan, evaluasi, serta publikasi. Pelatihan disampaikan secara luring melalui metode interaktif, diskusi kelompok, dan praktik langsung berbasis studi kasus. Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan guru dalam menerapkan AI pada pembelajaran. Berdasarkan umpan balik, 76,5% peserta menyatakan materi sesuai kebutuhan, 82,4% menyebut materi mudah dipahami, dan 88,2% puas terhadap pelayanan. Keberhasilan ini menunjukkan bahwa pendekatan pelatihan yang sistematis dan kolaboratif efektif dalam mendorong adopsi teknologi pendidikan. Kegiatan ini juga mendorong terbentuknya komunitas belajar digital dan menjadi model untuk sekolah lain dalam mengadopsi pembelajaran berbasis AI.
Schema-Guided Prompt Strategies for Text-to-SQL over Relational Databases Using Local LLMs Nurjayanti Nurjayanti; Adiwijaya Adiwijaya; Ade Romadhony; Alfian Akbar Gozali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.6966

Abstract

Text-to-SQL systems translate natural language questions into executable SQL queries, allowing users without SQL expertise to access structured data stored in relational databases. Although Large Language Models (LLMs) have substantially improved SQL generation capabilities, many state-of-the-art Text-to-SQL approaches continue to rely on cloud-based models with high computational requirements. Such dependence limits their deployment in environments with limited computing resources. This study addresses this limitation by proposing schema-guided prompting strategies for Text-to-SQL generation using local LLMs. A chat-based application was developed using the Django Web Framework, while model inference was performed through the Ollama platform to enable the deployment of local LLMs. The proposed framework incorporates database schema information, including table structures and column attributes, into structured prompts to improve the alignment between natural language questions and SQL generation. Experiment results across multiple databases demonstrate that schema-guided prompting significantly improves Text-to-SQL performance. The highest accuracy was achieved by LLaMA 3 (8B) with objective-aware prompting, reaching an Exact Matching (EM) accuracy of 71.96%. These findings suggest that structured prompt engineering provides a practical alternative to model fine-tuning for locally deployed LLMs, offering an effective balance between SQL generation accuracy, computational efficiency, and data privacy. Future work will investigate fine-tuning strategies, example selection methods, and cross-domain evaluation to enhance SQL generation.
WORD EMBEDDING ANALYSIS IN SENTIMENT ANALYSIS USING MACHINE LEARNING: A CASE STUDY OF STEAM RPG GAME REVIEWS Ardian Adam Alfarisyi; Mahendra Dwifebri Purbolaksono; Alfian Akbar Gozali
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10917

Abstract

User reviews on gaming platforms such as Steam have become a crucial source of information for potential players before making purchasing decisions. Due to the varied nature of user opinions, sentiment analysis is essential for processing and interpreting these reviews. This study investigates the application of sentiment analysis to RPG game reviews on Steam, aiming to assist users by summarizing reviews through sentiment results and providing insights into the general perception of a game. To achieve this, the study applies sentiment analysis using Word2Vec and Support Vector Machine (SVM). It focuses on evaluating the impact of lemmatization during preprocessing and analyzing the performance of Word2Vec in sentiment classification. Word2Vec transforms review text into vector representations that capture semantic relationships, enhancing the model’s ability to understand context. Meanwhile, SVM is chosen as the classifier for its effectiveness in distinguishing between positive and negative reviews and handling high-dimensional data. The system developed uses Word2Vec with 300-dimensional vectors combined with an SVM Polynomial classifier, resulting in the best performance among the tested models. The final model achieves a macro-average F1-score of 88.6%, indicating a strong capability in accurately classifying sentiments in user reviews. These results highlight the potential of combining word embedding and machine learning techniques for analyzing sentiment in gaming platforms.   Keywords: sentiment analysis, Word2Vec, SVM, Steam, RPG
Development of Taksonify Mobile Application for Learning Biological Taxonomy with Gamification and Computer Vision Muhammad Haulul Azkiyaa; Alfian Akbar Gozali; Raja Nur Dania binti Raja Arif Shah
Biosaintifika: Journal of Biology & Biology Education Vol. 18 No. 2 (2026): August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/biosaintifika.v18i2.45605

Abstract

Taxonomy and scientific names are the most important things that make up the foundation of biology education at the secondary level. But a lot of students have trouble with these subjects because of the old-fashioned way of teaching and all the Latin words. Traditional textbook-based teaching methods make this problem even worse. This study tackles the challenge students encounter in comprehending biological taxonomy by developing Taksonify, a mobile application aimed at enhancing taxonomy education through gamification and computer vision. The ADDIE development model (Analysis, Design, Development, Implementation, Evaluation) was used in the study. Expert validation was performed, and the application was evaluated on 35 tenth-grade students through pre-test and post-test assessments. An expert validation score of 4.73 (media) and 4.44 (content) showed that the evaluation was very likely to work. The average score went up from 16.91 (pre-test) to 20.44 (post-test), with a gain score of 0.69 (moderate). This means that comprehension improved a lot. These results show that Taksonify is a good way to learn about biological taxonomy because it helps students understand the subject much better. The use of Taksonify demonstrates the potential of mobile-based learning tools to improve science education quality, fostering greater interest and understanding of biology among secondary students. This research supports SDGs 4 (Quality Education) particularly Targets 4.1 and 4.4, by improving student learning outcomes and strengthening ICT skills. It develops innovative mobile-based, gamified, and computer vision supported learning media to enhance the effectiveness of biology education.
Hybrid Multi-Objective Metaheuristic Machine Learning for Optimizing Pandemic Growth Prediction Adiwijaya Adiwijaya; Syafrial Fachri Pane; Mahmud Dwi Sulistiyo; Alfian Akbar Gozali
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.981

Abstract

Pandemic and epidemic events underscore the challenges of balancing health protection, economic resilience, and mobility sustainability. Addressing these multidimensional trade-offs requires adaptive and data-driven decision-support tools. This study proposes a hybrid framework that integrates machine learning with multi-objective optimization to support evidence-based policymaking in outbreak scenarios. Six key indicators—confirmed cases, disease-related mortality, recovery count, exchange rate, stock index, and workplace mobility—were predicted using eight regression models. Among these, the XGBoost Regressor consistently achieved the highest predictive accuracy, outperforming other approaches in capturing complex temporal and socioeconomic dynamics. To enhance interpretability, we developed SHAPPI, a novel method that combines Shapley Additive Explanations (SHAP) with Permutation Importance (PI). SHAPPI generates stable and meaningful feature rankings, with immunization coverage and transit station activity identified as the most influential factors in all domains. These importance scores were subsequently embedded into the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to construct Pareto-optimal solutions. The optimization results demonstrate transparent trade-offs among health outcomes, economic fluctuations, and mobility changes, allowing policymakers to systematically evaluate competing priorities and design balanced intervention strategies. The findings confirm that the proposed framework successfully balances predictive performance, interpretability, and optimization, while providing a practical decision-support tool for epidemic management. Its generalizable design allows adaptation to diverse geographic and epidemiological contexts. In general, this research highlights the potential of hybrid machine learning and metaheuristic approaches to improve preparedness and policymaking in future health and socioeconomic crises.
Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset Nur Ghaniaviyanto Ramadhan; Warih Maharani; Alfian Akbar Gozali; Adiwijaya Adiwijaya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.798

Abstract

Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
Co-Authors A, Subaveerapandiyan Abduh Husaini Batubara, Muhammad Abdul Raihan Achmad Rizal Ade Romadhony Adiwijaya Ahmad Ibrahim A.M Akmal Natakusuma Amir Hasanudin Fauzi Angelina Prima Kurniati Anranur Uwaisy Marchiningrum Ardian Adam Alfarisyi Ario Harry Prayogo Artanto Ageng Kurniawan Asep Deffy Ciptady Asri Erbenca Gegeh, Yolando Assyifa, Cynthia Audyningrum, Nurul Qofifah Bondan Ari Bowo Bq Desy Hardianti Bq Desy Hardianti Cahyana Cahyana Chikal Fachdiana Citra Pangestu Diken Pradana Putra Diska Yunita Eko Darwiyanto Elly Susilowati Emir Septian Sori Dongoran Fachri Pane, Syafrial Fahri Alfiansyah Fat'hah Noor Prawita Fauzan, Muhammad Arief Gumilar, Ihshan Hariandi Maulid Harris Febryantony Z Hegar Aryo Dewandaru Hegar Aryo Dewandaru Hetti Hidayati I Gusti Bagus Ady Sutrisna Imelda Atastina J. Ratna Juita S Joshua Tanuraharja Julius Angger Satrio Wicaksono Kadek David Kurniawan Kemas Rahmat Saleh Wibowo Kemas Rahmat Saleh Wiharja Khoirul Fahmi Kusuma Handoyo, Amanda Putri Lailatuth Thohiroh, Elvira Mahendra Dwifebri Purbolaksono Mahmud Dwi Sulistiyo Majidah Majidah Miftahul Adnan Rasyid Mira Kania Sabariah Muhammad Agung Agung Muhammad Haulul Azkiyaa Muhammad Idris Muhammad Iqbal Muliadi Angga Wicaksono Nunit Prihatoni Siregar Nur Ghaniaviyanto Ramadhan Nurjayanti Nurjayanti Oktariani Nurul Pratiwi Pangestuaji Widodo, Akhdan Pratami, Rahmat Prawita, Fathah Noor Rafie Novianto Sudrajat Raja Nur Dania binti Raja Arif Shah Ramdana Putra, Haidar Rashid Ranita Windriani Reihaini Fikria Bunga Oktaviani Revanda Octavian Ria Aniansari Ruhallah, Muhammad Lutfi Shigeru Fujimura Syafrial Fachri Pane Syafrial Fachri Pane, Syafrial Fachri Tedy Gumilang Sejati Villy Satria Praditha Warih Maharani Wisnu Riyan Pratama Putra Yudhono, Efwandha Yusuf Basqara, Muhammad