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Prio Handoko
Pembangunan Jaya University
Computer Science & IT

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Web-Based Job Recommendation Based on LinkedIn Profiles Using Domain-Aware SBERT Retrieval and TF-IDF Reranking Maharaya Bintangku Aridhana; Prio Handoko
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/12r31r31

Abstract

Online job search often relies on keyword matching, while the semantic relationship between candidate profiles and job descriptions may not be captured adequately. This study develops a web-based job recommendation system based on LinkedIn-style candidate profiles using SBERT retrieval and domain-aware TF-IDF reranking. Candidate profiles are constructed from target role, headline, skills, experience, education, preferred location, and work preference, with non-English input translated into English when needed. The job corpus consists of approximately 1.3 million job postings represented by precomputed 384-dimensional SBERT embeddings. The system retrieves initial candidates using cosine similarity and reranks them using TF-IDF similarity with domain, experience, and location constraints. Manual evaluation on 1,012 judged profile-job pairs shows that the proposed method achieves Precision@5 of 0.428, Precision@10 of 0.368, NDCG@10 of 0.531, and MRR of 0.605. An additional validated pseudo-label evaluation achieves Precision@5 of 0.840, with 83.33% agreement and a Cohen’s Kappa of 0.75 against human-checked samples. These results indicate that semantic retrieval combined with explainable domain-aware reranking can improve the relevance of web-based job recommendations.
Weather Prediction Using CNN-LSTM-Based AI for Weather Pattern Analysis in Banten Province Advani Rayandra Kahfi; Prio Handoko
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ejaw3909

Abstract

Weather variability in Banten Province poses challenges across various sectors, including community activities, agriculture, and disaster preparedness, necessitating accurate weather prediction methods. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to predict air temperature and rainfall based on historical weather time-series data. The dataset was obtained from the Open-Meteo API and BMKG for the observation period from January 2019 to May 2024. Input variables include air temperature, rainfall, wind speed, sea surface temperature anomaly, and the El Niño–Southern Oscillation (ENSO) index. The data preprocessing stages involve data cleaning, normalization using Robust Scaler, and the construction of data sequences using the sliding window method prior to the model training process. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and compared against baseline models. The experimental results demonstrate that the CNN-LSTM model achieves an MAE of 0.60°C and an RMSE of 0.73°C for air temperature prediction and an MAE of 6.15 mm and an RMSE of 8.31 mm for rainfall prediction. The prediction outcomes were subsequently integrated into a web-based dashboard to facilitate information visualization. Initial validation against BMKG observation data in South Tangerang showed a relatively low temperature deviation during the testing period. These findings confirm that the proposed approach has adequate potential to support short-term weather prediction systems in Banten Province.
Low-Sugar Diet Recommendations for Bangrajanmuaythai Boxing Athletes Using Collaborative Filtering Ananda Gilang Ariyanto; Prio Handoko
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wxvz1f16

Abstract

Adjusting diet patterns according to nutritional requirements, training intensity, and an athlete's physical condition is often a challenge in implementing a healthy diet, particularly a low-sugar food diet. This study aims to develop an artificial intelligence (AI)-based recommendation system that can help boxing and Muay Thai athletes in implementing a more targeted diet program through food recommendations tailored to their individual behaviors and nutritional needs. The methods used are collaborative filtering with a nutrition scoring approach, athlete preference analysis, and dynamic nutrition planning. The results show that the developed system, namely the Smart Nutrition System, is able to provide recommendations based on similarities among athletes’ preferences and nutritional requirements, thus supporting more effective decision-making in managing athlete diet patterns. Furthermore, the Smart Nutrition System also has the potential to evolve into an “athlete intelligence nutrition platform" that supports the implementation of personalized nutrition for combat sports athletes to support athlete performance.
Co-Authors Advani Rayandra Kahfi Ananda Gilang Ariyanto Maharaya Bintangku Aridhana