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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Komputasi dan Teknologi Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Innovation Information Technology and Application (JINITA) Jurnal Teknik Informatika (JUTIF) Jurnal Profesi Insinyur Universitas Lampung CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Jurnal Puan Indonesia Tedc Journal of Informatics and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat
Ade Yuliana
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Journal : Jurnal Teknik Informatika (JUTIF)

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Improving Diabetes Prediction Performance Using Random Forest Classifier with Hyperparameter Tuning Anggreini, Novita Lestari; Yuliana, Ade; Ramdan, Dadan Saepul; Al-Dayyeni, Wissam
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4755

Abstract

Diabetes mellitus is a chronic metabolic disorder that poses a serious challenge to global healthcare systems due to its increasing prevalence and the high costs associated with treatment. Although machine learning has been widely adopted to support early diagnosis, many predictive models still underperform due to limited preprocessing strategies and inefficient hyperparameter settings. This study proposes a comprehensive machine learning pipeline to enhance diabetes prediction accuracy by utilizing a Random Forest classifier optimized through systematic hyperparameter tuning. The novelty of this method lies in its integrated approach, which includes thorough preprocessing such as removing duplicate records, handling inconsistent unique values, addressing missing data, and applying the SMOTE technique to overcome class imbalance. Additionally, hyperparameter tuning is conducted using GridSearchCV combined with 5-fold cross-validation, and only the most influential features are selected to improve model interpretability and efficiency. The proposed model achieved an accuracy of 95 percent, with a recall of 0.88 and an F1-score of 0.85, indicating its robustness in identifying diabetic cases more effectively than previous studies using standard machine learning algorithms. This model contributes to the development of a reliable and scalable early detection system for diabetes, applicable in clinical decision support environments. Further refinement can be achieved by testing on larger and more diverse datasets or by implementing more efficient tuning techniques such as Bayesian optimization.
A Hybrid LSTM–Smith Waterman Model for Personalized Semantic Search in Academic Information Systems Yuliana, Ade; Anggreini, Novita Lestari; Iskandar, Rachmat; Prasanth, G. Rafi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4763

Abstract

The growing complexity of digital learning environments presents a critical challenge in computer science, particularly in designing intelligent academic systems capable of delivering context-aware and personalized content. Traditional academic information systems often rely on literal keyword matching, failing to interpret the semantic intent behind user queries and ignoring historical learning behavior. This study addresses these limitations by proposing a hybrid semantic search and recommendation model that integrates Long Short-Term Memory (LSTM) networks with the Smith Waterman algorithm. The LSTM component models temporal sequences of user interactions, while Smith Waterman enables local semantic alignment between user queries and learning content. Historical query logs and user-clicked topics are transformed into semantic vectors, which are further enhanced through a contextual graph and semantic relation matrix. Experimental results demonstrate the model’s effectiveness, achieving 89% accuracy, an F1-score of 0.89, and an AUROC of 0.88 by epoch 50. The hybrid architecture successfully captures the evolution of user interest and semantic relevance, outperforming baseline approaches. This research contributes to the field of computer science by bridging natural language understanding and sequential modeling to improve adaptive learning technologies. The proposed model offers a scalable foundation for developing intelligent recommendation systems in academic platforms, fostering improved learner engagement and efficiency.
Co-Authors Agustian, Fajar Ahmad Yusuf Al-Dayyeni, Wissam Ananda Saputra, Ferdi Aris Haris Rismayana Aris, Nova Arianti Asmat Purba Castaka Agus Sugianto Devanti, Fifi Dini Rohmayani Eva Damayanti, Eva Fariha, Fariha Fazriani, Widi Heldi Akbar Kosasih Iskandar, Rachmat Jauhari, Mochamad Fakhrizal Julianto, Arjuna Rinaldi Julianto listiana, reni Lusi Marlina Mamay Syani Marlina, Lusi Maulana, Andrea Meilani Anggraeni Novita Lestari Anggreini Paryati, Retno Prasanth, G. Rafi Pratama, Arya Rizki Purba, Fahri Hidayah Rabella Rigustama Az Zahra Ramdan, Dadan Saepul Ratna Wulandari Reni Listiana Reni Raafidiani Rizky Herdiansyah, Moh Rizqia, Salsabila Samuel, Hizkia Septiani, Rafa Sukarya, Robby Rohman Taryana, Yudi Tri Pratiwi Olivia Riska Bokings Turnip, Ferlin Firdaus Yuda, Ibrahim Sawitro