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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Khatulistiwa Informatika JIEET (Journal of Information Engineering and Educational Technology) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmu Teknik dan Komputer JIKA (Jurnal Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Terpadu Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Software Engineering and Information System (SEIS) AMMA : Jurnal Pengabdian Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Informasi interaktif : jurnal informatika dan teknologi informasi
Prihartono, Willy
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Humanities Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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OPTIMASI MODEL XGBOOST UNTUK PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN OPTUNA Optarina, Yasni; Suarna, Nana; Bahtiar, Agus; Rahaningsih, Nining; Prihartono, Willy
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 1 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i1.10527

Abstract

Heart disease is one of the leading causes of mortality worldwide, emphasizing the need for accurate early detection systems. Machine learning models such as XGBoost have demonstrated strong performance in medical classification tasks; however, their effectiveness is highly dependent on optimal hyperparameter configurations. This study aims to improve the performance of XGBoost for heart disease classification by applying hyperparameter optimization using the Optuna framework with the Tree-structured Parzen Estimator (TPE) algorithm. The UCI Heart Disease dataset, consisting of 918 records, is used in this study. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to the training data. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The experimental results show that the optimized XGBoost model achieves an accuracy of 89.13%, outperforming the baseline model with 87.50%, and improves recall from 87.50% to 89.10%. In addition, the optimized model attains a higher ROC-AUC value of 0.9319, indicating improved classification stability. These findings demonstrate that Optuna-based hyperparameter optimization effectively enhances the performance and reliability of XGBoost, making it suitable for supporting early heart disease diagnosis in medical decision support systems.
Comparison of Balancing Strategies for Classifying Guava Fruit Diseases Putri Nabilla; Suarna, Nana; Bahtiar, Agus; Rahaningsih, Nining; Prihartono, Willy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1859

Abstract

The problem of class imbalance often poses an obstacle in deep learning-based image classification, especially in the domain of digital agriculture. The imbalance in data distribution makes it easier for models to recognize the majority class, while performance for the minority class declines. This study aims to analyze the effectiveness of three strategies for handling class imbalance: Weighted Loss Function, Oversampling, and a combination of Weighted Loss and Oversampling, in improving the performance of image classification of guava fruit diseases using a transfer learning-based MobileNetV2 architecture. The dataset consists of 3,784 images of three disease classes, namely Anthracnose, Fruit_Fly, and Healthy_guava, which show an imbalanced distribution. The research was conducted through the stages of Exploratory Data Analysis (EDA), pre-processing, augmentation, model training with four scenarios, and evaluation using Accuracy, Precision, Recall, F1-Score, and Macro Average F1-Score. The results showed that the Combination model (Oversampling and Weighted Loss) performed best on the minority class with an F1-score of 0.9630, the highest among all models. The Oversampling strategy produced the highest Macro F1-score of 0.9617, while Weighted Loss provided a significant improvement in classification sensitivity but was still below the combination model. Thus, it can be concluded that the combination strategy is the most effective approach in improving the sensitivity of the model to minority classes, while Oversampling excels in the overall performance stability of the model.
Segmentation of Coffee Purchasing Behavior Based on Transaction Time Using the K-Means Algorithm Yuslia Devitri; Rahaningsih, Nining; Ali, Irfan; Prihartono, Willy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1863

Abstract

This studyaims to identify customer behavior patterns based on the time of purchaseof beverages at a coffee shop using the K-Means method.Transaction data includes purchase time, payment type, product name,time category, day, and month. The research stages include data cleaning, time attribute transformation, and numerical feature normalization. The optimal number of clustersis determined through testing k = 2–10 with four evaluation metrics,namely Inertia, Silhouette Score, Davies–Bouldin Index, and Calinski–HarabaszIndex. Based on the validation results, k = 3 was selected because it provided the best balancebetween compactness and cluster separation. The clustering results showedthree main customer groups based on transaction time trends:nighttime buyers with a peak around 10:27 p.m., afternoon to early evening buyerswith a centroid of 7:01 p.m., and morning to noon buyers with a centroid11:13. The frequency distribution indicates that the morning–afternoon buyer groupis the largest, while the early evening–night group is thesmallest. Visualization of scatter plots, boxplots, and time category graphsemphasizes the differences in characteristics between clusters. Overall,this study proves that K-Means is effective in mapping the temporal patternsof customer behavior. These findings can be used to develop time-based marketing strategies, operational arrangements, and product stock management,as well as form the basis for further analysis in the industry.
Co-Authors ., Fathurrohman Agustin, Nia Aini Nurul Ainisa, Nurul Al Lutfani, Thariq Kemal Al Maeni, Nurul Amelia, Astri Ameliana, Nikan Andre Setiawan, Andre Apriliansyah, Rizal Dwi Rizki Aprilla, Anggita Arifqi, Tri Astuti , Rini Ayu Azzahra, Fadita Ayuni, Putri DAIPAH, IIP IMRON Dalifah, Nurul Dita, Fio Eka Permana, Sandy Erpian, Soni Fachry Abda El Rahman Fathur Rohman, Fathur Fathurrohman Fathurrohman Faturrohman, Faturrohman Faujia, Agnes Firmansyah, Fajar Gifthera Dwilestari Gunawan, Sepriyan Hadi Wicaksana, Arya Haikal, Harisman Hamonangan, Ryan Haryandini, Nur Anindya Putri Hayati, Umi Herdiana, Rulli Herdiana, Rully Hidayah, Nurni Hidayat, Pierre Galuh HIDAYATULLAH, NAUFAL ARIF Hoeriah, Dede Ilham Syahputra, Arief Irfan Ali, Irfan Irma Purnamasari, Ade Jannah, Nursuviyani Jihan, Aminatun Julianti, Okta Nur Kholifa, Nur Kusmawanti, Nisa Laksamana, Patria Gita Lita, Arlita lita Maulana, Aldi Maulida, Nida Muharromah, Oom Nining Rahaningsih Nur Amalia, Ocsana Nur Apriliani, Nur Nur Kirana, Anita Nur Pangestika, Fanny Nurdin Nurhakim, Bani Nurhayah, Nurhayah Nuri Nuri Nurjanah, Nurul Nurliana, Nicky NURUL AZIZAH Nurwanda, Nurwanda Nurzaman Nurzaman Odi Nurdiawan OKTAVIANI, ERNA Oktaviany, Nurul Optarina, Yasni Peni Peni Permana, Sandy Eka PUJI LESTARI Putri Nabilla Putriana, Puput RAHMAWATI, RULI Ramadhan, Niko Retnasari, Peni Rini Astuti RIZKI, ALVA FAUZIR Rohmat, Cep Lukman Rosiana, Rosa Sakarias Berek, Richardus Salsabila, Fauhan saninah, annisa Saniyah, Nilta Sayuti Hanapiah, Neneng Suarna, Nana Yudhistira Arie Wijaya Yuslia Devitri Zaelani, Nursehan