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JIEET (Journal of Information Engineering and Educational Technology)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 2549869X     DOI : http://dx.doi.org/10.26740/
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal Description: JIEET (Journal of Information Engineering and Educational Technology) is a scientific journal that publishes the peer-reviewed research papers in the field of Computer Engineering, Distributed and Parallel Systems, Business Informatics, Computer Science, Computer Security, System & Software Engineering and Educational Technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2025)" : 8 Documents clear
Implementasi Teknik Ensemble Stacking pada Klasifikasi Penyakit Anemia anita, anita desiani; Mukhlisah, Nur; Indira, Ria; Novi Rustiana Dewi; Yuli Andriani
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p51-55

Abstract

Abstrak— Anemia adalah penyakit yang disebabkan oleh kondisi seseorang yang memiliki kadar hemoglobin (Hb) darah dibawah normal. Deteksi penyakit dapat menggunakan bantuan data mining untuk mengklasifikasikan penyakit. Algoritma yang digunakan dalam penelitian ini adalah Gaussian Naive Bayes, K-Nearest Neighbor dan Support Vector Machine yang kemudian diterapkan pada teknik ensemble stacking. Penerapan Ensemble bertujuan untuk mendapatkan nilai keakurasian yang lebih baik dari klasifikasi individu. Pengujian algoritma ini menggunakan dua teknik pengujian yaitu percentage split dan k-fold cross validation. Untuk percentage split menggunakan ukuran split sebesar 80% data training dan 20% data uji dan pada k-fold cross validation dipilih nilai k=10. Hasil klasifikasi dari algoritma-algoritma tersebut memperoleh bahwa percentage split mendapatkan hasil akurasi yang lebih baik dibandingkan k-fold cross validation. Algoritma Support Vector Machine (SVM), Gaussian Naive Bayes dan k-Nearest Neighbor (kNN) dengan teknik pengujian percentage split memperoleh hasil akurasi secara berturut-turut sebesar 90,16%, 94,61% dan 96,49%. K-Nearest Neighbor (kNN) menghasilkan nilai akurasi tertinggi dari ketiga algoritma tersebut, namun dengan penerapan teknik ensemble memberikan kenaikan akurasi sebesar 1.05% dari hasil k-Nearest Neighbor (kNN). Ensemble dengan model stacking memperoleh hasil akurasi sebesar 97,19%. Berdasarkan hasil yang diperoleh dapat disimpulkan bahwa ensemble dengan model stacking dengan teknik pengujian percentage split memperoleh kinerja yang terbaik dari algoritma lainnya pada klasifikasi penyakit anemnia. Kata Kunci— Ensemble Learning, Support Vector Machine, Gaussian Naive Bayes, K-Nearest Neighbor, Anemia
Penerapan Gated Recurrent Unit dengan Bayesian Optimization dalam Prediksi Harga Saham Sektor FMCG Mas Diyasa, I Gede Susrama; Akmal, Mohammad Faizal; Junaidi, achmad
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p36-41

Abstract

Peningkatan partisipasi investor muda terutama dari Generasi Z dan Milenial menciptakan kebutuhan mendesak untuk menggunakan metode prediksi yang lebih akurat guna meminimalkan risiko investasi. Penelitian ini bertujuan untuk mengembangkan model prediksi harga saham pada sektor Fast-Moving Consumer Goods (FMCG) di Indonesia dengan memanfaatkan algoritma Gated Recurrent Unit (GRU) yang dioptimalkan menggunakan teknik Bayesian Optimization. Metode penelitian ini dimulai dengan pembagian data saham PT Hanjaya Mandala Sampoerna Tbk (HMSP) dari tahun 2019 hingga 2025, yang dibagi menjadi data train (60%), data validation (20%), dan data test (20%). Selanjutnya, dilakukan preprocessing data berupa normalisasi dan sequencing untuk mempersiapkan data. Model GRU yang diterapkan diuji dengan menggunakan metrik evaluasi seperti Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE), yang menghasilkan akurasi prediksi yang tinggi dengan RMSE 17.07, MAE 11.50, dan MAPE 1.48%. Penelitian ini menunjukkan bahwa penerapan Bayesian Optimization dapat memberikan efektivitas pemilihan hyperparameter menghasilkan model yang lebih presisi dalam memprediksi harga saham FMCG di Indonesia dan memberikan panduan yang lebih andal bagi investor dalam pengambilan keputusan investasi
Uncovering Hidden Issues in Audit Findings Through LDA-Based Topic Modeling Prastyo, Yoyok; Wiyli Yustanti; Yuni Yamasari
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p28-35

Abstract

Academic audit reports play an important role in assessing and monitoring the quality of higher education. However, most of these reports are arranged in an unstructured narrative descriptive form, making it difficult to analyze systematically and consistently, especially if done manually. This poses a challenge for auditors and decision makers in identifying patterns of findings and quality issues efficiently. This study aims to apply and evaluate the Latent Dirichlet Allocation (LDA) method in extracting keywords and abstracting main topics from academic audit report texts. The dataset was obtained from the Quality Management System (SIMUTU) of Surabaya State University, which includes hundreds of audit finding descriptions from various faculties over the past three years. The methodology used includes text preprocessing stages using tokenization, stopword removal, and stemming techniques, followed by topic modeling using LDA. Evaluation was carried out quantitatively using a coherence score to assess topic quality, and qualitatively through visualization of results in the form of word clouds and pyLDAvis. The results showed that the LDA model was able to produce meaningful, representative, and relevant topics in the context of academic quality, such as document management, lecturer involvement, and implementation of learning evaluations. Manual validation by internal quality experts showed that the generated topics can help in understanding audit findings trends more quickly and objectively. Thus, LDA has proven to be effective as an approach to extracting important information from unstructured audit reports and has great potential to be integrated into data-driven quality dashboard systems to support more informed and evidence-based decision making.
Optimasi Hyperparameter Deep Learning untuk Deteksi X-Ray Paru-Paru Menggunakan Bayesian Optimization Shahira, Fayza; Negara, Benny Sukma; Yanto, Febi; Sanjaya, Suwanto
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p53-63

Abstract

Penyakit paru-paru, seperti pneumonia dan COVID-19, merupakan ancaman serius terhadap kesehatan masyarakat, terutama jika diagnosisnya mengalami keterlambatan. Pendekatan deteksi dini melalui citra X-ray dada banyak digunakan, namun akurasinya sangat bergantung pada kemampuan sistem klasifikasi. Penelitian ini bertujuan untuk meningkatkan performa klasifikasi citra X-ray paru-paru dengan mengimplementasikan metode deep learning menggunakan arsitektur ResNet-101 yang dioptimasi menggunakan teknik Bayesian Optimization. Dataset yang digunakan dalam penelitian ini terdiri dari tiga kelas yaitu Normal, Pneumonia, dan COVID-19, masing-masing sejumlah 1.000 citra. Kinerja model hasil optimasi dibandingkan dengan model baseline pada tiga skenario split data yaitu 90:10, 80:20, 70:30. Hasil penelitian mengindikasikan bahwa model yang telah dioptimasi mampu meningkatkan performa pada seluruh metrik evaluasi mencakup akurasi, presisi, recall, spesifisitas, dan F1-score. Akurasi tertinggi tercatat sebesar 93,83% pada skenario 80:20, melampau akurasi baseline yang sebesar 91,83. Selain itu, kurva akurasi dan loss menunjukkan proses training yang stabil dan konvergen secara cepat tanpa indikasi overfitting yang signifikan. Penerapan Bayesian Optimization terbukti efektif dalam menemukan konfigurasi hyperparameter optimal yang berdampak pada peningkatan dalam tiap metrik evaluasi
Hybrid Clustering and Classification of At-Risk Customer Segments in Network Marketing Hartanto, Unung Istopo; Buditjahjanto, I Gusti Putu Asto; Yustanti, Wiyli
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p42-50

Abstract

Customer segmentation is a fundamental strategy for sustaining retention in network marketing businesses, where repeated transactions and multilayered relationships significantly impact long-term customer value. This study proposes a hybrid machine learning framework to classify at-risk customer segments—comprising regular customers, seasonal buyers, and churn-risk profiles—by integrating unsupervised clustering and supervised classification methods. A total of 36 engineered behavioral features were derived from longitudinal transaction data to capture spending behavior, recency, variability, and growth dynamics. Clustering algorithms including K-Means, Agglomerative Hierarchical Clustering, and Gaussian Mixture Models were applied and evaluated using standard clustering validity indices: Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index. K-Means with six clusters produced the most interpretable and balanced segmentation outcome. Cluster relabeling was conducted to align with business-relevant categories, followed by supervised validation using classifiers such as Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Random Forest and Support Vector Machine (SVM). Among these, SVM yielded the highest predictive accuracy (92.53%) and F1-Score (92.52). The results demonstrate the effectiveness of the proposed hybrid approach in enhancing segmentation precision and facilitating early detection of potential churn in a dynamic marketing environment.
Explainable Artificial Intelligence (XAI) for Identification of Using Obesity Factors Hybrid Artificial Neural Network Approach and SHapley Additive exPlanations Esti, Esti Yogiyanti; Yuni Yamasari; Ervin Yohannes
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p19-27

Abstract

This study aims to develop and evaluate an obesity classification model using an Artificial Neural Network (ANN) combined with Explainable Artificial Intelligence (XAI) techniques based on SHAP (SHapley Additive exPlanations). The model was trained and tested using two different optimizers, Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD), across multiple train-test ratios and epoch variations. The experimental results indicate that the Adam optimizer consistently outperformed SGD in terms of accuracy, loss value, and stability of evaluation metrics. The best performance was achieved with a 90:10 train-test ratio at 100 epochs, yielding an accuracy of 94.74%, a loss of 0.1899, precision, recall, and an f1-score of 0.95. To improve interpretability, SHAP was applied to identify the most influential features in the classification process. The analysis revealed that features such as Weight, Height, Gender, and Age significantly contribute to the model's predictions. Based on the SHAP interpretation, feature selection was conducted using the top nine features with the highest SHAP values. Retraining the ANN with these selected features resulted in improved performance, achieving 98.56% accuracy, a loss of 0.0638, and a precision, recall, and F1-score of 0.99 . These findings demonstrate that integrating XAI with ANN not only enhances transparency and interpretability but also boosts classification performance and computational efficiency. This approach shows strong potential for supporting decision-making in healthcare, particularly for early detection and intervention in cases related to obesity.
Automated Chest X-Ray Captioning Using Pretrained Vision Transformer with LSTM and Multi-Head Attention Aulia Akbar, Rafy; Putra, Ricky Eka; Yustanti, Wiyli
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p1-10

Abstract

Radiology report generation is a complex and error-prone task, especially for radiologists with limited experience. To overcome this, this study aims to develop an automated system for generating text-based radiology reports using chest X-ray images. The proposed approach combines computer vision and natural language processing through an encoder-decoder architecture. As an encoder, a Vision Transformer (ViT) model trained on the CheXpert dataset is used to extract visual features from X-ray images after Gamma Correction is performed to improve image quality. In the decoder section, word embeddings from the report text are processed using Long Short-Term Memory (LSTM) to capture word order relationships, and enriched with Multi-Head Attention (MHA) to pay attention to important parts of the text. Visual and text features are then combined and passed to a dense layer to generate text-based radiology reports. The evaluation results show that the proposed model achieves a ROUGE-L score of 0.385, outperforming previous models. The BLEU-1 score also shows competitive results with a value of 0.427. This study shows that the use of pre-trained ViT, combined with LSTM-MHA on the decoder, provides excellent performance in capturing visual and semantic context of text, as well as improving accuracy and efficiency in radiology report automation.
Perbandingan Algoritma Naïve Bayes Dan Random Forest Dalam Klasifikasi Obesitas Berdasarkan Faktor Gaya Hidup Prakoso, Rizkya Nanda; Rochim, Shidqi Ikmal; Subarnas, Ari; Kurniawan, Muhammad Erfan
JIEET (Journal of Information Engineering and Educational Technology) Vol. 9 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v9n1.p11-18

Abstract

Abstrak— Obesitas merupakan salah satu permasalahan kesehatan yang berkaitan erat dengan pola hidup modern. Tujuan dari penelitian ini adalah untuk mengevaluasi dan membandingkan kinerja algoritma Naïve Bayes dan Random Forest dalam mengelompokkan tingkat obesitas berdasarkan pola gaya hidup. Dataset diperoleh dari platform Kaggle yang memuat berbagai atribut terkait kebiasaan hidup, seperti pola makan dan aktivitas fisik. Penelitian dimulai dengan tahapan data preprocessing meliputi penghapusan atribut yang tidak relevan, transformasi label kelas menjadi bentuk kategorikal, serta pembersihan data kosong. Data kemudian dibagi menjadi data latih dan data uji. Model klasifikasi dibangun menggunakan aplikasi RapidMiner dengan algoritma Naïve Bayes dan Random Forest. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan classification error. Berdasarkan hasil pengujian, algoritma Random Forest menghasilkan akurasi 83,23%, precision 83,93%, dan recall 82,46% pada kelas Obesity, dengan classification error sebesar 16,77%. Sementara itu, Naïve Bayes mencatat akurasi 76,09%, precision 73,21% dan recall 71,93% pada kelas Obesity, sementara itu, hasil classification error sebesar 23,91%. Hasil analisis dari Weight by Information Gain menunjukkan bahwa atribut dengan bobot tertinggi adalah usia (0,290), diikuti frekuensi konsumsi sayuran (0,272) dan jumlah makan utama per hari (0,232) yang berperan penting dalam klasifikasi obesitas. Penelitian ini menyimpulkan bahwa algoritma Random Forest lebih unggul dibandingkan Naïve Bayes dalam memprediksi obesitas berdasarkan faktor gaya hidup dan faktor lainnya yang dapat memicu obesitas. Kata Kunci— Obesitas, Gaya Hidup, Naive Bayes, Random Forest, Rapidminer.

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