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All Journal Mimbar Sekolah Dasar Journal of Information Systems Engineering and Business Intelligence JIEET (Journal of Information Engineering and Educational Technology) Journal of Robotics and Control (JRC) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Abdimas: Papua Journal Of Community Service Journal of Informatics and Computer Science (JINACS) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Ranah Research : Journal of Multidisciplinary Research and Development PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Emerging Information Systems and Business Intelligence (JEISBI) Journal of Education Technology and Information System
Wiyli Yustanti
Prodi Manajemen Informatika, Fakultas Teknik, Universitas Negeri Surabaya
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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Sistem Deteksi Organ Liver Melalui Citra Iris Mata Menggunakan Convolutional Neural Network Aulia, Novi Rosidhatul; Yustanti, Wiyli
Journal of Informatics and Computer Science (JINACS) Vol. 6 No. 02 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v6n02.p417-426

Abstract

Analisis Topik Pinjaman Online Pada Sosial Media Twitter Menggunakan Latent Direchlet Allocation (LDA) Nautika, Puji Septiyana; Yustanti, Wiyli
Journal of Informatics and Computer Science (JINACS) Vol. 6 No. 02 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v6n02.p427-436

Abstract

Pemilihan Algoritma Klasifikasi Terbaik Untuk Prediksi Jenis Keluhan MI User Interface (MIUI) 14 Akbar, Mohammad; Yustanti, Wiyli
Journal of Informatics and Computer Science (JINACS) Vol. 6 No. 02 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v6n02.p445-452

Abstract

Studi Komparasi Local Outlier Factor (LOF) dan Isolation Forest (IF) pada Analisis Anomali Kinerja Dosen Mutmainah, Mutmainah; Yustanti, Wiyli
Journal of Informatics and Computer Science (JINACS) Vol. 6 No. 02 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jinacs.v6n02.p532-540

Abstract

Pada setiap semester dalam universitas terdapat kuisioner berupa penilaian terhadap kinerja dosen. Evaluasi kinerja dosen yang terdapat di Universitas Negeri Surabaya merupakan proses penting untuk memastikan bahwa dosen telah memenuhi tugas dan tanggung jawabnya dalam menyampaikan pendidikan berkualitas terhadap mahasiswanya. Pada penelitian ini terdapat 22 instrumen pertanyaan menggunakan Skala Likert yang diisi oleh mahasiswa untuk menilai kinerja dosen. Terdapat 1055 dosen yang diolah untuk mendeteksi bagaimana kinerja dosen apakah sesuai dengan Rancangan Pembelajaran Semeste (RPS) atau terdapat dosen yang ketika mengajar tidak sesuai RPS. Oleh karena itu, metode deteksi anomali diterapkan untuk mengetahui kinerja dosen yang menyimpang atau tidak seperti biasanya. Dengan metode tersebut, maka dapat digunakan algoritma Local Outlier Factor (LOF) dan Isolation Forest karena lebih efisien dalam menangani data yang besar dan bekerja dengan cepat dalam ruang fitur. Data yang digunakan belum terdapat label untuk menghitung sehingga digunakan metode klastering kmeans untuk memperoleh label dari LOF dan IF. Kemudian pada cluster kmeans didapatkan 3 cluster, yaitu cluster 0 terdiri dari 279 data points, cluster 1 terdiri dari 597 data points, dan cluster 2 terdiri dari 179 data points. Dari hasil cluster tersebut akan digunakan untuk memperoleh nilai dari label LOF dan label IF dalam perhitungan evaluasi hasil komparasi. Pada anomali yang diterapkan dengan algoritma LOF yaitu terdapat 19 dosen terdeteksi anomali dan pada algoritma IF terdapat 22 dosen terdeteksi anomali. Pada evaluasi yang digunakan untuk memperoleh hasil komparasi yaitu menggunakan rand index score dan silhouette score. Didapatkan nilai dari rand index dari LOF sebesar 0.438 dan IF sebesar 0.441. Kemudian hasil dari silhouette score LOF sebesar 0.0019 dan IF sebesar 0.0377. Kata Kunci : Kinerja dosen, LOF, IF, rand index, silhouette score
Assessment of the Potential of Elementary School Students in Terms of Physical Fitness Suroto, Suroto; Yustanti, Wiyli; Prakoso, Bayu Budi; Priadana, Benny Widya; Febriyanti, Irma
Mimbar Sekolah Dasar Vol 11, No 2 (2024)
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53400/mimbar-sd.v11i2.71731

Abstract

Primary education is an essential foundation for developing all the potential possessed by students, including physical fitness. The persistent inadequacy in children's physical fitness levels raises critical concerns regarding their overall health and well-being, necessitating urgent attention and intervention. This study aimed to assess the physical fitness of elementary school students, evaluate the effectiveness of the Indonesian Physical Fitness Test in measuring their physical fitness, and give policy recommendations. The quantitative descriptive method was used in this study due to its ability to provide numerical data analysis, facilitate systematic measurement, and allow comparative analysis and generalization. Incidental sampling was used to select 120 primary school learners in the Sidoarjo district. Two sets of fitness tests were administered based on child development phases: the Around the World Test for Phase B and a 600m running test for Phase C. Crosstabs analysis on SPSS revealed that male and female learners exhibited similar physical fitness levels, mainly within moderate and healthy categories. Differences were observed based on the phase or grade level (chi-squared=43.843, p=0.000), with Phase B learners generally having moderate to good fitness, while Phase C learners demonstrated lower and middle levels. Overall, there was a decline in fitness with higher grade levels, emphasizing the necessity to rejuvenate physical education for enhanced fitness. Instilling the importance of maintaining and enhancing physical fitness from a young age is crucial, and both teachers and students can actively contribute by enhancing their comprehension of physical literacy through training or by integrating it into the physical education curriculum.
Analisis Kepuasan Pengguna pada Aplikasi Jenius dengan Menggunakan Metode EUCS (End-User Computing Satisfaction) berdasarkan Perspektif Pengguna Istianah, Eva; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 3 No. 4 (2022): Vol. 03 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v3i4.47882

Abstract

Aplikasi Jenius merupakan aplikasi perbankan revolusioner yang telah dilengkapi kartu kredit Visa guna membantu nasabah saat melakukan aktivitas finansial dari satu tempat atau smartphone, seperti halnya menabung, bertransaksi, atau mengatur keuangan agar lebih aman, cerdas, dan simple. Selama pandemic Covid-19, jumlah pengguna aplikasi Jenius terus meningkat, sehingga dilakukan penilaian kepuasan pengguna pada aplikasi Jenius yang dapat dijadikan sebagai tolak ukur keberhasilan suatu aplikasi serta menjadikan bahan evaluasi perusahaan dalam meningkatkan layanan aplikasi. Penelitian yang dilakukan bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi kepuasan pengguna dan mengetahui tingkat kepuasan pengguna terhadap aplikasi jenius. Pengumpulan data dilakukan dengan penyebaran kuesioner secara online kepada pengguna aplikasi jenius yang berdomisili di Surabaya dan sekitarnya menggunakan teknik pengambilan sampel khusus pada sampel 100 responden. Kuesioner disusun dengan menggunakan dimensi metode End User Computing Satisfaction (EUCS). Teknik analisis data yang digunakan yaitu analisis deskriptif dan analisis Chi Square. Hasil, 1) Dapat ditentukan dari data survey yang diolah bahwa perhitungan range tertinggi 4,58 sedangkan nilai terendah range 3,80 yang berarti pengguna terhadap aplikasi Jenius berada dalam kategori Sangat Puas 2) Hasil yang didapatkan dari perhitungan uji chi square yaitu, terdapat 31 hipotesis awal ditolak dan 17 hipotesis awal diterima. Artinya terdapat 31 faktor yang berpengaruh terhadap kepuasan pengguna aplikasi Jenius.
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.
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.
I-Regs (Internet-Regression Analysis) as a Statistical Innovation in Nonparametric Regression Modeling Dani, Andrea; Budiantara, I Nyoman; Nuraini, Ulfa Siti; Yustanti, Wiyli; Sifriyani; Putra, Fachrian Bimantoro
Journal of Education Technology and Information System Vol. 1 No. 02 (2025): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jetis.v1i02.35288

Abstract

This research develops an information system based on the R-Shiny Dashboard, allowing users to perform nonparametric regression modeling. Internet-Regression Analysis (I-Regs) is the name of a dashboard that has been successfully developed. I-Regs provides a complete model library in regression analysis modeling, including parametric, nonparametric, and semiparametric regression. It is hoped that I-Regs can become a valuable tool for researchers, practitioners, and students in modeling regression analysis and solving various data analysis problems.
Optimizing Tuition Fee Determination with K-Means Cluster Relabeling Based on Centroid Mapping of Principal Component Pattern Yustanti, Wiyli; Iwan Nurhidayat, Andi; Iskandar Java, Muhammad
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.445-458

Abstract

Background: Tuition fee in Indonesian public universities is determined based on the socioeconomic status of prospective students. In this context, students are assigned to tuition fee groups after passing the selection process through achievement-based or computer-based exams. However, the current grouping system shows overlapping distributions, indicating the need for a more precise classification method.   Objective: This research aims to improve the accuracy of tuition fee group assignments by refining the clustering structure and relabeling the classification dataset.  Methods: A total of 13 socioeconomic variables were used to predict tuition fee groups. This research used K-Means clustering algorithm and a relabeling process using centroid mapping of principal components to balance original and newly generated labels. To assess the effectiveness of the relabeling process, six classification algorithms, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM), were used. Statistical tests at a 5% significance level were conducted to evaluate improvements in classification accuracy.  Results: The relabeling process significantly enhanced prediction accuracy compared to the original dataset. The refined clustering structure reported better classification performance across all six algorithms, showing the effectiveness of the proposed method.  Conclusion: The results showed that robust clustering and a relabeling method improved the precision of tuition fee classification systems. The proposed framework provided a data-driven solution for refining classification models, ensuring a fairer distribution of tuition fee based on socioeconomic indicators. The novelty lies in the centroid-based relabeling, which uses principal component patterns to enhance interpretability and classification accuracy. The method was adaptable for global use in any educational system using socioeconomic-based fee classification. Future research should explore alternative clustering methods and additional socioeconomic factors to enhance classification accuracy.    Keywords: K-Means Clustering, Machine Learning, Relabeling Process, Socioeconomic Indicators, Tuition Fee Classification   
Co-Authors Alpiana, Intan Andi Iwan Nurhidayat ANITA QOIRIAH ARI KURNIAWAN Aulia Akbar, Rafy Aulia, Novi Rosidhatul Aviana, Anisah Nurul Ayuningtyas, Nimas Bayu Budi Prakoso Dani, Andrea Ervin Yohannes Farid Baskoro Hartanto, Unung Istopo I Gusti Putu Asto Buditjahjanto I Nyoman Budiantara IRMA FEBRIYANTI Iskandar Java, Muhammad Istianah, Eva Istopo Hartanto, Unung Mohammad Akbar, Mohammad Muhammad Risalah Naufal Mutmainah Mutmainah Naim Rochmawati Nautika, Puji Septiyana Nurlyan, Reynisa Beta Prasetyo, Andhika Edo Priadana, Benny Widya Putra, Fachrian Bimantoro Putri, Windy Chikita Cornia Putu Asto Buditjahjanto, I Gusti Raden Mohamad Herdian Bhakti Rahmawati, Naim Ricky Eka Putra Rina Harimurti Rochmawati, Naim Saputra, Andika Dermawan Sifriyani, Sifriyani Suroto Suroto Syandika, Novliyan Dimas Ulfa Siti Nuraini Vebriani, Mutiara Widi Aribowo Yuni Yamasari