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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of ICT Research and Applications Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Ilmu Komputer dan Bisnis PREPOTIF : Jurnal Kesehatan Masyarakat Media Jurnal Informatika ProBisnis : Jurnal Manajemen Journal of Innovation Research and Knowledge Da'watuna: Journal of Communication and Islamic Broadcasting Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Journal of Advanced Studies in Management
Ignatius Wiseto Prasetyo Agung
Universitas Adhirajasa Reswara Sanjaya (ARS University)
Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
Exploring the Impact of Fear of Missing Out on Perceived Ease of Use and Usage Decisions in Digital Platforms Zoraifi, Renata; Agung, Ignatius Wiseto Prasetyo; Arifin, Samsul
JOURNAL OF ADVANCED STUDIES IN MANAGEMENT Vol. 1 No. 2 (2024): November 2024
Publisher : Magister Manajemen of Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research endeavors to examine the mediating function of perceived ease of use within the context of the relationship between Fear of Missing Out (FOMO) and usage decisions among Generation Z users of digital platforms. Employing a quantitative research design, a total of 125 participants were engaged through an online survey instrument. The collected data were subjected to analysis utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that FOMO exerts a significant impact on usage decisions, both directly and indirectly through perceived ease of use, underscoring the critical role of user perception in the adoption of technology. These findings enhance the comprehension of the psychological and technological determinants influencing decision-making and offer practical recommendations for platform developers aiming to engage Generation Z.
Pengaruh E-Service Quality dan E-Trust terhadap E-Satisfaction dan Dampaknya pada E-Repurchase Intention Pengguna Layanan Konsultasi Media Online Khrisna Ayuningtias, Tyagita; Erliany Syaodih; Wiseto P. Agung
Da'watuna: Journal of Communication and Islamic Broadcasting Vol. 4 No. 3 (2024): Da'watuna: Journal of Communication and Islamic Broadcasting (In Press)
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/dawatuna.v4i3.1163

Abstract

Apps for telemedicine are becoming increasingly popular as a result of the COVID-19 outbreak. Telemedicine thus competes with other providers to meet patient demand. Many tactics are used to overcome this, such as providing confidence and quality patients. The purpose of this study is to analyze the effect of E-Service Quality and E-Trust on E-Satisfaction and its impact on E-Repurchase Intention on users of Online Medical Consultation Services. This type of research is quantitative. The sample of this study amounted to 100 respondents who had an Online Medical Consultation Service application on a mobile phone and had at least consulted through the application or website at least once. The analysis method used is path analysis. The results of the study show that there is a direct influence between the variables of E-service Quality on E-Satisfaction in users of online medical consultation services. There is a direct influence between E-Trust variables on E-Satisfaction in users of online medical consultation services. There is a direct influence between the variables E-service Quality and E-Trust on E-Satisfaction in users of online consulting services. There is a direct influence between the variables E-service Quality, E-Trust, and E-Satisfaction, on E-Repurchase Intention in users of online medical consultation services. There is an Effect of E-service Quality and E-Trust on E-Satisfaction of 9.9% while the remaining 90.1% (100% - 9.9%) is contributed by other factors. There is an effect of E-service Quality, E-Trust, and E-Satisfaction on E-Repurchase Intention of 34% while the remaining 66% (100% - 34%) is contributed by other factors.
EVALUASI PENERAPAN MODEL EARLY WARNING SCORE DALAM MENDUKUNG MUTU PELAYANAN RAWAT INAP RSUD “X” KABUPATEN SUKABUMI Tresna Ridha Nurramadhani; Rohendi, Rohendi; Wiseto P. Agung
Journal of Innovation Research and Knowledge Vol. 5 No. 7 (2025): Desember 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jirk.v5i7.11809

Abstract

Peningkatan mutu pelayanan kesehatan di rumah sakit sangat bergantung pada kemampuan tenaga medis dalam mendeteksi dini kondisi kritis pasien. Early Warning Score (EWS) merupakan sistem skoring yang digunakan untuk memantau tanda vital pasien secara sistematis, sehingga dapat mencegah keterlambatan penanganan. Namun, implementasi EWS di rumah sakit daerah sering kali menghadapi kendala yang berdampak pada efektivitasnya. Penelitian ini bertujuan untuk mengevaluasi penerapan EWS di RSUD “X” Kabupaten Sukabumi, mengidentifikasi hambatan yang dihadapi tenaga kesehatan, serta memberikan rekomendasi strategis untuk optimalisasi sistem. Penelitian menggunakan metode campuran (mixed methods) dengan desain sequential explanatory. Data kualitatif diperoleh melalui wawancara mendalam dan observasi terhadap perawat, dokter, serta tim manajemen, sedangkan data kuantitatif dikumpulkan melalui kuesioner dan analisis rekam medis pasien. Hasil penelitian menunjukkan bahwa sebagian besar tenaga kesehatan memahami manfaat EWS sebagai alat deteksi dini. Meskipun demikian, tingkat kepatuhan dalam pencatatan dan eskalasi klinis masih belum optimal. Hambatan utama yang ditemukan meliputi keterbatasan sumber daya manusia, sarana-prasarana monitoring, sistem supervisi dan audit yang belum konsisten, beban kerja tinggi, serta budaya keselamatan pasien yang masih lemah.Berdasarkan temuan tersebut, disarankan beberapa langkah perbaikan, antara lain pelatihan berkesinambungan berbasis simulasi, penguatan supervisi klinis dan monitoring kepatuhan, digitalisasi formulir EWS yang terintegrasi dengan rekam medis, penyesuaian rasio perawat-pasien, serta pengembangan budaya keselamatan pasien melalui komunikasi dan dukungan manajemen. Dengan strategi komprehensif ini, penerapan EWS di RSUD “X” diharapkan lebih efektif dalam meningkatkan mutu pelayanan dan keselamatan pasien.
Emotion Detection in Indonesian Text Using the Logistic Regression Method Junianto, Erfian; Puspitasari, Mila; Zakaria, Salman Ilyas; Arifin, Toni; Agung, Ignatius Wiseto Prasetyo
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5927

Abstract

Emotion detection in Indonesian text has become a crucial topic in the advancement of human–computer interaction and sentiment analysis on digital platforms. Despite its importance, challenges arise from the linguistic complexity and frequent use of slang in Indonesian text. This study aims to evaluate the performance of three classification models—Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—in detecting emotions from Indonesian text. The dataset comprises 1,000 texts categorized into four emotions: happy, sad, angry, and fear. Preprocessing steps included slang normalization, text cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF weighting. Each model was trained and further optimized using ensemble bagging to improve classification performance. The optimized Logistic Regression model achieved the best performance, with an accuracy of 89%, precision of 0.90, recall of 0.89, F1-score of 0.89, and an average ROC-AUC score of 0.98. Both KNN and Naive Bayes models reached 81% accuracy after optimization, but their overall performance remained lower than Logistic Regression. The findings demonstrate that Logistic Regression is the most effective method for detecting emotions in Indonesian text, as it can effectively handle simple grammatical structures and slang variations. This study contributes to the development of emotion analysis models for Indonesian text, supporting applications in social computing and affective computing.
Stock Price Forecasting Using LSTM with Cross-Validation Rifki Ainul Yaqin; Anshori, Muhammad Iqbal; Angel, Reddis; Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.45130

Abstract

Stock price prediction remains challenging due to the market’s nonlinear, volatile nature, influenced by diverse economic and behavioral factors. Traditional models often suffer from overfitting and limited generalizability. This study addresses these limitations from prior research by other researchers for integrating Long Short-Term Memory (LSTM) with k-Fold Cross-Validation to improve prediction robustness. The proposed framework systematically evaluates model performance across varying market conditions. This methodological contribution enhances forecasting accuracy and stability, offering a more reliable approach to complex financial time series prediction. This study employs LSTM with one to two layers of 64–128 units, trained using Adam and dropout regularization, to capture long-term dependencies in stock price data. The workflow integrates feature selection, Min-Max scaling, and k-Fold Cross-Validation for robust evaluation. Model performance is assessed using RMSE, with reconfiguration applied to address underfitting or overfitting. The proposed model demonstrated substantial performance gains, achieving an average RMSE improvement of approximately 78.40% across all tested stocks compared to prior research. These enhancements are attributed to optimal hyperparameter tuning, consistent use of the Adam optimizer, and the implementation of k-Fold Cross-Validation, which reduced overfitting and provided more stable evaluations. Furthermore, findings revealed that simpler feature sets, such as using only closing prices, can outperform multiple technical indicators when normalization is inadequate, underscoring the importance of robust preprocessing and validation strategies. This study concludes that integrating LSTM with k-Fold Cross-Validation and optimized hyperparameters significantly improves stock price prediction accuracy.
Co-Authors Acep Rohendi Agustin, Dari Dianata Alfaruk, Muhammad Harist Angel, Reddis Anshori, Muhammad Iqbal Ardi, Wildan Khalifah Bambang Riyanto Erfian Junianto Erliany Syaodih Erliany Syaodih Hidayat, Fajar Mukti Junaedi, Novan Khrisna Ayuningtias, Tyagita Mayangsari, Ariefa Diah Muhammad Rizqi Prawasari, Nindyan Puspitasari, Mila Rabbani, Muhammad Ihsan Ramadhan, Fanky Fazdianki Renata Zoraifi, Renata Rifki Ainul Yaqin Rinaldi Munir Rizal Rachman, Rizal Rohendi, Rohendi Salam, Regi Samsul Arifin Sarwono Sutikno Sarwono Sutikno SATRIYAS ILYAS Toni Arifin Tresna Ridha Nurramadhani Wibowo, Ilham Rachmat Zakaria, Salman Ilyas