Article Per Year (5 Year)
p-Index From 2020 - 2025
4.598
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal Inspiratif Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control UICELL Conference Proceeding Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA Hearty : Jurnal Kesehatan Masyarakat Jurnal Biomedika dan Kesehatan Psikologi Konseling: Jurnal Kajian Psikologi dan Konseling Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Health Information : Jurnal Penelitian Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL LA MEDIHEALTICO Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering JUKEJ: Jurnal Kesehatan Jompa Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Holistik Jurnal Kesehatan
Fatma Indriani
Program Studi Ilmu Komputer, Fakultas Matematika Dan Pengetahuan Alam, Universitas Lambung Mangkurat, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 50 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 50 Documents
Search

 1   2   3   4   5 
Implementation of Copeland Method on Wrapper-Based Feature Selection Using Random Forest For Software Defect Prediction Agustia Kuspita Aryanti; Rudy Herteno; Fatma Indriani; Radityo Adi Nugroho; Muliadi Muliadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/2pgffc67

Abstract

Software Defect Prediction is crucial to ensure software quality. However, high-dimensional data presents significant challenges in predictive modelling, especially identifying the most relevant features to improve model performance. Therefore, efforts are needed to address these issues, and one is to apply feature selection methods. This study introduces a new approach by applying the Copeland ranking method, which aggregates feature weights from multi-wrapper methods, including Recursive Feature Elimination (RFE), Boruta, and Custom Grid Search, using 12 NASA MDP datasets. The study also applies Random Forest classification and evaluates the model using AUC and t-Test. In addition, this study also compares the accuracy and precision values produced by each method. The results consistently show that the Copeland ranking method produces superior results compared to other ranking methods. The average AUC value obtained from the Copeland ranking method is 0.7496, higher than the Majority ranking method with an average AUC of 0.7416 and the Optimal Rank ranking method with an average AUC of 0.7343. These findings confirm that applying the Copeland ranking method in wrapper-based feature selection can enhance classification performance in software defect prediction using Random Forest compared to other ranking methods. The strength of the Copeland method lies in its ability to integrate rankings from various feature selection approaches and identify relevant features. The findings of this research demonstrate the potential of the Copeland ranking method as a reliable tool for ranking features obtained from various wrapper-based feature selection techniques. The implementation of this approach contributes to improved software defect prediction and provides new insights for the development of ranking methods in the future
Performance Comparison of Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (H-ELM) Methods for Heart Failure Classification on Clinical Health Datasets Ichwan Dwi Nugraha; Triando Hamonangan Saragih; Irwan Budiman; Dwi Kartini; Fatma Indriani; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.904

Abstract

Heart failure is one of the leading causes of death worldwide and requires accurate and timely diagnosis to improve patient outcomes. However, early detection remains a significant challenge due to the complexity of clinical data, high dimensionality of features, and variability in patient conditions. Traditional clinical methods often fall short in identifying subtle patterns that indicate early stages of heart failure, motivating the need for intelligent computational techniques to support diagnostic decisions. This study aims to enhance predictive modeling for heart failure classification by comparing two supervised machine learning approaches: Extreme Learning Machine (ELM) and Hierarchical Extreme Learning Machine (HELM). The main contribution of this research is the empirical evaluation of HELM's performance improvements over conventional ELM using 10-fold cross-validation on a publicly available clinical dataset. Unlike traditional neural networks, ELM offers fast training by randomly assigning weights and analytically computing output connections, while HELM extends this with a multi-layer structure that allows for more complex feature representation and improved generalization. Both models were assessed based on classification accuracy and Area Under the Curve (AUC), two critical metrics in medical classification tasks. The ELM model achieved an accuracy of 73.95% ± 8.07 and an AUC of 0.7614 ± 0.093, whereas the HELM model obtained a comparable accuracy of 73.55% ± 7.85 but with a higher AUC of 0.7776 ± 0.085. In several validation folds, HELM outperformed ELM, notably reaching 90% accuracy and 0.9250 AUC in specific cases. In conclusion, HELM demonstrates improved robustness and discriminatory capability in identifying heart failure cases. These findings suggest that HELM is a promising candidate for implementation in clinical decision support systems. Future research may incorporate feature selection, hyperparameter optimization, and evaluation across multi-center datasets to improve generalizability and real-world applicability.
Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification Indriani, Fatma; Nugroho, Radityo Adi; Faisal, Mohammad Reza; Kartini, Dwi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6100

Abstract

Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigate the impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from student comments.
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN) Azizah, Azkiya Nur; Budiman, Irwan; Indriani, Fatma; Faisal, M. Reza; Herteno, Rudy
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 2 (2024)
Publisher : Universitas Sriwijaya

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

Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.
Perbandingan Tingkat Kepuasan Pasien JKN dan Umum Terhadap Kualitas Pelayanan Rawat Jalan di Rumah Sakit Umum Daerah Rantauprapat Ritonga, Egril Rehulina; Indriani, Fatma; Hasibuan, Rapotan
Health Information : Jurnal Penelitian Vol 17 No 2 (2025): Mei-Agustus
Publisher : Poltekkes Kemenkes Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36990/hijp.v17i2.1694

Abstract

Berdasarkan pernyataan dari Organisasi Kesehatan Dunia (WHO), rumah sakit merupakan elemen penting dalam sistem sosial maupun kesehatan yang memiliki peran dalam masyarakat menyediakan layanan medis secara menyeluruh, termasuk tindakan pencegahan serta penanganan darurat secara luas. Penelitian ini merupakan penelitian analitik dengan desain cross sectional yang dilakukan di RSUD Rantauprapat pada bulan Februari hingga Maret 2025. Penelitian menggunakan pendekatan kuantitatif dengan metode komparatif untuk membandingkan kepuasan pasien JKN dan pasien umum. Data yang digunakan berupa data primer dan sekunder, dengan instrumen berupa kuesioner berdasarkan lima dimensi kualitas pelayanan: Tangible, Reliability, Responsiveness, Assurance, dan Empathy, yang diukur menggunakan skala Likert. Sampel dipilih secara Accidental Sampling dari pasien rawat jalan yang memenuhi kriteria inklusi dan eksklusi. Jumlah populasi pasien rawat jalan tahun 2024 adalah 130.488, terdiri dari 5.023 pasien umum dan 125.465 pasien JKN. Sampel sebanyak 136 responden, masing-masing 68 pasien JKN dan 68 pasien umum, ditentukan menggunakan rumus Isaac dan Michael. Data dianalisis menggunakan uji Mann-Whitney U karena data tidak berdistribusi normal.
FACTORS AFFECTING WORK STRESS AMONG EMPLOYEES AT PT. MARINDA UTAMAKARYA SUBUR DELI SERDANG BRANCH Khairiyah Dwie Vanesa; Fatma Indriani; Reni Agustina Harahap
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 4 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i4.3080

Abstract

Work stress is a global problem with around 450 million people affected according to WHO, with stress levels in the Asia Pacific region reaching 48%. This study aims to analyze the factors that influence work stress in employees of PT. Marinda Utamakarya Subur, Deli Serdang Branch. The research method uses an analytical study with a quantitative approach and cross-sectional design. The study population includes all employees of PT. Marinda Utamakarya Subur, Deli Serdang Branch with a total sampling technique, so that the sample size is 36 people. Data analysis uses the Mann Whhitney, Kruskal Wallis, and Spearman tests. The results showed that there was no significant correlation between age and work stress (r = -0.028; p = 0.871) and education level with work stress (p = 0.190). While there was a significant correlation between workload and work stress (r = 0.410; p = 0.013) with a moderate negative correlation, gender with work stress ( p = 0.042) where male employees tend to experience higher work stress, and length of service with work stress (p = 0.015) with a tendency for employees who work more than 4 years to have higher stress levels. The conclusion of the study shows that gender, length of service and workload are significant factors that influence work stress and age and education status do not have a significant effect on work stress in employees of PT. Marinda Utamakarya Subur, Deli Serdang Branch.
Hubungan Posisi Kerja Dengan Keluhan Musculoskletal Disorder (MSDs) Pada Pemanen Sawit di PTPN IV Tanah Itam Ulu Ananda, Zahra; Astuty, Delfriana Ayu; Indriani, Fatma
Jurnal Biomedika dan Kesehatan Vol 8 No 2 (2025)
Publisher : Fakultas Kedokteran Universitas Trisakti

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

Abstract

Background Musculoskeletal disorders (MSDs) are a common occupational health issue among plantation workers due to unergonomic working postures. Oil palm harvesters are especially vulnerable because of repetitive physical activities and awkward body positions. Work processes involving strenuous physical effort, such as lifting tools, cutting bunches, bending, and reaching for fronds for extended periods, along with unergonomic postures, increase the risk of MSDs. This study aims to determine the relationship between work posture and MSDs complaints among oil palm harvesting workers at PTPN IV Tanah Itam Ulu. Methods A cross-sectional quantitative study was performed with a sample of 50 Fresh Fruit Bunch (FFB) harvesters. Data were collected through observations and interviews using the Rapid Entire Body Assessment (REBA) to evaluate work posture and the Nordic Body Map (NBM) to assess MSDs symptoms. Data analysis was conducted using the Chi-Square test with a significance level of α = 0.05 in SPSS. Results The study revealed that 52% of workers experienced high-risk MSDs, while 48% faced very high-risk MSDs. A statistically significant relationship was identified between work posture and MSDs complaints (p = 0.000).   Conclusions Poor ergonomic posture significantly increases the risk of MSDs among palm oil harvesters. Ergonomic interventions, including training on proper posture and the use of assistive tools, are strongly recommended to reduce risk and improve occupational health well-being.
Improving Diabetes Prediction Using Feedforward Neural Network with Adam Optimization and SMOTE Technique Wijaya Kusuma, Arizha; Mazdadi, Muhammad Itqan; Kartini, Dwi; Farmadi, Andi; Indriani, Fatma; P., Chandrasekaran
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.127

Abstract

Diabetes mellitus is a chronic metabolic disorder that demands early and accurate detection to prevent life-threatening complications. Traditional diagnostic procedures, such as blood glucose tests and oral glucose tolerance tests, are often invasive, time-consuming, and resource-intensive, making them less practical for widespread screening. This study aims to explore the potential of artificial intelligence, specifically Feedforward Neural Networks (FNN), in predicting diabetes based on clinical data from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The main contribution of this research lies in the application of the Adaptive Moment Estimation (Adam) optimization algorithm and the Synthetic Minority Oversampling Technique (SMOTE) to enhance the performance and generalization of the FNN on imbalanced medical datasets. The methodology involves preprocessing steps such as imputing zero values with feature means, normalizing input features using Min-Max scaling, and applying SMOTE to balance class distribution. Two model configurations were compared: a baseline FNN trained manually using full-batch gradient descent and a second FNN optimized using Adam. Experimental results demonstrated that the baseline model achieved an accuracy of 70.13%, precision of 56.06%, recall of 68.52%, and F1-score of 61.67%, while the Adam-optimized model achieved superior results with an average accuracy of 73.31%, precision of 60.97%, recall of 66.67%, and F1-score of 63.64% across ten independent runs. These findings indicate that combining adaptive optimization with oversampling significantly enhances the robustness and reliability of neural networks for medical classification tasks. In conclusion, the proposed method provides an effective framework for AI-assisted early diabetes detection and opens pathways for future development using deeper network architectures and explainable AI models for clinical applications.
Efektivitas Edukasi Dini Menggunakan Leaflet Terhadap Peningkatan Pengetahuan Diabetes Melitus pada Siswa MTsS Nurhasanah Labuhan Ruku Barus, Nency Utami Br; Mawandri, Dwi; Risma, Ade; Zahra, Fairuz; Susanti, Nofi; Indriani, Fatma
JUKEJ : Jurnal Kesehatan Jompa Vol 4 No 2 (2025): JUKEJ: Jurnal Kesehatan Jompa
Publisher : Yayasan Jompa Research and Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57218/jkj.Vol4.Iss2.1865

Abstract

Diabetes Mellitus (DM) is a global and national health issue with a continuously rising prevalence. Data from the 2023 Indonesian Health Survey (SKI) indicates that the prevalence of DM in individuals aged ≥15 years has reached 11.7%. This increase highlights the crucial need for early education, especially among students. This study aims to evaluate the effectiveness of education using a leaflet medium in improving students' knowledge of DM. The research employed a quantitative method with a quasi-experimental approach and a one-group pretest-posttest design. The sample consisted of 31 seventh and eighth-grade students from MTsS Nurhasanah Labuhan Ruku, selected through total sampling. Knowledge was measured using a questionnaire administered both before (pretest) and after (posttest) the educational intervention with the leaflet. A bivariate data analysis using the Paired Samples Test revealed a significant increase in students' knowledge levels. Before the education, 71% of students had poor knowledge, which decreased to 16.1% after the intervention. Conversely, the percentage of students with good knowledge increased from 29% to 83.9%. The statistical test result showed a p-value of 0.0001, confirming a significant difference in student knowledge before and after the intervention. Therefore, this study concludes that education through a leaflet medium is effective in improving students' knowledge of diabetes mellitus.
Performance Comparison of AdaBoost, LightGBM, and CatBoost for Parkinson's Disease Classification Using ADASYN Balancing Anshari, Muhammad Ridha; Saragih, Triando Hamonangan; Muliadi, Muliadi; Kartini, Dwi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4726

Abstract

Parkinson's disease is a neurodegenerative condition identified by the decline of neurons that produce dopamine, causing motor symptoms such as tremors and muscle stiffness. Early diagnosis is challenging as there is no definitive laboratory test. This study aims to improve the accuracy of Parkinson's diagnosis using voice recordings with machine learning algorithms, such as AdaBoost, LightGBM, and CatBoost. The dataset used is Parkinson's Disease Detection from Kaggle, consisting of 195 records with 22 attributes. The data was normalized with Min-Max normalization, and class imbalance was resolved with ADASYN. Results show that ADASYN-LightGBM and ADASYN-CatBoost have the best performance with 96.92% accuracy, 97.10% precision, 96.92% recall, and 96.92% F1 score. This improvement suggests that combining boosting methods and data balancing techniques can improve the accuracy of Parkinson's diagnosis. These results demonstrate the effectiveness of ADASYN in addressing data imbalance and improving the performance of boosting algorithms for medical classification problems. The findings contribute to the development of intelligent diagnostic systems in the field of medical informatics and computer science. These findings are essential for developing more accurate and efficient diagnostic tools, supporting early diagnosis and better management of Parkinson's disease.
Co-Authors Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Achmad Rizal Agustia Kuspita Aryanti Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Al Habesyah, Noor Zalekha Amini, Aisah Ananda, Zahra Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Azizah, Azkiya Nur Baron Hidayat Barus, Nency Utami Br Berutu, Marwiyah Carolina, Ayu Dendy Fadhel Adhipratama Dendy Difa Fitria Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Fahmi Setiawan Fairudz Shahura Faisal, M. Reza Faisal, Mohammad Reza Fajrin Azwary Friska Abadi Ghinaya, Helma Gustara, Rizki Asih Harahap, Helma Denisah Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudy Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Irwan Budiman Irwan Budiman Khairiyah Dwie Vanesa M. Apriannur M. Khairul Rezki Mahmud Mahmud Mawandri, Dwi Mohammad Mahfuzh Shiddiq Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Nita Arianty Nofi Susanti Nurhayani nurhayani Nurhayati Octavia, Mayang Dwi Oni Soesanto P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Purnajaya, Akhmad Rezki Radityo Adi Nugroho Rapotan Hasibuan Reni Agustina Harahap Ridha Afifa Risma, Ade Ritonga, Egril Rehulina Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Saragih, Triando Hamonangan Sa’diah, Halimatus Selvia Indah Liany Abdie Soesanto, Oni Sri Rahayu Suci Wulandari Triyoolanda, Anggun Wahyu Caesarendra Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zata Ismah Zida Ziyan Azkiya