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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Administrasi Kesehatan Indonesia Gadjah Mada Journal of Psychology Jurnal Ekonomika: Manajemen, Akuntansi & Perbankan Syari'ah Jurnal Psikologi Universitas Gadjah Mada Buletin Psikologi Jurnal Pengabdian Masyarakat Ilmu Kesehatan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Maya Fitria
Program Studi Psikologi Universitas Islam Negeri Sunan Kalijaga Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences

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Keadilan Gender dan Hak-hak Reproduksi di Pesantren Fitria, Maya; Helmi, Avin Fadilla
Jurnal Psikologi Vol 38, No 1 (2011)
Publisher : Faculty of Psychology, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (192.027 KB) | DOI: 10.22146/jpsi.7660

Abstract

This study was intended to understand how the phenomenon of gender equity and reproductive rights in pesantren using the theoretical framework of attitude. The research wasconducted in a qualitative approach through a case study method. Sources of data consisted of 10 interviewed subjects, 18 FGD subjects, and survey of 327 subjects. Subjects were varyingfrom pesantren’s supervisor, teacher, manager and doctor of pesantren’s Community Health Center, and santri itself. Data was also obtained from the observation of the supervisors’behaviors and the pesantren’s environment. Subjects tended to agree in distinguishing the male and female gender role based on what’s happening, different from the religious teachingsthey learned which tended to be gender fair. Regarding with women reproductive cases, subjects tended to be gender biased based on the interpretation of religious texts although theyadmitted that it was hard to be manifested in behavior, example: prefering monogamous marriage,never beating women, and not promoting early marriage. Subjects agreed andunderstood that women had different, and yet more complex anatomical processes and functions, however their health service were just considered the same as men.
Keadilan Gender dan Hak-hak Reproduksi di Pesantren Maya Fitria; Avin Fadilla Helmi
Jurnal Psikologi Vol 38, No 1 (2011)
Publisher : Faculty of Psychology, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (192.027 KB) | DOI: 10.22146/jpsi.7660

Abstract

This study was intended to understand how the phenomenon of gender equity and reproductive rights in pesantren using the theoretical framework of attitude. The research wasconducted in a qualitative approach through a case study method. Sources of data consisted of 10 interviewed subjects, 18 FGD subjects, and survey of 327 subjects. Subjects were varyingfrom pesantren’s supervisor, teacher, manager and doctor of pesantren’s Community Health Center, and santri itself. Data was also obtained from the observation of the supervisors’behaviors and the pesantren’s environment. Subjects tended to agree in distinguishing the male and female gender role based on what’s happening, different from the religious teachingsthey learned which tended to be gender fair. Regarding with women reproductive cases, subjects tended to be gender biased based on the interpretation of religious texts although theyadmitted that it was hard to be manifested in behavior, example: prefering monogamous marriage,never beating women, and not promoting early marriage. Subjects agreed andunderstood that women had different, and yet more complex anatomical processes and functions, however their health service were just considered the same as men.
PSIKOLOGI DALAM PROSES PERUBAHAN SOSIAL Maya Fitria
Buletin Psikologi Vol 10, No 1 (2002)
Publisher : Faculty of Psychology Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (43.156 KB) | DOI: 10.22146/bpsi.7450

Abstract

Berbagai teori sosial, ekonomi, politik, dan budaya lahir dari pemberian makna atas realitas sosial. Pemaknaan yang berbeda-beda tentu saja akan berimplikasi pada perlakuan yang berbeda pula. Pemaknaan yang berbeda atas satu realitas sosial yang sama sangat dipengaruhi oleh pengambilan sudut pemikiran atau katakanlah perspektif yang berbeda.
DETERMINE THE POLICY TARGET TO INCREASE INSTITUTIONAL DELIVERY AMONG INDONESIAN FEMALE WORKERS Syahri, Isyatun Mardhiyah; Laksono, Agung Dwi; Fitria, Maya; Rohmah, Nikmatur; Lolong, Dina Bisara; Alruwaili, Abdulah Saleh
Indonesian Journal of Health Administration (Jurnal Administrasi Kesehatan Indonesia) Vol. 12 No. 2 (2024): December
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jaki.v12i2.2024.228-237

Abstract

Introduction: Indonesia continues to face a significant challenge in terms of maternal and infant mortality. The government is working to promote the use of health facilities for childbirth to mitigate maternal mortality. Aim: The study aims to determine the policy target to increase the rate of institutional delivery among female workers in Indonesia. Methods: The study analyzed secondary data from the 2023 Indonesian Health Survey. It conducted cross-sectional research on 30,173 female workers. In addition to institutional delivery as the dependent variable, we examined eight independent variables: residence, age, education, marital status, wealth, insurance, antenatal care (ANC), and parity. The analysis involved bivariate method followed by binary logistic regression in the last stage. Results: Approximately 70.6% of female workers had institutional delivery. Female workers in urban areas were 1.157 times more likely than rural workers to perform institutional delivery (95%CI 1.153-1.161). Three worker characteristics (age, education, and marital status) were related to institutional delivery. Wealthier workers had a greater the possibility of executing institutional delivery. Insured workers were more likely than the uninsured ones to deliver in health facilities. Female workers with adequate ANC were 1.210 times more likely than those with inadequate ANC to execute institutional delivery (95%CI 1.166-1.256). Additionally, women with fewer childbirths had a higher probability of performing an institutional delivery. Conclusion: The policy target to increase institutional delivery was women workers in rural areas who were older, had poor education, were divorced/widowed, were the poorest, had inadequate ANC, were uninsured, and were grand multiparous. Keywords: institutional delivery, institutional birth, maternal health, female worker, public health.
ANALISIS BREAK EVEN POINT (BEP) SEBAGAI ALAT PERENCANAAN LABA Rohmah, Siti; Fitria, Maya
Jurnal Ekonomika: Manajemen, Akuntansi, dan Perbankan Syari'ah Vol. 13 No. 2 (2024): September
Publisher : Economic Faculty, University of Widya Gama Mahakam Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/je.v13i2.3129

Abstract

Penelitian bertujuan karena melihat kondisi penurunan dan peningkatan total penjualan dan laba bersih pada perusahaan UD. Heri Jaya pada Tahun 2018-2022. Pada Tahun 2022 penjualan mengalami penurunan menjadi Rp 61.967.950.000. Hal ini terjadi akibat dampak dari pandemi Covid-19. Jenis penelitian ini adalah deskriptif kuantitaif yang dilaksanakan pada data-data keuangan UD. Heri Jaya. Metode analisis data dalam penelitian ini adalah perhitungan Break Even Point (BEP), perhitungan margin kontribusi, dan perhitungan perencanaan laba. Berdasarkan hasil penelitian dan pembahasan dapat disimpulkan bahwa pada tahun 2018 perusahan UD. Heri Jaya memperoleh BEP Unit beras 5 kg sebesar 12.462 unit, beras 10 kg sebesar 4.492 unit, dan beras 25 kg sebesar 1.524 unit. Pada tahun 2019, diperoleh BEP Unit beras 5 kg sebesar 9.232 unit, beras 10 kg sebesar 3.941, dan beras 25 kg sebesar 1.472 unit. Pada tahun 2020 BEP Unit beras 5 kg sebesar 17.550 unit, beras 10 kg sebesar 7.201 unit, dan beras 25 kg sebesar 2.649. Pada tahun 2021 BEP Unit beras 5 kg sebesar 18.211, beras 10 kg sebesar 7.381, dan beras 25 kg sebesar 2.661. pada tahun 2022 BEP Unit beras 5 kg sebesar 12.684, beras 10 kg sebesar 5.750, dan beras 25 kg sebesar 2.411 unit. Setelah dilakukan analisis BEP terhadap perusahaan UD. Heri Jaya dismpulkan bahwa analisis BEP dapat mempermudah perusahaan untuk mengetahui seberapa besar unit yang harus dijual guna memperoleh laba yang diharapkan.
Impact of Image Quality Enhancement Using Homomorphic Filtering on the Performance of Deep Learning-Based Facial Emotion Recognition Systems Bahri, Al; Oktiana, Maulisa; Fitria, Maya; Zulfikar
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30409

Abstract

Facial emotion recognition technology is crucial in understanding human expressions from images or videos by analyzing distinct facial features.  A common challenge in this technology is accurately detecting a person's facial expression, which can be hindered by unclear facial lines, often due to poor lighting conditions. To address these challenges, it is essential to improve image quality. This study investigates how enhancing image quality through homomorphic filtering and sharpening techniques can boost the accuracy and performance of deep learning-based facial emotion recognition systems. Improved image quality allows the classification model to focus on relevant expression features better.  Therefore, this research contributes to in facilitating more intuitive and responsive communications by enabling system to interpret and respond to human emotions effectively. The testing used three different architectures: MobileNet, InceptionV3, and DenseNet121. Evaluasi kinerja dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Experimental results indicated that image enhancement positively impacts the accuracy of the facial emotion recognition system. Specifically, the average accuracy increased by 1-2% for the MobileNet architecture, by 5-7% for InceptionV3, and by 1-3% for DenseNet121.
Pelatihan Peregangan pada Pekerja Konveksi Sebagai Upaya Penurunan Keluhan Musculoskeletal Disorders (MSDs) Syahri, Isyatun Mardhiyah; Asfriyati, Asfriyati; Sanusi, Sri Rahayu; Mutiara, Erna; Fitria, Maya
Jurnal Pengabdian Masyarakat Ilmu Kesehatan Vol 6, No 2 (2025): Edisi Juli
Publisher : LPPM Institut Kesehatan Helvetia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33085/jpmik.v6i2.6530

Abstract

Keluhan Musculoskeletal Disorders (MSDs) merupakan salah satu permasalahan kesehatan kerja yang umum dialami oleh pekerja, khususnya di sektor konveksi yang melibatkan posisi duduk membungkuk dan aktivitas berulang dalam jangka waktu lama. Posisi seharusnya dilakukan dengan punggung tetap tegak dan siku sejajar meja kerja untuk mencegah ketegangan otot. Pekerja konveksi di Pusat Industri Kecil (PIK) Kota Medan tergolong kelompok yang sangat rentan terhadap gangguan ini sebab proses kerja yang monoton, repetitif, dan dilakukan tanpa penerapan ergonomi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan kesadaran pekerja mengenai bahaya MSDs serta memberikan solusi praktis melalui pelatihan peregangan yang mudah diterapkan di lingkungan kerja. Metode yang digunakan adalah kombinasi ceramah edukatif dan intervensi peregangan praktis, yang dilengkapi dengan media video peregangan dari Kementerian Kesehatan RI, diskusi interaktif, serta evaluasi dengan pre-test dan post-test. Kegiatan ini diikuti oleh 30 pekerja konveksi dan mencakup tahapan sosialisasi awal, penyuluhan materi, simulasi gerakan peregangan, hingga dokumentasi akhir. Hasil evaluasi menunjukkan peningkatan signifikan dalam tingkat pengetahuan pekerja setelah pelatihan, dari 47% menjadi 80%. Selain itu, keluhan nyeri pada leher, bahu dan punggung yang sebelumnya dirasakan banyak peserta mengalami penurunan. Pelatihan ini dinilai sangat relevan dengan kebutuhan mitra, bersifat praktis, tidak memerlukan alat tambahan, dan dapat dilakukan secara mandiri. Dampak positif kegiatan ini tidak hanya terlihat pada aspek pengetahuan, tetapi juga pada perubahan perilaku dan semangat menjaga kesehatan kerja. Keberhasilan program ini menunjukkan potensi replikasi di sektor kerja lain yang memiliki risiko serupa, dengan dukungan pemantauan berkelanjutan untuk hasil yang optimal
Performance Evaluation of EfficientNetB3-Based Deep Learning Model for the Classification of Acute Lymphoblastic Leukemia and Normal Blood Cells Muchallil, Sayed; Fitria, Maya; Arrahman, Ridha; Saddami, Khairun
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.v7i3.113

Abstract

Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer that predominantly affects children and requires early and accurate diagnosis to improve patient survival rates. Traditional diagnostic methods rely heavily on manual examination of blood smear images by pathologists, which is not only time-consuming but also susceptible to human error and variability. To address this limitation, this study proposed an automated detection model based on deep learning, specifically employing the EfficientNetB3 convolutional neural network architecture. A publicly available dataset containing microscopic images of ALL and normal blood cells was used for training and evaluation. The images were preprocessed using normalization and augmentation techniques and resized to 300×300 pixels to align with the EfficientNetB3 input requirements. The model was trained using the Adam optimizer and monitored with EarlyStopping to prevent overfitting. Experimental results showed that the proposed model achieved an accuracy of 92.23%, precision of 92.75%, and recall of 95.57%, significantly outperforming conventional approaches such as Canberra distance, K-Nearest Neighbor, and ensemble CNN methods. In addition to the classification model, a web-based ALL detection system was developed to make the solution more accessible and user-friendly. The frontend was built using ReactJS, while the backend API, built with Flask, handles image input, model inference, and output delivery. The interface allows users to upload cell images, input patient names, and receive instant classification results along with confidence scores. This integrated system demonstrates a practical application of AI in medical diagnostics and holds potential for use in real-world, resource-limited clinical settings.
The Role of U-Net Segmentation for Enhancing Deep Learning-based Dental Caries Classification Yassar, Muhammad Keysha Al; Fitria, Maya; Oktiana, Maulisa; Yufnanda, Muhammad Aditya; Saddami, Khairun; Muchtar, Kahlil; Isma, Teuku Reza Auliandra
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.75

Abstract

Dental caries, one of the most prevalent oral diseases, can lead to severe complications if left untreated. Early detection is crucial for effective intervention, reducing treatment costs, and preventing further deterioration. Recent advancements in deep learning have enabled automated caries detection based on clinical images; however, most existing approaches rely on raw or minimally processed images, which may include irrelevant structures and noise, such as the tongue, lips, and gums, potentially affecting diagnostic accuracy. This research introduces a U-Net-based tooth segmentation model, which is applied to enhance the performance of dental caries classification using ResNet-50, InceptionV3, and ResNeXt-50 architectures. The methodology involves training the teeth segmentation model using transfer learning from backbone architectures ResNet-50, VGG19, and InceptionV3, and evaluating its performance using IoU and Dice Score. Subsequently, the classification model is trained separately with and without segmentation using the same hyperparameters for each model with transfer learning, and their performance is compared using a confusion matrix and confidence interval. Additionally, Grad-CAM visualization was performed to analyze the model's attention and decision-making process. Experimental results show a consistent performance improvement across all models with the application of segmentation. ResNeXt-50 achieved the highest accuracy on segmented data, reaching 79.17%, outperforming ResNet-50 and InceptionV3. Grad-CAM visualization further confirms that segmentation plays a crucial role in directing the model’s focus to relevant tooth areas, improving classification accuracy and reliability by reducing background noise. These findings highlight the significance of incorporating tooth segmentation into deep learning models for caries detection, offering a more precise and reliable diagnostic tool. However, the confidence interval analysis indicates that despite consistent improvements across all metrics, the observed differences may not be statistically significant.
Co-Authors Agung Dwi Laksono Alruwaili, Abdulah Saleh Arrahman, Ridha Asfriyati ,MKes Avin Fadilla Helmi Bahri, Al Dina Bisara Lolong Erna Mutiara Isma, Teuku Reza Auliandra Muchtar, Kahlil Oktiana, Maulisa Rohmah, Nikmatur Saddami, Khairun Sayed Muchallil Siti Rohmah Sri Rahayu Sanusi Syahri, Isyatun Mardhiyah Yassar, Muhammad Keysha Al Yufnanda, Muhammad Aditya Zulfikar