Article Per Year (5 Year)
p-Index From 2021 - 2026
7.277
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 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 JOURNAL OF SCIENCE AND SOCIAL RESEARCH 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 International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL LA MEDIHEALTICO Journal of Gender and Social Inclusion in Muslim Societies MAHESA : Malahayati Health Student Journal Fitrah: Journal of Islamic Education Jurnal Kolaboratif Sains Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering Journal of Embedded Systems, Security and Intelligent Systems Jurnal Pengabdian Kepada Masyarakat Itekes Bali JUKEJ: Jurnal Kesehatan Jompa Jurnal Informatika Polinema (JIP) Jurnal Ilmiah Kesehatan Mandala Waluya Jurnal Kesehatan Masyarakat Perkotaan 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 Humanities Biochemistry, Genetics & Molecular Biology 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 76 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 76 Documents
Search

 4   5   6   7   8 
CARING FOR THE EARTH AND HEALING STUDENTS THROUGH THE SYNERGY OF ISLAMIC ECOTHEOLOGY AND COUNSELING IN WASTE EDUCATION Ashar, Yulia Khairina; Indriani, Fatma; Iqbal, M.; Safira, Putri; Lauchan, Agil Maritho
Journal of Gender and Social Inclusion in Muslim Societies Vol 6, No 2 (2025): Journal of Gender and Social Inclusion in Muslim Societies (JGSIMS)
Publisher : Pusat Studi Gender dan Anak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jgsims.v6i2.26522

Abstract

The issue of waste is a very pressing matter that requires serious attention because it has a direct impact on the environment, health, and the sustainability of human life. This study aims to increase the knowledge and attitudes of Islamic boarding school students. The objective of the study is to improve knowledge and attitudes to care for and heal Islamic boarding school students through the synergy of Islamic ecotheology and counseling in waste education. The type of research used in this study is quasi-experimental research with a one-group pretest-posttest design. This study involved 30 santri who were selected purposively based on recommendations from the pesantren. The statistical test used was the Paired Sample t-Test with the help of JASP version 16 computer software. The results of this study show that interventions in the form of counseling can significantly improve the knowledge and attitudes of santri regarding Islamic ecotheology and waste management in pesantren. The Wilcoxon test results showed a significant increase in knowledge (p < 0.001), while the Paired Sample t-Test showed an increase in attitude from an average score of 26.60 on the pretest to 36.10 on the posttest with a p-value < 0.001. This confirms that counseling not only improves conceptual understanding but also encourages positive attitude changes that have the potential to continue in real behavior in maintaining the cleanliness and sustainability of the pesantren environment.
Pengaruh Edukasi Media Poster Terhadap Peningkatan Pengetahuan Masyarakat Mengenai STBM Di Wilayah Kerja Puskesmas Bandar Senembah Indriani, Fatma; Ramadhanu, Suhada; Tarigan, David Brando Pratama; Daulay, Rangga Muriansyah; Zaki, Ahmad; Gunawan, Muhammad Khair; Aulia, Rizky Gunadi
JUKEJ : Jurnal Kesehatan Jompa Vol 4 No 4 (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.Iss4.2196

Abstract

Sanitasi Total Berbasis Masyarakat (STBM) merupakan pendekatan strategis dalam meningkatkan kesehatan lingkungan melalui perubahan perilaku masyarakat. Namun, masih banyak wilayah di Indonesia yang belum sepenuhnya memahami dan menerapkan lima pilar STBM secara menyeluruh. Penelitian ini bertujuan untuk mengetahui pengaruh edukasi menggunakan media poster terhadap peningkatan pengetahuan masyarakat mengenai STBM di wilayah kerja Puskesmas Bandar Senembah. Jenis penelitian ini adalah pra-eksperimen dengan desain one group pre-test and post-test. Penelitian pra-eksperimen dengan desain one group pre-test and post-test ini bertujuan menganalisis pengaruh edukasi menggunakan media poster terhadap peningkatan pengetahuan masyarakat mengenai Sanitasi Total Berbasis Masyarakat (STBM) di wilayah kerja Puskesmas Bandar Senembah.  yang melibatkan 52 responden menggunakan teknik accidental sampling. Data dikumpulkan melalui kuesioner tertutup yang telah diuji validitas dan reliabilitasnya, kemudian dianalisis menggunakan uji Wilcoxon. Penelitian melibatkan 52 responden yang dipilih melalui teknik accidental sampling. Pengumpulan data dilakukan menggunakan kuesioner tertutup yang telah diuji validitas dan reliabilitasnya, dengan pengukuran pengetahuan dinyatakan secara konsisten dalam skor pengetahuan. Analisis data menggunakan uji Wilcoxon menunjukkan adanya peningkatan rata-rata skor pengetahuan dari 4,02 pada pre-test menjadi 8,98 pada post-test, dengan nilai p = 0,000 (p < 0,05). Hasil ini membuktikan bahwa edukasi melalui media poster efektif dalam meningkatkan pengetahuan masyarakat tentang STBM. Media poster berperan sebagai sarana visual yang membantu penyampaian informasi secara jelas dan mudah dipahami, sehingga mendukung peningkatan pemahaman masyarakat terhadap lima pilar STBM dan penguatan upaya promosi kesehatan lingkungan.
Faktor-Faktor Yang Berhubungan Dengan Kelelahan Kerja Pada Pemanen Buah Kelapa Sawit Di Perkebunan Pt.Gruti Lestari Pratama Mandailing Natal: Factors Related to Work Fatigue in Oil Palm Fruit Harvesters at the PT. Gruti Lestari Pratama Mandailing Natal Plantation Anggi Cahya Utari; Fatma Indriani
Jurnal Kolaboratif Sains Vol. 9 No. 2: Februari 2026
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v9i2.10206

Abstract

Kelelahan kerja merupakan masalah kesehatan yang serius bagi pemanen kelapa sawit karena dapat menurunkan efisiensi kerja, meningkatkan risiko kecelakaan, dan berdampak negatif terhadap produktivitas. Pemanen kelapa sawit di PT. Gruti Lestari Pratama menghadapi berbagai tantangan fisik seperti mengangkat tandan buah sawit yang mencapai 25-30 kg, melakukan gerakan berulang, dan bekerja dalam kondisi cuaca ekstrem dengan target produksi 2-3 ton per hari. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang berhubungan dengan kelelahan kerja pada pemanen buah kelapa sawit di perkebunan PT. Gruti Lestari Pratama Mandailing Natal, khususnya hubungan antara umur, beban kerja, status gizi, dan masa kerja dengan tingkat kelelahan kerja. Penelitian ini menggunakan metode kuantitatif dengan desain observasi analitik dan pendekatan cross-sectional. Populasi penelitian adalah seluruh pemanen buah kelapa sawit yang bekerja di PT. Gruti Lestari Pratama Mandailing Natal berjumlah 180 orang. Teknik pengambilan sampel menggunakan probability sampling dengan rumus Lemeshow. Penelitian dilaksanakan pada bulan Maret sampai Mei 2025 di Kabupaten Mandailing Natal, Provinsi Sumatera Utara. Hasil analisis menunjukkan bahwa semua variabel yang diteliti memiliki hubungan positif dengan kelelahan kerja. Variabel umur menunjukkan hubungan sangat kuat dengan koefisien korelasi (r) = 0,003 dan p-value = 0,977. Beban kerja memiliki hubungan positif dengan r = 0,028 dan p-value = 0,781. Status gizi menunjukkan hubungan kuat dan positif dengan r = 0,044 dan p-value = 0,661. Masa kerja memiliki hubungan sangat kuat dan positif dengan r = 0,024 dan p-value = 0,807. Terdapat hubungan yang signifikan antara umur, beban kerja, status gizi, dan masa kerja dengan kelelahan kerja pada pemanen buah kelapa sawit di PT. Gruti Lestari Pratama Mandailing Natal.
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Hana, Elvina Nur; Faisal, Mohammad Reza; Kartini, Dwi; Mazdadi, Muhammad Itqan; Saputro, Setyo Wahyu; Indriani, Fatma; Satou, Kenji
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Improving Postprandial Glucose Forecasting Using Diagnosis-Aware Stacked Learning Fatma Indriani; Mohammad Reza Faisal; Naufal Said
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2566

Abstract

Predicting glucose levels after a meal (postprandial glucose) can help anticipate abnormal responses and improve diabetes management. Yet such prediction remains difficult because post-meal glucose depends on multiple interacting factors, including prior glucose trends, meal composition, and recent activity. This study develops machine learning models to forecast short-term post-meal glucose levels using the CGMacros dataset, which combines continuous glucose monitoring (CGM) data from Dexcom and Libre sensors with meal macronutrient annotations and activity measurements. Several feature combinations and regression models were evaluated to identify an optimal representation. Results show that combining baseline glucose statistics with meal composition yields the lowest error across all regressors. Building on this feature configuration, a stacked learning framework was implemented in which a global model provides initial predictions refined by diagnosis-specific CatBoost regressors for Healthy, Pre-diabetes, and Type 2 Diabetes groups. Across 18 configurations spanning two sensors and three horizons (30, 60, 120 minutes), stacking reduced normalized RMSE by 3.5 ± 3.7% on average, with the strongest improvements at 120-minute horizons (mean 5.5%) and for linear global models (up to 13.6% reduction). Gains varied by diagnosis group and sensor type, highlighting the importance of device-aware validation. These results demonstrate that diagnosis-aware stacking enhances both accuracy and robustness, offering a practical foundation for personalized glucose forecasting in digital health systems.
Evaluating CNN Robustness for Face Mask Classification under Environmental Variations Bagaskara Ridho Vandio; Fatma Indriani; Andi Farmadi; Dodon Turianto Nugrahadi; Friska Abadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2617

Abstract

Purpose – This study aims to analyze and compare the performance of ResNet50 and MobileNetV3 for multi-class face mask classification under various environmental conditions. Design/methods/approach – ResNet50 and MobileNetV3 are trained using transfer learning for three-class face mask classification and evaluated under normal conditions and environmental variations, including illumination changes, blur, low compression, and rotation. Findings – Experimental results show that ResNet50 achieves an accuracy of 94.32% under normal conditions, slightly outperforming MobileNetV3 at 94.10%. Under environmental variations, the largest performance degradation is observed under darkening and blur conditions, while low compression and rotation have relatively minor effects. ResNet50 demonstrates higher robustness across most perturbation settings, whereas MobileNetV3 provides competitive performance with substantially better computational efficiency. Research implications/limitations – This study is limited to a controlled evaluation using synthetic environmental perturbations on a single dataset and does not consider broader dataset diversity. Therefore, the findings should be interpreted within the evaluated experimental conditions. Originality/value – This study provides a comparative analysis of model robustness under controlled environmental perturbations, highlighting the trade-off between robustness and computational efficiency for face mask classification systems.
Co-Authors Abdilah, Muhammad Fariz Fata Abdul Azis Abdullayev, Vugar Achmad Rizal Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Al Habesyah, Noor Zalekha Amini, Aisah Ananda, Zahra Andi Farmadi Andi Farmadi Anggi Cahya Utari Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Aryanti, Agustia Kuspita Asti, Rahmah Dwi Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Aulia, Rizky Gunadi Azizah, Azkiya Nur Badali, Rahmat Amin Bagaskara Ridho Vandio Baharuddin Siregar, Baharuddin Baron Hidayat Barus, Nency Utami Br Berutu, Marwiyah Br Barus, Nency Utami br Damanik, Cici Rahayu Carolina, Ayu Daffa Dhiba Oesraini DALIMUNTHE, NADIYAH RAHMA Darmansyah, Rendi Daulay, Rangga Muriansyah Dendy Fadhel Adhipratama Dendy Dewi Sri Wahyuni, Dewi Sri Difa Fitria Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Effendi, Khairunnisa Fadilah, Sylva Qamara Nur Fahira Ramadhani Saragih Fahmi Setiawan Fairudz Shahura Faisal, M. Reza Faisal, Mohammad Reza Fajrin Azwary Fitriani, Karlina Elreine Friska Abadi Ghinaya, Helma Gunawan, Muhammad Khair Gustara, Rizki Asih Hafizah, Rini Hana, Elvina Nur Harahap, Helma Denisah Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudi Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Iqbal, M. Irwan Budiman Irwan Budiman Lauchan, Agil maritho Lilies Handayani Lubis, Masruroh 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 Muhammad Ridha Maulidi Muliadi Muliadi Muliadi Aziz Nafiz, Muhammad Fauzan Naufal Said Nita Arianty Nofi Susanti Nurhayani nurhayani Nurhayati Octavia, Mayang Dwi Oni Soesanto P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putra Apriadi Siregar Putri Maimunah Radityo Adi Nugroho Ramadhanu, Suhada Rapotan Hasibuan Riadi, Agus Teguh Risma, Ade Ritonga, Egril Rehulina Rizian, Rizailo Akfa Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno Safira, Putri Salianto Salianto, Salianto Saragih, Triando Hamonangan Satou, Kenji Sa’diah, Halimatus Selvia Indah Liany Abdie Setyo Wahyu Saputro Siregar, Nurul Syahputri Soesanto, Oni Sri Rahayu Suci Wulandari Tarigan, David Brando Pratama Triyoolanda, Anggun Umar Ali Ahmad Utami, Tri Niswati Wahyu Caesarendra Wardana, Muhammad Difha Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zakwan, M. Hadin Zali, Muhammad Zata Ismah Zida Ziyan Azkiya