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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 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 Jurnal INFOTEL 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

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Deep Learning-Based Lung Sound Classification Using Mel-Spectrogram Features for Early Detection of Respiratory Diseases Yabani, Midfai; Faisal, Mohammad Reza; Indriani, Fatma; Nugrahadi, Dodon Turianto; Kartini, Dwi; Satou, Kenji
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1256

Abstract

Respiratory diseases such as asthma, chronic obstructive pulmonary disease, and pneumonia remain among the leading causes of death globally. Traditional diagnostic approaches, including auscultation, rely heavily on the subjective expertise of medical practitioners and the quality of the instruments used. Recent advancements in artificial intelligence offer promising alternatives for automated lung sound analysis. However, audio is an unstructured data format that must be converted into a suitable format for AI algorithms. Another significant challenge lies in the imbalanced class distribution within available datasets, which can adversely affect classification performance and model reliability. This study applied several comprehensive preprocessing techniques, including random undersampling to address data imbalance, resampling audio at 4000 Hz for standardization, and standardizing audio duration to 2.7 seconds for consistency. Feature extraction was then performed using the Mel Spectrogram method, converting audio signals into image representations to serve as input for classification algorithms based on deep learning architectures. To determine optimal performance characteristics, various Convolutional Neural Network (CNN) architectures were systematically evaluated, including LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-50, and ResNet-152. VGG-16 achieved the highest classification accuracy of the tested models at 75.5%, demonstrating superior performance in respiratory sound classification tasks. This study demonstrates the potential of AI-based lung sound classification systems as a complementary diagnostic tool for healthcare professionals and the general public in supporting early identification of respiratory abnormalities and diseases. The findings suggest that automated lung sound analysis could enhance diagnostic accessibility and provide more valuable support for clinical decision-making in respiratory healthcare applications
Optimizing Input Window Length and Feature Requirements for Machine Learning-Based Postprandial Hyperglycemia Prediction Maulana, Muhammad Rafly Alfarizqy; Indriani, Fatma; Abadi, Friska; Kartini, Dwi; Mazdadi, Muhammad Itqan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1401

Abstract

Continuous glucose monitoring systems currently generate alerts only after blood glucose thresholds are breached, limiting their utility for proactive diabetes management. Predicting postprandial glucose excursions before they occur requires determining the optimal amount of historical data and identifying which features contribute most to prediction accuracy. This study systematically evaluates how the length of the pre-meal observation window and feature composition affect machine-learning predictions of hyperglycemia events 60 minutes after eating. We analyzed 1,642 meal events from 45 adults wearing continuous glucose sensors, constructing features from pre-meal glucose trajectories, meal macronutrients, time of day, and health status. Four observation windows (15, 30, 45, 60 minutes) and three feature sets (all features, glucose-only, meal-only) were evaluated using Random Forest, XGBoost, and CatBoost with 5-fold group cross-validation. CatBoost with a 30-minute window achieved the best performance: 72.6% F1-macro, 79.6% accuracy, and 64.0% recall for hyperglycemia detection. Extending windows beyond 30 minutes did not yield consistent benefits, whereas 15-minute windows yielded comparable results. Glucose trajectory features alone retained 94% of full model performance (68.5% F1-macro), whereas meal composition alone proved insufficient (59.4% F1-macro). These findings demonstrate that recent glucose history dominates short-term prediction, enabling practical real-time systems with minimal data requirements. A 30-minute observation window with glucose and meal features offers an effective balance between prediction accuracy and system responsiveness.
Analisis Faktor Yang Berhubungan Dengan Perilaku Safety Riding Sepeda Motor Siswa/I MAN 2 Langsa: Analysis of Factors Related to Motorcycle Safety Riding Behavior of Students at MAN 2 Langsa Daffa Dhiba Oesraini; Fatma Indriani
Jurnal Kolaboratif Sains Vol. 9 No. 1: Januari 2026 -In Progress
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v9i1.10065

Abstract

Perilaku Safety Riding menjadi faktor utama Kecelakaan lalu lintas yang membahayakan keselamatan para pengguna jalan, sehingga perilaku Safety Riding yang melibatkan transportasi darat belakangan ini menjadi perhatian yang serius. Tujuan penelitian ini untuk mengetahui faktor yang berhubungan dengan perilaku Safety Riding sepeda motor siswa/i MAN 2 Langsa. Metode penelitian yang digunakan kuantitatif dengan pendekatan cross-sectional. Besar sampel dalam penelitian ini 96 responden. Analisa data yang digunakan analisis univariat dan bivariat dengan menggunakan uji chi-square. Hasil penelitian menunjukkan terdapat hubungan jenis kelamin (p-value= 0,024 < 0,05), hubungan tingkat stress (p-value= 0,001 < 0,05), hubungan kepemilikan SIM (p-value= 0,004 < 0,05), hubungan peran teman sebaya (p-value= 0,000 < 0,05). Disarankan disarankan agar siswa laki-laki dan perempuan lebih meningkatkan sikap kehati-hatian saat berkendara, siswa/i mengelola stres dengan baik sebelum mengendarai kendaraan, siswa yang sudah cukup usia sebaiknya segera mengurus SIM secara resmi, siswa hendaknya lebih selektif dalam bergaul dan tidak mudah terpengaruh ajakan negative.
STRES AKADEMIK SEBAGAI PREDIKTOR KESEHATAN MENTAL PADA MAHASISWA FKM UIN SU Indriani, Fatma; Fadilah, Sylva Qamara Nur
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4992

Abstract

Abstract: Mental health among young people is receiving increasing attention. This study aims to determine the relationship between academic stress levels and mental health among students at the Faculty of Public Health, State Islamic University of Medan (UIN SU). The method used was a quantitative correlational survey approach. Data were obtained from 131 students at the Faculty of Public Health, State Islamic University of Medan (UIN SU), using incidental sampling. The measurement tools used were the Perceived Academic Stress Scale (PASS) and the Brief Mental Health Inventory (BMHI-12). Regression analysis was used. The results showed a negative effect of academic stress on mental health among students at the Faculty of Public Health, State Islamic University of Medan (UIN SU Medan), with an effective contribution of 19.3%. These findings demonstrate the importance of stress management strategies in higher education environments to maintain student mental well-being..Keyword: Mental Health; Collage Students; Academic Stress.Abstrak: Kesehatan mental pada generasi muda semakin mendapat perhatian. Penelitian ini bertujuan untuk mengetahui hubungan antara tingkat stres akademik dengan kesehatan mental pada mahasiswa FKM UIN SU. Metode yang digunakan adalah kuantitatif korelasional dengan pendekatan survei. Data diperoleh dari 131 orang mahasiswa FKM UIN SU yang diambil dengan teknik incidental sampling. Alat ukur yang digunakan yaitu Perceived Academic Stress Scale (PASS) dan Brief Mental Health Inventory (BMHI-12)). Teknik analisis data yang digunakan adalah uji regresi. Hasil penelitian menunjukkan terdapat pengaruh negatif dari stress akademik terhadap Kesehatan mental pada mahasiswa FKM UIN SU Medan dimana sumbangan efektif yang diberikan sebesar 19,3%. Temuan ini menunjukkan pentingnya strategi manajemen stres di lingkungan pendidikan tinggi untuk menjaga kesejahteraan mental mahasiswa.Kata kunci: Kesehatan Mental; Mahasiswa; Stres Akademik.
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 Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Aryanti, Agustia Kuspita Asti, Rahmah Dwi Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Azizah, Azkiya Nur Badali, Rahmat Amin 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 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 Gustara, Rizki Asih Hafizah, Rini Harahap, Helma Denisah Hartati Hartati Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudi Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Irwan Budiman Irwan Budiman Lilies Handayani Lubis, Masruroh M. Apriannur M. Khairul Rezki Mahmud Mahmud Mahmudah, Kunti Maulana, Muhammad Rafly Alfarizqy 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 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 Rapotan Hasibuan Riadi, Agus Teguh Risma, Ade Ritonga, Egril Rehulina Rizian, Rizailo Akfa Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rudy Herteno rusmining, rusmining Salianto Salianto, Salianto Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satou, Kenji Sa’diah, Halimatus Selvia Indah Liany Abdie Siregar, Nurul Syahputri Soesanto, Oni Sri Rahayu Suci Wulandari Sugiyarto Surono, Sugiyarto Triyoolanda, Anggun Umar Ali Ahmad Utami, Tri Niswati Wahyu Caesarendra Wardana, Muhammad Difha Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha Yabani, Midfai YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zakwan, M. Hadin Zali, Muhammad Zata Ismah Zida Ziyan Azkiya