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All Journal ELIT JOURNAL Electrotechnics And Information Technology MERDEKA: Jurnal Ilmiah Multidisiplin JPEMAS: Jurnal Pengabdian Kepada Masyarakat Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Diankara-Pengabdian Kepada Masyarakat
Maulidiyah, Nurul
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Analisis Kinerja Sprinkler Otomatis Berbasis Sensor Kelembapan dengan Optimalisasi Sudut Panel Surya Maulidiyah, Nurul; Maulana, Yusuf
Jurnal ELIT Vol. 6 No. 2 (2025): Jurnal ELIT
Publisher : Jurusan Teknik Elektro Politeknik Negeri Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31573/elit.v6i2.1124

Abstract

The development of technology in the field of electrical engineering and renewable energy provides great opportunities for the application of automation systems in agriculture. One of the common problems faced by farmers is the irrigation process, which is often carried out manually, requiring significant time, labor, and electrical energy consumption. This study aims to design and evaluate an automatic sprinkler system based on soil moisture sensors powered primarily by solar panels. The system employs an Arduino Uno as the main controller, a soil moisture sensor to detect soil conditions, and a water pump as the actuator for irrigation. Energy is supplied by a solar cell connected to a charging module and a battery as the power storage unit. The research method includes hardware design, software programming, and performance testing of the system under various solar panel tilt angles (30°, 37°, 45°, 53°, and 60°). The evaluation focuses on measuring the water discharge rate, irrigation time, and the efficiency of energy harvested from the solar panel. The results demonstrate that the tilt angle of the solar panel significantly influences irrigation performance. The optimal tilt angle was found at 45°, achieving a maximum discharge rate of 24.1 ml/s, which provides the most efficient irrigation compared to other angles. Although the discharge rate varies across different tilt angles, the total water volume delivered remains relatively constant, indicating that the difference is mainly reflected in the irrigation duration. In conclusion, the proposed automatic sprinkler system based on soil moisture sensors and solar energy offers an effective, efficient, and environmentally friendly solution to support sustainable agriculture. This study also contributes to the achievement of SDGs Goal 15 (Life on Land) by promoting renewable energy utilization and automation in agricultural land management.
Analisis Visualisasi Feature Map Lapisan Konvolusi AlexNet untuk Klasifikasi Diabetic Retinopathy pada Citra Fundus Retina Maulidiyah, Nurul; Dirgayussa, I Gde Eka
MERDEKA : Jurnal Ilmiah Multidisiplin Vol. 3 No. 4 (2026): April
Publisher : PT PUBLIKASI INSPIRASI INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/merdeka.v3i4.7325

Abstract

Diabetic Retinopathy (DR) is a diabetes complication and the leading cause of blindness in the working-age population. Convolutional Neural Network (CNN)-based automated detection systems have proven effective in classifying DR; however, model interpretability remains a challenge in clinical deployment. This study presents an in-depth analysis of feature map visualizations across five convolutional layers of the AlexNet architecture (Conv. 1 to Conv. 5), trained on the APTOS 2019 dataset for binary classification of Diabetic Retinopathy versus Non-Diabetic Retinopathy. Observations were conducted comparatively between DR and Non-DR fundus retinal images to understand how each convolutional layer extracts and transforms feature representations. Results indicate that early layers (Conv. 1–2) extract low-level features such as edges, orientations, and basic textures, while deeper layers (Conv. 3–5) build increasingly abstract and discriminative semantic representations. Significant differences in activation patterns between DR and Non-DR images are identifiable from Conv. 3 onward, becoming more defined in Conv. 4–5, confirming AlexNet's ability to hierarchically extract retinal pathological features. This study contributes to the explainability of deep learning models for medical applications, specifically providing a visual interpretive basis that supports clinician confidence in CNN-based CAD systems.
Peningkatan Pengetahuan Siswa Sekolah Dasar Tentang Bahaya Rokok dan Vaping Melalui Program Edukasi Kesehatan Nurul Maulidiyah
Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 4 (2026): DIANKARA: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPMK Universitas Ngurah Rai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70358/diankara.v2i4.1804

Abstract

This community service activity aims to improve the knowledge of 5th and 6th grade students at SDN 2 Marga Agung regarding the dangers of smoking and vaping. The low level of student understanding regarding the harmful substances contained in cigarettes and the risk of becoming a passive smoker was the background for the implementation of this educational program. The activity was carried out on October 17, 2025, using interactive counseling methods, visual media, and question-and-answer sessions. Evaluation used pre-test and post-test administered to 98 students on questions 11–15 covering knowledge of nicotine, lung damage from cigarette smoke, passive smoking, and diseases caused by smoking. Results showed significant improvement in all indicators. Understanding of passive smoking increased most sharply from 61.2% to 87.8%. Overall, the average student score increased from 79.5 in the pre-test to 86.8 in the post-test. This program proved effective in improving students' health literacy regarding the dangers of smoking and vaping.
Perbandingan Teknik Prapemrosesan Citra terhadap Akurasi CNN dalam Deteksi Diabetic Retinopathy Nurul Maulidiyah; I Gde Eka Dirgayussa; Rafli Filano; Yusuf Maulana
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.341

Abstract

Diabetic Retinopathy (DR) is a diabetes complication and the leading cause of blindness in the working-age population, affecting more than 93 million people worldwide. The quality of retinal fundus images is significantly affected by lighting conditions and contrast, making image preprocessing a critical factor that influences the accuracy of deep learning-based detection models. This study aims to compare the effect of four preprocessing techniques—original images, color Histogram Equalization (HE), grayscale HE, and Color Constancy—on the performance of a Convolutional Neural Network (CNN) based on the AlexNet architecture for DR detection. The APTOS 2019 Kaggle dataset was used, comprising 3,722 color retinal fundus images: 1,830 non-DR and 1,892 DR images. Model validation was performed using 10-Fold Cross Validation, and performance was evaluated using confusion matrix, ROC curve, accuracy, sensitivity, and specificity. Results show that original images yielded the best overall performance with accuracy of 96.10%, sensitivity of 97.98%, specificity of 94.29%, and AUC of 0.994. Grayscale HE produced the highest AUC (0.996), while Color Constancy had the lowest AUC (0.989). These findings indicate that color information in fundus images contains important discriminative features, and preprocessing does not always improve overall accuracy. The AlexNet model shows potential for implementation as a DR screening system based on Computer-Aided Diagnosis (CAD) with relatively low computational complexity
Peningkatan Kesehatan Mental Dini untuk Pencegahan Bullying di Kuttab Anas Bin Malik Retno Maharsi; Aldi Herbanu; Asy Syifa Labibah; Rafli Filano; Sekar Asri Tresnaningtyas; Dwi Susanti; Nova Resfita; Nurul Maulidiyah; Seiswondyshu Hannoudzaky Sonefisal; Rilensya Rahma Suci
JPEMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2026): JPEMAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : Yayasan Pendidikan Tanggui Baimbaian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71456/adc.v4i2.1782

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) yang dilaksanakan oleh Program Studi Teknik Biomedis ITERA di Kuttab Anas bin Malik bertujuan untuk meningkatkan kesadaran dan pengetahuan siswa mengenai pentingnya kesehatan mental sebagai faktor yang memengaruhi cara berpikir, berperilaku, dan berinteraksi sosial. Latar belakang kegiatan ini didasarkan pada meningkatnya permasalahan kesehatan mental pada anak dan remaja serta adanya perilaku bullying di lingkungan pendidikan yang berdampak negatif terhadap kondisi emosional, kepercayaan diri, dan perkembangan sosial akademik siswa. Selain itu, keterbatasan pemahaman siswa terkait pengelolaan emosi dan interaksi sosial positif, serta belum adanya program edukasi kesehatan mental yang terstruktur, menjadi dasar pelaksanaan kegiatan ini. Metode yang digunakan berupa pendekatan edukatif dan interaktif melalui penyampaian materi mengenai empati, bullying dan dampaknya, serta pembiasaan perilaku sosial positif dengan teknik role play, drama, presentasi interaktif, dan lagu edukatif yang dilaksanakan dalam tiga tahap, yaitu pra-sosialisasi, sosialisasi, dan pasca-sosialisasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa terhadap kesehatan mental serta terbentuknya perilaku sosial yang lebih positif. Kegiatan ini juga berkontribusi dalam menciptakan lingkungan sekolah yang lebih aman, nyaman, dan kondusif serta mendukung upaya pencegahan bullying secara efektif.
Co-Authors Aldi Herbanu Asy Syifa Labibah Dirgayussa, I Gde Eka Dwi Susanti I Gde Eka Dirgayussa Nova Resfita Rafli Filano Rafli Filano Retno Maharsi Rilensya Rahma Suci Seiswondyshu Hannoudzaky Sonefisal Sekar Asri Tresnaningtyas Yusuf Maulana Yusuf Maulana, Yusuf