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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal EECCIS Jurnal Edukasi dan Penelitian Informatika (JEPIN) JITK (Jurnal Ilmu Pengetahuan dan Komputer) SENTIA 2016 Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Jaringan Telekomunikasi Prosiding Konferensi Nasional PKM-CSR Jurnal ELTEK Jurnal Pengabdian Kepada Masyarakat
Kusumawardani, Mila
Politeknik Negeri Malang
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences Other

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Pembagian Beban Trafik pada Cluster Server Kusumawardani, Mila; Suharto, Nugroho; Zakaria, Muhammad Nanak
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 16 No. 1 (2022)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v16i1.619

Abstract

Permasalahan yang ada pada server adalah banyaknya user yang mengakses dalam waktu bersamaan. Untuk mengatasinya dapat digunakan konsep cluster server. Metode yang digunakan dalam artikel ini adalah membangun 3 cluster server web  dan 1 load balancer dari 4 buah Nanopi2 serta  mengimplementasikan algoritma round robin dalam pengaturan kerjanya. Hasil yang diperoleh adalah bahwa terjadi pembagian beban trafik yang berimbang di antara 3 server dengan rata-rata pada saat server menerima request dari client adalah 33.57%, 32.64%, dan 33.8%, serta rata-rata pada saat server memberikan response adalah 33.47%, 34.14%, dan 35.35%
Internet of things-based fuzzy controller for automatic irrigation and NPK nutrient monitoring of grapes Sarosa, Moechammad; Wirayoga, Septriandi; Kusumawardani, Mila; Firmanda Al Riza, Dimas; Mulyani Azis, Yunia
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9715

Abstract

Grape cultivation has gained increasing attention due to its short growing period and the high market value of its sweet, refreshing fruits. However, achieving optimal growth requires precise environmental and nutrient management, which can be challenging under conventional farming practices. This research aims to develop an automatic watering system that integrates soil moisture and nutrient monitoring to optimize grape cultivation. The system utilizes Nitrogen Phosphorus Potassium (NPK) sensors, soil moisture sensors, and a camera for growth observation, all connected through the internet of things (IoT) for remote monitoring via Android devices. A fuzzy logic controller is implemented to regulate watering duration based on environmental conditions such as temperature and humidity. Experimental results show that the system effectively adjusts watering duration to approximately six seconds when the temperature is between 25–32 °C and humidity is around 60%. The DS18B20 temperature sensor achieved an average error rate of only 0.12%, while the humidity sensor demonstrated 0.2% error, indicating high accuracy levels of 99.8%. Despite minor limitations related to internet stability and sensor calibration, the system demonstrates strong potential for commercial-scale smart farming applications, promoting resource-efficient and data-driven grape cultivation.
PELATIHAN INSTALASI CCTV PADA DESA BANDUNGREJOSARI SEBAGAI UPAYA MITIGASI RISIKO BANJIR DAN KEJAHATAN Chandrasena Setiadi; Isa Mahfudi; Mila Kusumawardani; Hadiwiyatno Hadiwiyatno; Yuri Ariyanto; Retno Damayanti
Jurnal Pengabdian kepada Masyarakat Vol. 12 No. 2 (2025): JURNAL PENGABDIAN KEPADA MASYARAKAT 2025
Publisher : P3M Politeknik Negeri Malang

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Abstract

Bandungrejosari Village RT.05/RW.09, located in Sukun District, Malang City, is vulnerable to flooding due to its proximity to a river and has also faced increasing motorcycle theft. These conditions have raised community concerns and emphasized the need for preventive measures through technology. This program introduced the installation of Closed Circuit Television (CCTV) to support flood mitigation and strengthen neighborhood security, while also providing technical training and awareness on technology-based monitoring systems. The implementation followed a participatory and educational approach, involving stages of preparation, planning, training, installation, and evaluation. Residents actively participated in site surveys, practical device installation, and system simulations, ensuring community ownership of the initiative. Evaluation was conducted through direct observation and questionnaires distributed to 30 respondents. The results indicate that CCTV installation at strategic points effectively assisted in monitoring river conditions and areas prone to crime. Most residents expressed satisfaction, felt supported, and showed willingness to maintain the system. Overall, the program not only improved community preparedness and security but also enhanced collective awareness and participation in sustaining technological solutions for local challenges.
COMPARATIVE ANALYSIS OF YOLOV5SM, YOLOV8, AND YOLOV11 FOR IMAGE-BASED TEMPEH QUALITY RECOGNITION Isa Mahfudi; Mila Kusumawardani; Moechammad Sarosa; Chandrasena Setiadi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7930

Abstract

Tempeh is a traditional Indonesian fermented food whose quality is influenced by fermentation and environmental conditions. Quality assessment is still commonly performed manually, leading to subjectivity and inconsistency. This study compares three modern object detection models—YOLOv5sM, YOLOv8, and YOLOv11—for digital image–based tempeh quality recognition. A dataset of 1,000 images (500 good and 500 defective) was collected using a Logitech C270 camera under controlled lighting conditions. YOLOv5sM was trained with data augmentation (Mosaic, flip, rotation), while YOLOv8 and YOLOv11 were trained without augmentation to isolate architectural differences. All models were trained for 100 epochs using identical hyperparameters and evaluated on a 10% test set. Results show that YOLOv11 achieved the highest accuracy (98%), outperforming YOLOv8 (94%) and YOLOv5sM (88%). Although mAP@0.5 reached 99.5% across models, stricter evaluation using mAP@0.5:0.95 revealed performance differences (96.2%, 96.9%, and 97.0%, respectively). The superior performance of YOLOv11 is attributed to its C3K2 and C2PSA modules, which enhance fine-grained feature extraction and localization precision. These findings indicate that YOLOv11 is the most suitable architecture for automated tempeh quality inspection
Co-Authors Abdul rasyid Achmad Sudrajat Agum Sabda Ilhamy Aisyah Nanda Kartina Asyiva Nurbaeti Aulia, Hillyatul Chandrasena Setiadi Dimas Firmanda Al Riza Dimas Firmanda Al Riza Elfa Mas’udia, Putri Farida Arinie Fitriana, Dina Nurika Fitriana Hadiwiyatno Hadiwiyatno Hapsari, Ratih Indri Hartadi, Berlian Mei hendro darmono Isa Mahfudi Isa Mahfudi Koesmarijanto Koesmarijanto Ma'rifah, Puteri Nurul Mas’Udia, Putri Elfa Mochammad Junus Moechammad Sarosa Monica Wahyuni Mulyani Azis, Yunia Muzzakhim Imamuddin, Azam Narulita Dwi Nugrahaini Nur Ahmad Wasian Nurul Hidayati Prayogo Khanua Almufaridz Putri Ayu Zartika Putri Elfa Masudia Putri, Atika Yulia Sari Rachmad Saptono Rachmad Saptono Rafi Muflih, Muhammad Farras Rakhmania, Amalia Eka Retno Damayanti ridho hendra yoga perdana Robby Juniansyah Arifandi Sari, Zamah Septriandi Wirayoga Sri Wahyuni Dali Suharto, Nugroho Suyono, Achmad Widjajanti, Kristina Widyantari, Nabila Yoyok Heru Prasetyo Yuri Ariyanto Zakaria, Muhammad Nanak Zulfa Anggraini Kusumaningtyas