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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Semantik Jurnal Simetris Jurnal Transformatika Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika International Journal of Artificial Intelligence Research Tekinfo | Scientific Journal of Industrial and Information Engineering Applied Industrial Engineering Journal Elektrika Indonesia Journal of Halal IJID (International Journal on Informatics for Development) Repertorium: Jurnal Ilmiah Hukum Kenotariatan Journal of Electronics, Electromedical Engineering, and Medical Informatics Abdimasku : Jurnal Pengabdian Masyarakat JTEIN: Jurnal Teknik Elektro Indonesia Jurnal Locus Penelitian dan Pengabdian
Sari Ayu Wulandari
Universitas Dian Nuswantoro
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Pelatihan Peningkatan Mutu Produk Recycle Speaker Pada UKM Nusantara Recycle Centre Wulandari, Sari Ayu; Kurniatie, Menik Dwi; Nurcipto, Dedi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.51 KB) | DOI: 10.33633/ja.v2i2.39

Abstract

Mitra kami adalah Nusantara Recycling Center (selanjutnya disingkat NRC), hingga saat ini telah mampu mendaur ulang sampah an-organik. Produk hasil dari NRC, diantaranya adalah emas, aluminium, palladium, beberapa logam lain serta sampah speaker. Pada penampungan speaker, terdapat paparan gas Freon dan gas pospor yang berbahaya bagi organ pernafasan manusia. Di satu sisi, speaker yang sudah tertampung, belum dapat dijual, karena masih belum ada yang mencari speaker bekas dalam jumlah besar. Hal ini menjadikan sebuah pemikiran, bagaimana selanjutnya pemanfaatan speaker bekas, agar lebih bernilai guna? Permasalahan dari mitra diantaranya adalah bagaimana melakukan pemanfaatan speaker bekas dilokasi mitra? Serta bagaimana meningkatkan nilai guna dari speaker bekas yang melimpah di lokasi mitra?. Solusi yang diusulkan pada pengabdian kepada masyarakat ini adalah pemanfaatan speaker bekas menjadi produk Li-Fi berteknologi tinggi. Pada kegiatan ini, dilakukan sosialisasi dan workshop pemanfaatan speaker bekas untuk Li-Fi (Light Fidelity). Urutan kegiatan dari program pengabdian ini adalah merancang workshop dan pendampingan reuse speaker, sebagai upaya transfer of knowledge dari perguruan tinggi kepada masyarakat.
Sistem Monitoring Sungai Berbasis IoT Pambudi, Arga Dwi; Wulandari, Sari Ayu
Elektrika Vol. 14 No. 2 (2022): October 2022
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/elektrika.v14i2.5754

Abstract

Water pollution often occurs due to the disposal of household waste (solid waste and liquid waste) as well as industrial waste, small industry and non-organic waste. This waste will be disposed of through channels which then flow into rivers. Thepurpose of this research is to make a device that can determine the condition of river water, which is polluted or not from along distance. The method used in this study is by taking samples from river water that can represent polluted and unpolluted,then data is taken from these samples which are then processed so that their condition can be known. The conditions that havebeen obtained are then displayed via the web.
Monitoring Sistem Kontrol Mesin Drying Kopi Secara Real Time Berbasis IoT Kusmiyati, Kusmiyati; Pambudi, Arga Dwi; Arifin, Zaenal; Wulandari, Sari Ayu; Purnomo, Muhammad Agus; Setiadi, Kristoforus Ardian; Listianingrum, Nia Yunita
Elektrika Vol. 15 No. 2 (2023): October 2023
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/elektrika.v15i2.7857

Abstract

The process of drying coffee beans often done by manually using sunlight which has its drawbacks, where coffee farmers cannot predict the weather which may rain at any time. If exposed to rain water, coffee beans that are slightly dry will become wet and moist again, which will affect the quality of the coffee beans. Therefore, in this research a literature study will be carried out, followed by the construction of an IoT-based coffee drying machine so that its condition can be monitored at any time. In the drying process, a PTC fan and heater will be used to regulate the temperature and humidity in the coffee drying machine to get better results in drying coffee. This research will also test how much temperature and humidity are optimal in the coffee drying machine, because it will also affect the drying time and the quality of the coffee produced. To determine the quality of the coffee beans and develop a coffee drying machine, researchers will collaborate with UKM Boyolali which has experience in drying coffee beans. IoT-based dying coffee machine has been made, with dimensions (80 x 47 x 115) cm, drying capacity of 30 grams, using electric fuel which is integrated with temperature and humidity sensors which function as drying temperature controllers, with measurement error calibration results of 1.2 and organoleptic tests show that the quality of the coffee produced by the tool/machine is better than the coffee produced by manual heating, where the coffee beans heated by the machine have a strong coffee aroma but do not smell burnt, the color is even, light brown in color and has a bitter taste and when crushed and dissolved there is no precipitate on the surface. The optimum temperature for the IoT-based dying coffee machine is 80ºC in 883 seconds or the equivalent of 15 minutes, which is equivalent to traditional drying by relying on sunlight for 10 days, while the optimal humidity for the IoT-based dying coffee machine is 15 %, this is in accordance with the quality standards of coffee as a result of heating.          Keywords: Coffee beans, Drying coffee machine, IoT ABSTRAK  Pada proses pengeringan biji kopi saat ini masih sering dilakukan secara manual menggunakan sinar matahari yang memiliki kekurangan, dimana petani kopi tidak bisa memprediksi cuaca yang kemungkinan bisa terjadi hujan setiap saat. Jika terkena air hujan biji kopi yang agak kering akan menjadi basah dan lembab kembali, dimana akan jadi berpengaruh pada kualitas dari biji kopi tersebut. Oleh karena itu pada penelitian ini akan dilakukan study literature, yang dilanjutkan dengan pembuatan mesin drying kopi berbasis IoT sehingga bisa dipantau kondisinya setiap saat. Pada proses pengeringan akan digunakan fan dan heater PTC untuk mengatur suhu dan kelembapan di dalam mesin drying kopi untuk mendapatkan hasil yang lebih bagus dalam pengeringan kopi. Pada penelitian ini juga akan diujikan seberapa besar suhu dan kelembapan yang optimal di dalam mesin drying kopi, karena akan berpengaruh juga pada waktu pengeringan dan kualitas kopi yang dihasilkan. Untuk menentukan kualitas dari biji kopi dan mengembangkan mesin drying kopi peneliti akan bekerjasama dengan UKM Boyolali yang sudah berpengalaman dibidang pengeringan biji kopi. Telah dibuat mesin dying kopi yang berbasis IoT, dengan dengan dimensi (80 x 47 x 115) cm, kapasitas pengeringan 30 gram, menggunakan bahan bakar listrik yang terintegrasi dengan sénsor suhu dan kelembaban yang berfungsi sebagai pengontrol suhu pengeringan, dengan hasil kalibrasi error pengukuran sebesar 1,2 dan uji organoleptic menunjukan bahwa kualitas kopi hasil alat/ mesin lebih bagus dibandingkan dengan kopi hasil pemanasan manual, dimana biji kopi hasil pemanasan dengan mesin mempunyai aroma kopi kuat namun tidak beraroma gosong, warna merata, berwarna coklat muda dan mempunyai rasa pahit dan ketika ditumbuk dan dilarutkan tidak ada endapan dipermukaan. Suhu optimal pada mesin dying kopi yang berbasis IoT adalah 80ºC dalam kurun waktu 883 detik atau setara dengan 15 menit yang mana hasil tersebut setara dengan pengeringan secara tradisional dengan mengandalkan sinar matahari selama 10 hari, sedangkan kelembapan optimal pada mesin dying kopi yang berbasis IoT adalah 15%, hal ini sesuai dengan baku mutu kopi hasil pemanasan.
Adopsi dan Difusi Teknologi Pengukuran Tekanan Intraokular Non-Invasif Pada Pemeriksaan Kesehatan di Kota Semarang Wulandari, Sari Ayu; Prasetyanto, Wisnu Adi; Arsita, Cynthia; Pambudi, Arga Dwi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3236

Abstract

The Community Service Program (PKM) in Semarang City, Central Java, aims to address the low adoption of smart city technology, particularly in health screening initiatives. Amid a rising prevalence of glaucoma, the rate of eye disease screening in this region remains low. This PKM initiative offers the development of an affordable, non-invasive tonometer for intraocular pressure measurement to facilitate early glaucoma screening and monitoring. The implementation includes device design, screening participation surveys, and analysis of measurement results compared to a reference tonometer. This tool is expected to aid in early glaucoma detection and help reduce adoption barriers to technology. Surveys conducted before and after the program indicate increased public understanding, while calibration tests show the device has high accuracy with a minimal error rate of 1.71 mmHg, demonstrating its effectiveness for intraocular pressure screening.
MK–TripNet: A Deep Learning Framework for Real-Time Multi-Class Lung Sound Classification Erini, Widya Surya; Thomas, Gracia Putri; Badia, Giulia Salzano; Rahadian, Arief; Raharjo, Sofyan Budi; Wulandari, Sari Ayu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1403

Abstract

Respiratory diseases such as asthma, pneumonia, and Chronic Obstructive Pulmonary Disease (COPD) remain major global health challenges, particularly in resource-limited settings where access to pulmonary specialists and early diagnostic tools is limited. Automatic lung sound classifications have emerged as a promising non-invasive screening approach; however, existing methods often rely on single-scale feature extraction, conventional loss functions, and offline analysis, which limit their discriminative capability and real-time applicability. The aim of this study is to develop and evaluate a deep learning framework for real-time multi-class lung sound classifications that improves discriminative representation and temporal sensitivity. To address limitations, this study proposes MK-TripNet, a novel deep learning architecture designed to integrate multi-scale feature extraction, discriminative embedding learning, and real-time inference within a unified framework. The main contribution of this work is the unified integration of a Multi-Kernel convolutional architecture, Triplet Loss-based embedding learning, and Sliding Window segmentation within a single end-to-end framework, enabling accurate segment-level lung sound classifications in real-time scenarios. Unlike prior approaches, the proposed method simultaneously captures fine-grained temporal patterns and broader spectral characteristics while explicitly maximizing inter-class separability in the embedding space. The proposed model was evaluated using a newly constructed dataset comprising 1,409 lung sound segments obtained from primary digital stethoscope recordings and publicly available respiratory sound databases. Experimental results demonstrate that MK-TripNet consistently outperforms several strong baseline models, including CNN-BiGRU, CNN-BiGRU-UMAP, and VGGish-Triplet, achieving an accuracy of 89.1%, an F1-score of 0.89, and a recall of 0.88. Ablation studies further confirm that the combined use of Multi-Kernel convolution, Triplet Loss, and Sliding Window segmentation yields the most robust and generalizable performances. These findings highlight the clinical potential of MK-TripNet for real-time digital auscultation and point-of-care respiratory screening, particularly in resource-limited and telemedicine settings.
Co-Authors Achmad Syarifuddin Affandy Affandy Ahmad Zainul Fanani Arga Dwi Pambudi Aripin Arsita, Cynthia Badia, Giulia Salzano Dedi Nurcipto, Dedi Dian Retno Sawitri Dita Ayu Mayasari, Dita Ayu Dwi Nurul Izzhati Erini, Widya Surya Etika Kartikadarma Firdausy, Farah Mutia Harun Al Azies Hasan Mastrisiswadi Heryanto, M Ary I Ketut Swakarma JUANANTA, DENY Kurniatie, Menik Dwi Kusmiyati Kusmiyati Listianingrum, Nia Yunita M Ary Heryanto Madnasri, Sutikno Maghfi, Risya Ulayyar Menik Dwi Kurniatie Muhammad Naufal, Muhammad Muljono Muljono Muslim, Zulkifli Alif Nadlirah, Tasnim Ahya Nurdin, Muhammad Rizal Paryanto Pramitasari, Ratih Pujiono Pujiono Pulung Nurtantio Andono Purnomo, Muhammad Agus Rachelina Nur Ilyasaf Rahadian, Arief Raharjo, Sofyan Budi Ramadhan, Rifky Daffa rexza berliyana Ridwan Ridwan Rudy Tjahyono Rudy Tjahyono, Rudy SADANARESWARI, AGATA Sari Wijayanti Sari Wijayanti Setiadi, Kristoforus Ardian SETYAWAN, YOGA Soeleman, M. Arief Susilo Susilo Thomas, Gracia Putri Tita Talitha Tjahyono, Rudi Triyotomo, Triyotomo Wisnu Adi Prasetyanto Zaenal Arifin Zami , Farrikh Al