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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.
Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument Wulandari, Sari Ayu; Madnasri, Sutikno; Pramitasari, Ratih; Susilo, Susilo
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09203

Abstract

The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.
Adopsi dan Difusi Teknologi Pengukuran Tekanan Intraokular Non-Invasif Pada Pemeriksaan Kesehatan di Kota Semarang Sari Ayu Wulandari; Wisnu Adi Prasetyanto; Cynthia Arsita; Arga Dwi Pambudi
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.