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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI Jurnal Geosaintek Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Innovation Information Technology and Application (JINITA) Teknika Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Nasional Teknik Elektro dan Teknologi Informasi IJCOSIN : Indonesian Journal of Community Service and Innovation EINSTEIN (E-JOURNAL) Jurnal Geosaintek
Nia Annisa Ferani Tanjung
Institut Teknologi Telkom Purwokerto, Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Pemberdayaan Perempuan melalui Inovasi Teknologi IoT dan Energi Terbarukan dalam Pengelolaan Bank Sampah Nur, Yohani Setiya Rafika; Tanjung, Nia Annisa Ferani; Ginting, Melinda Br.; Sulaeman, Gilang; Al Faiz, M. Hanif
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 5, No 5 (2025): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v5i5.2084

Abstract

Waste banks are a form of circular economy initiative that increase the added value of inorganic waste while empowering local communities. However, their operations often face limitations such as inadequate production facilities, high energy costs, and manual record-keeping systems. This community service program aimed to strengthen the capacity of the “Sampah Sahabatku” Waste Bank in Muntang Village through the application of appropriate technology based on solar panels and IoT sensors, as well as by enhancing women’s roles in management. The implementation methods included situation analysis, solar panel installation, technical and managerial training, mentoring, and evaluation using pre-test and post-test assessments. The results showed that solar panels successfully reduced electricity costs and supported the operation of plastic and paper shredding machines, while IoT sensor integration improved the accuracy of record-keeping. The average knowledge level of participants increased by +58% after training. The community’s response was highly positive, reflected in improved technical skills and greater confidence among women in waste bank management. In conclusion, the combination of appropriate technology and renewable energy has strengthened the independence, efficiency, and sustainability of community-based waste banks.ABSTRAKBank sampah merupakan salah satu instrumen ekonomi sirkular yang dapat meningkatkan nilai tambah sampah anorganik sekaligus memberdayakan masyarakat. Namun, pengelolaan bank sampah sering menghadapi keterbatasan sarana produksi, tingginya biaya energi, dan sistem pencatatan yang masih manual. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kapasitas Bank Sampah “Sampah Sahabatku” di Desa Muntang melalui penerapan teknologi tepat guna berbasis panel surya dan sensor IoT, serta penguatan peran perempuan dalam pengelolaan. Metode pelaksanaan meliputi analisis situasi, pemasangan panel surya, pelatihan teknis dan manajerial, pendampingan, serta evaluasi melalui pre-test dan post-test. Hasil kegiatan menunjukkan bahwa panel surya mampu mengurangi biaya listrik dan mendukung operasional mesin pencacah plastik maupun kertas, sementara integrasi sensor IoT meningkatkan akurasi pencatatan. Nilai rata-rata pemahaman mitra meningkat sebesar +58% setelah pelatihan. Respon mitra juga positif, ditandai dengan meningkatnya keterampilan teknis dan kepercayaan diri perempuan dalam pengelolaan bank sampah. Kesimpulannya, kombinasi teknologi tepat guna dan energi terbarukan mampu memperkuat kemandirian, efisiensi, serta keberlanjutan bank sampah berbasis komunitas.
A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets Adhinata, Faisal Dharma; Ramadhan, Nur Ghaniaviyanto; Fauzi, Muhammad Dzulfikar; Tanjung, Nia Annisa Ferani
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1164

Abstract

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies
Co-Authors Abdul Hakim Prima Yuniarto Adinda Hashina Al Faiz, M. Hanif Anisia Kholidah Ardianne Luthfika Fairuz Aulia Musa, Nila Condro Kartiko Dasril Aldo Fadlan Raka Satura Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Fitran Dwi Pramakrisna Fitran Dwi Pramakrisna Gracia Rizka Pasfica Indah Permatasari Indah Permatasari Kholidah, Anisia Listiaji, Prasetyo Melinda Br Ginting Muhamad Taufiq Nila Aulia Musa Nur Ghaniaviyanto Ramadhan Pangestu, Farhan Aryo Ramadhani, Rima Dias Sehat Simatupang Sehat Simatupang Sintiya Sulaeman, Gilang Tirtasari, Ni Luh Tri Ginanjar Laksana Tri Ginanjar Laksana Widi Afandi Widi Widayat Widyawarman, Danang Widyawarman, Danang Yasin, Feri Yohani Setiya Rafika Nur Yuniarto, Hakim Prima Zen, Nur Afifah