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ANALISIS PEAK GROUND ACCELERATION (PGA) KOTA TEGAL MENGGUNAKAN METODE HVSR (HORIZONTAL TO VERTICAL SPECTRA RATIO) Tanjung, Nia Annisa Ferani; Permatasari, Indah; Yuniarto, Abdul Hakim Prima
Jurnal Geosaintek Vol. 7 No. 1 (2021)
Publisher : Institut Teknologi Sepuluh Nopember

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Abstract

Seismisitas kota Tegal dipengaruhi oleh keberadaan segmen sesar baribis-kendeng yang melewati kota Tegal dengan kecepatan rata-rata 4,5 mm/tahun. Kota Tegal merupakan kota yang sedang berkembang, sehingga mikrozonasi kegempaan perlu dilakukan untuk mendukung tata letak pembangunan di Kota Tegal. Mikrozonasi dilakukan dengan menganalisis nilai dari PGA (Peak Ground Acceleration) data mikrotremor di 37 titik di Kota Tegal. Data diolah dengan menggunakan metode HVSR (Horizontal to Vertical Spectral Ratio) untuk mendapatkan nilai frekuensi dominan (fo) dan amplifikasi (A) daerah penelitian. Analisis PGA (Peak Ground Acceleration) dilakukan dengan menggunakan metode Kannai dan didapatkan nilai PGA di Kota Tegal mulai dari 5.88 – 27.59 gal.
Penerapan Teknologi IoT dan Energi Terbarukan untuk Meningkatkan Efisiensi Budidaya Ikan di Desa Kasegeran Aldo, Dasril; Ginting, Melinda Br; Tanjung, Nia Annisa Ferani; Yasin, Feri; Sulaeman, Gilang; Pangestu, Farhan Aryo
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 4, No 5 (2024): 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.v4i5.836

Abstract

This service activity aims to overcome the high energy costs due to the use of excavated wells by fish farmers in Kasegun Village. Solar panel technology and Internet of Things (IoT) systems are applied to reduce energy costs and monitor pond water quality in real-time. This program is carried out through a series of technical stages including situation analysis, Focus Group Discussion (FGD) to identify technology needs, installation of solar panels and IoT sensors, as well as training on the use and maintenance of equipment. The results of the application of technology show a reduction in energy costs by 80%, a reduction in energy waste of up to 85%, and an increase in operational efficiency from 50% to 90%. The fish mortality rate also dropped from 25% to 3.25%. The success of the program is supported by partners' active participation in FGDs and trainings, which increases their understanding and independence in utilizing technology. However, the sustainability of the program depends on ongoing technical assistance.ABSTRAKKegiatan pengabdian ini bertujuan untuk mengatasi tingginya biaya energi akibat penggunaan sumur galian oleh pembudidaya ikan di Desa Kasegeran. Teknologi panel surya dan sistem Internet of Things (IoT) diterapkan untuk mengurangi biaya energi serta memantau kualitas air tambak secara real-time. Program ini dilaksanakan melalui serangkaian tahapan teknis meliputi analisis situasi, Focus Group Discussion (FGD) untuk mengidentifikasi kebutuhan teknologi, pemasangan panel surya dan sensor IoT, serta pelatihan penggunaan dan perawatan alat. Hasil penerapan teknologi menunjukkan penurunan biaya energi sebesar 80%, pengurangan pemborosan energi hingga 85%, dan peningkatan efisiensi operasional dari 50% menjadi 90%. Tingkat kematian ikan juga turun dari 25% menjadi 3,25%. Keberhasilan program ini didukung oleh partisipasi aktif mitra dalam FGD dan pelatihan, yang meningkatkan pemahaman dan kemandirian mereka dalam memanfaatkan teknologi. Namun, keberlanjutan program bergantung pada pendampingan teknis yang berkelanjutan.
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