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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi JUTEKIN (Jurnal Manajemen Informatika) Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Teknomatika: Jurnal Informatika dan Komputer
dayat subekti
Universitas Jenderal Achmad Yani Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Rancang Bangun Sistem Peringatan Dini Bencana Banjir Di Kabupaten Madiun Berbasis Website Dan SMS Gateway Menggunakan Mikrokontroller Arduino Halillur Nur Afandi; Landung Sudarmana; Dayat Subekti; Nafisa Alfi Sa'diya
Jurnal Teknomatika Vol 13 No 2 (2020): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v13i2.1111

Abstract

Madiun Regency is one of the districts on the island of Java, precisely in the province of East Java. Based on research from the Center for Volcanology and Geological Disaster Mitigation (PVMBG), Madiun City has several potential disasters, one of which is flooding with a high category. This is because Madiun Regency has two streams, namely Kali Sono and Kali Piring which head up on the slopes of Mount Wilis, Madiun Regency. Both times it ended in the Jerohan River in the Balerejo District, Madiun Regency which is a tributary of the Bengawan Madiun River. When heavy rains fell, parts of Madiun City were immediately inundated by flood waters. To anticipate floods, this research proposes a prototype design of a flood disaster early warning system using multiple sensors based on the Global System for Mobile Communications (GSM) in Madiun Regency. Which in the design uses the waterfall and Arduino methods. The prototype built is able to provide early warning information. The data that has been taken can be displayed on the web which functions as a monitoring system, so that officers can carry out supervision more easily and take action when the situation is dangerous. With the prototype and the system made, it is expected to be able to minimize losses caused by floods.
PERBANDINGAN METODE DECISION TREE DAN NAIVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN PENGGUNA LAYANAN PT PERUSAHAAN LISTRIK NEGARA (PLN) ABIYOGA BAGUS MUSTRIYANTO; Muhammad Habibi; Dayat Subekti; Fajar Syahruddin
Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v15i2.1131

Abstract

Background : PLN is a state-owned company that is tasked with supplying electricity to all regions of Indonesia which certainly cannot be separated from the various obstacles experienced, to find out public sentiment on the services that have been provided, an analysis is carried out to determine public sentiment. The results of these sentiments are created in the dashboard using the Flask framework by comparing the Naive Bayes and Decision tree methods. To create a sentiment analysis dashboard for PT. PLN and make a research analysis model using a comparison of the Naive Bayes Classification and Decision tree methods. The method used in this research is Naive Bayes and Decision tree. The data obtained with a total of 40,745 Tweet data taken in the period 1 May 2022 - 4 June 2022 with the keyword "PLN". Making a dashboard that displays the results of the analysis where there is a menu to display the data and each analysis process. The use of 900 training data and 300 testing data resulted in the Naive Bayes method getting an accuracy of 83% on the training data and 80% for the Testing data, while the Decision tree method got an accuracy of 77% on the Training data and 56% on the Testing data. The analysis obtained for the method in this study also shows that the Naive Bayes method is better for classifying large amounts of data than the Decision tree. The sentiment generated by the highest number is negative, with most of the Tweets being complaints about the response to complaints and handling of damage reported by the public.
Co-Authors ABIYOGA BAGUS MUSTRIYANTO Achmad Faisal Agung Permana Mukti Agung Permana Mukti Agung Priyanto Arief Ikhwan Wicaksono Arif Himawan Dimas Rully Azzuhry Dimas Rully Azzuhry Fajar Syahruddin Halillur Nur Afandi Kusrini Landung Sudarmana Muhammad Habibi Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Pambudi Lono Sudarmawan