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Journal : Building of Informatics, Technology and Science

Monitoring Keamanan Toko Menggunakan Sensor Pir dan Pintu Berbasis Arduino dengan Notifikasi SMS Gateway Putra, Jaka; Sumarno, Sumarno; Damanik, Bahrudi Efendi; Hartama, Dedy; Gunawan, Indra
Building of Informatics, Technology and Science (BITS) Vol 1 No 2 (2019): December 2019
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.045 KB) | DOI: 10.47065/bits.v1i2.49

Abstract

The author has conducted research at UD Stores. Variant Jl. Kabu-Kabu No. 31 Pematangsiantar and found a problem where the theft was prone to the place and no employees guarded the store during the closing. Then another problem arises how to monitor stores from a distance by using SMS Gateway notifications. So here the author provides a solution so that the store can be monitored remotely with SMS Gateway Notifications. The author implements a Magnetic switch on the front door so that when the door is forced open the buzzer will ring and the owner receives an SMS, then in the front room the writer implements a PIR sensor where when a human passes the sensor the buzzer will turn on and the shop owner will get SMS notification.
Prediksi Perkembangan Jumlah Pelanggan Listrik Menurut Pelanggan Area Menggunakan Algoritma Backpropagation Saragih, Irfan Christian; Hartama, Dedy; Wanto, Anjar
Building of Informatics, Technology and Science (BITS) Vol 2 No 1 (2020): June 2020
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.497 KB) | DOI: 10.47065/bits.v2i1.341

Abstract

Electricity is one of the vital needs of humanity. Without electricity, it is certain that the wheels of the economy will not be able to run properly. So that electricity customers are increasingly increasing, as they increase the needs and population of the community. Therefore this study aims to determine the development of the number of electricity customers using the backpropagation algorithm. The research data used was electricity customer data by area (customer) in North Sumatra in 2013-2017, obtained from the Central Statistics Agency of North Sumatra. This study uses 5 architectural models, namely 4-2-1, 4-3-1, 4-4-1, 4-5-1, and 4-6-1. Of the five architectural models used, one of the best architectural models is obtained 4-4-1 with an accuracy rate of 88%, epoch 716 iterations in a short amount of time, 15 seconds, with MSE Training 0,00099763 and MSE testing 0.00109935. Based on the best architectural model, this will be used to predict the Development of Electricity Customers by Area Customers in North Sumatra from 2018 to 2020
Pengelompokan Algoritma K-Means dan K-Medoid Berdasarkan Lokasi Daerah Rawan Bencana di Indonesia dengan Optimasi Elbow, DBI, dan Silhouette Hartama, Dedy; Wanayumini, W; Damanik, Irfan Sudahri
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5851

Abstract

The study examines the use of K-Means and K-Medoids algorithms in the grouping of disaster area locations in Indonesia, with the aim of identifying patterns and optimizing disaster re-sponse strategies. The data used includes geographical and historical information of various disaster events in Indonesia, such as Aceh Besar, Asahan, Badung, Bangkalan, Bekasi, and others. In the clustering process, optimization techniques such as the Elbow Method, the Da-vies-Bouldin Index (DBI), and the Silhouette Score are used to determine the optimal number of clusters. Research results show that the K-Means algorithm tends to be more stable in deal-ing with outliers than K- Means, with the results of the DBI (Davis-Booldin Index) 0.3737248981 and the cluster 7, resulting in the silhouette score of 0.868728638 and cluster 2, resulting at the elbow 98106477130.371 and claster 2. The Silhouette Score and Elbow index-es also provide a strong indication that the clustering algorithm used is capable of forming significant and meaningful clusters. The study has made important contributions to the opti-mization of clustering with three methods used so that it can be the basis for authorities in planning and implementing more effective disaster mitigation policies.