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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Journal of Computer System and Informatics (JoSYC) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer BEES: Bulletin of Electrical and Electronics Engineering Journal of Computing and Informatics Research Bulletin of Artificial Intelligence Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Dedy Hartama
STIKOM Tunas Bangsa, Pematangsiantar
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation Muhammad Syafiq; Dedy Hartama; Ika Okta Kirana; Indra Gunawan; Anjar Wanto
JURIKOM (Jurnal Riset Komputer) Vol 7, No 1 (2020): Februari 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (256.554 KB) | DOI: 10.30865/jurikom.v7i1.1963

Abstract

Product is the one of thing which more important in the business especially for the retail industry. Ramayana is the one exact place for selling retail products such as clothing, shoes, or slipper. On 2012-2018, the number of sales of products in Ramayana experience curve up and down. That thing can cause profit and lose for Ramayana, to avoid that thing need to be held a prediction for the next months so that Ramayana side can know what will happen in the next months in selling it’s product and can take a step for more effective in selling it’s products. The data which used in this research is the data report monthly product sales of shoes & sandal sourced from Ramayana from 2012 until 2018. This research uses the Backpropagation neural network method using 5 architectures namely 3-26-1, 3-31-1, 3-35-1, 3-39-1 and 3-40-1. The best architecture is 3-35-1 with an accuracy rate of 92%. The results obtained are the results of the prediction of the number of sales for 2019, 2020, 2021 and 2022
PkM: Pelatihan Pemanfaatan Teknologi Cloud Sebagai Media Pembelajaran Masa Pandemi M Safii; Dedy Hartama; Yuli Septya Ayunda
TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Vol. 4 No. 2 (2021): TRIDARMA: Pengabdian Kepada Masyarakat (PkM)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/abdimas.v4i2.1372

Abstract

Dimasa pandemi yang belum diketahui kapan berakhirnya berdampak pada seluru aspek kehidupan manusia. Dampak ini juga dirasakan oleh generasi muda yang saat ini sedang melaksanakan kegiatan pendidikan. Di Pematangsiantar kegiatan belajar mengajar masih dilaksanakan secara online yang memaksa tenaga pengajar dan siswa harus menguasai teknologi khususnya Internet. Secara umum pihak penyelenggara pendidikan menggunakan google classroom yaitu salah satu produk dari perusahaan google untuk belajar secara online. Pada pelatihan ini akan memperkenalkan fasilitas yang disediakan oleh teknologi cloud sehingga siswa bisa melatih kompetensinya terhadap penguasaan teknologi khususnya Teknologi Cloud. Fasilitas yang diperkenalkan kepada peserta pelatihan diantaranya media penyimpanan cloud menggunakan Google Cloud Datastore dan Pembuatan Formulir online. Pelatihan ini dilaksanakan pada tanggal 7-10 Juni 2021 secara daring menggunakan Zoom yang diikuti oleh 48 orang siswa dari berbagai sekolah di Pematangsiantar dan Kabupaten Simalungun yang diundang secara online melalui media sosial. Siswa yang mengikuti kegiatan pelatian ini dari berbagai jurusan yang berbeda. Setelah mengikuti kegiatan pelatihan secara umum dapat diamati bahwa selama pelatihan 4 hari siswa menjadi lebih menguasai dan memahami fasilitas teknologi cloud khususnya keterampilan membuat formulir online dan penyimpanan data menggunakan google cloud.
Analisis Model Backpropagation Dalam Meramalkan Tingkat Penjualan Saldo “Link Aja” Dwi Findi Auliasari; Gita Febrianti; Agus Perdana Windarto; Dedy Hartama
Journal of Computing and Informatics Research Vol 2 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.942 KB) | DOI: 10.47065/comforch.v2i1.382

Abstract

Analysis of a prediction (forecasting) is very important in a study, so that research becomes more precise and directed (Wanto and Windarto, 2017). As is the case in predicting the level of Link Aja's balance sales. This research is expected to be useful for an agency as one of the study materials in business development. A system to predict the level of sales of Link Aja balance at PT. Wahana Putra Yudha. Artificial Neural Network is a method that is able to perform a mathematical process to predict the level of sales of Link Aja Balance at PT. Wahana Putra Yudha. By using the backpropagation method, the previous data processing process is carried out which will be used as input to predict the sales level of Link Aja Balance. The data were taken from January 2021 to April 2022. January 2021 to August 2021 were used as training data, while September 2021 to April 2022 were used as test data. The training architecture model used to predict the sales level of Link Aja's Balance is: 4-2-1; 4-25-1; 4-50.1; 4-75-1; and 4-100-1. The best architecture is 4-50-1, the percentage result is 75% in each test
Penerapan Algoritma C4.5 Untuk Analisa Tingkat Keberhasilan Mahasiswa Dalam Pembelajaran Praktikum di Masa Pandemi Dedy Hartama; Kristin Daya Rohani Sianipar
Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i1.2584

Abstract

Practical learning is a method of learning in which students conduct experiments directly by applying the theories they have learned and proving what they have learned in order to better understand what they have learned. The success rate of students towards practical learning is one of the factors that can improve the quality of higher education and can facilitate lecture activities. To analyze the success rate of students towards practicum learning, they can use the C4.5 algorithm by looking at the highest gain. The C4.5 algorithm is one of the algorithms in data mining whose results are identical to the decision tree. Several parameters used for this research are motivation factor, learning method factor, learning material factor, and facility factor. This research was conducted with the aim of helping universities determine the level of student success in practicum learning using the C4.5 algorithm as much as 200 data. The results of the study showed an accuracy of 82.00% with the facility factor being the highest factor. So that the results obtained can help the campus in improving the learning process.
Peramalan Nilai Penjualan Gas Elpiji 3 Kg di Sumatera Utara dengan bantuan Analisis Metode Jaringan Saraf Tiruan Maulidya Rahma Siregar; Adinda Putri Azhari; Dedy Hartama; Agus Perdana Windarto
Bulletin of Artificial Intelligence Vol 1 No 2 (2022): October 2022
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research is related to forecasting the sales value of 3 Kg LPG in North Sumatra. The level of sales is influenced by customer satisfaction, service and customer needs. The purpose of this study is to determine the level of sales of 3 Kg LPG in North Sumatra and can overcome problems and overcome the amount of LPG demand in North Sumatra. So this research is needed using an artificial neural network method with a backpropagation algorithm to find the best sales results. The data used is divided into 2 parts, namely training and test data. The best network is taken from the Mean Square error (MSE) value and the smallest test. The experiments carried out in this study used a data rotation pattern, with 6 training and testing models. The experimental results of the 3-10-1 model are tests with the highest accuracy value, which is 100% and the MSE test is 0.00100005
Analisis Data Mining Pesebaran Siswa Smp Di Pematangsiantar Dengan Metode Algoritma K-Means Clustering Kurnia Sandi Purba; Dedy Hartama; S Suhada
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 3, No 1 (2022): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v3i1.91

Abstract

Data Mining is an automatic analysis of large or complex data with the aim of finding important patterns or trends that are usually not realized. However, the student data has not been utilized optimally, making it difficult for the school to carry out student development at school. Each student will make a comparison between the desired service with the service received. The K-Means algorithm is an iterative cluster technique algorithm. This algorithm starts with random selection, which is the number of clusters that you want to form. The attributes needed in processing are the student's hometown, the student's school origin, and the student's grade. Based on these attributes, the data will then be grouped into junior high school students based on city origin, school origin, and student grades using the K-Means Clustering algorithm. The results of processing this data will greatly help the junior high school, so that it is known that the distribution of each junior high school student is the most. In this study, the data used were data from junior high school students from the 2018-2019 class in Pematangsiantar as many as 300 data samples
Co-Authors Adinda Putri Azhari Agus Perdana Windarto Alan Boy Sandy Damanik Angga Sihasani Bahrudi Efendi Damanik Bellarmino Wira Permana Cici Astria Dewinta Marthadinata Sinaga Dinda Nabila Batubara Dwi Findi Auliasari Eka Irawan Elsa Indah Sari Ferdinal Rahmat Gea Fitri Anggraini Friskila Parhusip Gita Febrianti Handri Al Fani Heru Satria Tambunan Iin Parlina Ika Okta Kirana Ika Purnama Sari Ilham Saputra Saragih Indra Fransiskus Tarigan Indra Gunawan Indra Gunawan Irfan Christian Saragih Irfan Sudahri Damani, Irfan Sudahri Jalaluddin Jalaluddin Jalaluddin Jalaluddin Jalaluddin Jalaluddin Khairunnissa Fanny Irnanda Kristin Daya Rohani Sianipar Kurnia Sandi Purba M Safii M Safii M. Ridwan Lubis Maulidya Rahma Siregar Mawaddah Anjelita Muhammad Afifuddin Javan Muhammad Aliyul Amri Muhammad Ridwan Lubis Muhammad Syafiq Nurul Rofiqo Okprana, Harly Poningsih Primatama Kurniandisyah S Suhada Saifullah Saifullah Saifullah Solikhun Solikhun Sri Vivi Wahdini Suhada Suhada Suhada Sumarno Sumarno Sumarno Sundari Retno Andani Wanto, Anjar Yuli Septya Ayunda Zepri Pratama Zulaini Masruro Nasution Zulaini Masruroh Nasution