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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Journal of Informatics Management and Information Technology Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Dedi Suhendro
STIKOM Tunas Bangsa, Pematangsiantar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Analisis Pemilihan Guru Konseling dengan Metode VIKOR pada SMK TPI Alhasanah Pematang Bandar Delima Syah Putri Sinaga; Sundari Retno Andani; Dedi Suhendro
Journal of Computer System and Informatics (JoSYC) Vol 3 No 1 (2021): November 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i1.951

Abstract

This study aims to determine the right counseling teacher at SMK TPI Al-Hasanah to be able to help, direct and change the deviant behavior of students that occur in the school environment. The decision support system applied in this study is by using the VIKOR method. The data obtained in this study by direct observation to the field and also requesting accurate data from the leadership. The selection of counseling teachers is done with several criteria. The criteria are: (K1) last graduation, (K2) Length of Work, (K3), Discipline, (K4) Supporting certificates. It is expected that the results of this research using the VIKOR method can determine appropriate counseling teachers and be able to direct students at SMK TPI Al-Hasanah to become more virtuous individuals both in the school environment and also in the community
Penerapan Metode Adaptie Neuro Fuzzy Inference dalam Memprediksi Penjualan Buku Zulfia Darma; Agus Perdana Windarto; Dedi Suhendro
Journal of Informatics Management and Information Technology Vol. 2 No. 1 (2022): January 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v2i1.153

Abstract

The increase in sales of items or product componies continues to increase based on the needs of the community. With the increase in sales will greatly affect the income of a company. So a mature sales strategy is needed. The number of visitors has a great influence on sales transactions. The more visitors, the more likely the transaction can be predicted. The number of visitors every day is different and has an unequal percentage in making sales transactions. One way to increase sales revenue is to predict sales based on the average number of stock so that sales strategy planning can be right on target. Related to this, the author conducts research to predict the number of book sales based on the pattern that occurs from the number of book stock at PT. Tiga Serangkai Pustaka Mandiri Pematangsiantar. By applying the Adaptive Neuro Fuzzy Inference System (ANFIS) method in predicting book sales in 2018-2020. The amount of data used is 36 data, divided into training data (23 data) and testing (13 data) using 2 input variables namely orders and stock, 1 input variable is sales. In testing with matlab software, the training process uses mf trimf with 9 rules which produces the smallest error value of 1.045 at epoch 36, with an accuracy rate of 57%
Penerapan Data Mining Dalam Mengelompokkan Provinsi Rawan Kejahatan Menggunakan Algoritma K-Means Eka Desriani Aritonang; Heru Satria Tambunan; Jaya Tata Hardinata; Eka Irawan; Dedi Suhendro
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2576

Abstract

Kemajuan Teknologi informasi saat ini berkembang sangat cepat yang mengakibatkan peningkatan pada data dalam jumlah besar. Meningkatnya jumlah kejahatan pada setiap provinsi di Indonesia menyebabkan penumpukan pada data. Beragam jenis kejahatan terjadi di lingkungan masyarakat, seperti pembunuhan, penganiayaan, pemerkosaan, pencurian, penipuan, penyalahgunaan narkoba, dan perjudian. Dengan melihat banyaknya jumlah kejahatan tersebut, masyarakat menjadi khawatir dan merasa tidak nyaman sehingga perlu dilakukan penelitian agar dapat mengetahui wilayah/provinsi yang rawan akan kejahatan. Tujuan dari penelitian yaitu sebagai referensi bagi pemerintah untuk meningkatkan keamanan untuk setiap wilayah pada tahun-tahun berikutnya. Penelitian ini menggunakan metode data mining dengan algoritma k-means clustering dan dibantu dengan aplikasi Rapidminer. Penelitian ini mengelompokkan provinsi dengan dua cluster yaitu cluster tinggi dan cluster rendah. Hasil dari penelitian ini diperoleh 4 provinsi dengan jumlah kejahatan tertinggi (C1), 30 provinsi dengan jumlah kejahatan rendah (C2) dan pengujian menggunakan Rapidminer mendapatkan hasil yang sama. Algoritma K-means dapat diterapkan dan memberikan informasi tentang provinsi yang rawan terjadinya kejahatan.Kata kunci: k-means, clustering, kejahatan
ANALISIS TINGKAT KEPUASAN PENGGUNA GOOGLE CLASSROOM DALAM PEMBELAJARAN ONLINE MENGGUNAKAN ALGORITMA NAÏVE BAYES Fildzah Nadya Arieni; Eka Irawan; Dedi Suhendro
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 2 No. 3 (2022): November : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Barenlitbangda Kabupaten Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v2i3.327

Abstract

SMK Negeri 3 Pematangsiantar is one of the schools affected by the COVID-19 pandemic, which at that time the whole world was facing an outbreak of this infectious disease. The Covid-19 pandemic that was hitting the whole world at that time, required all students and students to carry out the online learning process in order to prevent the spread of the Covid-19 virus. This study aims to classify the level of satisfaction of Google classroom users using nave Bayes data mining techniques. Sources of data obtained from questionnaires given to students randomly as many as 100 students. The criteria used as Google Classroom user satisfaction include: system quality, service, information, usage, user satisfaction. The model generated by researchers and Rapid Miner Software with training data as much as 75 data. There are 25 test data that are processed in Rapid Miner 5.3. get test results with an accuracy of 96.00%, namely 15 satisfied users and 10 dissatisfied users.
Co-Authors Agus Perdana Windarto Delima Syah Putri Sinaga Eka Desriani Aritonang Eka Irawan Fildzah Nadya Arieni Heru Satria Tambunan Jaya Tata Hardinata Sundari Retno Andani Zulfia Darma