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All Journal Jurnal Kajian Ilmiah Journal of Students‘ Research in Computer Science (JSRCS)
Putra , Tri Dharma
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Analisis Keranjang Belanja dengan Algoritma Apriori Klasik pada Data Mining Putra , Tri Dharma
Jurnal Kajian Ilmiah Vol. 20 No. 1 (2020): Januari 2020
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (208.59 KB) | DOI: 10.31599/jki.v20i1.70

Abstract

Association Rule Mining is an area of data mining that focus on pruning candidate keys, to find frequent item set. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set is a frequent itemset. A subsequence, such as buying first PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern, also knwon as market basket analysis. This paper describes the step by step classical apriori on market basket analysis. Keywords: apriori algorithm, frequent item set, market basket analysis, association rule Abstrak Penambangan Aturan Asosiasi adalah area data mining yang fokus pada pemangkasan kunci kandidat, untuk menemukan frequent itemset. Sebagai contoh, satu set item, misalnya susu dan roti, yang muncul sering bersama-sama di set data transaksi adalah frequent itemset. Berikutnya, pelanggan, misalnya membeli PC dahulu, lalu kamera digital, lalu kartu memori, jika ini sering terjadi dalam riwayat basisdata belanja, adalah pola sekuensial berurutan (sering), juga dikenal sebagai analisis keranjang belanja. Tulisan ini menjelaskan langkah demi langkah algoritma apriori klasik pada analisis keranjang belanja. Kata kunci: algoritma apriori, frequent itemset, analisis keranjang belanja, aturan asosiasi
Sistem Informasi Geografis Mencari Rute Terpendek Pada Pemetaan SMP di Kecamatan Mustikajaya Dengan Algoritma A-Star A(*) Berbasis Web Novandra , Deka Darma; Putra , Tri Dharma; Mayadi , Mayadi
Journal of Students‘ Research in Computer Science Vol. 4 No. 1 (2023): Mei 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/755m7j40

Abstract

Many parents have few references to send their children to school, and are only based on suggestions or invitations from those closest to them. Lack of information can be one of the reasons for the lack of references for parents to choose a junior high school for their child. The lack of information regarding the location of junior high schools is the reason for the lack of references for parents to send their children to school. Based on the existing problems, the authors propose a web-based geographic information system that uses the A-Star Algorithm (A*) to be used as a means of information and also to add references for parents/guardians. The A-Star (A*) algorithm is a method for searching for information about the distance to reach a destination by selecting the closest route.
Implementasi Deep Learning Untuk Rekomendasi Aplikasi E-learning Yang Tepat Untuk Pembelajaran jarak jauh Priatna, Wowon; Purnomo , Rakhmat; Putra , Tri Dharma
Jurnal Kajian Ilmiah Vol. 21 No. 3 (2021): September 2021
Publisher : Lembaga Penelitian, Pengabdian Kepada Masyarakat dan Publikasi (LPPMP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (554.294 KB) | DOI: 10.31599/jki.v21i3.521

Abstract

The purpose of this study is to recommend e-learning applications that are appropriate for use in online learning in college environments. The large number of e-learning platforms used by lecturers for online lecture activities results in students being forced to use several e-learning applications depending on the lecturer who teaches the courses taken, for the university also finally gives lecturers policies for distance learning reports each finished giving the material. In this study the data collection method began by taking data from the faculty to find out which e-learning applications were widely used by lecturers, then distributing questionnaires to students and lecturers who used the e-learning application to measure the e-leaning application with the e-learning criteria. Appropriate. The data is then processed into a dataset. The algorithm used in implementing deep learning is Artificial Neural Network (ANN). For the implementation of ANN, 27 variables were determined from the e-learning criteria and 1 target. In this ANN stage, prediction was used with classifications based on preparation, training, learning, evaluation and prediction using the python programming. The results obtained in this study that the Moodle application gets the highest score with an accuracy of 97% to be used as a recommendation for e-learning applications that are appropriate for universities to conduct online lectures.
Co-Authors Mayadi , Mayadi Novandra , Deka Darma Priatna , Wowon Purnomo , Rakhmat