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All Journal Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Jurnal Teknologi Informasi dan Multimedia Scientific Journal of Informatics
Muhammad Ali Ridla
Universitas Ibrahimy
Computer Science & IT

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PARTICLE SWARM OPTIMIZATION SEBAGAI PENENTU NILAI BOBOT PADA ARTIFICIAL NEURAL NETWORK BERBASIS BACKPROPAGATION UNTUK PREDIKSI TINGKAT PENJUALAN MINYAK PELUMAS PERTAMINA Muhammad Ali Ridla
Jurnal Ilmiah Informatika Vol. 3 No. 1 (2018): Jurnal Imliah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v3i1.473

Abstract

The lubricating oil industry is one part of the oil and gas sector which is still one of the main pillars of economic growth in Indonesia. Sales predictions are needed by companies and policy makers as planning materials and economic development strategies to increase income in the future. Predictions that have a better level of accuracy can provide appropriate decisions. Various methods have been used, the Artificial Neural Network algorithm is one of the most widely used, especially in the Backpropagation (BPNN) structure which can predict non linear time series data. Backpropagation has been proven to have a better level of accuracy compared to econometric methods such as ARIMA. The integration of Backpropagation algorithm with other algorithms needs to be done to overcome the shortcomings and improve the ability of the National Land Agency itself. Particle Swarm Optimization (PSO) which is used as an optimization determinant of attribute weight values in the network structure of BPNN shows good results. After testing, BPNN without PSO has a Squared Error (SE) level of 0.012 and a Root Mean Aquared Error (RMSE) of 0.111. While BPNN with PSO has SE levels of 0.004 and RMSE of 0.059. This shows that there is a significant decrease in the error rate after the PSO algorithm is added to the BPNN structure which is 46.85%.
Perancangan dan Implementasi Sistem Informasi Pelayanan Jam'iyah Umroh Hafas Irma Yunita; Muhammad Ali Ridla
Jurnal Ilmiah Informatika Vol. 4 No. 2 (2019): Jurnal Imliah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v4i2.533

Abstract

The servicing done by jam'iyah umroh hafas (a group which provide umrah service) still use conventional procedure, namely by using form paper during the registration which obliges pilgrims to meet with the organizer. Also in the next process all is done with a face to face model, so that for the management of pilgrims each package the officer must carefully sort it out. This greatly affects the service process, in which officers must be really careful to group installments from pilgrims, as well as prepare data for processing to passports and travel. Therefore, in an effort to provide optimal, fast and appropriate services for hafas pilgrims, it is necessary to adopt the Customer Relationship Management contained in e-business, as well as to engineer software that can facilitate officers in completing all pilgrims administrative who will go to makkah. Designing this system uses the V-Model method in which each stage is validated and verified so that the system that is produced really meets the needs of the users and helps the officers to better administer the needs of the pilgrims.
Implementasi Data Mining terhadap Pola Penjualan Bahan Material Bangunan di TB. Murah Rejeki Menggunakan Algoritma Apriori Muhammad Ali Ridla; Achmad Baijuri; Ubaidillah Ahmad
Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Vol 6, No 2 (2023): Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data)
Publisher : Institut Informatika dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/simada.v6i2.3800

Abstract

The Murah Rejeki Building Store makes sales transactions every day, but these transactions are only used for reporting and data bookkeeping purposes. The Murah Rejeki Building Store has not conducted an analysis of the relationship between building material products purchased by consumers in the future. This study aims to process sales transaction data from consumer purchases using the Apriori Algorithm, which is one of the data mining processing methods. In this study, the authors use the Apriori Algorithm to find association rules by determining the Minimum Support and Minimum Confidence values. The results showed that with a Minimum Support value of 5% and a Minimum Confidence of 60%, 14 patterns of consumer purchase transactions were found. This information becomes very valuable in making decisions on preparing supplies of building materials and determining the layout of goods in TB. Cheap Fortune. The association rules formed can be used to plan sales strategies and layout of building materials at TB. Murah Rejeki.
Implementasi Algoritma Apriori untuk Menentukan Pola Transaksi Penjualan Berbasis Web Muhammad Ali Ridla; Fajriyanto; Misbahul Marzuqi
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 3 (2023): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i3.399

Abstract

The Apriori algorithm is an algorithm that is well known for searching frequent itemsets using the association rule technique. The calculation of the Apriori algorithm uses minimal support and minimal confidence to determine the limit for calculating goods. The a priori algorithm functions to determine the pattern of sales of goods that are often purchased together by customers. The history of sales transactions owned by a store can be calculated for its frequent itemset pattern by using an a priori algorithm so that customers can find patterns of items that are often purchased simultaneously by customers. Therefore, the a priori algorithm is very important to be used by shop owners because it can determine sales strategies and the placement of goods that are often purchased simultaneously by customers. In this study, the authors succeeded in calculating a sales transaction by determining a minimum support limit of 10% and a minimum confidence of 10%. With the minimum support and minimum confidence that has been set by the author to see the results of the a priori algorithm for sales, then the results of 2 combinations of itemsets that meet the calculation requirements are obtained.
Co-Authors Achmad Baijuri Fajriyanto Irma Yunita Misbahul Marzuqi Ubaidillah Ahmad