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bit-Tech
Published by Komunitas Dosen Indonesia
ISSN : 2622271X     EISSN : 26222728     DOI : https://doi.org/10.32877/bt
Core Subject : Science,
Computer Science & IT
The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol. 2 No. 3 (2020): Pandemik ICT" : 6 Documents clear
Junior Class Preparedness Classification Faces A National Exam Using C.45 Algorithm with A Particle Swarm Optimization Approach Asep Suherman; DIDI KURNAEDI; Sofian Lusa; Rizqi Darmawan
bit-Tech Vol. 2 No. 3 (2020): Pandemik ICT
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v2i3.133

Abstract

These studies are counter to a trend of falling students' graduation rates on the national exam. This is because of the way students prepare their readiness to face national tests is inaccurate. On this study the hybrid method c4 algorithm.5 and the swarm particle optimization to produce a class readiness of students with high and accurate accuracy. This research suggests that by using hybridmethodC4.5 andParticle Swarm Optimizationgenerates accuracy as 97.13 %, Precisionas 96,58 %, andRecallas 100 %. Then implemented through a web-based prototype application using programming javascriptlanguage.
Perbandingan Metode SAW dan CPI dalam Sistem Pendukung Keputusan untuk Menilai Kinerja Guru Andi Loa; Benny Daniawan; Tugiman Tugiman; Amat Basri
bit-Tech Vol. 2 No. 3 (2020): Pandemik ICT
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v2i3.141

Abstract

At educational institutions like Junior High School, Human Resources especially teachers determines the quality of the school. To determine Junior High School have good quality teachers, then the best teacher selection is needed to spur the teacher's performance. However, the best teacher selection at Santa Maria 2 Junior High School which in Tangerang still doing direct observation and no method implements the calculation. To overcome those problems, then Decision Support System is needed to do a calculation and rating the teachers at ease and accurate. The proposed Decision Support System is using Simple Additive Weighting and Composite Performance Index methods, where’s the calculation is obtained from each alternatives score and value weight from each criterion. The criteria in best teacher selection are reviewed from the absence aspect, professionalism, solidarity corps, personality, involving in activities from inside or outside school events. The final result from this calculation formed to ranking. The execution time of the SAW method has a faster average time of 0.489005 than the CPI method with an average time of 0.62258 seconds. On Relative Standard Deviation Testing CPI percentage greater than SAW with 3.90% and CPI 6.48%.
Prediction of Water Use Using Backpropagation Neural Network Method and Particle Swarm Optimization Afdhal Rizki Yessa; Mardi Hardjianto
bit-Tech Vol. 2 No. 3 (2020): Pandemik ICT
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v2i3.158

Abstract

Clean water production has not been well considered between the balance of water use by the community and the production of clean water that is in accordance with the needs of the community. Prediction of water use in meeting the daily needs of the community is very necessary in order to be able to produce efficient water. This research can help PDAM Kota in Kalimantan to be able to produce clean water in accordance with the use of clean water by the community. The Backpropagation Neural Network method focuses on the recapitulation of water use by the community. For better prediction results, optimization is done with Particle Swarm Optimization (PSO). It is expected that the results in this study can predict community water use in daily activities. The test results showed that the Prediction results had RMSE of 0.040 with parameters for training cycle 600 values, learning rate 0.1 and momentum 0.2, and neuron size was 3 and in particle swarm optimization population size 8, max.of gene 100, inertia weight value 0.3, the value of local best weight 1.0 and global value of best weight 1.0
Analisis Penerapan Data Mining Analisa Pola Pembelian Pelanggan Pada Penjualan Cat Menggunakan Algoritma Apriori (Studi Kasus: Pt Indowarna Cemerlang Indonesia) Rino - -; Maman Novian
bit-Tech Vol. 2 No. 3 (2020): Pandemik ICT
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v2i3.161

Abstract

Sales transaction data is one thing that can be used for making business decisions. Most sales transaction data is not reused, and is only stored as an archive and only used for making a sales report. Paint sales data is one science that can be applied in cases like this. Sales transactions that are not utilized properly can be extracted and reprocessed into useful information using data mining techniques. Using one of the data mining methods, namely the a priori algorithm, sales transaction data can be reprocessed so that it can produce a consumer buying pattern. This consumer buying pattern will later help companies make business decisions. PT Indowarna Cemerlang Indonesia is a company engaged in the paint trade, where the main activity is selling various wall paints, oil / wood paints, NC paints (car paints), epoxy paints (floor paints), depo-proof (anti leaked). PT Indowarna Cemerlang Indonesia does not reuse sales transaction data resulting from its sales activities. This data is only used as a reference for making sales reports and as an archive only, causing accumulation of data and unknown paint brands that are often sold or those that are of interest to customers. Therefore, the author takes the title application of data mining analysis of customer purchase patterns in paint sales using a priori algorithm. By doing this research, it is expected to provide results in the form of information that can be useful for related parties and can design sales strategies to increase company turnover.
Inventory Management with Forecasting Method: Single Moving Average and Trend Projection Amesanggeng Pataropura; Ivan Darmawan Sabatino; Riki Riki
bit-Tech Vol. 2 No. 3 (2020): Pandemik ICT
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v2i3.162

Abstract

Inventory management using forecasting methods aims to improve effectiveness and efficiency that facilitate trading businesses in the process of business transactions, improve delivery of information quickly, accurately, and transaction data properly and minimize errors. The system running in the system of selling goods is still manual, that is, it is not well computerized. The method used is forecasting which helps determine the estimated future stock of goods. Single Moving Average and Trend Projection. It can be concluded that the results of implementing this new system can assist trading businesses in recording transactions in the system. We can predict the current flow of goods which has been calculated based on 2 modules that have a connection with the system.
Alleged Bad Credit at Saving Cooperatives Borrow Flamboyant Assistance PPSW Jakarta With Comparasion the Algorithms Naive Bayes and C4.5 Renaldi Renaldi; Yusuf Kurnia
bit-Tech Vol. 2 No. 3 (2020): Pandemik ICT
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v2i3.163

Abstract

Data mining is often used in the financial sector, one of which is cooperatives. According to Law No. 25 of 1992, what is meant by cooperatives are business entities whose members are individual or cooperative legal entities based on activities based on the principles of cooperatives as well as as a people's economic movement based on the principle of kinship. One of the things that needs to be considered is the provision of credit or borrowing in the Flamboyan cooperative, which in this study there are many bad crediting occurrences that occur in the Flamboyan cooperative. By using a lot of data mining techniques, data can be utilized optimally. From the above problems, it can be overcome by utilizing data mining techniques, namely Predicting Bad Credit at the Flamboyant Savings and Loan Cooperative Fostered by PPSW Jakarta Using Comparative Algorithms Naive Bayes and C4.5. The algorithm used in the system is the best result of the Naive Bayes and C4.5 comparison based on data from the Flamboyan cooperative. The results obtained from the comparative data processing between the Naïve Bayes algorithm and the C4.5 using a dataset of 2282 transaction data obtained the results of the accuracy of the Naïve Bayes algorithm of 69.19% and the C4.5 algorithm of 71.87%, based on the accuracy results state that the C4 algorithm .5 is superior to the Naïve Bayes algorithm. Then the results from the C4.5 decision tree are translated into the bad credit prediction system in the Flamboyan cooperative.

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