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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Mantik INFOKUM Jurnal Sistem Informasi dan Teknologi Jaringan (SISFOTEKJAR) DISTANCE: Journal of Data Science, Technology, and Computer Science
N P Dharshinni
Universitas Prima Indonesia
Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media

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Menu Package Recommendation using Combination of K-Means and FP-Growth Algorithms at Bakery Stores: Menu Package Recommendation using Combination of K-Means and FP-Growth Algorithms at Bakery Stores N P Dharshinni; Elvana Bangun; Sarah Karunia; Ruth Damayanti; Gabriel Rophe; Roy Pandapotan
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.931.pp1272-1277

Abstract

Bakery shop is a shop that sells variants menu like bread, cakes, and drinks. The main problem with this store's sales is still not knowing which product items are best sellers and the shop still markets a lot of non-selling menus, causing the shop to lose money. So it takes the right strategy to increase the sales of bakery shop menus by making a menu package recommendations from the menus most frequently purchased by customers. The k-means algorithm performs grouping on menus to get menu packages. Furthermore, the fp-growth algorithm looks for linkages between frequently purchased menus to get menu package recommendations. The results of the research that the dominant items often purchased in cluster0 packages are hotdogs, pancakes, milk, garlic breadsticks with a confidence value of 92%, cluster1 packages are garlic breadsticks, hotdogs, chicken sand, pancakes with a confidence value of 92% and the last cluster2 packages are garlic breadstick, pastry, milk with a confidence value of 79%.
IMPROVEMENT OF DIGITAL IMAGE USING A COMBINATION OF ALPHA TRIMMED MEAN FILTER AND ARITHMETIC MEAN FILTER Insidini Fawwaz; N P Dharshinni; Irfan Hindrawan
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (944.539 KB)

Abstract

The development of technology at this time causes the provision of information to increase through social media. Many social media users convey information by including digital images. Digital images are very important in conveying the accuracy of information. However, digital images often experience various disturbances, such as decreased pixel quality, less sharpness, blurring, and the appearance of noise in the image. Noise contained in the image causes a decrease in image quality. Image degradation can be caused by uneven light intensity and can also be caused by dirt adhering to the camera lens. There are various types of noise found in digital images, including Salt And Pepper Noise, Speckle Noise, and Rayleigh Noise. There are many filtering methods that can improve digital images from noise interference. Some of them are the Mean Filter method, Geometric Mean Filter, Harmonic Mean Filter, Arithmetic Mean Filter, Median Filter, Midpoint filter, Alpha Trimmed Mean Filter and so on. Based on the research conducted, the combination of the Alpha Trimmed Mean Filter and Arithmetic Mean Filter methods can reduce Salt and Pepper noise, Speckle noise and Rayleigh noise better than the Alpha Trimmed Mean Filter and Arithmetic Mean Filter methods based on the MSE, RMSE and PSNR parameters.
Designing Applications For Damage Reporting Of Public Facilities Using K-Means Clustering Algorithm Rika Saljuni; Muhammad Sholahuddin; Fanema Putra Hartaret Harefa; Thines Raman; Juliansyah Putra Tanjung; N P Dharshinni
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2900

Abstract

Public facilities are facilities provided for public purposes such as roads, street lighting, bus stops, sidewalks, and pedestrian bridges. The facilities provided are facilities that provide convenience for the community so that they must be maintained properly. Data mining is a process of dredging or collecting important information from large data. The data mining process often uses statistical, mathematical methods, to utilize artificial intelligence technology. The application designed uses 50 datasets which, after normalization, the number of data becomes 350 data, and after preprocessing the data used in the study is 81 data, with 4 attributes and 3 clusters. The results of the data processing resulted in the first data clustering based on the facility attributes produced as many as 29 data, the second data clustering based on the year attribute produced was 12 data, the third data clustering based on the attribute the resulting amount was 40 data
Co-Authors Amir Saleh Andre Putra Persada Tarigan Andre Tarigan Azmi, Fadhillah Elvana Bangun FADHILLAH AZMI Fanema Putra Hartaret Harefa Gabriel Rophe Insidini Fawwaz Irfan Hindrawan Juliansyah Putra Tanjung Muhammad Sholahuddin Muhathir Muhathir Muhathir, Muhathir Rika Saljuni Roy Pandapotan Ruth Damayanti Sarah Karunia Thines Raman