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All Journal JTIK (Jurnal Teknik Informatika Kaputama) Journal of Artificial Intelligence and Engineering Applications (JAIEA)
I Gusti Prahmana
STMIK Kaputama
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Implementasi Algoritma Apriori dalam Perencanaan Persediaan Alat Kesehatan pada Apotek Muhammad Yoga Sabilla; Katen Lumbanbatu; I Gusti Prahmana
JTIK (Jurnal Teknik Informatika Kaputama) Vol 6, No 2 (2022): Volume 6, Nomor 2 Juli 2022
Publisher : STMIK KAPUTAMA

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Abstract

Dalam penelitian ini dilakukan proses Salah   satu   cara mengatasinya   adalah   dengan   tetap tersediaannya berbagai   jenis   alat-alat   kesehatan   secara   kontinu digudang   Apotik.   Untuk mengetahui   alat-alat kesehatan apa saja   yang   dibeli   oleh para   konsumen, dilakukan   teknik analisis  keranjang  pasar  yaitu analisis dari kebiasaan membeli konsumen. Adapun hasil penelitian adalah  Hasil analisis pola diatas menunjukkan bahwa nilai support yang semakin besar dari sebuah kombinasi alat kesehatan memberikan rekomendasi alat kesehatan yang paling sering dibeli oleh konsumen adalah Termometer, Kain Kasa, Plaster, Perban elastis. Sebaliknya semakin kecil nilai support suatu kombinasi alat kesehatan artinya rekomendasi diberikan berdasarkan berdasarkan alat kesehatan yang jarang dibeli. Adapun hasil dari penerapan metode apriori dengan minimum support 30% dengan kombinasi 3 dan 4 itemset adalah jika Termometer, Kain Kasa, Plaster, Perban elastis. Metode apriori yang digunakan cukup efektif dalam memberikan hasil akhir kombinasi obat yang sering dibeli oleh konsumen. Tingkat keakuratan pengujian menggunakan metode apriori yaitu 100 %. 
Knearst Algorithm Analysis – Neighbor Breast Cancer Prediction Coimbra I Gusti Prahmana; Kristina Annatasia Br Sitepu
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 3 (2022): June 2022
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (250.231 KB) | DOI: 10.53842/jaiea.v1i3.97

Abstract

A process to explain the results of the KNN algorithm analysis with the prediction of Breast Cancer Coimbra disease (Breast Cancer). The prediction output of the KNN algorithm will be added with the Simple Linear Regression algorithm modeling to measure the predictive data through a straight line as an illustration of the correlation relationship between 2 or more variables. Linear regression prediction is used as a technique for the relationship between variables in the prediction process of the Breast Cancer Coimbra data set (Breast Cancer). for the value of K in analyzing the KNN algorithm, take the nearest neighbor with the ranking results with K = 5 nearest neighbors which are taken in the KNN calculation. Which is where the output of the KNN algorithm classification will be analyzed with the Simple Linear Regression algorithm with Dependent (Cause) and Independent (effect) variables. The test results determine that the patient has breast cancer and the number of predictions based on age with glucose means that the patient is predicted to have breast cancer. analyze the KNN algorithm with Simple Liner Regression modeling with Python programming language.
Identification Identification of land and water Centella asiatica leaf herbal plants using digital imagery with the Sobel Edge Detection algorithm I Gusti Prahmana; Kristina Annatasia Br Sitepu
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 2 No. 2 (2023): February 2023
Publisher : Yayasan Kita Menulis

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Abstract

Centella asiatica leaves or gotu kola leaves are wild plants that grow in Asian countries such as China, Indonesia, Japan and India. Since thousands of years ago, this gotu kola leaf has been known to treat various diseases. This plant is even used as a traditional herbal medicine in China and India. Centella asiatica is an annual herbaceous plant that grows and flowers throughout the year. Plants will thrive if the soil and environment are suitable to be used as a ground cover. Types of gotu kola that are often found are red gotu kola and green gotu kola. Centella asiatica is also known as antanan taman or antanan batu because it is found in rocky, dry and open areas. Centella asiatica grows with stolons and has no stems, but has rhizomes (short rhizomes). Meanwhile, green gotu kola is often found in rice fields and on the sidelines of the grass. Based on this problem, a study is needed to develop a system to determine the shape of leaf fiber density with a comparison of ground gotu kola and water gotu kola using image processing techniques to find the diameter. This measurement process uses the Matlab application and tests with the Sobel edge detection method and image processing to see edges that are more clearly visible. The results showed that the developed system was capable of obtaining images and identifying the fiber density of Centella asiatica leaves. The system was designed with Jupyter Notebook Python-based programming language analysis with image data taken via internet sources as research material.
Co-Authors Katen Lumbanbatu Kristina Annatasia Br Sitepu Muhammad Yoga Sabilla