Articles
<![CDATA[Implementasi Algoritma Apriori dalam Perencanaan Persediaan Alat Kesehatan pada Apotek ]]>
<![CDATA[Muhammad Yoga Sabilla]]>;
<![CDATA[Katen Lumbanbatu]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ JTIK (Jurnal Teknik Informatika Kaputama) Vol 6, No 2 (2022): Volume 6, Nomor 2 Juli 2022 ]]>
Publisher : <![CDATA[STMIK KAPUTAMA ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[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 %. ]]>
<![CDATA[Knearst Algorithm Analysis – Neighbor Breast Cancer Prediction Coimbra ]]>
<![CDATA[I Gusti Prahmana]]>;
<![CDATA[Kristina Annatasia Br Sitepu]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 1 No. 3 (2022): June 2022 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (250.231 KB)
|
DOI: 10.53842/jaiea.v1i3.97
<![CDATA[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.]]>
<![CDATA[Identification Identification of land and water Centella asiatica leaf herbal plants using digital imagery with the Sobel Edge Detection algorithm ]]>
<![CDATA[I Gusti Prahmana]]>;
<![CDATA[Kristina Annatasia Br Sitepu]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 2 No. 2 (2023): February 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[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.]]>
<![CDATA[PENERAPAN ALGORITMA FIXED LENGTH BINARY ENCODING (FLBE) KOMPRESI CITRA ]]>
<![CDATA[Maulida Azmy]]>;
<![CDATA[Akim M.H. Pardede]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ Jurnal Sistem Informasi Kaputama (JSIK) Vol. 6 No. 2 (2022): Volume 6, Nomor 2, Juli 2022 ]]>
Publisher : <![CDATA[STMIK KAPUTAMA ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.59697/jsik.v6i2.187
<![CDATA[Perkembangan teknologi yang pesat, sangat berperan penting dalam pertukaran informasi yang cepat. Pada pengiriman informasi dalam bentuk citra masih mengalami kendala, diantaranya adalah karena besarnya ukuran citra sehingga solusi untuk masalah tersebut adalah dengan melakukan kompresi. Kompresi bertujuan untuk mengurangi ukuran data tersebut menjadi sekecil mungkin. Ada banyak metode kompresi citra, namun pada tugas akhir ini akan dibahas prinsip kerja algoritma Fixed Length Binary Encoding (FLBE) dengan implementasi menggunakan bahasa pemrograman visual basic. Analisis kinerja algoritma ini bertujuan untuk mengetahui performansi algoritma pada file citra. Untuk mengetahui hasil proses kompresi dilakukan melalui perhitungan Ratio of Compression (????????), Compression Ratio (????????), Redudancy (Rd), waktu kompresi (ms) dan waktu dekompresi (ms) pada file citra. Dalam percobaan yang dilakukan didapatkan bahwa algoritmaFixed Length Binary Encoding (FLBE) dengan rasio kompresi rata-rata sebesar 2.276%.]]>
<![CDATA[Sentiment Analysis Using Text Mining Techniques On Social Media Using the Support Vector Machine Method Case Study Seagames 2023 Football Final ]]>
<![CDATA[Muhammad Rifa'i]]>;
<![CDATA[Relita Buaton]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.59934/jaiea.v3i1.274
<![CDATA[This thesis aims to analyze sentiment on text data from social media related to the 2023 SEA Games, especially in the final match of the soccer sport. The method used is the Text Mining Technique with the SVM (Support Vector Machine) algorithm to classify user sentiment as positive or negative regarding the match. Text data is retrieved from various social media platforms during and after the match. The results of the sentiment analysis are expected to provide insight into the public's view of the sporting event. This research can contribute to the understanding of public sentiment towards the 2023 SEA Games final football match through the analysis of text data from social media.]]>
<![CDATA[Categorying Sugarcane Production Based On Factors Affecting Productivity With The K-Nearest Neighbor Algorithm ]]>
<![CDATA[Anggi Pratiwi]]>;
<![CDATA[A M H Pardede]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.59934/jaiea.v3i1.295
<![CDATA[Sugarcane (Saccharum Officanarum is an annual plantation crop, which has its own characteristics, because the stem contains sugar. To classify the results of sugarcane production, currently still using the manual method by only looking at the current conditions of sugarcane production. This is less efficient because there is no calculation process in grouping sugarcane. So that mistakes can occur in grouping sugarcane production to get good results or not in the assessment of sugarcane grouping at PTPN II Kwala Madu. For this reason, the author will create an alternative application system that can group sugarcane production with the K-Nearest Neighbor algorithm to find out the best type of sugarcane production based on the factors. The application made by the author uses the PHP programming language and uses the MySQL database as data storage. The system is made as easy as possible to make it easier for users to use and understand later.]]>
<![CDATA[Prediction Of New Students Using The Exponential Smoothing Method (Case Study: STMIK Kaputama) ]]>
<![CDATA[Tria Pusvita Dewi]]>;
<![CDATA[A M H Pardede]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.59934/jaiea.v3i1.325
<![CDATA[With this prediction system for the number of new students, it functions to determine the priority of how many new students will be accepted in the following year. For each new teaching according to the factual and quite accurate new student data reports by implementing a computerized system, the data processing will be more precise and reduce errors in predicting it. This prediction will provide an overview based on the trend of the number of new students at STMIK KAPUTAMA. The method used is the exponential smoothing method by looking for how big the error is with different alpha values, namely 0.1 to 0.9. Each alpha tested will give different results. The purpose of the above calculation is to find out (a) alpha which produces the smallest forecast error. By taking data on the number of new students in the previous period, forecasting by determining the value of weight (a) alpha. The value of weight (a) alpha depends on the number of new students, where the nature of this forecasting determination of the value that is closest to the actual conditions. The forecasting results above are the closest to the overall number of new students alpha 0.9 is 1,207.9 and forecast error is 15.75.]]>
<![CDATA[Implementasi Algoritma Apriori dalam Perencanaan Persediaan Alat Kesehatan pada Apotek ]]>
<![CDATA[Muhammad Yoga Sabilla]]>;
<![CDATA[Katen Lumbanbatu]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ JTIK (Jurnal Teknik Informatika Kaputama) Vol. 6 No. 2 (2022): Volume 6, Nomor 2, Juli 2022 ]]>
Publisher : <![CDATA[STMIK KAPUTAMA ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.59697/jtik.v6i2.214
<![CDATA[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 %.]]>
<![CDATA[IMPLEMENTASI ALGORITMA OTP DAN STEGANOGRAFI EOF DALAM PENYISIPAN PESAN TEKS PADA CITRA ]]>
<![CDATA[Muhammad Arief]]>;
<![CDATA[Magdalena Simanjuntak]]>;
<![CDATA[I Gusti Prahmana]]>
<![CDATA[ JTIK (Jurnal Teknik Informatika Kaputama) Vol. 6 No. 2 (2022): Volume 6, Nomor 2, Juli 2022 ]]>
Publisher : <![CDATA[STMIK KAPUTAMA ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.59697/jtik.v6i2.216
<![CDATA[Penggunaan informasi media citra mempunyai beberapa kelemahan, salah satunya adalah mudahnya dimanipulasi oleh pihak-pihak tertentu dengan bantuan teknologi yang berkembang sekarang ini. Upaya yang dapat dilakukan dalam peningkatan pengamanan pengiriman informasi citra adalah kriptografi, yaitu ilmu dan seni untuk menjaga keamanan pesan. Pada penelitian ini diterapkan metode One Time Pad dan Stegnografi End Of File yang bertujuan untuk memperoleh cipher yang lebih kuat dengan menyisipkan pesan kedalam citra sehingga susah untuk di sadap. Algoritma One Time Pad untuk mengenkripsi dan dekripsi, Stegnografi End Of File yang digunakan untuk mengencoding dan mendecoding citra. Hasil dari penelitian ini menunjukkan bahwa dengan menerapkan algoritma One Time Pad dan Stegnografi End Of File dapat mengamankan pesan yang disisipkan kedalam citra dan mengamankan kunci untuk kebutuhan data. Waktu proses encoding dan decoding di pengaruhi oleh banyaknya pesan yang akan dirahasiakan.]]>
