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Selection of Outstanding Course Participants for Award Recipients Using the Topsis Method of Case Studies of Multilogic Course Institutions Riza Mahyuda; Rahmadani; Br Sitepu, Kristina Annatasia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.656

A course institution is an organization or institution that provides educational or training programs in various fields. This institution aims to improve the skills, knowledge, and competencies of course participants. Course institutions can operate formally or informally and offer different types of courses, ranging from short-term courses to more intensive programs. Examples of course institutions include vocational training centers, language schools, computer centers, and other professional educational institutions. In this study, the author wants to explain the determination of outstanding course participants by applying the TOPSIS method to get alternative students who are close to positive ideals and far from negative ideals, based on data on participant values, among others, course certificate assessment criteria, course average scores, skills, final project scores and attendance, the value of giving awards to participants in order to further improve the quality of participant achievement so that the participants are more enthusiasm in learning. The purpose of selecting outstanding students to give awards is to appreciate and recognize the efforts, abilities, and outstanding achievements shown by students. This award aims to motivate students to continue to excel, increase their enthusiasm for learning, and encourage healthy competition among participants. In addition, this award also serves as an inspiration for other students to strive to achieve the best results in their field of pursuit. After doing the preference value of each alternative, the largest score is owned by alternative V14 (Dwi Intan Sari) with a value of 0.801. It can be concluded that the recipient of the award for the outstanding participant in Multi Logika is Dwi Intan Sari.

Decision Support System for Extracurricular Determination to Increase Student Involvement in Activities Outside the Classroom Using the AHP Method Rizky Ramadhan; Yani Maulita; Br Sitepu, Kristina Annatasia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.664

SMK Dharma Pancasila Medan is a private school located at Jl. Dr. T. Mansyur No.71 C Medan, Kel. PB Selayang. Currently, the selection of extracurricular activities at the school relies on a conventional manual system, lacking a formal system to identify the best extracurricular activities among the available options. This study aims to develop a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) method to evaluate and prioritize extracurricular activities based on four main criteria: achievement, short-distance running, height, and discipline. The system is designed to provide objective and measurable recommendations, facilitating students in choosing the most suitable extracurricular activities to enhance their success in out-of-class activities. The research findings demonstrate that the DSS developed with the AHP method is effective in providing accurate recommendations and increasing student engagement in extracurricular activities. The analysis and assessment process using AHP results in more targeted decisions aligned with the goal of enhancing student involvement. Overall, the system not only aids students in optimal extracurricular selection but also contributes to the development of better extracurricular activity strategies at SMK Dharma Pancasila Medan. This research enriches educational theory and practice by offering practical solutions to the challenges in extracurricular selection and enhances the overall educational experience for students.

SISTEM MONITORING DAN KONTROL PINTU BENDUNGAN BERBASIS NODEMCU DAN ENERGI TERBARUKAN Rifai, Abas; Sembiring, Arnes; Br Sitepu, Kristina Annatasia; Jalil, Saifuddin Muhammad
Jurnal TIMES Vol 14 No 1 (2025): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pintu bendungan memiliki peran strategis dalam pengelolaan sumber daya air, mencakup irigasi, pengendalian banjir, hingga penyediaan air bersih bagi masyarakat. Namun, di banyak daerah, sistem pengoperasian pintu masih bersifat manual dan sangat tergantung pada intervensi manusia. Ketergantungan ini menyebabkan keterlambatan dalam merespons fluktuasi volume air secara tiba-tiba, terutama pada saat cuaca ekstrem, yang berisiko menimbulkan bencana seperti banjir dan kekeringan. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem otomatisasi pintu bendungan berbasis Internet of Things (IoT), dengan menggunakan mikrokontroler NodeMCU ESP8266 sebagai unit kendali utama. Sensor ultrasonik digunakan untuk mengukur ketinggian permukaan air secara real-time, sementara sensor hujan memantau kondisi cuaca. Sistem ini dilengkapi panel surya sebagai sumber energi terbarukan guna mendukung keberlanjutan operasional. Motor servo digunakan untuk membuka dan menutup pintu bendungan secara otomatis berdasarkan data sensor yang dianalisis secara cerdas. Seluruh data dikirimkan ke aplikasi Blynk, memungkinkan pemantauan dan pengendalian jarak jauh melalui perangkat seluler. Hasil pengujian menunjukkan bahwa sistem ini bekerja secara akurat, responsif, dan andal dalam berbagai kondisi simulasi. Temuan ini menawarkan solusi teknologi yang inovatif, hemat energi, dan berpotensi diterapkan secara luas dalam sistem pengelolaan air terpadu di masa depan.

Implementation of a decision support system for selecting palm oil processing waste disposal location in Pagar Merbau using the topsis method Nikpani, Karen; Syahputra, Siswan; Br.Sitepu, Kristina Annatasia
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 2 (2025): Journal of Engineering, Tecnology and Computing (JETCom)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i2.311

The management of palm oil solid waste, particularly Empty Fruit Bunches (EFB), remains a major challenge in the palm oil industry due to its potential to cause environmental pollution if not properly handled. One solution to this problem is selecting appropriate disposal sites by considering various technical, environmental, and social criteria. This study aims to implement a Decision Support System (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for selecting palm oil waste disposal locations in Pagar Merbau. The criteria applied include land area, number of trees, soil type, plant age, nutrient requirements, and soil fertility. The system was developed using PHP and MySQL, and tested with the blackbox testing method. The results show that the system effectively supports decision-making by ranking alternative sites based on their highest preference values. Therefore, the implementation of a TOPSIS-based DSS proves to be effective in determining the most optimal location for palm oil waste disposal.

Kombinasi Algoritma Deteksi Tepi Prewitt dan Canny untuk Identifikasi Citra Invert Golongan Darah A+ Br Sitepu, Kristina Annatasia; Hanafiah, Mhd Ali
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.286

Information about blood types is a crucial aspect that must be known in the medical field, especially in the process of blood transfusion and healthcare services. Identifying blood types is a vital step to ensure patient safety during blood transfusions. In this research, the primary focus is on blood type A+. Blood type A+ is one of the common and sought-after blood types because it can donate blood to individuals with blood types A or AB positive. Blood type A+ can receive blood from donors with blood types A or O positive. One method that can be utilized in the process of identifying blood type A+ is using digital image processing and identification methods with edge detection algorithms. The use of edge detection algorithms on an image will result in the edges of objects in that image. The goal is to highlight the details in the image and improve blurred points in vision that may arise due to errors or effects from the image acquisition process. This research aims to evaluate the capabilities of the combination of Prewitt and Canny edge detection algorithms in detecting inverted images. The image dataset used consists of 10 original images of blood type A+ and 10 inverted images. The research dataset was obtained from the IEEE DataPort website. Based on the analysis of 10 conducted experiments, the combination of Prewitt and Canny algorithms is excellent in edge detection, achieving a high accuracy level of 100%. Therefore, it can be concluded that for this issue, the combination of Prewitt and Canny algorithms is capable of identifying inverted images of blood type A+.

PENGELOMPOKAN DATA KELUHAN PASIEN PADA LAYANAN RUMAH SAKIT BERDASARKAN KATEGORI MASALAH MENGGUNAKAN METODE CLUSTERING Anggita, Refa; Buaton, Realita; Br Sitepu, Kristina Annatasia
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.7432

Penelitian ini membahas pengelompokan data keluhan pasien pada layanan RSU Artha Medica Binjai berdasarkan kategori masalah menggunakan algoritma K-Means Clustering. Data penelitian mencakup periode 2023–2024 dengan variabel umur pasien, kategori keluhan, dan kategori masalah. Proses pengolahan dilakukan menggunakan perangkat lunak Matlab R2014a, menghasilkan enam cluster dengan karakteristik berbeda. Hasil pengujian menunjukkan konfigurasi enam cluster memiliki nilai cluster variance terendah sebesar 4,5682, menandakan distribusi data paling kompak dibanding konfigurasi lainnya. Secara khusus, cluster keenam memiliki variance 5,0008 dengan Vmin 0,2472 dan Vmaks 10,3912, menunjukkan variasi yang terkendali dan sebaran data yang merapat ke pusat cluster. Temuan ini membuktikan bahwa penerapan K-Means Clustering dapat membantu rumah sakit dalam memahami pola keluhan pasien secara lebih akurat dan menjadi acuan strategis untuk peningkatan kualitas pelayanan.

OPTIMASI PENINGKATAN AKURASI KLASIFIKASI STUNTING MENGGUNAKAN ALGORITHM SUPPORT VECTOR MACHINE I Gusti Prahmana; Kristina Annatasia Br Sitepu; Adek Maulidya; Toni Prabowo
Djtechno: Jurnal Teknologi Informasi Vol 7, No 2 (2026): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i2.9391

Stunting merupakan tantangan kesehatan masyarakat yang berdampak signifikan pada perkembangan fisik dan kognitif anak di masa depan. Penelitian ini bertujuan untuk mengoptimalkan akurasi klasifikasi status gizi balita menggunakan algoritma Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 161 data sekunder dari Dinas Pengendalian Penduduk dan Keluarga Berencana (PPKB) Kota Binjai, mencakup atribut umur, tinggi badan, dan berat badan. Metodologi penelitian meliputi tahap praproses, seleksi fitur, serta optimasi model melalui penyetelan parameter kernel, nilai cost (C), dan gamma. Evaluasi kinerja dilakukan menggunakan confusion matrix dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model SVM yang dioptimasi berhasil mencapai tingkat akurasi sebesar 84,85%. Model ini menunjukkan performa yang sangat kuat dalam mengidentifikasi status gizi normal, meskipun masih terdapat kendala dalam mengklasifikasikan kategori kurus akibat ketidakseimbangan data. Secara keseluruhan, algoritma SVM terbukti efektif dalam memetakan risiko stunting secara dini. Temuan ini diharapkan dapat menjadi instrumen pendukung bagi otoritas kesehatan dalam merancang program intervensi yang lebih presisi dan tepat sasaran bagi masyarakat.

PENGELOMPOKAN DATA KELUHAN PASIEN PADA LAYANAN RUMAH SAKIT BERDASARKAN KATEGORI MASALAH MENGGUNAKAN METODE CLUSTERING Refa Anggita; Realita Buaton; Kristina Annatasia Br Sitepu
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol. 6 No. 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.7432

Penelitian ini membahas pengelompokan data keluhan pasien pada layanan RSU Artha Medica Binjai berdasarkan kategori masalah menggunakan algoritma K-Means Clustering. Data penelitian mencakup periode 2023–2024 dengan variabel umur pasien, kategori keluhan, dan kategori masalah. Proses pengolahan dilakukan menggunakan perangkat lunak Matlab R2014a, menghasilkan enam cluster dengan karakteristik berbeda. Hasil pengujian menunjukkan konfigurasi enam cluster memiliki nilai cluster variance terendah sebesar 4,5682, menandakan distribusi data paling kompak dibanding konfigurasi lainnya. Secara khusus, cluster keenam memiliki variance 5,0008 dengan Vmin 0,2472 dan Vmaks 10,3912, menunjukkan variasi yang terkendali dan sebaran data yang merapat ke pusat cluster. Temuan ini membuktikan bahwa penerapan K-Means Clustering dapat membantu rumah sakit dalam memahami pola keluhan pasien secara lebih akurat dan menjadi acuan strategis untuk peningkatan kualitas pelayanan.

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

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v2i2.158

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.

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