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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) TEKNIK INFORMATIKA Jurnal TIMES Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Informasi MURA Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Science, Engineering, Technology, and Education U-NET Jurnal Teknik Informatika Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Minfo Polgan (JMP) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) PUBLIDIMAS CSRID SISFOTENIKA
Rika Rosnelly, Rika
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Penerapan Data Mining Pada Penerimaan Dosen Tetap Menggunakan Metode Naive Bayes Classifier dan C4.5 Sadikin, Muhammad; Rosnelly, Rika; Roslina, Roslina; Gunawan, Teddy Surya; Wanayumini, Wanayumini
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2434

Abstract

Recruitment is an important step in creating professional HR (Human Resources). The application of classification methods such as the Naïve Bayes method and C4.5 can be used in the classification of potential lecturers and can be accepted by the campus by calculating the equations for each criterion. The difficulty experienced is the ineffective use of the method to generate the required lecturer acceptance so that it is not in accordance with the applicant's expertise. One of the classification methods applied to data mining is the naïve Bayes method and C4.5. The purpose of this study is to determine the level of accuracy of the two methods used by using the Weka 3.8 tool based on the calculation of Correctly Classified Instance and Incorrectly Classified Instance. The accuracy results obtained with the naïve Bayes method are 83.7838% and the C4.5 method is 91.8919% from 37 training data. So the C4.5 method is a more appropriate method to use than naïve Bayes.
Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes Azhari, Mulkan; Situmorang, Zakaria; Rosnelly, Rika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2937

Abstract

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm
Pengenalan Masker Wajah Menggunakan VGG-16 dan Multilayer Perceptron Margolang, Khairul Fadhli; Riyadi, Sugeng; Rosnelly, Rika; Wanayumini, Wanayumini
Jurnal Telematika Vol. 17 No. 2 (2022)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v17i2.519

Abstract

Penggunaan masker wajah pada masa pandemi Covid-19 dapat diidentifikasi berdasarkan citra yang diambil dari wajah seseorang kemudian diklasifikasi berdasarkan hasil ekstraksi fiturnya. VGG 16 merupakan sebuah pre-trained CNN model yang dapat mengekstrak 4.096 fitur dari sebuah citra dan melakukan transfer learning kepada algoritme multilayer perceptron dalam mengklasifikasikan seseorang menggunakan masker wajah atau tidak. Hasil dari penelitian ini menunjukkan bahwa kombinasi aktivasi ReLu dengan optimasi adaptive moment (Adam) dan stochastic gradient descent (SGD), kombinasi ReLu dan Adam, menghasilkan performa klasifikasi terbaik dengan nilai accuracy, precision, dan recall sebesar 98,1%.
DETEKSI PENGENALAN WAJAH ORANG BERBASIS AI COMPUTER VISION Laia, Finis Hermanto; Rosnelly, Rika; Naswar, Alvinur; Buulolo, Karuniaman; Lase, Mega Christin Morys
Jurnal Teknologi Informasi Mura Vol 15 No 1 (2023): Jurnal Teknologi Informasi Mura Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v15i1.2024

Abstract

Teknologi kecerdasan buatan (AI) telah menjadi perhatian utama dalam penerapan Personal Identification (PI). Visi komputer sebagai subkategori AI bertujuan untuk mengekstrak informasi yang berguna dari gambar. Pengenalan wajah menjadi penting karena kompleksitas wajah manusia yang memiliki ciri-ciri berbeda. Penelitian ini berfokus pada pengenalan dan verifikasi wajah menggunakan computer vision dengan tujuan mendeteksi dan mengenali citra wajah seseorang secara akurat. Algoritma Histogram of Oriented Gradients (HOG) digunakan sebagai solusi praktis untuk meningkatkan efisiensi dan efektivitas dalam bidang keamanan dan aplikasi lainnya. Penelitian ini berkontribusi dalam mengembangkan teknik dan metode yang lebih baik untuk deteksi wajah dan pengolahan gambar dalam bidang teknologi informasi, khususnya dalam aplikasi pengenalan wajah. Hasil dari perancangan dan pengujian deteksi pengenalan dan verifikasi wajah berbasis computer vision menunjukkan bahwa program yang dibuat dari model algoritma HOG dengan fitcecoc multiclass SVM mampu mendeteksi citra wajah orang dengan baik setelah melewati proses testing, dengan tingkat akurasi mencapai 98.5714%.
Effect Effect of Gradient Descent With Momentum Backpropagation Training Function in Detecting Alphabet Letters Alkhairi, Putrama; Batubara, Ela Roza; Rosnelly, Rika; Wanayaumini, W; Tambunan, Heru Satria
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12183

Abstract

The research uses the Momentum Backpropagation Neural Network method to recognize characters from a letter image. But before that, the letter image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimension of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computer systems compared to humans is recognizing character patterns if not using supporting methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In ANN, there are several methods used to train computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train and detect an image is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize the character pattern of handwritten letters well which can make it easier for humans to recognize patterns from letters that are difficult to read due to various error factors seen by humans. The results of the testing process using the Backpropagation algorithm reached 100% with a total of 90 trained data. The test results of the test data reached 100% of the 90 test data.
Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.; Tambunan, Fazli Nugraha; Rosnelly, Rika; Wanayumini, Wanayumini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12195

Abstract

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation.
Modeling Digital Image Segmentation with Canny Method Ptr, Agus Fahmi Limas; Rosnelly, Rika; Junaidi, Junaidi; Amrullah, Amrullah
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12263

Abstract

In general, the segmentation process is divided into three parts. classification, by edge, and by area. The digital image segmentation process divides an object whose surface or background maintains the RGB value of all pixels of a digital image so that the object can be processed for other purposes. This system aims to execute drawing objects using intelligent methods. In the Canny method, the process begins with taking digital images and continues with the grayscale process. In addition, technique selection starts with performing a complex operator or Laplacian edge detection and finally unlocks it. Successful segmentation results using edge detection with intelligent operators to separate objects. This system uses the Matlab 2017 application.
Classification of Big Data Stunting in North Sumatra Using Support Vector Regression Method Simanullang, Maradona Jonas; Rosnelly, Rika; Riza, Bob Subhan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.32177

Abstract

Stunting in children is a serious issue in society, especially in areas with high levels of malnutrition like North Sumatra. Therefore, it is important to develop an effective approach to identify the factors contributing to stunting and predict its risks in children, considering the high prevalence of stunting in this region. The high rate of stunting in North Sumatra indicates the urgency of this problem, making research on Big Data classification using Support Vector Regression (SVR) methods highly important. This study aims to offer profound understanding into factors influencing stunting in the region, thus enabling the development of more effective and targeted intervention strategies. The objective of this research is to categorize Big Data related to stunting in North Sumatra using SVR methods, taking into account factors such as wasting and malnutrition. The main focus of this research is to identify patterns related to stunting, predict the risk of stunting in children, and design more effective intervention strategies while addressing the issues of wasting and malnutrition. The research process encompasses several steps including data collection, pre-processing to handle missing values and outliers, normalization, and the application of Support Vector Regression (SVR). The final outcomes were achieved using a Voting Classifier that integrates Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), resulting in an accuracy rate of 91.78%. This method effectively pinpoints the main factors contributing to stunting, which supports clinical decision-making and intervention strategies. The study highlights the potential of big data and machine learning in the healthcare sector, offering a model for enhancing health services and tracking children’s health conditions.
Design of agrivoltaic system with internet of things control for chili fruit classification using the neural network method Wanayumini, Wanayumini; Satria, Habib; Rosnelly, Rika
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp176-183

Abstract

Agriculture is a leading sector in the economy as well as the most dominant provider of employment for the Indonesian people. The fertile soil factor allows various types of fruit to be grown, including chilies. However, complex problems make chili farmers have limitations in implementing conventional farming systems. Therefore, the development of an agrivoltaic system with internet of things (IoT) integrated sensors on chili plants can help farmers more easily control, add vitamins, fertilizers, and provide plant nutrients that can be done automatically periodically based on a real-time clock schedule. This system also operates using photovoltaic (PV) as a pumping machine for water circulation. Other technologies such as mini smart cameras are also being developed to monitor and take pictures of chilies which will later be converted using the graphical user interface (GUI) application for segmentation. The method used in this chili fruit classification uses an artificial neural network in classifying ripe, raw, and rotten chilies. The classification results obtained an R value of 0.9, which means it is close to a value of 1 in the suitability of the chili image. Therefore, farmers will find it easier to sort the chilies that will be harvested.
Aplikasi Identifikasi Pola Perkakas dengan Menggunakan Chain Code dan SVM ElisaBeth S, Noprita ElisaBeth S; Harahap, Sarwedi; Rahma, Intan Dwi; Rosnelly, Rika
SISFOTENIKA Vol. 14 No. 1 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/sisfotenika.v14i1.415

Abstract

Dalam dunia ini, meskipun sudah banyak yang beralih ke teknologi atau sistem yang modern. Namun, tentunya tidak semua dapat dilakukan atau digunakan tanpa adanya alat pembantu, baik itu membangun teknologi, memperbaikinya, hingga lain sebagainya yang berkaitan. Nah alat-alat yang digunakan pun pada dasarnya tidak hanya terdiri dari 1, melainkan ada beberapa yang biasa itu disebut sebagai perkakas. Pengenalan pola (pattern recognition) sudah menjadi bidang kajian selama ini. Beberapa metode yang dipakai dalam pengenalan pola adalah metode chain code digunakan untuk menggambarkan batas obyek atau jumlah piksel yang berada dalam satu obyek. Kode rantai (chain code) merupakan suatu teknik pengolahan citra yang didasarkan pengkodean dengan berdasarkan arah mata angin pada suatu objek citra dua dimensi. Selama ini, kode rantai banyak yang digunakan dalam pengolahan citra untuk merepresentasikan garis, kurva atau batas tepi dari suatu area. Proses pengenalan pola pada penelitian ini menggunakan gambar atau image perkakas sebanyak 3 tipe yaitu : botol (bottle), garpu (fork) dan palu (hammer), gambar yang digunakan untuk penelitian ini didesain atau digambar sendiri, aplikasi pengujian yang digunakan dalam penelitian ini dengan menggunakan matlab. Dari hasil pengujian yang dilakukan metode chain code dengan menggunakan aplikasi matlab dapat mengenali atau mendeteksi masing-masing tipe perkakas dengan tingkat akurasi 100%, serta gambar yang digunakan pada penelitian ini berekstension gif.
Co-Authors -, Mubarak Agung Rizky, Muhammad Dipo Agus Fahmi Limas Ptr Aji, Eko Setyo Budi Putra Akbar, Muhammad Barkah Alkhairi, Putrama Amrullah Amrullah Ashari, Annisa Bambang Suhardi Batubara, Ela Roza Bob Subhan Riza, Bob Subhan Daifiria Dian Maya Sari ElisaBeth S, Noprita ElisaBeth S Fahriyani, Tasya Finis Hermanto Laia Gea, Muhammad Nasri Habib Satria Habib, Nurhayati Harahap, Charles Bronson Harahap, Gilang Harahap, Sarwedi HARDIANTO - Hartono Hartono Haryanto S., Edy Victor Heru Satria Tambunan, Heru Satria Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Indra Kelana Jaya Junaidi Junaidi Kelvin Leonardi Kohsasih Khairi, Ibni Krismona, Lumi Limas, Agus Fahmi Manza, Yuke Margolang, Khairul Fadhli MARIA BINTANG Mega Christin Morys Lase Mochammad Imron Awalludin Muhammad Sadikin Mulkan Azhari Nasution, Ammar Yasir Nasution, M. Irfan Aldy Naswar, Alvinur Ndruru, Agus F.S. Nur Hayati Nursie, Aly Paramitha, Cindy Putra, Reza Ananda Rahma, Intan Dwi Rahmadi, Diky Ramadhan, Muhammad Yakub Rambe, Lima Hartima Rambe, Lima Hartimar Rofiqoh Dewi Roslina Roslina, Roslina Sagala, Tamado Simon Sari, Rita Novita Setiawan, Adil Simanullang, Maradona Jonas Siregar, Kiki Putri Ani Situmorang, Zakaria sri lestari rahayu Subhan, Zhafira Nur Sugeng Riyadi Suhada WD, Muhammad Sukriatna Sumantri, Ekoliyono Wahyu Suyono Suyono Syahrian, Achmad Tambunan, Fazli Nugraha Tarigan, Dede Ardian Teddy Gunawan, Teddy Teddy Surya Gunawan Tri Nowo, Suryandika Veronica Wijaya, Veronica Wahyudi, Diky Wahyuni, Linda Wanayaumini, W Wanayumini Zai, Andreas Zakarias Situmorang Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.