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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
Unknown Affiliation
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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Rancang Bangun Sistem Keamanan Pintu Rumah Menggunakan Arduino Berbasis Android Aji, Eko Setyo Budi Putra; Rosnelly, Rika
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 1: February 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.1.2023.37-46

Abstract

Many people still find it difficult to secure their doors with the current technological orientation. Many people look for safety devices for their doors on various websites, but they are still constrained by the price and how to install the equipment purchased on their door. The existence of tools and applications for designing a home door security system is made with the aim of helping people to secure their door in an easier and more efficient way. The design of the home door security system with the available Android-based Arduino makes it easier for people to secure their home doors, of course, with guides and understanding of the tools described by the author. Tools made using Arduino and applications are made using the Java programming language. The system development, method for making this application uses the MIT App Inventor.
Pelatihan Pembelajaran Rancang Bangun Aplikasi Metode Segmentasi Dengan Thresholding Rosnelly, Rika; Wahyuni, Linda; Hardianto, Hardianto; Akbar, Muhammad Barkah; Daifiria; Subhan, Zhafira Nur
Publikasi Pengabdian Masyarakat Vol 4 No 1 (2024): PUBLIDIMAS Vol. 4 No. 1 MEI 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/publidimas.v4i1.345

Abstract

Image processing technology has attracted human attention to be studied so that it has become a science that can be accepted and understood in everyday life. An image is a representation, likeness, or imitation of an object. The output of a data recording system can be a photo, an optical signal such as a photo, an analog signal such as an image on a TV monitor, or a digital signal that can be stored directly on storage media. One of the processes in image processing (image preprocessing). One image segmentation technique is thresholding, which uses differences in brightness or darkness to distinguish objects from the background. Thresholding is based on groupings in the histogram according to certain backgrounds or objects that can be extracted by separating the groups of the histogram. Otsu thresholding is used in a variety of applications ranging from medical imaging to low-level computer vision. The Otsu method takes an approach, namely discriminant analysis, namely determining a variable that can divide foreground and background objects. This training provides excellent benefits for students in understanding objects for applying thresholding segmentation.
Pelatihan Perakitan Komputer Pada SMK Tritech Indonesia Hardianto, Hardianto; Wahyuni, Linda; Rosnelly, Rika; Nasution, M. Irfan Aldy; Harahap, Charles Bronson
Publikasi Pengabdian Masyarakat Vol 4 No 2 (2024): PUBLIDIMAS Vol. 4 No. 2 NOVEMBER 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/publidimas.v4i2.395

Abstract

In the current era of globalization, computers are data processing machines and calculating tools, where the results or output can be printed via a printer. Input, process and output of a computer are the work processes of the 3 most important elements, namely hardware, software and users. Before users use a computer, in terms of assembling a computer, they need to know how to put together the parts needed to run the computer properly. Knowing the functions of the hardware components so that there are no mistakes in assembling a computer. Although assembling a computer is basically quite easy, some research is needed to get good results. There are many different price and quality options for computer assembly components. By assembling a computer yourself, we can choose the type of components, features, and computer facilities that best suit our needs. Before assembling a computer, you must prepare well, especially if there is hardware and software installed. Assembling and installing a computer is an important step in creating and maintaining a reliable and effective computer system. To ensure optimal system performance and security, a thorough understanding of assembly and installation methods is essential.
Pelatihan Pembuatan Website Menggunakan Wordpress Pada SMK Pangeran Antasari Wahyuni, Linda; Rosnelly, Rika; Hardianto, Hardianto; Sari, Rita Novita; Rahayu, Sri Lestari; Khairi, Ibni
Publikasi Pengabdian Masyarakat Vol 5 No 1 (2025): PUBLIDIMAS Vol. 5 No. 1 MEI 2025
Publisher : LPPM Universitas Potensi Utama

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Abstract

The rapid advancement of digital technology highlights the importance of mastering website development skills, especially for vocational high school students who are being prepared for the workforce. This training aimed to improve the understanding and skills of students at SMK Pangeran Antasari in creating and managing websites using the WordPress platform, known for its user-friendly interface and minimal programming requirements. The activity involved 30 students from grades XI and XII in the Computer and Network Engineering program, divided into two groups to ensure effective learning. The training was carried out in five sessions: an introduction to WordPress and the importance of website development in the digital era; WordPress installation; content creation using the Gutenberg editor; theme and plugin management; and an independent implementation session where students designed their own websites. The training adopted a hands-on approach, with direct assistance provided to ensure thorough understanding of each technical stage. The results indicated a significant improvement in students' ability to independently create websites. They were able to manage website layouts, add multimedia content, and integrate additional features such as contact forms and galleries. Beyond technical skills, the training also fostered students’ creativity in designing websites for portfolios, blogs, or entrepreneurial purposes. In conclusion, WordPress-based training effectively strengthened the digital competencies of vocational high school students, particularly in creating informative and functional websites. This initiative is expected to provide valuable preparation for the technology-driven job market and can serve as a model for similar training programs in other schools. Keywords: Wordpress, Training, Website Development, Vocational Students, Digital Skills
Implementasi Teknologi Informasi untuk Meningkatkan Strategi Penjualan pada UKM Driply Coffee Berbasis Online Wahyuni, Linda; Hardianto, Hardianto; Rosnelly, Rika; Syahrian, Achmad; Rahmadi, Diky
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 1 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

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Abstract

Small and medium enterprises (SMEs) are similar to the category of Small and medium enterprises (SMEs), but are usually used to refer to small and medium enterprises only, without including micro businesses. Community service carried out at UKM Driply Coffee, which provides types of drinks in the form of coffee and several other types of drinks besides, also providing food that can accompany customers when they come and sit down to relax either alone, with friends, colleagues, or with family. The research conducted looked at the process of its marketing system, which uses social media such as Facebook, Instagram, and also WhatsApp. The system is indeed seen by many customers, only the process for ordering and selecting menus and the payment process cannot all be seen in the display due to the limitations of the image display, from the presentation of these conditions this research was conducted for approximately 3 months, and will be designed and implemented an online-based application that uses a web program and a MySQL database. Customers can access remotely and can also make payments online, and delivery of drinks or food will be carried out according to the intended location. The convenience felt is not only for customers, but partners will also get convenience and be able to compete in the world of small and medium enterprises (UKM).
Optimasi Algoritma Genetika pada Perbandingan ANN dan KNN untuk Klasifikasi Penyakit Jantung Zai, Andreas; Rambe, Lima Hartima; Putra, Reza Ananda; Rosnelly, Rika; Sagala, Tamado Simon; Jaya, Indra Kelana
Majalah Ilmiah METHODA Vol. 15 No. 1 (2025): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol15No1.pp10-23

Abstract

A comparative analysis of genetic algorithm optimization methods on the performance of Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) in heart disease classification shows significant results. The research used a heart disease dataset consisting of 303 samples with 14 attributes. Genetic algorithm optimization produced substantial performance improvements in both models. The optimized ANN model achieved 94.85% accuracy, 93.00% precision, 97.00% recall, and 97.00% ROC AUC, demonstrating excellence in positive case identification. Meanwhile, the optimized KNN model achieved 93.30% accuracy, 92.00% precision, 95.00% recall, and 96.77% ROC AUC, yielding more balanced performance. The genetic algorithm optimization method proves its effectiveness in improving heart disease classification accuracy, where ANN is optimal for applications requiring high sensitivity and KNN is more stable for small datasets.
Application of Digital Image Processing for Orchid Image Segmentation in Morphological Plant Analysis Riza, Bob Subhan; Rosnelly, Rika; Haryanto S., Edy Victor
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci3772

Abstract

The deployment of digital image processing in orchid image segmentation for plant morphological analysis is investigated in this study. The goal of this study is to increase the accuracy of orchid species identification using color-based segmentation approaches using 90 photos of three different orchid species—Cattleya, Dendrobium, and Onchidium—that were retrieved from Kaggle. Pre-processing is the first step in the process, which involves shrinking the size of the photos, separating them into RGB components, and converting them to HSV color space for additional analysis. Segmentation is done using the K-Means technique, which clusters pixels according to the color features that have been retrieved. Centroid updates are made until convergence is reached. With an identification accuracy of 92%, the binary and RGB segmentation results show how well this method works to distinguish the flower item from the backdrop. By advancing image processing methods in botany, this study aids in the identification of rare orchid species and conservation initiatives.
ANALISIS SENTIMEN KOMENTAR YOUTUBE TERHADAP ISU KESEHATAN MENTAL MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN K-NEAREST NEIGHBOR (KNN) Hayati, Nur; Tri Nowo, Suryandika; Suhardi, Bambang; Rosnelly, Rika
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 1 (2025): Juni 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i1.6060

Abstract

Kesehatan mental merupakan salah satu isu yang semakin mendapat perhatian di seluruh dunia. Masalah kesehatan mental sering kali diabaikan, namun dampaknya dapat merusak kualitas hidup individu dan masyarakat secara keseluruhan. Faktor lain yang mempengaruhi upaya penyuluhan kesehatan mental adalah pemahaman yang kurang baik dan kesadaran yang rendah tentang kesehatan mental. Dari penjelasan permasalahan tersebut maka perlu adanya analisis sentimen untuk mengetahui opini masyarakat terhadap kesehatan mental di media sosial youtube. Analisis sentimen merupakan suatu proses untuk memahami emosi atau sentimen dari suatu teks yang ditulis oleh pengguna baik berupa sentimen positif, netral ataupun negatif.  Proses pengambilan dataset dilakukan mengunakan platform Google Colab untuk crawling data dan terkumpul sekitar 2.703 komentar. Setelah dilakukan proses cleaning dan preprocessing jumlah data yang tersisa adalah sebanyak 1700. Metode yang digunakan pada penelitian ini menggunakan algoritma Naïve Bayes dan K-Nearest Neighbor (k-NN).  Dalam penelitian ini, dua metode yang digunakan yaitu pelabelan manual dan pelabelan otomatis menggunakan tools RapidMiner. Pada tahap pertama, pelabelan manual dilakukan pada 305 data menghasilkan nilai akurasi 95% untuk algoritma Naïve Bayes dan nilai akurasi 85.88% untuk algoritma k-NN. Pada tahap kedua, pelabelan otomatis digunakan dengan data latih sebanyak 305 data dan data uji 1.395 data menghasilkan nilai akurasi 68.01% untuk algoritma Naïve Bayes dan nilai akurasi 48.97% untuk algoritma k-NN. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes memiliki akurasi yang lebih tinggi dibandingkan dengan algoritma K-Nearest Neighbor dalam mengklasifikasikan sentimen dari komentar YouTube terkait isu kesehatan mental.
Analisis Sentimen Aplikasi Playstore Sirekap 2024 Pasca Pilpres Dengan Perbandingan Metode Support Vector Machine (SVM), Naïve Bayes Classifier Dan Random Forest. TARIGAN, Dede Ardian; Situmorang, Zakarias; Rosnelly, Rika
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025129608

Abstract

Aplikasi Sirekap merupakan sebuah aplikasi berbasis website yang mengandalkan teknologi Optical Character Recognition (OCR) dan Optical Mark Reader (OMR) dalam pengoperasiannya. Perkembangan teknologi ini digunakan untuk mempermudah proses perhitungan suara dengan QuickCount yang sifatnya sementara oleh KPU pada Pemilihan Presiden dan Wakil-Presiden Indonesia Periode 2024. Kedua teknologi tersebut memiliki peran penting dalam mengotomatisasi pola proses baca dan hitung secara real-time. Dengan demikian, analisis sentimen diperlukan untuk mengekstraksi komentar teks dari opini publik tentang aplikasi Sirekap 2024 di Play Store. Penelitian ini berkaitan dengan analisis sentimen terkait hasil perhitungan suara yang menimbulkan ketidaksesuaian di aplikasi Sirekap 2024, apakah bersifat positif atau negatif. Tahapan teknik yang digunakan dalam penelitian ini meliputi scraping data, pre-processing data, pelabelan pola, ekstraksi fitur/pembobotan, pembagian data, dan proses klasifikasi analisis sentimen. Pengumpulan data primer dilakukan menggunakan program Python di aplikasi Google Colab dengan teknik google play scraper di aplikasi playstore android Sirekap 2024. Metode klasifikasi yang digunakan adalah Support Vector Machine, Naïve Bayes, dan Random Forest untuk mengklasifikasikan data. Hasil klasifikasi SVM adalah 82%, Naïve Bayes adalah 71%, dan Random Forest adalah 81%. Dari ketiga metode klasifikasi, kinerja terbaik dalam mengidentifikasi adalah metode klasifikasi SVM dengan akurasi 82%, presisi 82%, recall 82%, dan F1-Score 82%.   Abstract The Sirekap application is a web-based application that relies on Optical Character Recognition (OCR) and Optical Mark Reader (OMR) technology in its operation. The development of this technology is used to facilitate the vote counting process with a temporary QuickCount by the KPU in the 2024 Indonesian Presidential and VicePresidential Elections. Both technologies play an important role in automating the reading and counting process patterns in real-time. Thus, sentiment analysis is necessary to extract text comments from public opinion about the Sirekap 2024 application on the Play Store. This research is related to sentiment analysis concerning the vote count results that cause discrepancies in the Sirekap 2024 application, whether they are positive or negative. The technical stages used in this research include data scraping, data pre-processing, pattern labeling, feature extraction/weighting, data splitting, and the sentiment analysis classification process. Primary data collection was conducted using a Python program in the Google Colab application with the Google Play scraper technique in the Sirekap 2024 Android Play Store application. The classification methods used are Support Vector Machine, Naïve Bayes, and Random Forest to classify the data. The SVM classification result is 82%, Naïve Bayes is 71%, and Random Forest is 81%. Among the three classification methods, the best performance in identification is the SVM classification method with an accuracy of 82%, precision of 82%, recall of 82%, and F1-Score of 82%.
ANALISIS SENTIMEN PADA ULASAN PENGGUNA TIKTOK DAN TOKOPEDIA MENGUNAKAN MESIN LEARNING BERBASIS NAIVE BAYES CLASSIFIER -, Mubarak; Ashari, Annisa; Harahap, Gilang; Rosnelly, Rika
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 1 (2025): Juni 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i1.6960

Abstract

Abstrak−Analisis sentimen merupakan teknik penting dalam memahami opini dan pengalaman pengguna terhadap layanan atau produk, terutama di platform e-commerce yang berkembang pesat seperti TikTok Shop. Tujuan dari penelitian ini adalah mengetahui dan analisis terhadap sentimen keluhan penjual di TikTok Shop dengan menggunakan algoritma Naive Bayes Classifier. Dataset yang digunakan terdiri dari ulasan penguna TikTok. yang diambil dari dataset kaggle. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna TikTok menggunakan algoritma Naïve Bayes untuk menganalisis data sentimen tersebut. Algoritma Naive Bayes diterapkan untuk mengklasifikasikan sentimen ulasan menjadi dua kategori: positif, dan negatif. Penelitian ini mengunakan Model Algiritma Naive Bayes berhasil mencapai nilai akurasi 97%, nilai CA 82%, nilai F1 82%, nilai Prec 86%, nilai Recall 82% dan nilai MCC 68% tergantung pada pengaturan dataset. Pada penelitian ini jumlah dataset yang digunakan berjumlah 3145 data yang diambil dari dataset kaggle, dengan pengaturan dataset traning sebesar 90% dan dataset test sebesar 10%. Temuan ini memberikan wawasan berharga bagi pengelola TikTok Shop untuk meningkatkan fitur dan layanan mereka berdasarkan umpan balik pengguna. Rekomendasi untuk penelitian selanjutnya mencakup penggunaan dataset yang lebih besar dan penerapan algoritma lain untuk perbandingan efektivitas serta eksplorasi lebih lanjut terhadap faktor-faktor yang mempengaruhi sentimen pengguna. Kata kunci : TikTok, Analisis Sentimen, Algoritma Naive Bayes, Ulasan Pengguna Abstract− Sentiment analysis is an important technique in understanding user opinions and experiences towards services or products, especially on rapidly growing e-commerce platforms such as TikTok Shop. The purpose of this study is to determine and analyze the sentiment of seller complaints on TikTok Shop using the Naive Bayes Classifier algorithm. The dataset used consists of TikTok user reviews. taken from the kaggle dataset. This study aims to analyze the sentiment of TikTok user reviews using the Naive Bayes algorithm to analyze the sentiment data. The Naive Bayes algorithm is applied to classify review sentiment into two categories: positive and negative. This study uses the Naive Bayes Algorithm Model successfully achieving an accuracy value of 97%, a CA value of 82%, an F1 value of 82%, a Prec value of 86%, a Recall value of 82% and an MCC value of 68% depending on the dataset settings. In this study, the number of datasets used was 3145 data taken from the kaggle dataset, with a training dataset setting of 90% and a test dataset of 10%. These findings provide valuable insights for TikTok Shop managers to improve their features and services based on user feedback. Recommendations for further research include the use of larger datasets and the application of other algorithms for comparison of effectiveness as well as further exploration of factors influencing user sentiment. Keywords: TikTok, Sentiment Analysis, Naive Bayes Algorithm, User Reviews
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.