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All Journal bit-Tech ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Indonesia Sosial Teknologi MDP Student Conference JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yoannita
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Penerapan Algoritma $Q Super Quick Recognizer Untuk Pengenalan Angka Muhammad Yudha Setiawan; Yohannes Yohannes; Yoannita Yoannita
MDP Student Conference Vol 2 No 1 (2023): The 2nd MDP Student Conference 2023
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.398 KB) | DOI: 10.35957/mdp-sc.v2i1.4370

Abstract

Materi pembelajaran pada umumnya dituliskan pada papan tulis di depan kelas sebagai media pembelajaran, namun dengan menggunakan pen tablet sebagai alat bantu dalam pembelajaran layaknya menggunakan pena dan kertas dapat dilakukan lebih mudah dan menarik. Bagaimana tulisan angka anak-anak itu bisa dikenali yaitu menggunakan algoritma pengenalan gestur untuk mendeteksi apakah benar angka yang ditulis oleh anak-anak itu benar. Algoritma $Q Super Quick Recognizer dapat mengenal gerakan 2D yang dirancang untuk pembuatan prototipe cepat antarmuka pengguna berbasis Gerakan. Pada penelitian ini menggunakan algoritma $Q Super Quick Recognizer pada game pengenalan angka. Dengan tingkat rata-rata akurasi sebesar 99.50% , precision sebesar 97.56% dan Recall 95.50%.
Pelatihan aplikasi screen recorder dan video conference guna meningkatkan proses belajar mengajar Triana Elizabeth; Dicky Pratama; Derry Alamsyah; Yoannita Yoannita; Inayatullah Inayatullah; Tinaliah Tinaliah
ABSYARA: Jurnal Pengabdian Pada Masayarakat Vol 2 No 1 (2021): ABSYARA: Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/ab.v2i1.3495

Abstract

At the beginning of 2020, Indonesia faced the Covid-19 virus pandemic. As a result, changes occurred in all sectors, including the education sector. The change in the learning model from offline to online makes it difficult for Indonesian because they are not accustomed to using digital technology in the learning process. Therefore, this service activity aims to train 10 Dewi Sartika Palembang Kindergarten teachers using video conferencing applications (Google Meet) and screen recorders (AZ Screen Recorder) to support a more optimal learning process. This activity is carried out by implementing health protocols. Based on the results of the evaluation of the training activities, it was obtained that all teachers were able to use the Google Meet and AZ Screen Recorder applications and were able to make learning videos and use them in the learning process. However, many students cannot participate in the online learning process because of poor internet signals. This problem can be overcome by sending a video recording of the learning process.
Implementasi Virtual Reality pada Game Edukasi Protokol Kesehatan Gerry Jeven Timoti; Yohannes Yohannes; Yoannita Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4065

Abstract

In the year of 2020 until now has been a tough year for almost all industries and the general public due to the coronavirus pandemic. However, the increase in cases per day illustrates that the community has not complied with health protocols optimally. The impact of the pandemic has increased gamers in Asia by more than 75%. With a positive impact on society, the role of games can be used as a medium for education. Virtual Reality will be implemented in the health protocol educational game to see the good role of virtual reality in educational game interactions. The implementation of Virtual Reality in the health protocol educational game is carried out using the GDLC (Game Development Life Cycle) approach with stages starting from initiation, pre-production, production, testing, and release. The results of the recapitulation of the questionnaire on the acceptance and satisfaction of the health protocol educational game application showed the results of 44,9.% strongly agree, 44,9% agree, 9,15% are neutral and 1% are not agree that the application has functioned well and can be selected as one of the learning media especially about health protocol to reduce the growth rate of people infected with the coronavirus.
Implementasi Algoritma $P Point Cloud Recognizer pada Pengenalan Angka Berbasis Game Muhammad Farid Athar; Yohannes Yohannes; Yoannita Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5472

Abstract

More devices use gestures as their input method. Recognizing these gestures becomes more important for app development. One of the methods used for gesture recognition is Point Cloud Recognizer or $P. Gesture recognition can be used to recognize written characters like numbers or letters. Result of this recognition can be used for education involving apps, like games. This study is done by implementing $P in games to show that $P can be used as one of the methods for gesture recognition when developing games that need such features. In this study $P is implemented with the help of the game engine Unity with C# programming language. 3 sets of numerals 1 to 10 are used as data with $P configured to use 32 points. Total of 100 tests are done in the game resulting in 99% accuracy, showing $P is able to recognize the gesture well.
Klasifikasi Jenis Bunga Menggunakan Metode Svm Berdasarkan Citra Dengan Fitur Hsv Millenia Mudita Chandra; Yoannita
Jurnal Indonesia Sosial Teknologi Vol. 4 No. 02 (2023): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v4i02.585

Abstract

Bunga mempunyai banyak jenis yang beraneka ragam, dari warna, bentuk, ukuran, makna, dan lain – lain. Adapun bunga yang terkadang terlihat mirip sehingga sulit dibedakan, contohnya saja bunga matahari dan bunga daisy. Keduanya memiliki kelopak bunga berwarna kuning cerah, inti bunga yang mirip, memiliki kelopak bunga yang banya. berbentuk mekar, dan batang tumbuhan yang berwarna hijau. Dengan ciri – ciri yang sulit dibedakan tersebut, maka dilakukanlah penelitian dengan melakukan resize sebesar 320x240 piksel, kemudian citra akan di ekstraksi dengan fitur HSV. Algoritma Support Vector Machine yang digunakan dalam klasifikasi ini memiliki akurasi keseluruhan sebesar 63.66%.
KLASIFIKASI JENIS TANAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK BERBASIS MOBILENETV2 Ayu Sri Oktaviani; Yoannita Yoannita
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.7083

Abstract

Tanah merupakan komponen penting dalam pertanian karena berfungsi sebagai media tumbuh yang menentukan ketersediaan nutrisi dan kemampuan lahan mendukung pertumbuhan tanaman. Identifikasi jenis tanah di lapangan umumnya masih dilakukan secara manual dengan memperhatikan warna, tekstur, dan kondisi permukaan. Cara ini bersifat subjektif dan sering menimbulkan kesalahan dalam menentukan jenis tanaman yang sesuai, sehingga berpotensi menurunkan produktivitas pertanian. Penelitian ini bertujuan mengklasifikasikan empat jenis tanah, yaitu alluvial, black soil, clay soil, dan red soil, menggunakan metode Convolutional Neural Network dengan arsitektur MobileNetV2. Dataset yang digunakan berasal dari Kaggle dan melalui proses praproses, pembagian data menjadi train, validation, dan test lalu diaugmentasi, serta Model dilatih menggunakan pendekatan transfer learning dan dievaluasi dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian diharapkan menghasilkan model klasifikasi tanah yang akurat, efisien, dan dapat digunakan sebagai bagian dari sistem pertanian cerdas untuk membantu petani menentukan jenis tanah secara lebih objektif.
Machine Learning Classification of Liver Disease Using Clinical Data with SVM and PCA Daniel Johan; Yoannita Yoannita
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3494

Abstract

Liver disease remains a major global health problem that requires early and accurate diagnosis to prevent severe clinical complications and mortality. In recent years, Support Vector Machine (SVM) combined with Principal Component Analysis (PCA) has been widely applied for liver disease classification. However, existing studies are often limited by small or moderately sized datasets, a lack of systematic comparison among SVM kernel functions, and insufficient discussion of clinical relevance and data representativeness. These limitations restrict model generalizability and hinder practical clinical adoption. To address these gaps, this study evaluates a PCA–SVM classification framework using a large-scale Liver Disease Patient Dataset comprising 30,691 clinical records, thereby improving robustness and population representativeness. The main contribution of this research lies in a systematic and controlled comparison of four SVM kernel functions linear, radial basis function (RBF), polynomial, and sigmoid—under identical preprocessing and dimensionality reduction conditions. PCA is applied to reduce feature redundancy while preserving over 97% of clinically relevant information, supporting efficient learning without increasing model complexity. Experimental results indicate that kernel selection has a substantial impact on diagnostic performance. The RBF kernel consistently outperforms other kernels, achieving an accuracy of 83.63% and an area under the ROC curve of 92.09%, while maintaining strong generalization on unseen data. From a clinical perspective, these findings demonstrate that the proposed PCA–SVM framework has significant potential as a clinical decision support tool for early liver disease screening based on routine laboratory data, offering a balance between predictive performance, computational efficiency, and practical applicability.
Co-Authors Ayu Sri Oktaviani Daniel Johan Derry Alamsyah Dicky Pratama Gerry Jeven Timoti Inayatullah Inayatullah Millenia Mudita Chandra Muhammad Farid Athar Muhammad Yudha Setiawan Tinaliah, Tinaliah Triana Elizabeth, Triana Yohannes, Yohannes