Article Per Year (5 Year)
p-Index From 2021 - 2026
2.096
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Dinamika Informatika Jurnal Penelitian Pertanian Terapan JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Buletin Ilmiah Sarjana Teknik Elektro Jurnal Berdaya Mandiri Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Dinamika Informatika (JDI) Vortex Jurnal Rekam Medic
Tentua, Meilany Nonsi
Universitas PGRI Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
Respon Pertumbuhan Dan Produksi Mentimun Baby (Cucumis Sativus L.) Pada Berbagai Aplikasi Eco-Enzym Dan Plant Growth Promoting Rhizobacteria Okti; Saptaningsih Sumarmi; Meilany Nonsi Tentua; Hani Andrasasi
Jurnal Penelitian Pertanian Terapan Vol 23 No 2 (2023)
Publisher : Politeknik Negeri Lampung.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/jppt.v23i2.2646

Abstract

Consumption of baby cucumbers in fresh form by the community is one of the factors driving the cultivation of environmentally friendly cucumbers so that they are safe for health and the environment. The use of PGPR (plant growth promoting rhizobacteria) and eco-enzymes to sufficient the nutrient needs of cucumber plants. This study aims to examine the use of PGPR and eco-enzymes in environmentally friendly baby cucumber cultivation. The study was arranged in an RCBD (Randomized Complete Block Design) design in 3 blocks with 6 treatments, namely control (without fertilizer), eco-enzyme concentration of 5 ml/l water, bamboo root PGPR 10 ml/l water, banana weevil PGPR 10 ml/l water, Eco-enzyme 2.5 ml/l water + PGPR bamboo root 5 ml/l water, eco-enzyme 2.5 ml/l water + PGPR banana hump 5 ml/l water. The research was conducted in Sawahan Hamlet, Pandowoharjo Village, Sleman DIY, alluvial soil type. The research data were analyzed using the Analysis of Variance at the 5% real level. To find out the real difference between treatments, Duncan's multiple distance test was carried out at the 5% real level. The results showed that the use of eco-enzymes could increase root dry weight, fruit number and fruit weight of baby cucumbers. The use of PGPR can increase the number of baby cucumbers. PGPR can increase cucumber fruit weight when applied with eco-enzymes.
Sentiment Analysis of Electronic System Provider (PSE) Method Using Support Vector Machine Turiyan Turiyan; Nurirwan Saputra; Ahmad Riyadi; Meilany Nonsi Tentua
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i2.7416

Abstract

The study discusses the policy issued by the Indonesian Ministry of Communication and Information on Electronic System Providers (PSE), with a special emphasis on the impact of the policy that has triggered public reactions on social media platforms. Based on the regulations that have been made, it has an impact on the public who believe that the regulation is made to take away access freedom and data privacy on digital systems used by the public. Sentiment analysis can see public opinion on the policy issued by the Ministry of Communication and Information. The data used is taken from social media which is labeled "positive" and "negative". The research method used in sentiment analysis is the Support Vector Machine method. The contribution of this research is to determine the boundary line between the two classes in this research data. The result of the F1 score can be measured in the model, with a achieved value of 75%.
PELATIHAN DAN PENDAMPINGAN PEMBUATAN LOGO SERTA DIGITALISASI OPERASIONAL UNTUK PENINGKATAN BRANDING DAN EFISIENSI Sumarmi, Saptaningsih; Triutami, Septi; Sitrawati, Puput; Agustin, Lia; Angganingrum, Vika; Tentua, Meilany Nonsi; Kurnia, Nova
Abdimas Galuh Vol 7, No 2 (2025): September 2025
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v7i2.20619

Abstract

Program pengabdian masyarakat ini bertujuan untuk meningkatkan branding dan efisiensi operasional Fazza Store melalui pelatihan dan pendampingan dalam pembuatan logo serta digitalisasi operasional. Pelatihan pembuatan logo berfokus pada pengembangan identitas visual yang khas untuk memperkuat kehadiran pasar dan pengenalan merek Fazza Store. Sementara itu, pendampingan digitalisasi mencakup penerapan sistem berbasis teknologi untuk pengelolaan gudang dan penggajian karyawan guna meningkatkan efisiensi operasional. Program ini juga mencakup pengembangan sumber daya manusia agar staf mampu beradaptasi dengan perangkat dan proses digital. Dengan mengintegrasikan desain modern dan teknologi, inisiatif ini dirancang untuk memberdayakan Fazza Store agar lebih kompetitif dan terorganisir di pasar yang terus berkembang. Kegiatan ini dilaksanakan melalui pelatihan langsung, pendampingan teknis, dan pengembangan kapasitas, yang menghasilkan peningkatan performa branding dan operasional Fazza Store.
Optimizing nitik batik classification through comparative analysis of image augmentation Suprapto, Suprapto; Tentua, Meilany Nonsi; Maulana, Ahmad Rizki
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3970-3981

Abstract

Nitik batik is one of the most intricate and culturally significant motifs in Yogyakarta's batik tradition, characterized by its complex, geometric dot-based patterns. The unique challenges of automatically classifying nitik batik motifs stem from the high variability within the class and the limited availability of training data. This study investigates how different image data augmentation techniques can enhance the performance of a random forest classifier for nitik batik motifs. Techniques such as geometric transformations (flip, rotate, and scaling), intensity transformations (cut-out, grid mask, and random erasing), non-instance level augmentation (pairing samples), and unconditional image generation (deep convolutional generative adversarial network (DCGAN)) were used to expand the dataset and improve the model's ability to generalize. The results show that specific techniques, notably flip, cut-out, and DCGAN, significantly improved classification accuracy, with flip achieving the highest accuracy improvement of 20.20%, followed by cut-out at 19.27% and DCGAN at 16.25%. Moreover, DCGAN demonstrated the lowest standard deviation (0.78%), indicating high stability and robustness in classification performance across multiple validation folds. These findings suggest that augmentation techniques effectively improve classification accuracy and enhance the model's ability to generalize from limited and complex datasets.
Web Service Pertukaran Data Rekam Medis (Studi Kasus pada Klinik Rawat Inap Pelayanan Medik Dharma Husada dan Klinik Pratama PKU Muhammadiah Semukti, Deni Kurniawan; Tentua, Meilany Nonsi; Wardani, Setia
Jurnal Rekam Medic Vol 8, No 2 (2025): Edisi Agustus
Publisher : LPPM Institut Kesehatan Helvetia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33085/.v8i2.6656

Abstract

Latar Belakang: Kemajuan teknologi terutama dibidang teknologi komputer membuat Rumah Sakit maupun perusahaan harus bisa memenuhi kebutuhan akan komputer karena dirasa bahwa teknologi komputer sangat berpengaruh penting bagi perkembangan dan kemajuan Rumah Sakit maupun perusahaan. Tujuan penelitian ini yaitu membangun Web service pertukaran data rekam medis. Bertujuan mengola untuk mengelola data – data pasien yang mendaftar untuk berobat pada periode waktu tertentu.Dalam kegiatan ini sistem dirancang sangatlah penting karena untuk mencegah terjadinya kesalahan prosedur dalam pelaksanaan pendaftaran pasien pengolahan data dapat dilakukan dengan sebaik –baiknya. Web service pertukaran data rekam medis dengan bahasa pemograman PHP dan database MySQL. Metode penelitian: metodeperancangan yang digunakan adalah dengan menggunakan diagram konteks, DAD Sistem, relasi antar tabel dan tampilan antar muka dengan sistem yang diusulkan. Tahap penyembangan aplikasi meliputi analisis, perancangan sistem, implementasi, dan pengujian. Pengujian sistem dilakukan dengan black box test dan alpha test. Hasil: Berdasarkan hasil dari penelitian, sistem dapat membantu petugas untuk mendaftarkan pasien , data rekam medis serta merujuk. Kesimpulan: Web Service Pertukaran Data Rekam Medis telah dirancang menggunakan bahasa pemrograman PHP dengan database MySQL, yang memiliki pengguna seperti: pegawai dan administrasi. Sistem juga memiliki fasilitas untuk bertukar data rekam medis. Berdasarkan pengujian blackbox, dapat disimpulkan bahwa modul pengujian yang diuji semuanya berfungsi. Saran: Membuat sistem perhitungan biaya secara otomatis
Prediksi Promosi Pegawai Menggunakan Metode Extremely Randomized Trees Tentua, Meilany Nonsi
Jurnal Dinamika Informatika Vol. 12 No. 2 (2023): Jurnal Dinamika Informatika Vol.12 No.2
Publisher : Universitas PGRI Yogyakarta

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

Abstract

With the advancement of information technology and data analytics, new opportunities arise in decision-making within the field of human resources. The implementation of ensemble methods, such as extremely randomized trees, holds promise in predicting promotion decisions with higher accuracy. Despite numerous studies in this domain, there is a need for a deeper exploration in adapting ensemble methods to the context of employee promotions, considering various factors like skills, experience, and individual achievements. This research aims to develop an effective predictive model for employee promotions by integrating extremely randomized trees with relevant human resources data. The model derived from this study achieved an accuracy rate of 99% on the training data, indicating highly precise predictions. The use of test data confirmed the model's performance with an accuracy score of 98%, illustrating its consistent predictive capabilities. Factors such as avg_training_score, department, total_score, age, and sum_metric were identified as the most influential in shaping the model, providing crucial insights for human resources management.
Penerapan LightGBM Dengan Kombinasi Ekstraksi Fitur HSV, GLCM dan HOG Untuk Klasifikasi Bunga Sedik, Stevani Gabriella Ayuk; Tentua, Meilany Nonsi
Vortex Vol 7, No 1 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/vortex.v7i1.3787

Abstract

This study applies the LightGBM algorithm with a combination of GLCM, HOG, and HSV feature extraction for flower image classification. The dataset used consists of five types of flowers, namely Sunflower, Rose, Tulip, Dandelion, and Daisy, with a total of 4,242 images. Each image undergoes preprocessing and feature extraction of texture, shape, and color before being trained using LightGBM. The results show that the proposed model achieves an accuracy above 70% in distinguishing the five flower classes. This study provides evidence that the combination of GLCM, HOG, and HSV with LightGBM is able to improve classification performance and can serve as a reference for further research in the field of digital image processing
Klasifikasi Ulasan Aplikasi E-KTP Menggunakan Bidirectional Encoder Respresentations from Transformers Rusdi, Alfida Hari; Tentua, Meilany Nonsi
Vortex Vol 7, No 1 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/vortex.v7i1.3788

Abstract

The Digital Population Identity application (E-KTP Digital) is part of e-government development aimed at improving the quality of public services. However, user reviews on the Google Play Store are still grouped based on star ratings, so the level of user satisfaction is not yet described in depth. This study aims to classify the sentiment of user reviews of the E-KTP Digital application using the Bidirectional Encoder Representations from Transformers (BERT) method with the Multilingual BERT (mBERT) model. A total of 15,000 reviews were collected from July 3, 2023 to May 31, 2025 and filtered into 1,750 reviews through data cleaning and manual labeling processes. The dataset is divided into training and testing data with ratios of 60:40, 70:30, and 80:20. The training process is conducted using the AdamW optimizer for 4 epochs with a batch size of 16. Model evaluation is planned using accuracy, precision, recall, and F1-score metrics to measure the performance of user review sentiment classification.
Co-Authors Ahmad Riyadi Ahmad Riyadi Angganingrum, Vika Ardiansa, Picky Nanda Dwi Winarti Fadia Ayu Cahyani Fanny Aura Salsabila Hani Andrasasi Karandi Nurbagja Kurnia, Nova Lia Agustin Mahendra, Rizqa Gardha Marti Widya Sari, Marti Maulana, Ahmad Rizki Muhammad Fairuzabadi Nurirwan Saputra Nurirwan Saputra, Nurirwan Okti Picky Nanda Ardiansa Pradana Feri Ariyanto Rusdi, Alfida Hari Saptaningsih Sumarmi Saptaningsih Sumarmi, Saptaningsih Sedik, Stevani Gabriella Ayuk Semukti, Deni Kurniawan Setia Wardani Sitrawati, Puput Suprapto Suprapto Triutami, Septi Turiyan Turiyan Vicky Fidiantoro Wisnulaksito, Febri Aji