Article Per Year (5 Year)
p-Index From 2021 - 2026
2.862
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Charity : Jurnal Pengabdian Masyarakat Jurnal Dialektika Informatika (Detika) Indonesian Community Journal Jurnal Pengabdian Masyarakat Akademisi Journal of Artificial Intelligence and Digital Business Indonesian Journal of Multidisciplinary on Social and Technology Journal of Engineering and Science Application As-Salam: Journal Islamic Social Sciences And Humanities Journal of Education and Computer Applications
Lidya Ningsih
Telkom University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
Predict Students' Dropout and Academic Success with XGBoost Ridwan, Achmad; Priyatno, Arif Mudi; Ningsih, Lidya
Journal of Education and Computer Applications Vol. 1 No. 2 (2024)
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jeca.v1i2.13

Abstract

The attrition rate of students in higher education is a worldwide issue that profoundly affects both individuals and institutions. Students who fail to complete their studies often encounter economic and social difficulties, while educational institutions suffer a deterioration in reputation and operational efficacy. This paper proposes the creation of a prediction model utilizing the XGBoost algorithm to assess students' academic progress and dropout risk. The model incorporates several elements, such as academic, demographic, and socio-economic, to yield comprehensive insights into students' educational trends. This research utilizes the Predict Students' Dropout and Academic Success dataset, comprising 4,424 data points and 36 attributes. The data underwent normalization via StandardScaler and was divided into five scenarios for training and testing, ranging from a 50:50 to a 90:10 split. The evaluation of the model was conducted utilizing accuracy, precision, recall, and F1-Score criteria. The findings indicate that the model attains peak performance in the 80:20 scenario, exhibiting 88% precision and an 81% F1-Score, signifying an ideal equilibrium between predictive accuracy and risk identification capability. This study demonstrates that XGBoost can serve as a dependable predictive instrument to aid decision-making in the education sector. These findings establish a foundation for formulating targeted interventions aimed at enhancing student retention. Subsequent study may investigate the use of real-time data and sophisticated models to enhance predictive accuracy.
Pengembangan jaringan dan sistem ujian LMS berbasis Moodle di Lingkungan MAN 4 Sukabumi Aaz M Hafidz Azis; Lidya Ningsih; Untari Novia Wisesty
Charity : Jurnal Pengabdian Masyarakat Vol. 9 No. 1 (2026): Charity-Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v9i1.9507

Abstract

Transformasi pendidikan berbasis digital di MAN 4 Sukabumi, yang memiliki 509 siswa (248 laki-laki dan 261 perempuan), mulai diterapkan melalui penggunaan Learning Management System (LMS) sebagai platform utama dalam ujian daring. Implementasi awal menghadapi kendala signifikan, di mana sekitar 80% responden melaporkan gangguan jaringan berupa lambatnya akses dan terputusnya koneksi, dengan keterbatasan perangkat yang menyebabkan satu access point harus digunakan oleh 2–3 kelas sekaligus. Kegiatan pengabdian masyarakat ini bertujuan mengembangkan sistem ujian berbasis LMS yang andal melalui penguatan infrastruktur jaringan dan peningkatan kapasitas pengguna. Metode yang diterapkan meliputi analisis kebutuhan, perancangan dan instalasi empat access point berkapasitas tinggi, konfigurasi Quality of Service (QoS), pelatihan teknis bagi guru dan siswa, serta evaluasi berbasis kuesioner. Hasil kegiatan menunjukkan peningkatan stabilitas koneksi internet, efisiensi penggunaan kertas berkurang hingga 80% dengan penghematan sekitar Rp1.200.000, serta percepatan proses penilaian berkat fitur otomatisasi LMS. Selain itu, 91% siswa yang mengikuti pelatihan menyatakan mampu menyelesaikan ujian tanpa kendala, dan mayoritas dari 15 responden memberikan penilaian “Setuju” hingga “Sangat Setuju” terhadap relevansi materi dan keberlanjutan program. Temuan ini menegaskan bahwa penguatan infrastruktur digital dan pemberdayaan pengguna berkontribusi signifikan dalam mendukung pelaksanaan ujian daring yang efektif dan berkelanjutan di lingkungan madrasah.   Kata Kunci : Pxendidikan digital, LMS, Moodle, infrastruktur jaringan, ujian daring
Perbandingan Kinerja Algoritma Klasifikasi Status Mutu Air Lidya Ningsih; Jajam Haerul Jaman; Naufal Ibnu Salam; Muhammad Haikal
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 2 No. 1 (2024): November - Februari
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijmst.v2i1.298

Abstract

Klasifikasi mutu air adalah salah satu teknik dalam melakukan penilaian terhadap air sebagai objek penelitian. Penelitian ini dilakukan dengan tujuan agar dapat memberikan pengetahuan terhadap mutu atau kualitas air, sehingga dapat menjadi solusi terbaik yang dapat dilakukan terhadap air tersebut sebelum dikonsumsi. Penelitian ini menggunakan dua skenario dengan beberapa teknik klasifikasi, diantaranya adalah Algoritme Naive Bayes, KNN, Multiclass classification, MLP, SVM, dan Random Forest. Berdasarkan hasil peneltiian yang dilakukan dengan beberapa algoritma klasifikasi tersebut, didapatkan hasil akurasi terbaik menggunakan Algoritme Random Forest dengan persentase akurasi sebesar 99,5% pada skenario pertama dan 99,7% pada skenario kedua . Sedangkan tingkat akurasi terendah ditemukan pada Algoritme Naive Bayes dengan persentase akurasi sebesar 22,3% pada skenario pertama dan 21,9% pada skenario kedua. Hal ini disebabkan karena dataset mutu air yang diperoleh tidak seimbang atau tidak terdistribusi normal (Gaussian). Selain itu, algoritme Naive Bayes memiliki kinerja baik dalam pekerjaan klasifikasi dengan data teks.
Benchmarking YOLOv12 Variants for Indonesian Traditional Cuisine Detection Fauzan Firdaus; Lidya Ningsih; Aminah Indahsari Marsuki; Angel Metanosa Afinda
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12625

Abstract

YOLOv12 is one of the latest YOLO versions currently. Several studies have proven that YOLOv12 has better performance compared to previous versions. YOLOv12 itself has five model variants based on its architectural complexity, namely nano, small, medium, large and extra larges. This study tests the performance of YOLOv12 model variants (n, s, m, l, x) for traditional Indonesian culinary detection using a domain-specific object detection dataset. The dataset contains 718 images with 720 bounding-box instances annotated across 20 culinary classes, divided into 418/150/150 images for training/validation/testing. Data processing was performed in Roboflow with automatic orientation and stretching resizing to 640×640, while the training split was enriched using augmentation (horizontal and vertical flips) to increase sample diversity. All YOLOv12 variants were trained with the same configuration and environment, for 50 epochs using the Ultralytics framework with default hyperparameters on an NVIDIA A100-SXM4 80GB GPU. On the validation set, all variants achieved high detection accuracy (mAP@0.5 = 0.985–0.991), while differences emerged under a more stringent localization criterion (mAP@0.5:0.95). The best overall localization performance was achieved by YOLOv12-L (mAP@0.5:0.95 = 0.874), while YOLOv12-N provided the fastest inference (0.8 ms/image) with competitive accuracy (mAP@0.5:0.95 = 0.822). These findings provide preliminary guidance for selecting YOLOv12 variants based on the trade-off between accuracy and speed.
A New Triple-Weighted K-Nearest Neighbor Algorithm for Tomato Maturity Classification Lidya Ningsih Lidya; Arif Mudi Priyatno Arif; Addini Yusmar dini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6441

Abstract

As climatic products, tomatoes are highly sensitive to harvesting and processing. The sorting of tomatoes can be significantly improved by utilizing Hue Saturation Value (HSV) color features that are classified using neighboring algorithms, such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (W-KNN), and DW-KNN. However, the DW-KNN algorithm does not consider the relative relationship between the farthest, nearest, and surrounding neighbors, which may impact the classification accuracy, particularly in datasets with uneven distributions. This study proposes a Triple Weighted K-Nearest Neighbor (TW-KNN) algorithm for tomato image classification. This algorithm effectively handles the problem of sensitivity and outliers in the data distribution and considers the relationship between neighboring distances. The classification data consisted of 400 tomato images with five maturity levels divided into training and testing sets using k-fold cross-validation. Tests were conducted using several variations of parameter k, namely 4, 6, 9, and 15, to evaluate the classification performance. The results show that the proposed TW-KNN algorithm consistently outperforms other methods by producing better classification results. This is demonstrated by an accuracy rate of 95.52% across different values of k. The superior performance of the TW-KNN highlights its ability to provide robust and stable classification results compared to conventional KNN variants. This finding indicates that the TW-KNN is more effective in consistently classifying tomato fruits, making it a promising approach for automated fruit sorting applications.
Co-Authors Aaz M Hafidz Azis Achmad Ridwan, Achmad Acihmah Sidauruk Addini Yusmar dini Adini Yusmar Afrianto, Nurdi Agung Pambudi Agus Buono AMINAH INDAHSARI MARSUKI Angel Metanosa Afinda Arif Mudi Priyatno Arif Mudi Priyatno Arif Auzi Asfarian Bariqy Budy Satria Cholidhazia, Putri Ema Rachmawati Ema Rachmawati Fauzan Firdaus Fazmah Arif Yulianto Harun Al-Rasyid Hery Wibowo Jaman, Jajam Haerul Laud Pulungan Liga Mayola Muhammad Haikal Muhammad Noor Mushthofa Naufal Ibnu Salam Rimba Whidiana Ciptasari RR. Ella Evrita Hestiandari SAKINAH, PUTRI Syifa Shabrina Siregar Toto Haryanto Untari Novia Wisesty