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All Journal JURIKOM (Jurnal Riset Komputer) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian
Hilda Yulia Novita
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Religion Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Other

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Perbandingan Algoritma Logistic Regression dan K-Nearest Neighbor Dalam Klasifikasi Kematangan Buah Pepaya Wildan Amin Wiharja; Tohirin Al Mudzakir; Hilda Yulia Novita; Jamaludin Indra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.550

Abstract

Visual assessment of papaya ripeness often leads to inconsistent and low accuracy results. To address this, the study applies Logistic Regression and K-Nearest Neighbor (K-NN) algorithms for automatic classification using digital image processing. The initial dataset consisted of 300 images, which were expanded to 1,200 through preprocessing and augmentation. Features were extracted using the Gray Level Co-occurrence Matrix (GLCM) method, and the data was split into 80% for training and 20% for testing. The study aims to compare the performance of both algorithms and understand their classification mechanisms. Results show that K-NN with k=1 achieved an accuracy of 87%, while Logistic Regression with L2 regularization reached 73%, indicating that K-NN outperforms Logistic Regression in classifying papaya ripeness levels.
ANALYSIS AND IMPLEMENTATION OF AES-128 ALGORITHM IN SUKAHARJA KARAWANG VILLAGE SERVICE SYSTEM Fariz Duta Nugraha; Kiki Ahmad Baihaqi; Hilda Yulia Novita; Siregar, Amril Mutoi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2038

Abstract

Data security in databases is needed in the industrial era 4.0 to prevent attacks and unwanted things from happening, one of the biggest cases that has been widely reported is data leakage, in this study aims to implement and analyze the Advanced Encryption Standard Algorithm, one of the data security algorithms with a block chiper type that has 4 transformations (SubByte, ShiftColumn, MixColumn, AddRoundKey), or what we usually call the Cryptography method. Cryptography is a method that is often used to secure important data in databases, in this article the Advanced Encryption Standard Algorithm is used to secure citizen data and family card data in the Sukaharja Karawang Village service system. The method in this research is the observation method, the data is obtained from each head of the neighborhood in Sukaharja Karawang Village with the permission of the head of Sukaharja Karawang Village. Citizen data and family cards were encrypted and analyzed for resource requirements in storing encryption results and time in returning and displaying original data. The results of the analysis obtained the amount of resources required 1.5MB to store family card data, which before encryption required 352KB. Citizen data requires a resource of 6.5MB, before encryption it takes 1.5MB. As for the AES resilience test stage using the Bruteforce attack method with the help of Hashcat software version 6.2.5 with 4 trial processes, One encrypted address data was taken for this test, but out of 4 attempts none of them showed that the data could be cracked.
Penerapan Algoritma Support Vector Machine (SVM) untuk Klasifikasi Keluarga Sejahtera Dwi Tian Tonara; Ahmad Fauzi; Hilda Yulia Novita
Scientific Student Journal for Information, Technology and Science Vol. 7 No. 1 (2026): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Setiap keluarga menginginkan kehidupan yang sejahtera serta mampu memenuhi kebutuhan, baik primer maupun sekunder. Kartu Keluarga Sejahtera merupakan salah satu program pemerintah yang bertujuan untuk menanggulangi kemiskinan. Penentuan penerima bantuan pemerintah agar tepat sasaran dapat dilakukan melalui klasifikasi keluarga yang layak dan tidak layak menerima bantuan. Data yang digunakan dalam penelitian ini diperoleh dari Pemerintah Desa Mekarmaya sebanyak 432 data, yang terdiri atas 25 data keluarga layak dan 407 data keluarga tidak layak. Data tersebut kemudian diklasifikasikan menggunakan algoritma Support Vector Machine (SVM). Pengujian dilakukan menggunakan Orange dan bahasa pemrograman Python, dengan hasil klasifikasi berupa 7 data keluarga layak dan 80 data keluarga tidak layak, serta tingkat akurasi sebesar 94%.
PENGENALAN TEKNOLOGI INFORMASI DAN MULTIMEDIADALAM MENINGKATKAN SISTEM PEMBELAJARAN DI PONDOKPESANTREN AL-KAUTSAR KARAWANG Tatang Rohana; Hilda Yulia Novita
JURNAL BUANA PENGABDIAN Vol. 8 No. 1 (2026): JURNAL BUANA PENGABDIAN
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/77vx1v89

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkenalkan teknologi informasi dan multimedia dalam meningkatkan sistem pembelajaran di Pesantren Al- Kautsar Karawang.Pesantren merupakan lembaga pendidikan yang memegang peranan penting dalam membentuk generasi yang tidak hanya berkarakter Islami tetapi juga siap menghadapi perkembangan teknologi di era digital. Namun, banyak pesantren masih menghadapi keterbatasan dalam mengakses teknologi, sehingga pembelajaran cenderung bersifat konvensional. Melalui pengenalan teknologi informasi dan penggunaan multimedia, diharapkan terjadi peningkatan dalam kualitas pembelajaran, baik dari segi efektivitas maupun keterlibatan santri. Kegiatan ini meliputi pelatihan dasar tentang penggunaan perangkat teknologi, seperti komputer dan internet, serta pengenalan berbagai aplikasi multimediayang dapat mendukung proses belajar mengajar. Selain itu, dilakukan juga pendampingan dalam penerapan teknologi untuk menunjang pengelolaan administrasi dan pembelajaran berbasis digital di pesantren. Hasil dari kegiatan ini menunjukkan adanya peningkatan kemampuan tenaga pengajar dan santri dalam mengoperasikan perangkat teknologi serta pemanfaatan multimedia dalam kegiatan pembelajaran. Dengan demikian, pesantren dapat mengintegrasikan metode pembelajaran modern yang lebih interaktif dan relevan dengan kebutuhan zaman.
Penerapan Metode YOLOv8 untuk Deteksi Jalan Berlubang Berbasis Object Detection Rafli Muhammad Fauzi; Ramadhan Putra Al Kariim; Nurul Latifah; Hilda Yulia Novita
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9441

Abstract

Road infrastructure damage, particularly potholes, is a critical issue that contributes to an increased risk of traffic accidents in Indonesia. This condition highlights the need for a road monitoring system that is capable of operating quickly, accurately, and continuously. This study aims to develop and evaluate an automated pothole detection system based on computer vision using the YOLOv8 (You Only Look Once version 8) method, a deep learning algorithm designed for real-time object detection. Dataset collection was conducted through two approaches, namely acquiring data from the Kaggle platform and capturing real-world road images directly using a smartphone camera to enhance data diversity and represent actual road conditions. All collected images were annotated using the Roboflow platform to generate labeled data suitable for model training. The YOLOv8 model was trained using a total of 345 images, consisting of 241 training images, 69 validation images, and 35 testing images. The training results indicate that the model achieved a mean Average Precision (mAP) of 93%, with a precision of 88% and a recall of 82%, demonstrating strong detection and localization performance. Furthermore, real-time testing using a smartphone camera showed that the system achieved an accuracy of over 85% under real-world conditions. These findings demonstrate that YOLOv8 has strong potential as an efficient and reliable automated pothole detection system, supporting road infrastructure maintenance and enhancing road user safety
Co-Authors Ahmad Fauzi Baihaqi, Kiki Ahmad Dwi Tian Tonara Fariz Duta Nugraha Indra, Jamaludin nurul latifah Rafli Muhammad Fauzi Ramadhan Putra Al Kariim Siregar, Amril Mutoi Tatang Rohana Tohirin Al Mudzakir Wildan Amin Wiharja