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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Prosiding Snastikom Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Poningsih
STIKOM Tunas Bangsa
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Other

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Penerapan Data Mining Dalam Menentukan Pola Penjualan Alat Tulis Kantor (Atk) Menggunakan Algoritma Apriori Pada Toko Novacom Aldo Daniel Purba; Poningsih; Widodo Saputra; Dedy Kristianto Lumbantobing; Sumarno
SNASTIKOM Vol. 2 No. 1 (2023): SEMINAR NASIONAL TEKNOLOGI INFORMASI & KOMUNIKASI (SNASTIKOM) 2023
Publisher : Unit Pengelola Jurnal Universitas Harapan Medan

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

Abstract

Office stationery is a necessity that is indispensable in the world of offices and education because it has usefulness in helping and lightening when doing daily tasks in various types of work and circles. This research place sells various kinds of office stationery such as pens, books, pencils and various other types of equipment. Many piles of data that are not used effectively are a problem for Novacom office stationery shop owners. Because of the pile of data, a settlement or pattern formation is needed in order to assist shop owners in determining sales patterns and provide recommendations to shop owners what items are most often purchased by consumers. The algorithm that will be used as an analysis process for office stationery sales is the a priori algorithm to show the results that have met the determination of the pattern of buying office stationery based on sales to consumers
MACHINE LEARNING WITH LIGHTWEIGHT CNN (RESNET-18) FOR EARLY DETECTION OF RICE LEAF DISEASES Poningsih; Suhendra; Ahmad Zamsuri
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7358

Abstract

Rice leaf diseases such as blast and brown spot significantly threaten rice productivity, especially in agrarian countries like Indonesia. Manual diagnosis methods remain subjective, slow, and inconsistent across field conditions, highlighting the need for an automated and reliable detection system. This study presents a lightweight deep learning framework for the automatic classification of rice leaf diseases from image data. To assess its effectiveness, four Convolutional Neural Network (CNN) architectures ResNet-18, VGG-16, Inception V3, and MobileNetV2 were evaluated. The dataset, obtained from Kaggle, consists of three classes healthy, blast-infected, and brown spot with all images preprocessed through normalization and augmentation before being split into training and validation sets. Experimental results show that ResNet-18 achieves the best overall performance, with 96.94% accuracy, 100% precision, 95.45% recall, an F1-score of 96.18%, and an AUC of 1.0000. Compared to the other architectures, ResNet-18 demonstrates higher stability, stronger generalization, and lower overfitting tendencies while maintaining computational efficiency. The findings indicate that ResNet-18 is a promising lightweight model for practical deployment in mobile or IoT-based agricultural monitoring systems, supporting early disease detection and enhancing local food security efforts
Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning Sundari Retno Andani; Poningsih; Abdul Karim
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.v9i4.6357

Abstract

TThis study aimed to compare the performance of various Convolutional Neural Network (CNN) architectures, including LeNet, ResNet, AlexNet, GoogleNet, VGGNet, and the proposed model, in medical image classification for disease detection. The proposed model was developed by adding additional layers and fine-tuning the hyperparameters in the ResNet architecture to enhance its ability to extract complex features. The training and testing processes were conducted using an augmented X-ray image dataset to increase the data diversity. The results indicate that the proposed model achieved the highest testing accuracy of 76.33%, surpassing other models in terms of accuracy, precision, recall, and F1-score. Although there are some limitations in specificity and the Matthews Correlation Coefficient (MCC), the proposed model still demonstrates better generalization ability, with an AUC-ROC score approaching an optimal value. These findings suggest that the proposed model has advantages in medical image classification and holds potential for further development to enhance disease classification accuracy.
Implementasi Penilaian Kinerja Dosen Menggunakan Metode Fuzzy Mamdani dan Sistem Pendukung Keputusan Menggunakan Metode AHP Rafiqa Dewi; Poningsih
Jurnal Manajemen, Pendidikan Dan Ilmu Komputer Vol. 3 No. 2 (2026): Volume 3 No 2 Juli 2026
Publisher : Yayasan Darus Soleh Parung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65309/ebareb12

Abstract

Penilaian kinerja dosen merupakan aspek penting dalam pengelolaan mutu pendidikan tinggi. Namun, sistem penilaian konvensional sering menghadapi kendala subjektivitas, ambiguitas data, dan kurangnya struktur dalam pembobotan kriteria. Penelitian ini mengusulkan sistem evaluasi kinerja dosen berbasis integrasi metode Fuzzy Mamdani dan Analytic Hierarchy Process (AHP) untuk meningkatkan objektivitas dan akuntabilitas penilaian. Metode Fuzzy Mamdani digunakan untuk mengolah data linguistik dan menangani ketidakpastian pada tiga variabel utama: jumlah publikasi per tahun, kedisiplinan, dan penilaian dari mahasiswa serta pimpinan. Hasil fuzzy kemudian diolah dengan metode AHP guna memberikan bobot antar kriteria secara sistematis dan menyusun pemeringkatan dosen secara menyeluruh. Hasil penelitian menunjukkan bahwa model gabungan ini mampu menghasilkan evaluasi kinerja dosen yang lebih fleksibel, logis, dan dapat dipertanggungjawabkan secara manajerial. Implementasi dilakukan pada sembilan dosen Universitas Global Mandiri dan berhasil mengidentifikasi peringkat kinerja secara objektif.
Co-Authors Abdul Karim Ahmad Zamsuri, Ahmad Aldo Daniel Purba Dedy Kristianto Lumbantobing Dewi, Rafiqa Suhendra Sumarno Sundari Retno Andani Widodo Saputra