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All Journal Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer JITCoS : Journal of Information Technology and Computer System Jurnal Teknik Informatika dan Teknologi Informasi
Diah Putri Kartikasari
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Ekstraksi Fitur Citra Grayscale dengan Convolutional Neural Networks Diah Putri Kartikasari; Fiqri Dian Priyatna Sinaga; Tiara Ayu Triarta Tambak; Zahra Humaira Kudadiri; M. Khalil Gibran
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 1 (2025): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i1.5175

Abstract

This study aims to explore the use of Convolutional Neural Networks (CNN) in feature extraction from grayscale images for avocado object identification. The process begins with taking a grayscale image of the avocado object to be recognized. Convolution is applied using a 3x3 horizontal Sobel kernel filter with a stride of 1 to the right, and a ReLU (Rectified Linear Unit) activation function to improve the network's ability to extract relevant features. After the convolution stage, pooling is carried out using the max pooling method to reduce the image dimension while retaining important information, thereby speeding up the training process and reducing the risk of overfitting. The processed image is then flattened to produce a feature vector that is ready to be used in classification. The results of the study indicate that the CNN approach can be used as an effective method for feature extraction and edge detection on avocado objects from grayscale images.
Analisis Sentimen Pengguna X terhadap Kebijakan PPN 12% Menggunakan Naive Bayes Alwi Andika Panggabean; Diah Putri Kartikasari; Rafif Risdi Aulia; Tiara Ayu Triarta Tambak; Siti Fadiyah Nabila; Mhd Furqan
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 5 No 1 (2025): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v5i1.1002

Abstract

Kebijakan kenaikan Pajak Pertambahan Nilai (PPN) dari 11% menjadi 12% yang direncanakan berlaku pada tahun 2025 telah menimbulkan berbagai reaksi publik, terutama di media sosial. Penelitian ini bertujuan untuk menganalisis sentimen pengguna media sosial X (sebelumnya Twitter) terhadap kebijakan tersebut menggunakan metode Naive Bayes yang diimplementasikan dalam bahasa pemrograman R. Data diperoleh dari tweet yang relevan dengan topik PPN 12%, kemudian diproses melalui tahapan pra-pemrosesan dan pelabelan manual. Hasil analisis menunjukkan bahwa sentimen negatif mendominasi dengan proporsi 39%, diikuti sentimen netral 32%, dan sentimen positif 29%. Evaluasi performa model Naive Bayes menunjukkan akurasi sebesar 50%, dengan ketepatan klasifikasi tertinggi pada kategori negatif. Analisis lebih lanjut terhadap istilah kunci dan topik diskusi mengungkapkan bahwa kekhawatiran terhadap beban ekonomi dan dampak terhadap UMKM menjadi sumber utama sentimen negatif, sementara sentimen positif dikaitkan dengan harapan terhadap perbaikan layanan publik dan pembangunan. Penelitian ini memberikan wawasan penting bagi pembuat kebijakan untuk memahami persepsi publik terhadap kebijakan fiskal secara lebih mendalam dan berbasis data.
Hybrid Demand Forecasting and Monte Carlo Simulation for Retail Supply Chain Inventory Optimization Diah Putri Kartikasari; Tiara Ayu Triarta Tambak; Aero Rizal Ridwanto
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.40

Abstract

Retail inventory optimization must balance service levels against holding, ordering, and stockout costs under uncertain demand and lead time. We develop an integrated framework that couples hybrid demand forecasting with Monte Carlo simulation (MCS) to evaluate continuous‑review policies. Historical daily sales are modeled using statistical baselines (naive and exponential smoothing) and gradient‑boosted trees with quantile objectives to obtain distributional forecasts. Predictive means and residual‑based dispersion calibrate a Negative Binomial demand model; because lead-time is not present in the dataset, we treat it as a scenario parameter in the simulator (baseline mean ~2 days, SD ~1 day) and probe it via sensitivity analyses. Using a representative retail subset, we simulate 90‑day horizons with 300 replications per item across a grid of values. Results reveal a convex cost–service frontier: (15,120) minimizes total cost in the tested grid, while (25,140) achieves the highest fill rate. Sensitivity analyses show costs are most responsive to safety stock and lead‑time variability. The framework links forecast uncertainty to inventory policy selection, offering a reproducible, data‑driven tool for practitioners and a baseline for future multi‑echelon and decision‑focused extensions.
Co-Authors Aero Rizal Ridwanto Alwi Andika Panggabean Fiqri Dian Priyatna Sinaga M. Khalil Gibran Mhd Furqan Rafif Risdi Aulia Siti Fadiyah Nabila Tiara Ayu Triarta Tambak Zahra Humaira Kudadiri