Article Per Year (5 Year)
p-Index From 2021 - 2026
2.013
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Desimal: Jurnal Matematika JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Telematika Jambura Journal of Mathematics JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Al-Aqlu : Jurnal Matematika, Teknik dan Sains MILANG Journal of Mathematics and Its Applications Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Khatizah, Elis
School Of Data Science, Mathematics, And Informatics, Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Physics Public Health Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Sentimen Publik Terhadap Kebijakan Pemindahan Ibu Kota Indonesia di X Menggunakan Model BiLSTM-CNN Wanda Nugraha; Mochamad Tito Julianto; Mohamad Khoirun Najib; Elis Khatizah
Jurnal Telematika Vol. 20 No. 2 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i2.796

Abstract

The development of Indonesia's new capital city, Ibu Kota Nusantara (IKN), is an innovative government policy that has sparked diverse public responses. This study aims to explore sentiment trends on the social media platform X to understand public perceptions of the policy. Additionally, a sentiment classification model combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) was developed and optimized through hyperparameter tuning. Exploratory analysis showed that positive sentiment dominated at 46%, followed by negative at 30% and neutral at 24%. The classification model achieved a test accuracy of 78% and an average accuracy of 81% across 10-fold cross-validation, with a standard deviation of 0.006. The achieved accuracy, together with the low cross-validation standard deviation, indicates that the BiLSTM-CNN model demonstrates stable and reliable performance.
ANALISIS PERFORMA YOLOV8 PADA DETEKSI KEMATANGAN BIJI KOPI DENGAN BERBAGAI KONFIGURASI PELATIHAN: Performance Analysis of YOLOv8 in Coffee Seed Detection with Various Training Configuration Roma Rio Simbolon; Muhammad Faza Hanifan; Mohamad Khoirun Najib; Elis Khatizah; Sri Nurdiati
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 4 No. 2 (2026): Juli 2026
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v4i2.570

Abstract

Coffee is a strategic plantation commodity in Indonesia, whose quality is strongly influenced by fruit maturity at harvest. However, maturity assessment in the field is still largely conducted manually, leading to subjectivity and low efficiency. This study aims to analyze the performance of YOLOv8 in detecting three coffee fruit maturity levels—unripe, semi-ripe, and ripe—and to evaluate the impact of different training configurations on model performance. The experiments were conducted using the public “Coffee Cherry” dataset from Kaggle, consisting of 432 images with 1000 bounding box annotations. Several configurations were evaluated, including YOLOv8 variants (n, s, m), image sizes, batch sizes, number of epochs, and data augmentation techniques based on HSV color space, object scaling, and mixup. The results show that YOLOv8s provides the best balance between detection accuracy and computational efficiency. The optimal configuration was achieved using an image size of 416, batch size of 8, and 30 epochs, with augmentation improving recall and mAP@50, particularly for challenging classes. Nevertheless, limited dataset size and diversity remain the main constraints affecting performance. Overall, YOLOv8 demonstrates strong potential for coffee fruit maturity detection under real-world conditions.
Co-Authors Abel Prayoga Abisha, Nicholas Akbar, Raihan Alifah, Nayla Nur Aulia Rizki Firdawanti David Vijanarco Martal Fatmawati, Linda Leni Hauralia Rahmadanti Finan Hendri Irwandi Lilis Sri Wahyuni Linda Leni Fatmawati Mirza Farhan Azhari Mochamad Tito Julianto Muhammad Faza Hanifan Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad Khoirun Nicholas Abisha Nurdiari, Sri Redytadevi, Tita Putri Rizkian Agung Jamaesa Roma Rio Simbolon Saputra, Rika Ardiansyah Sri Nurdiati Wanda Nugraha