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All Journal Jurnal Sistem Komputer Journal of Information System Jurnal Komputer Terapan Techne : Jurnal Ilmiah Elektroteknika Jurnal ULTIMA Computing KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Manajemen Informatika Jurnal Teknologi Terapan Indonesian Community Service and Empowerment Journal (IComSE)
Hidayat, Hidayat
Program Studi Teknik Komputer Universitas Komputer Indonesia
Arts Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Design of Crop Leaf Area Measurement using Webcam and Raspberry Pi Hidayat; Muhammad Mahardiansyah
Techné : Jurnal Ilmiah Elektroteknika Vol. 21 No. 2 (2022)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v21i2.327

Abstract

This paper describes an electronic system for measuring the number of leaves and the total leaf area. This system is built to make it easier for users to compute several leaves and measure leaf area electronically. The number of leaves and leaf area are used to determine the level of fertility of a plant. Several important factors in measuring leaf area are the accuracy and the speed of measurement. The stages conducted in this research consist of needs analysis, design, implementation and testing. The system built uses a mini PC Raspberry Pi as the data processor and a webcam to capture images. Moreover, the image is processed using Binary threshold and Otsu threshold methods. The results showed that the designed system was functioning properly with an error rate of 0% for the number of leaves calculation and a 0.39% error rate in the of leaf area measurement.
RANCANG BANGUN PENDETEKSI TINGKAT KEHIJAUAN WARNA DAUN PADI MENGGUNAKAN SENSOR WARNA TCS230 Hidayat Hidayat; Yazid Baihaqy
Jurnal Teknologi Terapan Vol 8, No 2 (2022): Jurnal Teknologi Terapan
Publisher : P3M Politeknik Negeri Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31884/jtt.v8i2.435

Abstract

Fertilization of rice plants according to the dose of their needs is one of the important things to produce an optimal rice harvest. Giving less or more fertilizer can cause rice plants not to grow optimally and even cause crop failure. The need for fertilizer doses can be determined by changing the color of the rice leaves using the Leaf Color Chart (LCC). However, obstacles in the field are challenging for novice farmers to predict fertilizer needs just by looking at the color of the leaves with the naked eye. The application of information technology is expected to help farmers, especially novice farmers, in measuring the dose of fertilizer needed for rice plants. The technology that will be applied is an electronic device that can detect the color of rice leaves and provide information for users from the measurement results through an android application on a smartphone device. The electronics modules used are the TCS320 color sensor module which functions to detect the color of objects, the Arduino UNO microcontroller module which contains ATMega128 as a data processor, and the Bluetooth module as a communication liaison between the microcontroller device and the android application on the smartphone. The test results show that the built device can function properly. All tested leaves can be classified according to the greenish level of the leaf color.
Komparasi TF-IDF dan BoW pada Analisis Sentimen Shopee-Tokopedia Jihan Salsabila; Silvia Meida; Efelien Anindya Shifani; Hana Mar’atul Afifah; Hidayat Hidayat
Jurnal Manajemen Informatika JAMIKA Vol 16 No 1 (2026): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v16i1.17552

Abstract

The rapid growth of e-commerce in Indonesia has led to an increase in user interactions in the form of reviews and opinions on services and products. These textual data contain valuable information that can be processed through sentiment analysis to better understand user perceptions. This study aims to compare the effectiveness of Term Frequency–Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) feature extraction methods in classifying user sentiments, as well as to evaluate the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms on Shopee and Tokopedia platforms. A total of 5,000 user reviews were analyzed through text preprocessing, lexicon-based sentiment labeling, application of TF-IDF and BoW feature extraction methods, model training using SVM and RF algorithms, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results show the combination of BoW and SVM achieved the highest accuracy of 90% on Shopee reviews, making it the most optimal configuration in this study. Additionally, in Tokopedia reviews, the same configuration (BoW and SVM) also produced a strong accuracy of 88%. In general, the SVM algorithm showed more stable performance than RF, while the BoW method proved to be more effective (measured at up to 90% accuracy) in representing this Indonesian-language e-commerce review data. These findings contribute to the development of more accurate sentiment analysis systems in the local e-commerce domain.
Co-Authors A.A. Ketut Agung Cahyawan W Abd. Rasyid Syamsuri Afrizal Imanullah Andre Ryadi Wardana Boni Al Amin Efelien Anindya Shifani Fajarianti, Oktavia Hana Mar’atul Afifah Jihan Salsabila Linda Nurjanah Muhammad Mahardiansyah Mustopa Nur Hayat Myrna Dwi Rahmatya Riani Lubis Silvia Meida Sri Nurhayati Tri Pamungkas Wendi Zarman Wicaksono, Mochamad Fajar Yazid Baihaqy