Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer Aceh
Heri Gustami
Universitas Almuslim

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Monitoring Kualitas Tanah pada Tanaman Cabai Rawit Menggunakan Sensor Soil Moisture dan Sensor pH Tanah Berbasis IoT Ela Firliza; Imam Muslem; Heri Gustami
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.28

Abstract

Chili peppers are one of the agricultural commodities that have a lot of commercial potential or high economic potential. Chili peppers require ideal soil pH and water content to produce maximum yields. Monitoring of agricultural land is generally done manually which can be time-consuming and labor-intensive. Therefore, a monitoring system is needed that can detect soil pH and water content in real time to increase the productivity and effectiveness of chili plants. This study developed an Internet of Things (IoT) based monitoring system. This system uses a soil moisture sensor to monitor soil moisture in chili plants and a soil pH sensor to monitor pH levels in the soil. This monitoring system was built using a soil moisture sensor and a pH sensor as input, an ESP32 DEV KT V1 microcontroller as a process and Telegram as an output. The system workflow is the sensor reads soil moisture and pH data, the data is sent to the ESP32 microcontroller for processing, from the Wi-Fi module the data is transferred to the server and the server sends the obtained data to the Telegram Bot to be displayed to the user
Prototype Peringatan Banjir Berbasis Internet of Things Khalisah Khalisah; Imam Muslem; Heri Gustami
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.29

Abstract

Flooding is a disaster that frequently strikes Indonesia and causes various negative impacts on the community. Generally, there are two categories of flooding events: flooding in areas not normally submerged in water and flooding caused by overflowing rivers due to water volume exceeding the capacity of the existing river flow. Parameters often used as data to monitor and analyze changes are river water levels during certain seasons as an early warning effort for natural disasters such as flooding. Currently, monitoring river levels is still carried out manually using a water level scale installed on the riverbank, similar to a measuring instrument. Therefore, direct monitoring of the numbers indicated by the scale is necessary. Information obtained by the community is also still relatively inadequate. Therefore, by designing and building a river water level monitoring system based on IoT (Internet of Things), it is hoped that it can provide a solution to this problem. This system utilizes an HC-SR04 Ultrasonic sensor to measure the distance between the sensor and the object using ultrasonic waves. Data obtained from the sensor will be sent to an ESP32 DEV KT V1 microcontroller connected to the internet, so that users can access it through the Telegram application on their mobile phones.
Klasifikasi Nilai Nominal Uang Logam Indonesia Menggunakan Support Vector Machine Triee Salsabila; Riyadhul Fajri; Heri Gustami
Jurnal Ilmu Komputer Aceh Vol 3 No 1 (2026): Jurnal Ilmu Komputer Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/ilka.v3i1.39

Abstract

This study focuses on the classification of Indonesian Rupiah coin denominations using the Support Vector Machine (SVM) method based on digital image processing. The research objects consist of Rp100, Rp200, Rp500, and Rp1,000 coins issued from 2016 to the present. The pre-processing stage includes resizing the images to 128×128 pixels and converting them into grayscale to ensure data uniformity. Feature extraction is performed by combining shape features, Haralick texture, Local Binary Pattern (LBP), and HSV color features to represent the main characteristics of each coin. The classification model is developed using an SVM with a Radial Basis Function (RBF) kernel, with 80% of the data used for training and 20% for testing. The experimental results show an accuracy of 75%, indicating that the proposed approach is reasonably effective in distinguishing Indonesian coin denominations. However, further improvements can be achieved through parameter optimization and dataset expansion in future studies.
Co-Authors Ela Firliza Khalisah Khalisah Muslem R, Imam Riyadhul Fajri Triee Salsabila