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Infotek : Jurnal Informatika dan Teknologi
Published by Universitas Hamzanwadi
ISSN : 26148773     EISSN : 26148773     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
INFOTEK Jurnal Informatika dan Teknologi Fakultas Teknik Universitas Hamzanwadi selanjutnya disebut Jurnal Infotek (e-ISSN: 2614-8773) merupakan Jurnal yang dikelola oleh Fakultas Teknik Universitas Hamzanwadi yang mempublikasikan artikel ilmiah hasil penelitian atau kajian teoritis (invited authors) dalam bidang (1) keilmuan informatika, (2) Rekayasa Perangkat Lunak, (3) Multimedia, (4) Jaringan Komputer, (5) Data Mining, (6) Image Processing, (7) Komputer Vision, (8) Mikrokontroller, (9) Robotik, (10) IOT yang belum pernah dipublikasikan. Jurnal Infotek diterbitkan oleh Fakultas Teknik Universitas Hamzanwadi dua kali setahun yaitu pada bulan Januari dan Juli. Jurnal Infotek Telah Terindeks pada Google Scholar.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 388 Documents
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Pengembangan Model Convolutional Neural Network (CNN) untuk Klasifikasi Penyakit Kulit Berbasis Citra Digital Imam Fathurrahman; Mahpuz; Muhammad Djamaluddin; Lalu Kerta Wijaya; Ida Wahidah
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28655

Abstract

This study aims to develop a skin disease classification model based on Convolutional Neural Networks (CNN) specifically designed to classify three types of skin diseases: acne, ringworm, and tinea versicolor. Unlike some previous studies that utilized datasets from various domains such as textile images, plants, and blood cell images, this research specifically employs a dataset relevant to skin diseases. The dataset used in this study consists of 810 skin disease images, divided into 600 images for training (200 images each for acne, tinea versicolor, and ringworm) and 210 images for testing. To enhance data variation and support model generalization, the dataset was processed using augmentation techniques. The model's performance evaluation showed promising results, with an average accuracy of 87.14%. Additionally, the model achieved precision, recall, and F1-score values of 87% each, demonstrating its ability to detect and classify skin diseases consistently. This study is expected to serve as a foundation for developing more accurate and efficient technology-based diagnostic tools, particularly for skin diseases, in the future
Survei Teknik Pemilihan Fitur Untuk Sistem Deteksi Intrusi Berbasis Machine Learning Ahmad, Ramli
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28657

Abstract

With the increasing threat to cybersecurity, Machine Learning (ML)-based Intrusion Detection Systems (IDS) are becoming increasingly important for detecting and preventing network attacks. The selection of appropriate features is a key factor in improving the performance of IDS, as it can enhance detection accuracy, reduce model complexity, and save computation time. This article examines various feature selection techniques used in ML-based IDS, including filter, wrapper, embedded, and hybrid techniques. Each technique has its advantages and disadvantages, depending on the characteristics of the dataset and the type of attack encountered. This research also evaluates the effectiveness of these techniques using popular datasets such as KDD Cup 99, NSL-KDD, and CICIDS 2017. The results show that filter techniques are more efficient in terms of time, while wrapper and hybrid techniques offer higher detection accuracy, although they require more resources. The embedded technique combines efficiency and accuracy with time savings in model training. This article also discusses the importance of good feature selection for classification in IDS, as well as the challenges faced by IDS in overcoming its limitations. This research provides a comprehensive overview of feature selection in ML-based IDS and recommendations for further development and implementation to address increasingly complex threats.
Penerapan Automatic Drip Irrigation System (ADIS) berbasis Internet Of Things (IoT) untuk Monitoring dan Meningkatkan Produktivitas Cabai Samsu, L.M.; Yahya; Nurhidayati
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28667

Abstract

Padamara Village is one of the villages in Sukamulia District with the largest agricultural expanse reaching 155 Ha, of which 90% is a productive area in the first and second planting seasons after rice. The cayenne pepper plant is a plant that is suitable for cultivation in highland and lowland areas. The optimal growth and yield of cayenne pepper plants is very dependent on adequate soil quality, proper watering management and fertilizer application. Checking soil conditions is very important for the growth of cayenne pepper plants which have optimal humidity of 50%-70% so that they are not too dry or wet with a soil pH of 6-7 and a temperature of 24-28 degrees Celsius. The "SEHATI" farmer group in Padamara Village, Sukamulia District, East Lombok Regency has around 45 farmers with an agricultural land area of ± 15.89 Ha as partners in the Implementation of an Automatic Drip Irrigation System (ADIS) based on the Internet of Things (IoT) for Monitoring and Increasing Productivity Chili in solving problems. Components such as Soil Moisture YL-69 for monitoring soil moisture and pH which will then be sent to the web server for subsequent reading of the results. ESP 32 as a microcontroller to turn on/off the pump water drops.
Pengaruh Pelanggaran Privasi Data Terhadap Keputusan Pembelian Pengguna Tokopedia Menggunakan Theory Of Planned Behavior (TPB) Pakpahan, Farida Manin; Suratno, Tri; Lestari, Dewi
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30169

Abstract

The rapid growth of e-commerce in Indonesia, led by platforms like Tokopedia, has transformed consumer behavior while also raising serious data privacy concerns. The 2020 Tokopedia data breach, involving 91 million user records leaked on the dark web, highlighted the vulnerability of personal data and risks such as identity theft and online fraud. This study investigates the impact of privacy breaches on Tokopedia users’ purchasing decisions in Jambi City using a quantitative approach grounded in the Theory of Planned Behavior (TPB). The study examines the influence of perceived data security, risk perception, attitude, subjective norms, and perceived behavioral control on purchase intention and actual buying behavior. Data were collected from 240 respondents through a questionnaire and analyzed using PLS-SEM with SmartPLS 4.0. Findings reveal that perceived data security has a significant positive effect on purchase intention, while risk perception has a significant negative effect. However, attitude and subjective norms show no significant impact on purchase intention, and behavioral intention does not significantly affect actual purchasing behavior. These results suggest that after a privacy breach, consumers prioritize data security over social or personal factors. E-commerce platforms must enhance data protection and maintain transparent communication to rebuild and retain consumer trust.
Pengenalan Bahasa Isyarat Indonesia Dengan Algoritma YOLOv8 Berbasis Mobile Hidayah, Firmansyah Nur; Rahmawati, Yunianita; Findawati, Yulian; Azizah, Nuril Lutvi
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30189

Abstract

Indonesian Sign Language (BISINDO) is the primary means of communication for deaf people in Indonesia, but the general public's understanding of BISINDO is still limited, thus hampering inclusive social interaction. To overcome this obstacle, the development of an artificial intelligence-based BISINDO detection system is a promising solution. One of the latest approaches is the utilization of the YOLOv8 algorithm, which is known to have advantages in real-time object detection with high accuracy and better model efficiency compared to previous versions. The BISINDO detection system using YOLOv8 is trained with image and video datasets of Hand gestures, so that it is able to recognize various BISINDO gestures in various lighting conditions and backgrounds. The main challenge in developing this system is the limited variety of datasets and image quality, so that more diverse data collection and optimization of model parameters are needed. Integration of supporting Augmented Reality (AR) and Transfer Learning technologies also has the potential to improve the learning experience and detection accuracy. Thus, the BISINDO detection system based on YOLOv8 is expected to expand communication access, increase public awareness of BISINDO, and support the realization of a more friendly and inclusive social environment for deaf people in Indonesia
Analisis Kinerja dan Keamanan Protokol PPTP dan L2TP/IPSec VPN pada Jaringan MikroTik M. Khofikur R.A; Eka Putra, Fauzan Prasetyo; Ridho G, Moh. Wahid; Huda, Valentino
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30230

Abstract

Virtual Private Network (VPN) has become a critical solution for ensuring secure communication over public networks. This research conducts a comprehensive analysis of the performance and security of two major VPN protocols: Point-to-Point Tunneling Protocol (PPTP) and Layer 2 Tunneling Protocol with Internet Protocol Security (L2TP/IPSec) implemented on MikroTik devices. The research methodology uses an experimental approach by testing throughput, latency, packet loss, and CPU utilization in various network scenarios. The results show that PPTP provides higher throughput with minimal overhead, but has significant weaknesses in security aspects. Conversely, L2TP/IPSec offers superior security levels with strong encryption, although it results in higher latency and lower throughput. These findings provide important insights for network administrators in choosing VPN protocols that suit organizational needs, considering the trade-off between performance and security
Analisis Protokol Keamanan Jaringan dalam Era Internet of Things (IoT) Hidayatullah, Imam; Khairi, Muh Hafiz; Maulana, Irfan; Eka Putra, Fauzan Prasetyo
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30257

Abstract

This study explores the challenges and solutions related to information system security in the Internet of Things (IoT) era, focusing on smart homes and industrial settings. Using a qualitative approach through a comprehensive literature review, this research analyzes academic sources including journals, technical reports, and research publications. Findings show that cyber threats such as malware, ransomware, and denial of service (DoS) attacks significantly affect IoT system performance and reliability. Key issues include the absence of standardized security measures, limited device interoperability, and low user awareness. The study contributes by mapping critical security concerns in IoT ecosystems and offering strategic recommendations such as enhancing security protocols, developing data protection policies, and promoting user education. This research aims to support the development of secure, adaptive, and sustainable IoT systems in an increasingly connected digital world
Identifikasi Penyakit Daun Durian Menggunakan Penerapan Algoritma Residual Network (RESNET-50) Ramadhan, Arga Satria; Rahmawati, Yunianita; Indra Astutik, Ika ratna; Sumarno
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30293

Abstract

Durian is one of Indonesia’s leading horticultural commodities, but its productivity can decline due to leaf diseases that are difficult for farmers to identify visually. This study aims to develop an automated durian leaf disease classification system using a deep learning algorithm based on the ResNet-50 architecture. The dataset consists of 420 durian leaf images classified into four categories: Algal Leaf Spot, Leaf Blight, Leaf Spot, and No Disease, collected from the Roboflow platform. Preprocessing steps included annotation, augmentation, and resizing the images to 240x240 pixels.The model was trained using TensorFlow with pretrained ImageNet weights. Three data split scenarios (70:20:10, 75:15:10, and 80:10:10) were applied using both binary and multiclass classification approaches. Model performance was evaluated using confusion matrix and metrics such as accuracy, precision, recall, and F1-score. The best binary classification result achieved 99.8% accuracy and 99.9% F1-score, while the best multiclass result achieved 99.6% accuracy and 96.9% macro F1-score. These results demonstrate that ResNet-50 is effective in accurately detecting durian leaf diseases and can be implemented in mobile applications to assist farmers in early diagnosis and improving crop productivity.
Sistem Pendukung Keputusan Penerima Bantuan Langsung Tunai Dana Desa (BLT-DD) Menggunakan Algoritma Preference Selection Index (PSI) Tampubolon, Putri Tasya Agustina; Chairani, T.Sofia; Niska, Debi Yandra
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30343

Abstract

The Village Fund Direct Cash Assistance (BLT-DD) program is one of the government's efforts to help poor families in the village. However, the recipient selection process is still done manually through deliberations of village officials, making it prone to mistargeting and lack of transparency. This research aims to build a Decision Support System (SPK) that is able to determine prospective BLT-DD recipients objectively and efficiently. The method used is the Preference Selection Index (PSI), which can solve multicriteria decision-making problems without the need to assign weights between criteria. The five main criteria used include welfare decile, health status, program participation, marital status, and gender of the family head. Testing was conducted on 10 alternative data. The calculation results show that alternative A3 has the highest score of 1.66. This system is able to improve accuracy, efficiency, and transparency in BLT-DD distribution.
Penerapan Metode Naïve Bayes Untuk Mendeteksi Secara Dini Stunting Pada Balita Kusumawardhany, Nidya; Abdullah, Indra Nugraha
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30385

Abstract

This study focuses on early detection of stunting in toddlers at integrated health service posts (posyandu) in the Paninggilan Utara Ciledug Tangerang area using the Naive Bayes method. Stunting is a chronic nutritional problem that arises due to prolonged malnutrition. Early detection of stunting in toddlers is very important because it can have an impact on the growth and development of toddlers in the long term. The study used a dataset of 250 toddler records collected randomly from 17 posyandus in the Paninggilan Utara Ciledug Tangerang area. Toddlers were divided into three age groups: Group A (0-11 months), Group B (12-35 months), and Group C (36-59 months). The Naive Bayes method, which is a statistical classification technique, is used to detect early the possibility of stunting in infants under five years old. So that earlier medical action can be given to overcome the stunting condition. The results of the study showed that the Naive Bayes method obtained a higher accuracy of 94.80% compared to the K-Nearest Neighbor (KNN) method which had an accuracy of 85.80%. The high accuracy and speed of the Naive Bayes method make it a suitable tool for screening and early detection of stunting in toddlers

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