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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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Komparasi Naive Bayes dan Gradient Boosting Machine untuk Klasifikasi Sentimen Publik terkait Kenaikan Harga Pertalite di Media Sosial Twitter Sandi, Arnila; Yahya
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.v8i2.31102

Abstract

The increase in the price of pertalite fuel in Indonesia has caused various reactions from the public, which are widely expressed through social media such as twitter. Fuel oil (BBM) is one of the basic needs that is very important for the Indonesian people because it plays a major role in supporting various daily activities. BBM is used not only as fuel for motor vehicles, but also as a source of energy for various industrial equipment. This study aims to classify public sentiment towards the issue using the naive bayes algorithm and Gradient Boosting Machine (GBM) as a classification method. The data used in this study were obtained from the Kaggle platform, which contains a collection of tweets related to the issue of fuel prices, especially pertalite. The analysis process begins with text preprocessing, such as data cleaning, tokenization, stopword removal, and stemming. The data already has a sentiment label (positive, negative, neutral) and is divided for model training and testing. The evaluation results show that the GBM algorithm is able to classify sentiment with an accuracy rate of 60% while the Naive Bayes algorithm has an accuracy rate of 90%. These results prove that naive bayes has a higher level of accuracy than the GBM algorithm, so it can be used in processing text data from social media to understand public opinion on government policies, especially regarding fuel price increases.
Analisis Komparatif Feture Selection dan Metode Klasifikasi Intrution Detection System (IDS) Ahmad, Ramli; Saiful, Muhammad
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.v8i2.31103

Abstract

An Intrusion Detection System (IDS) is a computer system that analyzes user traffic and data on a network. IDS will provide a warning signal to network users that are attacked by intruders. However, IDS still needs to be developed due to the ever-evolving attack pattern. Feature Selection and Classification Network data are important components of IDS development to identify attack patterns based on network traffic data. To identify attack patterns, many researchers have proposed Feature Selection methods such as Chi-square, Gini Index, Information Gain, Relief, Gain Ratio, and Uncertainty. Researchers have also proposed Machine Learning Classification methods such as Random Forest, SVM, Naive Bayes, AdaBoost, and Neural Network. However, among the methods they propose, the Selection and Classification of Features method, the best, has not been studied. Therefore, we propose to conduct a comparative study of this method to find a better method of selection and classification of features. This study compares Feature Selection and Classification methods to identify those suitable for IDS by comparing Recall, Precision, F1, Accuracy, and Area Under Curve (AUC) for each method. The results showed that the Gain Ratio Feature Selection method was better than other Feature Selection methods. In addition, this study shows that the AdaBoost classification method is more accurate than other classification methods.
Pengembangan Aplikasi Mobile Sistem Pengendalian Oven Tembakau Berbasis Internet of Things Jumawal; Sudianto, Aris; Zulkipli; Putra, Yupi Kuspandi
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.v8i2.31104

Abstract

Tobacco leaf drying is an important process in determining the final quality of tobacco as a raw material for the cigarette industry. This process is generally carried out using a tobacco oven which is influenced by two main factors, namely temperature control and oven ventilation. If the temperature and ventilation settings are wrong, it can reduce the quality of the tobacco leaves produced. To overcome this, this study developed a tobacco oven control system based on the Internet of Things (IoT) which is integrated with a mobile application. This system is designed to monitor and regulate the temperature and open and close the oven ventilation automatically. The DHT11 temperature sensor is used to read the temperature in the oven, while the DC Servo motor is used to control the ventilation. All sensor data is displayed in real-time via an Android application connected to the esp8266 Ethernet module to the Arduino Uno microcontroller. The test results show that the system can run well, where the oven temperature and ventilation control work according to the specified threshold values. In addition, the oven owner can easily monitor the condition of the oven via a mobile application, so that the tobacco leaf drying process becomes more efficient and of higher quality.
Penerapan Algoritma Apriori Dalam Mengidentifikasi Pola Perilaku Belanja Konsumen Muliawan Nur, Amri; Bahtiar, Hariman; Suhartini; Fathurrahman, Imam; Muzarrofah, Liana Rozita
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.v8i2.31105

Abstract

The advancement of digital technology has brought significant changes to data management in the commercial sector, especially in retail stores. This study aims to apply the Apriori algorithm to identify patterns of consumer shopping behavior at D&D Mart, Jerowaru District, East Lombok, Indonesia. Data were collected through observation and interviews with customers. Data processing was conducted using Google Colaboratory to obtain visualizations and calculate association rules. The results show that the combination of coffee, detergent, and sugar products has a support value of 31% and a confidence level of 100%, indicating a very strong purchasing pattern for these products to occur together. This finding provides valuable insights for store owners in developing marketing strategies, such as product placement for items frequently purchased together, bundled promotional offers, and more optimal stock management. Overall, this study demonstrates that the Apriori algorithm is effective for analyzing sales transaction data in retail stores and can support data-driven business decision-making to enhance operational effectiveness and customer satisfaction.
Klasifikasi Motif Batik Nusantara Menggunakan Vision Transformer (ViT) Berbasis Deep Learning Fathurrahman, Imam; Djamaluddin, Muhammad; Amri, Zaenul; Wathani, M. Nurul
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.v8i2.31108

Abstract

Batik is a cultural heritage of Indonesia that reflects local philosophies and identities through its diverse motifs. In the digital era, automatic classification of batik patterns plays a crucial role in cultural preservation, education, and commercialization. This study aims to develop a batik motif classification system using Vision Transformer (ViT), a deep learning architecture based on self-attention capable of capturing global spatial relationships in images. The dataset comprises 800 images spanning 20 batik motif classes from various regions, divided into training and testing subsets. The ViT model was fine-tuned using pretrained weights from ImageNet-21k, with standard preprocessing and data augmentation applied to the training set. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and prediction visualization. Results indicate that ViT achieved an overall accuracy of 96%, with most classes recording F1-scores above 0.90. Evaluation on unseen batik images demonstrated excellent generalization capability, achieving 99.94% confidence in prediction. These findings suggest that ViT is an effective and efficient architecture for batik motif classification and offers valuable contributions to cultural preservation through artificial intelligence.
Penerapan Kecerdasan Buatan Untuk Menentukan Gejala Utama Polycystic Ovary Syndrom (PCOS) Berdasarkan Data Klinis Pasien Permana, Baiq Andriska Candra; Zulkipli; Wasil, Muhammad; Harianto
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.v8i2.31123

Abstract

Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder among women of reproductive age, often leading to menstrual irregularities, infertility, and metabolic issues. Diagnosing PCOS remains challenging due to the wide range of symptoms and varying patient responses to treatment. Therefore, this study aims to apply artificial intelligence (AI) to identify key symptoms contributing to PCOS based on patients' clinical data.This study employs a machine learning approach, with the Support Vector Machine (SVM) algorithm as the primary method for classifying patients with and without PCOS. The data used was sourced from patient medical records, which included clinical data parameters obtained from the Kaggle website, with a total of 541 patient data samples. The research stages include data collection and preprocessing, selection of main features using feature selection technique, model training with SVM algorithm. The AI model developed produces 10 main features that affect the diagnosis with an accuracy value of 90.74% which shows that the model has the ability to classify PCOS and non-PCOS sufferers. In addition, the matrix shows a balance between the matrix values for precision 87.5%, recall 82.35% and F1 score 84.85%. The results of this study are expected to contribute to the medical field, especially in supporting faster and more accurate early diagnosis and personalization of PCOS treatment.
Pengembangan Model AI Menggunakan Algoritma Intensity Of Character (IoC) dan Reduced Support Vector Machine (RSVM) untuk Transliterasi Citra Aksara Sasak Samsu, Lalu Muhammad; Hidayat, Muh.Adrian Juniarta; Bagja, Amir; Saiful, Muhammad
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.v8i2.31124

Abstract

The limited human resources who are able to read Sasak script palm leaves are the main motivation for developing a transliteration tool for Sasak script images on palm leaves. By using the Reduced Support Vector Machine or RSVM algorithm as one of the classification methods, transliteration efforts can be facilitated with maximum results. The principle of the RSVM method in classifying objects by separating two different classes using a hyperplane has been proven to be able to produce maximum accuracy performance in this study. The research data in the form of Sasak script images resulting from the palm leaf image segmentation process that has been divided into 18 classes. The feature extraction algorithm used is Intensity of Character (IoC) with window sizes of 3x3, 4x4, and 5x5 and 3-Fold, 5-Fold, 7-Fold data Imbalance. The test results at the RSVM classification stage using the Linear Kernel, Polynomial Kernel,  Radial Basic Function (RBF) and One against One modeling on the 18 classes tested, where each class contains 20 handwritten Jejawen Sasak script image data on palm leaves, were recorded to produce the highest accuracy, which was 93.6%.
Pengembangan Media Pembelajaran Android Pada Mata Pelajaran Informatika Untuk Siswa SMA Kelas X Wirasasmita, Rasyid Hardi; Uska, Muhammad Zamroni; Kholisho, Yosi Nur; Ulfa, Delvia Samara; Hamdani, Fahri
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.v8i2.31282

Abstract

The learning media used in schools are still classified as conventional and have not utilised technology optimally. One solution that can be offered is the development of Android-based learning media as an innovative alternative in supporting the learning process. This research aims to develop Android-based informatics learning media, measure its feasibility level based on the assessment of media experts and material experts, and evaluate user responses. The method used is Research and Development (R&D) with the ADDIE development model, which includes the stages of Analysis, Design, Development, Implementation, and Evaluation. The subjects of this study consisted of 27 class X students at SMAN 1 Sukamulia. The results showed that the learning media developed obtained a feasibility score of 90.7% from media experts and 90% from material experts, both of which were included in the very feasible category. Meanwhile, the user response to the application reached 94% with a very high category. These findings indicate that the developed Android-based learning media is not only feasible to use, but also receives positive acceptance from students. The academic contribution of this research lies in the development of digital media that is contextual and relevant to 21st century learning needs. Therefore, this media is recommended to be implemented more widely as well as being the basis for further development in the study of technology-based learning.

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