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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 431 Documents
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Pengembangan Sistem Pakar untuk Skrining Awal Penderita Penyakit Tuberkulosis Menggunakan Forward Chaining Sidqiyah, Elis Dhia; Hindayati, Mustafidah; Fitriani, Maulida Ayu; Hamka, Muhammad
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8621

Abstract

Tuberkulosis (TB) merupakan penyakit menular yang menjadi tantangan besar bagi kesehatan masyarakat di Indonesia, dengan banyak kasus yang belum terdeteksi. Berdasarkan data dari Sistem Informasi Tuberkulosis (SITB) per 2 Oktober 2023, sekitar 36% dari total kasus TB belum terlaporkan, yang berpotensi menjadi sumber penularan di masyarakat. Banyak masyarakat yang kurang memahami gejala TB, sehingga tidak menyadari pentingnya melakukan deteksi dini dan sering kali terlambat dalam mencari penanganan yang tepat. Kriteria yang diperlukan untuk skrining TB meliputi gejala seperti batuk berkepanjangan, demam, berkeringat pada malam hari, penurunan berat badan, sesak napas, dan pembesaran kelenjar getah bening. Penelitian ini bertujuan untuk mengembangkan sistem pakar berbasis web yang dapat digunakan untuk skrining awal TB. Metode yang diterapkan dalam penelitian adalah forward chaining, menggunakan aturan logika IF-THEN untuk menentukan hasil berdasarkan gejala yang dimasukkan oleh pengguna. Hasil pengujian kesesuaian aturan menunjukkan bahwa semua aturan yang diterapkan dalam sistem dapat menghasilkan kesimpulan yang tepat berdasarkan kombinasi gejala yang dilaporkan. Pengujian ini dilakukan dengan menggunakan metode berbasis kasus uji, di mana setiap kombinasi gejala diuji untuk memastikan bahwa sistem memberikan keluaran yang sesuai. Selain itu, sistem ini dilengkapi dengan antarmuka yang intuitif, sehingga masyarakat dapat dengan mudah melakukan skrining awal TB. Pengembangan sistem pakar ini diharapkan dapat memberikan kontribusi yang signifikan dalam pengendalian TB di Indonesia.
Performance Improvement of Machine Learning Algorithm using PCA on IoV Attack Putra Hartanto, Octaviano Ryan Eka; Ghozi, Wildanil; Rafrastara, Fauzi Adi; Paramita, Cinantya
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8064

Abstract

In the transportation industry, the Internet of Vehicles (IoV) is an advancement of the Internet of Things (IoT), allowing automobiles to connect to networks to provide a range of features. This connectivity transforms traditional vehicles into intelligent systems, fostering innovations like autonomous driving and traffic optimization. However, this increased connectivity exposes IoV to cybersecurity threats, particularly because the networks utilized are often public and lack robust security measures. Cyberattacks targeting IoV can involve data packet modification, traffic flooding, or spoofing, potentially disabling critical vehicle components, compromising passenger safety, and increasing the risk of accidents. Consequently, accurate and efficient attack detection systems are essential to counter these threats and ensure IoV security. This study leverages the CICIoV2024 dataset and applies Principal Component Analysis (PCA) to enhance computational efficiency in detecting IoV attacks. The algorithms employed in this research include Random Forest, AdaBoost, Logistic Regression, and Deep Neural Networks. Experimental results demonstrate that implementing PCA significantly improves computational efficiency across all algorithms while maintaining consistent accuracy and F1-Score, highlighting its effectiveness in securing IoV systems. 
Optimizing Road Safety with MobileNet-Based Classification of Over-dimensioned Trucks Arifuddin, Nurul Afifah; Capri, Hary; Setiawan, Deni; Amalia, Rifka Dwi; Gusti, Kharisma Wiati
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8239

Abstract

This study aims to automatically detect overdimension trucks using a lightweight and efficient deep learning model based on MobileNet. Overdimension trucks pose serious threats to road infrastructure, traffic safety, and contribute to increased economic costs due to road damage and congestion. The developed model utilizes MobileNet as a feature extractor without the standard fully connected layers, and is equipped with additional layers including Flatten, Batch Normalization, Dense with Leaky ReLU activation, and Dropout to enhance training stability and prevent overfitting. The dataset consists of two classes—normal trucks and overdimension trucks—with images sized 128×128 pixels, collected from internet sources and field photos. The training process employs binary crossentropy loss, the Adam optimizer with an initial learning rate of 0.0001, and an Early Stopping mechanism. Fine-tuning is performed by unfreezing layers from the 100th layer upward and lowering the learning rate to 0.00001. Evaluation results show an accuracy of 97.92%, with consistent loss and accuracy visualization, demonstrating the model's capability in classifying overdimension trucks to support automatic traffic monitoring systems. This model has the potential to be implemented in toll gate systems to automatically deny access to overdimension vehicles. Furthermore, integration with roadside CCTV allows real-time monitoring of vehicle dimension violations across various traffic checkpoints.
Klasterisasi Pola Penjualan Menu Makanan pada Rumah Makan menggunakan Metode K-Means Clustering Rusvinasari, Dian; Annisa, Lolanda Hamim
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8511

Abstract

The culinary industry is one of the fastest-growing business sectors in Indonesia, as evidenced by the increasing number of restaurants emerging across the country. This intense competition demands that each restaurant develop effective strategies to attract customers and enhance profitability. One such strategy is analyzing menu sales patterns. This study contributes to the field of informatics, particularly in the application of data mining and machine learning techniques to support strategic decision-making in the culinary sector. The K-Means Clustering method was employed to analyze 12,404 daily sales transactions from a restaurant. The sales data were collected and analyzed to identify groups of menu items with similar sales characteristics. The research stages included data preparation, processing using RapidMiner and Microsoft Power BI, and analysis of the Clustering results. The quality of the clusters was evaluated using the Davies-Bouldin Index, which yielded a score of 0.354, indicating good separation and compactness between clusters. The analysis revealed that the optimal number of clusters is five, representing categories of highly popular, moderately popular, and less popular menu items. The most popular items include Chicken Rice, Tea, Catfish Rice, Chicken, and Potato Fritter. Meanwhile, the least preferred menu items include Minced Meat, Beef Tendon Rice, Jackfruit Curry, Beef Tendon, and Tempe. This Clustering provides valuable insights for restaurants to focus on developing popular menu items and consider improving or removing those that are less favored. The implementation of these Clustering results supports strategic decisions related to ingredient inventory management, menu promotion, and improvements in operational efficiency and customer satisfaction.
Manfaat Blockchain pada Sistem Registrasi Tanah: Systematic Literature Review Suratmanto, Bekti; Emanuel, Andi Wahyu Raharjo
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.6505

Abstract

The robust economic growth in Indonesia in the second quarter of 2023 indicates a projected population increase, leading to higher population density and driving the conversion of agricultural land. Land ownership has become a valuable source of capital, triggering intense competition. Land properties have emerged as valuable assets, emphasizing the importance of land registration as a process for recording ownership rights. The current centralized and manual land registration system faces challenges such as record duplications, unauthorized document reforms, and excessive departmental involvement. These weaknesses can result in pending cases, slow verification processes, and document manipulation.Digital transformation with blockchain technology is proposed as a solution for transparent, efficient, and legally certain land administration. This technology offers decentralized storage, resilience to changes, and peer-to-peer verification in transaction recording. While some countries have successfully implemented blockchain, others have faced failures due to environmental factors, state intervention, socio-political readiness, and institutional factors. In Indonesia, land conflicts have escalated, recording 562 cases from 1988 to July 2023. The lack of capacity and competence in local government human resources, coupled with suboptimal administration, complicates handling and hampers regional revenue. This research proposes a land registration framework with the implementation of blockchain as a solution to land administration issues in Indonesia.
Sistem Otomatisasi Pengendalian Perangkat Listrik Dan Penguncian Pintu Ruangan Menggunakan Komunikasi Bluetooth Rendah Energi Busran, Busran; Ilahi, Riski Pratama; Putra, Eko Kurniawanto; Warman, Indra; Mandarani, Putri
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.7826

Abstract

This research aims to design and implement an automation system for controlling electrical devices and door locking based on an ESP32 microcontroller with support for Bluetooth Low Energy (BLE) technology. The system was realized in prototype form using the iTag device as a communication medium between the user and the ESP32. The main aim of this system is to increase energy efficiency and room security through automatic control of electrical devices and a door locking mechanism when the room is not in use. Registered iTag devices will be connected to the ESP32 through the BLE pairing process, enabling detection of the user's presence within a 3 meter radius. When a user is detected, the system automatically activates the electrical device and unlocks the door; instead, the device will be disabled and the door locked when the user leaves the area. System testing was carried out to evaluate the BLE signal range and system response to various environmental conditions. Test results show that the system is able to detect iTag devices up to a maximum distance of 12 meters without physical obstacles and 9 meters with wall obstacles. 
Perbandingan Metode KNN dan Naïve Bayes dalam Deteksi Tingkat Stres Berdasarkan Ekspresi Wajah Alamsyah, Malik Fajar; Wijaya, Ardi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8513

Abstract

Stress is a feeling in which a person feels under pressure, overwhelmed, and has difficulty in dealing with a problem. Stress can be caused by various factors, such as academic pressure, work, personal problems, or social environment. If not addressed immediately, stress can have adverse effects on an individual's health, such as causing high blood pressure, heart disease, sleep disturbances, and a decreased immune system, which makes a person more vulnerable to various diseases. Therefore, monitoring stress levels is very important to prevent more serious negative impacts. Generally, stress detection is done through consultation with a psychologist, but this method has a subjective nature and requires a lot of time and money. Therefore, this research develops a computer vision-based stress detection system using OpenCV and Dlib, with K-Nearest Neighbors and Naïve Bayes algorithms. The data of 500 samples is divided into 80% training data and 20% test data. Features were extracted, and stress was classified into three levels: low, medium and high. Evaluation using k-fold cross-validation (n_split=10, random_state=42) based on accuracy, precision, recall, and F1-score. The results showed that K-Nearest Neighbors with k=5 excelled with 74% accuracy, 73% precision, 73% recall, and 73% F1-score. Meanwhile, Naïve Bayes only achieved 52% accuracy, 51% precision, 48% recall, and 41% F1-score. This shows that KNN is more effective in stress level classification. However, the accuracy of the model is still limited due to the small amount of training data. Parameter optimization and dataset addition are required to improve the overall system performance.
Analisis Data Warehouse Pada Perpustakaan Universitas XYZ Untuk Efisiensi Manajemen Menggunakan Metode Kimball 4 Langkah Uddin, Badie; Wijayadi, Eneng Mila Lestari; Maharani, Aprilia zahra; Barren, Kailal Wafa Auladal
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.7323

Abstract

The XYZ University Library faces recurring book losses that affect the efficiency of library collection management. This study aims to develop a data-driven analysis system to identify loss patterns and support decision-making. The proposed solution uses a Data Warehouse approach with the Kimball Four-Step method and the ETL (Extract, Transform, Load) process. This methodology includes business process selection, grain declaration, dimension identification, and fact determination. Library transaction data from 2022 to 2024 was extracted, transformed, and loaded into a MySQL-based warehouse and visualized using Power BI. The analysis revealed that popular book categories, such as novels, were the most frequently lost. The visualization also enabled trend analysis based on time, book types, and user segments. The findings highlight a significant decline in loss cases, from 27 in 2022–2023 to 13 in 2023–2024, suggesting improved monitoring and management. The study demonstrates that the Data Warehouse approach effectively supports historical data analysis and provides accurate insights for sustainable library policy formulation.
HARMONI: Home Automation Module Berbasis Internet of Things dan Deep Learning Juniyanto, Muhammad Ma'sum; Aji, Bernadus Anggo Seno; Kamali, Muhammad Adib
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.7109

Abstract

Alat listrik yang tidak dimatikan saat tidak digunakan seringkali menyebabkan terjadinya korsleting listrik yang berakibat bencana kebakaran. Selain itu, hal ini juga berpotensi dalam pemborosan penggunaan energi listrik. Orang-orang menyambungkan alat listrik langsung pada sumber listrik melalui stop kontak atau melalui jalur listrik kemudian dihubungkan dengan sakelar, dalam pengoperasiannya. Ini cukup efektif, namun, seringkali dialami kelalaian dalam mematikan atau mencabutnya, sehingga berpotensi membahayakan. Modul otomasi rumah berbasis IoT dan Deep Learning dibuat untuk melakukan digitalisasi dan otomasi sakelar. Terdiri dari mikrokontroler ESP32-S3 dan ESP32 sebagai pengendali sistem, modul relay sebagai sakelar otomatis, modul kamera untuk mendeteksi orang, integrasi Google Home dengan platform Sinric.Pro, website Mowny dengan integrasi protokol HTTPS. Mikrokontroler, modul, relay disusun pada papan-sirkuit-cetak. Website Mowny untuk mengontrol saklar dan monitoring ruangan. Pendeteksian keberadaan orang menggunakan YOLO sebagai pemicu otomasi sakelar. Model deteksi dimuat melalui API untuk diakses pada website. Pengujian sistem meliputi empat skenario untuk menyala-matikan sakelar secara digital dan otomatis, menghasilkan waktu respon sebagai berikut (dalam satuan detik): Google Home (±3,468), Google Assistant (±4,348), website Mowny (±1,042), dan otomasi deteksi objek (±19,375). Modul otomasi ini dapat mengontrol alat listrik dari secara digital dan otomatis, yang berdampak pada kemudahan pengoperasian sakelar ketika mengalami kelalaian mematikan alat listrik
Analisis Pengaruh Luas Area Pertanian Terhadap Prediksi Hasil Pertanian di Kebumen Menggunakan Metode Regresi Linier Ikhsanuddin, Rohmatulloh Muhamad; Rusvinasari, Dian
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8471

Abstract

Kebumen as an agricultural area whose people mostly play a role in agriculture has an important role in the southern part of Java. The size of the agricultural area will affect agricultural results, especially rice yields. Large agricultural areas will be beneficial for the community in their role as well as food self-sufficiency programs so that dependence on foreign agricultural production is reduced. However, agricultural conditions have not been managed maximally. It is hoped that agricultural yield predictions can help the government in making decisions on the management of agricultural areas in Kebumen. The linear regression method is one of the methods in data mining for data forecasting that relies on historical data so it requires agricultural yield data for the period from 2013 to 2019. The prediction process uses data on the area of the harvest which will influence the harvest in tons. Previous research shows that the linear regression method produces very small error values so it is very suitable for use in prediction cases. The aim of this research is to determine the predicted influence of harvested land area on the amount of harvest in Kebumen as analysis material. The stages in the linear regression method are determining the intercept and coefficient values with the a value of -317.231 and the b value of 6.0123, determining the regression equation to determine predictions, calculating the difference in predicted data, calculating the error value using MAPE with a result of 5,60%.

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