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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 28 Documents
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Search results for , issue "Vol 10, No 2 (2025)" : 28 Documents clear
Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin Menggunakan Correlation-Based Feature Selection (CFS) dengan Backpropagation Neural Network (BPNN) Ma'rifah, Laila Alfi; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
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.v10i2.8616

Abstract

Abstract – In forensic anthropology, sex identification is the initial step in individual identification, with a probability level of 50%, influencing subsequent examinations such as age and height estimation. The skull is the second-best choice after the pelvis for determining sex, with an accuracy of up to 90%. Morphological and metric methods are less reliable due to the high variability of skulls, while DNA analysis is ineffective on burned or damaged bones. Therefore, this study applies Correlation-Based Feature Selection (CFS) with a Backpropagation Neural Network (BPNN) to improve classification accuracy. The dataset used originates from Dr. William Howells, consisting of 2,524 skull samples with 85 variables. CFS was applied with two thresholds, 0.1 and 0.01, and the division of training data and test data using k-fold cross validation with k=10. The BPNN parameters included learning rates of 0.01 and 0.001, along with three different architectures based on the number of input neurons. The results indicate that CFS improved accuracy from 92.06% to 93.25% under the CFS threshold of 0.01, with a learning rate of 0.001 and a BPNN architecture of [72; 95; 1]. This study confirms that combining CFS and BPNN enhances sex classification accuracy based on skull bones.Abstrak – Pada antropologi forensik, identifikasi jenis kelamin adalah langkah awal dalam mengidentifikasi individu dengan tingkat probabilitas 50%, yang berpengaruh pada pemeriksaan lain seperti perkiraan usia dan tinggi badan. Tulang tengkorak menjadi pilihan terbaik kedua setelah tulang panggul dalam menentukan jenis kelamin dengan akurasi hingga 90%. Metode morfologi dan metrik kurang dapat diandalkan karena variabilitas tengkorak yang tinggi, sementara analisis DNA tidak efektif pada tulang yang terbakar atau rusak. Oleh karena itu, penelitian ini menerapkan Correlation-Based Feature Selection (CFS) dengan Backpropagation Neural Network (BPNN) untuk meningkatkan akurasi klasifikasi. Dataset yang digunakan berasal dari Dr. William Howells, terdiri dari 2.524 sampel tengkorak dengan 85 variabel. Pada CFS digunakan dua ambang batas yaitu 0,1 dan 0,01, serta pembagian data latih dan uji data menggunakan k-fold cross validation dengan k=10. Parameter BPNN yang digunakan meliputi learning rate (0,01 dan 0,001) serta tiga arsitektur berbeda sesuai dengan jumlah neuron input. Hasil menunjukkan bahwa CFS meningkatkan akurasi dari 92,06% menjadi 93,25% pada konfigurasi ambang batas CFS 0,01 dengan learning rate 0,001 dan arsitektur BPNN [72; 95; 1]. Penelitian ini menunjukkan bahwa kombinasi CFS dan BPNN dapat meningkatkan akurasi klasifikasi jenis kelamin berdasarkan tulang tengkorak.
Rancang Bangun Sistem Perpustakaan Web Universitas Esa Unggul dengan Metode Scrum untuk Pengelolaan Digital Azzahra, Fayza; Dzikrya, Kaysa; Prabowo, Ary
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.v10i2.8207

Abstract

Conventional library systems that are still manual-based often face various obstacles, such as delays in the transaction process, the risk of recording errors, and low efficiency in collection management. This research aims to design and build an integrated web library system at Esa Unggul University by applying the Object Oriented Programming (OOP) approach and Scrum method. The development process is carried out iteratively through the stages of Sprint Planning, Execution, Review, and Retrospective. The system was developed using Python programming language with Flask framework and MySQL database. The main features include book data management, members, loan and return transactions, automatic notifications, and time-based fine calculations. Evaluation was conducted using the Black Box Testing method on 35 scenarios, including input validation, transaction processing, and system resilience to extreme conditions. The test results showed a 100% success rate and a 60% increase in transaction efficiency compared to the manual system. End-user validation showed that the system has a responsive interface, easy to use, and supports digital library management. This research contributes to the digital transformation of libraries and opens up opportunities for development towards mobile platforms and data analytics.
A Systematic Review: Aggregation Methods for Production Processes in Supply Chain Management Cahya Pratama, Yudha Herlambang; Naristi, Keysa; Arifianti, Clariza
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.v10i2.8248

Abstract

In the modern era, technology has significantly changed the way businesses operate, leading to the need for faster and more efficient processes, new technologies like robotics, machine learning, and artificial intelligence. This has enabled organizations to increase operational efficiency, increase customer experience, and remain competitive in the rapidly changing business environment. One strategy for implementing technology is through ideal generation planning, which involves planning the entire production process and operational planning. This approach helps companies optimize resources, reduce costs, and increase efficiency. In business management, aggregate planning is crucial for integrating various business functions, such as sales, production, and financial management, to achieve a company's full potential. However, implementation can be challenging due to various challenges, such as high production volumes and low volume. This study aims to explore the implementation of aggregate planning in business management, focusing on the impact of technology on efficiency and effectiveness of production planning. Based on the results of the analysis in the journal, it was found that production planning is important at the MSME business unit level. Chase Strategy is the right choice for production planning at the MSME business unit level. Information technology integration has proven critical to improving aggregate planning efficiency, although interdepartmental coordination challenges are a major obstacle. Therefore, it is necessary to implement centralized information technology that is able to unite the needs of each department to achieve overall business process efficiency and effectiveness.
Rancang Bangun Aplikasi Dashboard Laporan Pengaduan Kendala Sistem Internal pada PT. Telkom Indonesia Witel Purwokerto berbasis Website menggunakan Metode Prototype Fahrezi, Raihan Ahmad; Prasetyo, Novian Adi
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.v10i2.6767

Abstract

The development of Internet technology has increased the number of internet users and affected people's lives at large. As a telecommunications company, PT. Telkom Indonesia Tbk. (Telkom) provides Indihome services, has an increasing number of customers. To meet customer needs, Telkom gave responsibility to the Access Service Operation (ASO) unit at Witel Purwokerto to supervise and control Indihome services. However, the recording of complaints by the Access Service Operation (ASO) unit is still done manually, resulting in duplicate orders and recording imperfections. Based on the existing problems, the author designed a complaint report system that uses methods such as the System Development Life Cycle (SDLC) Prototype model to overcome these problems and uses black box and white box testing methods. The purpose of this study is to build a system that will facilitate Witel Purwokerto's Access Service Operation (ASO) unit in accommodating data on complaint reports made by whistleblowers. The system is built using PHP programming language with the help of Laravel framework, and as database management using MySQL. The test results of the system with the black box and white box methods showed 99.74% success for black box testing, and the white box test results showed 100% success. It can be concluded that every function on the dashboard website reports complaints of internal system constraints in this study runs well.
KaGeo: Aplikasi Geologi Berbasis Web Untuk Manajemen Data Koleksi di Museum Geologi Bandung Maulani, Muhammad Ruslan; Rahmatuloh, Marwanto; Mubassiran, Mubassiran; Choldun R, Muhammad Ibnu; Yanuar, Amri; Fayaqun, Reza; Wibowo, Unggul Prasetyo
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.v10i2.8237

Abstract

Pengelolaan data koleksi geologi di Museum Geologi Bandung menghadapi tantangan yang cukup besar, seperti keterbatasan sistem penyimpanan data yang terpadu, sulitnya aksesibilitas informasi koleksi, dan potensi kehilangan data akibat pengelolaan secara manual. Penelitian ini bertujuan untuk merancang dan mengembangkan KaGeo, yaitu aplikasi berbasis web yang dirancang untuk mengelola data koleksi geologi secara efisien dan terpadu. Metodologi yang digunakan adalah System Development Life Cycle (SDLC), yang meliputi tahapan perencanaan, analisis kebutuhan, perancangan, pengembangan, pengujian, dan implementasi. Pengembangan aplikasi dilakukan dengan menggunakan framework Laravel 11 dan Filament 3 untuk backend serta Livewire untuk front end. Data koleksi dikelola dalam database relasional dengan optimasi struktur untuk mendukung pencarian dan klasifikasi berbasis metadata. Validasi sistem dilakukan melalui pengujian black box dan pengumpulan umpan balik dari staf museum. Hasil penelitian menunjukkan bahwa KaGeo meningkatkan efisiensi pencatatan dan pencarian data koleksi hingga 75% dibandingkan dengan metode sebelumnya. Aplikasi ini juga mendukung pelaporan dan visualisasi data secara otomatis, sehingga memudahkan proses pengelolaan koleksi. Selain itu, umpan balik pengguna menunjukkan peningkatan kepuasan terhadap aksesibilitas dan antarmuka aplikasi. Studi ini menyimpulkan bahwa KaGeo dapat menjadi solusi inovatif untuk mengelola data koleksi geologi di Museum Geologi Bandung, dengan potensi untuk diadopsi oleh lembaga sejenis. Rekomendasi untuk pengembangan lebih lanjut meliputi integrasi teknologi berbasis IoT untuk melacak lokasi fisik koleksi dan penerapan algoritma pencarian berbasis kecerdasan buatan.
SCANOCULAR: Application for Early Detection of Eye Diseases Using AI and Blockchain Technology Pratomo, Dinar Nugroho; Alfian, Ganjar; Putri, Divi Galih Prasetyo; Kusnady, Rasyid; Pinandhita, Pudyasta Satria; Yusuf, Muhammad Abyan Farras; Dharmawan, Edeline Felicia; Zhafarizza, Ghifari Nafhan 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.v10i2.8014

Abstract

Eye diseases such as cataracts, glaucoma, and diabetic retinopathy affect approximately 2.2 billion people globally, with 1 billion cases being preventable. In Indonesia, cataracts remain the leading cause of blindness. This research presents SCANOCULAR, a mobile application that integrates artificial intelligence (AI) and blockchain technology for early detection of eye diseases. The system utilizes a modified EfficientNetB4 Convolutional Neural Network (CNN) for analyzing eye images, achieving 95.50% accuracy, 95.92% precision, and 94.95% recall in cataract detection with an AUC of 0.9932. The blockchain implementation using Polygon Amoy platform ensures secure data transmission and storage while maintaining efficient transaction processing. Testing results demonstrate the system's capability in identifying various eye conditions while maintaining data integrity through blockchain verification. SCANOCULAR contributes to informatics by implementing a hybrid AI-blockchain architecture optimized for medical imaging applications, with a lightweight CNN model design that reduces computational requirements while maintaining diagnostic accuracy. This integration of technologies provides a potential solution for improving accessibility to eye disease screening and early intervention in Indonesia.
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.

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