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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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Prediksi Kesehatan Mental Remaja Berdasarkan Faktor Lingkungan Sekolah Menggunakan Machine Learning Rahma, Mutiara; Fikry, Muhammad; Afrillia, Yesy
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.v10i2.8556

Abstract

Adolescent mental health is a crucial aspect that affects academic performance, social relationships, and overall well-being. The school environment is one of the primary factors influencing adolescents' mental conditions. This study aims to predict adolescent mental health levels based on school environmental factors using the Random Forest algorithm. Data were collected from 229 adolescents in Lhokseumawe and categorized into four classes of mental health conditions. The research methodology includes data preprocessing, model training, and performance evaluation using accuracy and other relevant metrics. The results show that the model achieved an accuracy of 80.43%, with the highest F1-score of 0.90 in the category indicating no mental health issues. Feature importance analysis identified loneliness, feelings of worthlessness, academic pressure, and home-related stress as the most influential factors in the predictions. While the model effectively classified most data, some misclassifications occurred at certain mental health levels. Thus, the Random Forest model proves to be an effective predictive tool for detecting potential adolescent mental health issues. The findings of this study can serve as a reference for educational institutions in designing more targeted intervention strategies to support adolescent mental well-being.
Prediksi Stok Barang di Toko Eko Helm Menggunakan Metode Time series Analysis Fadillah, Betran Dwi; Hendrastuty, Nirwana
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.v10i2.8584

Abstract

Eko Helm Store located in South Lampung, faces challenges in managing helmet inventory, particularly in determining the optimal stock levels for two categories: affordable and premium helmets. This study aims to forecast helmet stock requirements for the year 2024 using the ARIMA method. Weekly sales data from January to December 2024 were analyzed through stationarity testing using the Augmented Dickey-Fuller (ADF) test and differencing, followed by parameter identification based on ACF and PACF plots. The best-fitting models were identified as ARIMA(2,1,0) for premium helmets, with a Mean Squared Error (MSE) of 24.5101 and an Akaike Information Criterion (AIC) of 249.4062, and ARIMA(1,1,0) for affordable helmets, with an MSE of 32.6102 and an AIC of 250.5381. ARIMA was selected due to its ability to capture trends and seasonal fluctuations more effectively than methods such as moving average or exponential smoothing. The forecasting results estimate a stock requirement of 112 units for affordable helmets and 64 units for premium helmets over the next four weeks. The ARIMA model is integrated into an automated forecasting system that runs scheduled scripts without manual intervention. This system supports timely and precise inventory procurement decisions.
Perancangan Ulang Desain UI/UX Aplikasi I-Nusaplant Dengan Metode Design Thinking dan A/B Testing Saputra, M Ari; Khaira, Ulfa; Saputra, Edi
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.v10i2.8236

Abstract

I-Nusaplant is a mobile-based application that can detect types of medicinal plants. The I-Nusaplant application was designed by Information Systems students using Android-based leaf images. This application aims to help the community in detecting medicinal plants. The ease of detecting medicinal plants using the application must also be supported by a good appearance and user experience. I-Nusaplant has 3 menus in it, namely Home, Detection, and About. The I-Nusaplant application has shortcomings after conducting interviews with medicinal plant experts, medicinal plant enthusiasts, and general users. The majority of respondents chose the I-Nusaplant application to be redesigned for several reasons related to user experience in running the application. Ease of obtaining information, time to move around each menu, and some features that users need. In doing a redesign, it is necessary to have an in-depth design using UI/UX. The design process in solving problems and generating user needs, researchers use the Design Thinking method to generate ideas to solve user problems. At the analysis stage, problem analysis is carried out, finding solutions to user problems, analyzing user needs. In the design stage, UI/UX design is produced, namely architecture information, user flow, and interface design. UI/UX design will be tested using the Maze tool. After that, compare the results of the old and new I-Nusaplant interface designs using the A/B Testing method. This method aims to see the performance of the old and new application designs. Our A/B testing revealed that the new design, while more complex, is just as efficient as the old one, both scoring 99 this shows that the new design is easy to use by users despite its different design.
Klasifikasi Pertanyaan Quora Menggunakan Metode Keyword-based dan Analisis Sentimen dengan ComplementNB Adiuntoro, Alwan; Hendrawan, Aria
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.v10i2.7965

Abstract

Text classification is a fundamental task in Natural Language Processing (NLP) that supports the categorization of data based on predefined labels. This study aims to evaluate the effectiveness of keyword-based labeling and sentiment analysis methods for text classification using the Quora Questions dataset. The dataset comprises 16,921 samples with imbalanced class distribution, where the opinion category dominates, while the hypothetical category is a minority class. The labeling process utilized a keyword-based approach for the fact and hypothetical categories, while the opinion category was labeled using sentiment analysis with the Vader Lexicon library. TF-IDF was employed as the feature representation method, with two approaches explored: n-gram range tuning (1–3) and without tuning. ComplementNB, designed for handling imbalanced datasets, was utilized for classification, with a training-test split of 70:30. The results show that the approach without n-gram tuning achieved the highest accuracy of 93.89%, with zero variance in cross-validation. Evaluation revealed that ComplementNB effectively handles class imbalance, as demonstrated by high precision and recall in the minority class. This study demonstrates that a simple approach combining keyword-based labeling and sentiment analysis can be effectively implemented for category-based text classification tasks, particularly in platforms like Quora. These findings are relevant for similar applications requiring real-time text classification with minimal complexity.
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.

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