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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 29 Documents
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Search results for , issue "Vol 10, No 3 (2025)" : 29 Documents clear
Pengembangan Media Pembelajaran Pengenalan Perangkat Keras Jaringang Komputer Berbasis Augmented Reality Setiawan, Muhamad Anjas; Widodo, Tri
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8253

Abstract

In Indonesia, many educators still apply conventional methods, namely by reading questions to students which are then answered using paper media, which are often considered less effective in attracting students' interest in learning. One of them is in the material on the introduction of computer network hardware. The purpose of this study is to create Augmented reality (AR)-based learning media for the introduction of computer network hardware in order to create a fun and non-boring learning process for students. The process of creating learning media is carried out by applying the Multimedia Development Life Cycle (MDLC) method. This study resulted in an application of computer network hardware learning media based on augmented reality. This application is equipped with learning materials, learning objectives, images and 3D objects. Based on the validation results, the application obtained a feasibility level of 86.12% from media experts, 84.45% from material experts, and 79.37% from user tests (students). These findings indicate that the application of AR not only increases visual appeal, but also supports conceptual understanding through immersive visual representations. The main contribution of this study lies in the systematic application of the MDLC framework in the development of AR learning media, as well as the integration of 3D objects designed according to pedagogical needs in the context of ICT education.
E-Assessment untuk Menentukan Tipe Gaya Belajar Menggunakan Algoritma Rule-Based Reasoning Latifah, Isnaenti Nur; Harani, Nisa Hanum
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8349

Abstract

Setiap manusia dilahirkan dengan perbedaan fisik, psikologis, genetik, dan eksternal yang signifikan yang mungkin mempengaruhi karakter individu. Perbedaan karakter ini terkadang diabaikan oleh individu, terutama selama proses pembelajaran, karena kemampuan siswa dalam menyerap pengetahuan pasti berbeda-beda. Akibatnya, diperlukan sistem untuk mengidentifikasi jenis gaya belajar siswa dan membantu guru dalam menghasilkan kualitas pengajaran yang mudah diterima oleh siswa. Penelitian ini menciptakan sistem e-assessment gaya belajar yang menggunakan algoritma penalaran berdasarkan aturan untuk menentukan gaya belajar siswa seperti visual, auditori, kinestetik, dan campuran. Teknik evaluasi melibatkan perhitungan setiap jawaban tergantung pada kategori gaya belajar, yang menghasilkan persentase untuk menunjukkan jenis pembelajaran yang berlaku. Sistem ini dibangun menggunakan model penelitian ADDIE, dengan VueJS di frontend dan PHP Laravel di backend. Pengujian dilakukan dengan menggunakan metode blackbox dan User Acceptance Testing (UAT). Hasil pengujian blackbox dengan alat Selenium IDE menunjukkan bahwa sistem bekerja sesuai dengan skenario yang ditentukan, dan UAT dengan 30 siswa memperoleh skor 86%, yang menunjukkan bahwa sistem ini dapat digunakan. Metode ini dirancang untuk membantu pengembangan cara belajar yang lebih tepat bagi siswa dan guru.
A Supervised Learning Model for Sentiment Analysis Based on Regional Dialects in Tourism-Related Issues Munandar, Tb Ai
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8627

Abstract

Indonesia has an exceptionally rich diversity of regional languages, one of which is the Bekasi dialect, often used in social media communication. The uniqueness of this dialect presents specific challenges in extracting public opinion, especially in text-based sentiment analysis. This study aims to develop a sentiment analysis framework that incorporates regional dialects from social media data and evaluate the effectiveness of various supervised learning algorithms. Data were collected from the Facebook group “Explore Bekasi Tourism,” totaling 1,257 posts and comments, which were filtered down to 1,000 relevant instances. A manual validation process was conducted by linguistic experts to convert non-standard terms and regional dialects into standardized Indonesian, followed by translation into English for annotation purposes. The analysis method involved preprocessing steps (tokenizing, case folding, stemming), feature weighting using TF-IDF, and sentiment classification using four algorithms: Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Tree. The evaluation results show that Naive Bayes achieved the best performance with an accuracy of 76%, followed by K-Nearest Neighbor (67.5%), SVM (65.5%), and Decision Tree (28%). These findings highlight the crucial role of expert judgment in processing dialect-based data to ensure accurate sentiment classification. The study recommends developing a broader annotated corpus of regional dialects and exploring deep learning methods in future research to enhance classification performance and generalizability.
Monk Skin Tone Classification: RMSprop vs Adam Optimizer in MobileNetV2 Aryaputra, Firman Naufal; Sari, Christy Atika; Rachmawanto, Eko Hari
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8886

Abstract

The lack of accurate and accessible skin tone classification systems poses significant challenges in personalized fashion recommendations and inclusive technology development. This study aims to develop a skin tone classification system utilizing the Monk Skin Tone (MST) scale through the implementation of Convolutional Neural Network with MobileNetV2 architecture enhanced by transfer learning techniques. The MST scale encompasses ten distinct categories providing comprehensive representation of human skin color diversity. The methodology leverages efficient MobileNetV2 architecture suitable for web deployment, transfer learning to enhance accuracy despite limited training data, and strategic dataset balancing. A dataset of 1,729 facial photographs representing the complete MST spectrum was utilized. Preprocessing involved scaling images to 224×224 pixels, normalization, and augmentation through various transformations to address class imbalance challenges. The dataset was partitioned using a 70:15:15 ratio for training, validation, and testing respectively. The system was implemented as a web platform called SkinToneAI that enables users to upload facial images for skin tone analysis and receive personalized clothing color recommendations. Evaluation demonstrated classification accuracy of 97.83% on the test dataset with a loss value of 0.1166 when using Adam optimizer, while RMSprop optimizer achieved better performance with 98.26% accuracy and 0.0548 loss value. The implemented web application successfully translates technical capabilities into practical fashion assistance. The system provides users with customized apparel color suggestions based on their identified skin tone category, effectively connecting advanced AI technology with everyday fashion needs.
Rancang Bangun Webgame Edukasi Sebagai Media Pembelajaran Bahasa Inggris Maritim Kurniawan, Fajar Sari; Sari, Dhesi Wulan; Purwanto, Purwanto
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.7931

Abstract

The phenomenon of online game addiction is increasing among students, including students at the Indonesian State Maritime Polytechnic (Polimarin). They tend to spend time to playing game which decrease their academic performance. Apart from that, students also face challenges in learning through traditional learning methods which tend to be less interesting and boring, especially when learning English. English language skills are an important aspect that must be mastered by shipping students, because this language is widely used in the international maritime world. To improve students' English skills, an interactive and fun maritime English learning web game was built that can increase their interest in learning English in a more interesting way. The development method used the waterfall model, which consisted of the stages of analysis, design, implementation, testing and maintenance. At the analysis stage, user needs were identified to understand the most relevant and interesting content for students. Then, the interface and game mechanisms were designed to be attractive and easy to access. The implementation was carried out using responsive web technology so that it can be accessed via computer or mobile devices. The features in the web game were designed to enrich vocabulary, sentence understanding and English communication skills, which were relevant to the needs of the maritime studies field. Web game-based learning media provided a good learning experience in improving the quality and effectiveness of the teaching and learning process. Web games can be an innovative solution in increasing student learning motivation and helping students understand the material in a fun and interactive way.Keywords: learning media; maritime English; webgame.
CesLA (Cegah Stunting Lewat Anemia): Deteksi Anemia Non-Invasif pada Remaja Putri Berbasis Citra Konjungtiva Ilmadina, Hepatika Zidny; Nisa, Juhrotun; Apriliani, Dyah; Anisa, Lulu Nadhiatun; Rakhmah, Firda Aulia
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8873

Abstract

Stunting is a chronic nutritional problem that will directly affect the quality of human resources in the future. One of the contributing factors to stunting is anemia during pregnancy, which often originates from adolescence. Early detection of anemia in women of reproductive age is a crucial preventive measure to reduce the risk of stunting. This study aims to develop an anemia classification model based on conjunctival images using a combination of MobileNetV2 architecture and Support Vector Machine (SVM), and to implement the model into a mobile application named CeSLA (Cegah Stunting Lewat Anemia). The model was built using a dataset of female conjunctival images annotated based on haemoglobin levels and visual characteristics of the conjunctiva. Evaluation results explain that the model achieved precision, recall, and f1-score values ranging from 0.91 to 0.92 for each class, with a macro average of 0.92, indicating accurate and balanced classification performance. The trained and evaluated model was then integrated into the CeSLA mobile application. This application allows users, particularly adolescent girls, to detect potential anemia non-invasively by scanning the lower eyelid using a smartphone camera. CeSLA is also equipped with educational features such as health articles and a detection history log. With this approach, CeSLA is expected to serve as an innovative solution that supports early, self-administered anemia detection and contributes to the national effort to prevent stunting.
Dinamika Opini Publik Terkait Quarter Life Crisis Pada Media Sosisal X Menggunakan Support Vector Machine Septyorini, Talitha Dwi; Umam, Khothibul; Handayani, Maya Rini
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8648

Abstract

This study aims to analyze the dynamics of public opinion related to quarter life crisis on platform X through a sentiment analysis approach based on machine learning Support Vector Machine (SVM) algorithm is used to classify positive and negative sentiments from text data. A total of 6.312 tweets were collected with the keyword “quarter life crisis” from January 2024 to January 2025. The data was then processed through the stages of text cleaning, tokenization, stopword removal, stemming, and lexicon-based sentiment labeling. The classification process is carried out using SVM with a data division of 80% training and 20% test. The results showed an accuracy of 81.57% with a sentiment distribution of 59.3% negative and 40.7% positive. Implementation was done on Google Colab platform with evaluation using confusion matrix and classification report. The fingdings prove the effectiveness of SVM in analyzing psychosocial phenomena on social media and provide an empirical basis for the development of digital data-based mental health interventions. The machine learning pipeline optimized in this study can be used as a reference for other studies in analyzing psychological phenomena on social media
Optimasi AdaBoost dan XGBoost untuk Klasifikasi Obesitas Menggunakan SMOTE Sukmawati, Cici Emilia; Pratama, Adi Rizky; Hikmayanti, Hanny; Juwita, Ayu Ratna
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8536

Abstract

Obesity is a condition in which a person's weight exceeds the normal limit due to excessive accumulation of fat tissue. Thus, obesity is considered a global public health challenge. This is evidenced by the latest data from the World Health Organization (WHO) in 2022, namely that 2.5 billion adults aged 18 years and over are overweight and 890 million of them are obese. Therefore, it is very important to accurately identify these risk factors in order to implement effective interventions in the prevention and management of obesity. However, in previous studies there has been no application of SMOTE with the AdaBoost and XGBoost algorithms, so this study aims to compare the performance of the AdaBoost and XGBoost algorithms with SMOTE. The stages of this research begin with problem identification, data collection, preprocessing and model evaluation and model comparison. This study also applies the SMOTE technique to balance unbalanced data. Based on the results of the research that has been carried out, it shows that the accuracy and recall values of the XGBoost algorithm with SMOTE are 0.945 and precision 0.947. Meanwhile, the accuracy and recall values on AdaBoost with SMOTE are 0.388. Then, the precision is 0.371. Thus, it is expected that the results of the XGBoost model with SMOTE can be a source for other research and can help in efforts to prevent and manage obesity.
Verifikasi Wajah untuk Menghitung Jumlah Transaksi Pengunjung Menggunakan Metode Deep Metric Learning Maulana, Rifqi Affan; Sigit, Riyanto; setiawardhana, setiawardhana
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8922

Abstract

This research carries the theme of facial recognition to detect visitors' faces by counting the number of times visitors make transactions. The objective of this research is to develop and implement a face verification system for public purposes, such as commercial purposes. One potential application of this system is in the realm of promotions, where it could be utilized to track the number of transactions conducted by visitors. The method employed utilizes deep metric learning (DML) to generate a model capable of verifying various facial images through the Convolutional Neural Network (CNN) architecture, which is designed to train human face image data. The triplet loss method is employed in training data due to its recognition as a more flexible approach in utilizing labels (in the form of face images) to facilitate comparison with the detected face images. The model employed for face recognition applications is facenet, a system that has been demonstrated to achieve a high degree of accuracy. The research's output is an application capable of swiftly and precisely verifying facial images of visitors and calculating the number of visitor transactions. The number of visitor transactions can subsequently be utilized as a promotional or discount strategy in commercial services.
PDF-Document Chatbot Responses using Large Language Models to Enable Smart City Engagement Khadija, Mutiara Auliya; Nurharjadmo, Wahyu; Aziz, Abdul; Primasari, Ina
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8262

Abstract

Traditional documents, including Rencana Pembangunan Jangka Menengah Daerah (RPJMD), Strategic Plans (Renstra), and e-masterplans, have undergone a remarkable transformation, evolving from their conventional printed formats to the dynamic realm of electronic versions. While this shift holds the promise of enhanced accessibility and convenience for the public, the full potential of these resources remains unrealized due to inherent challenges. On the other hand, a Generative AI approach is employed for the creation of an intelligent chatbot. Our primary contribution lies in the PDF-Document Chatbot Response utilizing Large Language Models (LLMs) GPT 3.5 Turbo from OpenAI, aimed at fostering engagement within Smart City. The dataset consists of Masterplan documents for Smart City development in Yogyakarta City, presented in PDF format and employing the Indonesian language. This research leverages the Large Language Models (LLMs) GPT-3.5 Turbo from OpenAI, in conjunction with user input and prompts. The development process for crafting this chatbot utilizes the LangChain Framework and Pinecone for storing vector embeddings. The results underscore the chatbot's capability to generate coherent responses closely aligned with the context found within the PDF document.

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