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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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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.
Design and Implementation of IoT-Based Smart Election Using ESP32 and RFID Irawan, Bambang; Ardiyanto, Anggasta Rukma
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.v10i3.8412

Abstract

This research aims to design and implement a smart election system leveraging Internet of Things (IoT) technology through the integration of the RFID RC522 module, ESP32 microcontroller, and the MQTT communication protocol, with the goal of improving the efficiency, transparency, and security of the voting process. The research adopts a prototyping approach consisting of four main stages: requirement analysis, system design, performance evaluation, and refinement leading to final implementation. The system enables voter authentication through e-KTP verification using RFID sensors, which is cross-checked against a centralized database. Voting data are transmitted securely via the MQTT protocol and displayed in real-time through a Node-RED dashboard, allowing for continuous monitoring and rapid vote recapitulation. Experimental results indicate a 100% accuracy rate in UID verification, prevention of duplicate voting, and stable system responsiveness. The platform significantly reduces human intervention and the risk of vote manipulation, supporting the credibility and auditability of election results. In conclusion, the proposed IoT-based smart election prototype offers an efficient, scalable, and user-friendly technological solution suitable for local deployment. Future improvements may include the integration of cryptographic techniques, cloud-based data storage, and biometric authentication to enhance system robustness and security.
Pengembangan Prototipe untuk Prediksi Tingkat Penyeduhan Kopi Menggunakan Data Spektroskopi dan Deep Learning Prananto, Muhammad Teguh; Raafi'udin, Ridwan; Adrezo, Muhammad; Pradana, Musthofa Galih; Arifuddin, Nurul Afifah
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.v10i3.8710

Abstract

Consistency in coffee flavor is a crucial factor for coffee enthusiasts, thus requiring a method capable of objectively measuring the coffee brewing level in accordance with the standard brewing chart. This study utilizes the AS7265X spectroscopy sensor to capture the characteristics of coffee based on the resulting light spectrum. The spectral data is then used in a deep learning model using the Convolutional Neural Network (CNN) algorithm to classify the coffee brewing level into five distinct classes. A total of 150 data samples were used in the training and testing process. Initial results show that the model achieved a very high average accuracy of approximately 97%. After hyperparameter tuning using the Random Search method, the model's accuracy further improved, reaching a very high accuracy. However, this performance improvement resulted in a trade-off in computational time, with execution time increasing from 15 seconds to 1 minute and 43 seconds. This research is expected to contribute to ensuring consistent coffee brew quality and to open opportunities for further studies that combine sensor technology and artificial intelligence in the food and beverage sector.
Analisis Berbasis Convolutional Neural Network untuk Pendeteksian Kanker Prostat dengan Citra Magnetic Resonance Imaging (MRI) Rosyidan, Fikri Yoma; Hendradi, Rimuljo; Wuryanto, Eto
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.v10i3.8397

Abstract

Kanker prostat adalah tumor ganas yang berada dari kelenjar prostat, yang merupakan bagian penting dari sistem reproduksi pria. Adanya peningkatan prevalensi kanker prostat maka diperlukan deteksi dini yang akurat. Penelitian ini memfokuskan pada pemanfaatan deep learning, khususnya metode Convolutional Neural Network (CNN) untuk mendiagnosis kanker prostat melalui citra MRI. Diperlukan penelitian untuk mengkaji tiga arsitektur CNN: U-Net, nnU-Net, dan nnDetection agar didapatkan arsitektur yang terbaik. Data penelitian ini menggunakan data sekunder sejumlah 1294 citra MRI dari The PI-CAI Challenge “Artificial Intelligence Radiologists Prostate Cancer Detection in MRI” tahun 2022. Data tersebut menjalani proses pre-processing, termasuk normalisasi intensitas piksel, augmentasi data seperti rotasi dan scaling, serta pemotongan gambar untuk menghilangkan area yang tidak relevan. Proses selanjutnya data tersebut akan masuk ke tahap pelatihan model dengan menggunakan ketiga arsitektur. Hasil dari pelatihan tersebut akan dievaluasi kinerja modelnya dengan menggunakan metrik Area Under the Receiver Operating Characteristic Curve (AUROC) dan Average Precision (AP). Hasil evaluasi menunjukkan bahwa arsitektur U-Net mencapai AUROC 89,94% dan AP 51,22%, arsitektur nnU-Net mencapai AUROC 97,75% dan AP 86,67%. dan arsitektur nnDetection mencapai AUROC 83,66% serta AP 49,91%. Dari perbandingan hasil ketiga arsitektur maka didapatkan hasil terbaik adalah arsitektur nnU-Net dengan capaian AUROC 97,75% dan AP 86,67%. Penelitian ini menunjukkan potensi penggunaan CNN dalam diagnosis kanker prostat melalui citra MRI. Temuan penelitian menegaskan pentingnya pemilihan arsitektur yang tepat dalam aplikasi deep learning untuk citra medis.
Pengembangan Oven Pengering Ikan Teri Dengan Algoritma Fuzzy dan Pemantauan Melalui Telegram Alfat, Lathifah; Fadillah, Rizki
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.v10i3.8073

Abstract

Perubahan cuaca yang tidak menentu sering kali menyebabkan fluktuasi intensitas pencahayaan, terutama ketika sinar matahari terhalang oleh awan atau polusi udara. Kondisi ini berdampak signifikan terhadap proses pengawetan ikan teri, yang sangat rentan terhadap pembusukan. Salah satu upaya untuk menjaga kualitas ikan teri adalah dengan memastikan suhu dan kelembapan terkendali sehingga kadar air tetap berada pada angka 40% setelah proses pengawetan. Pengendalian ini penting agar kualitas ikan teri tetap terjaga hingga diterima oleh konsumen.Di Indonesia, pengawetan ikan teri dilakukan dengan berbagai metode, salah satunya memanfaatkan energi listrik untuk memastikan stabilitas suhu selama proses pengeringan. Teknologi yang umum digunakan adalah PTC Heater, yang terbukti mampu menjaga kestabilan suhu meskipun terjadi fluktuasi cuaca. Selain itu, integrasi algoritma fuzzy dalam sistem pengeringan memberikan keuntungan berupa prediksi waktu pengeringan yang lebih akurat. Algoritma ini berperan dalam meningkatkan efisiensi proses dan memastikan hasil pengeringan yang merata. Berdasarkan hasil pengujian, sistem ini mampu mengeringkan ikan teri dengan performa MAE 0.81, RMSE 2.69, Akurasi 0.95. Temuan ini menunjukkan potensi signifikan akan teknologi IoT dan algoritma Fuzzy dalam meningkatkan kualitas pengawetan ikan teri di tengah tantangan perubahan iklim yang dinamis.
Pengembangan Web Antrian Terapi RSUD Syarifah Ambami Rato Ebu Menggunakan Waterfall dan SUS Rokhim, Imam Fadhkur; Rahmatullah, Asfani; Faqih, Fauziah Nur; Ifada, Noor
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.v10i3.8546

Abstract

During the pandemic, the COVID-19 virus spread very quickly through the air so that the government implemented a social distancing policy. However, the high number of patients in the therapy waiting room of Syarifah Ambami Rato Ebu Bangkalan Hospital is feared to make the policy not run optimally. The purpose of this study is to create an online therapy queue system abbreviated as SMART in order to reduce crowds in hospitals. In developing SMART, the Waterfall method is used so that it is sequential starting from system needs analysis, design, to implementation and maintenance. The results of functional testing show that all application features can run well. Furthermore, the usability evaluation using the System Usability Scale (SUS) method produced a score of 70, which indicates that the system has a good level of acceptability and usability. Other values obtained in the Acceptability Ranges are Marginal, the Grades Scale value is C, the Adjective value is Good, and the Promoters and Detractors value is Passive. The implementation of this SMART system has the potential to increase the operational effectiveness of hospitals in managing patient flow and significantly improve user experience through usability evaluation.
Segmentasi Pelanggan Berdasarkan Model LRFM Menggunakan Algoritma K-Means dan Optimasi Klaster Dinamis Wahyudi, Riyan; Solihin, Achmad
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.v10i3.8735

Abstract

The number of tax training participants often does not meet the minimum quota, resulting in the cancellation of many training classes. Throughout 2022, there were 27 training classes that failed to take place due to a lack of participants. One of the reasons is that promotions have not utilised historical customer data to set marketing targets more precisely. By utilising historical customer data, companies can design more targeted promotional strategies and increase the number of training participants. Therefore, this research aims to segment customers using the dynamic K-Means algorithm based on the Length, Recency, Frequency, and Monetary (LRFM) model, so that customer behaviour patterns when registering for training can be identified. The clustering results are then visualised to facilitate analysis and decision-making. This research resulted in three customer segments, namely Loyal customers (Gold, 17%), Lost customers (Diamond, 64%), and New customers (Silver, 17%). With this segmentation, it is expected that the company can conduct more effective promotions and increase the number of trainees in the future.
Pemodelan Topik pada Komentar YouTube Arra: Komparasi LDA dan K-Means Menggunakan Fitur Leksikal dan Semantik Nuradilla, Siti; Kamila, Sabrina Adnin; Zahra, Latifah; Suhaeni, Cici; Sartono, Bagus
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.v10i3.8763

Abstract

YouTube has become a platform for sharing content, including positive material and stereotypes that often trigger debates. One noteworthy phenomenon is the video of Arra, a toddler known for her remarkable communication skills. This uniqueness has drawn significant attention and sparked debates about the mismatch between her age and cognitive development. The diverse comments on Arra’s videos reflect sharply differing perspectives among netizens, making manual analysis highly challenging. Therefore, it is important to examine the topics discussed by netizens to understand the dominant issues emerging in these discussions. Through this approach, the public can gain insights, and parents may receive valuable input regarding child-rearing practices. The main objective of this study is to explore the effectiveness of the two methods and their combinations of text representations in identifying key topics within comments by comparing the coherence performance of the models. This research applies topic modeling to analyze comments using two primary approaches: Latent Dirichlet Allocation (LDA) and K-Means clustering. The study involves data collection through comment crawling, followed by text preprocessing and text representation using TF-IDF and GloVe embeddings. LDA and K-Means are then used to identify dominant topics appearing in the comments. The results show that LDA with TF-IDF achieved the highest coherence score of 0.662, although the resulting topics were still difficult to interpret due to overlap. Meanwhile, K-Means with GloVe 100D yielded a slightly lower coherence score of 0.6538 but outperformed in terms of interpretability. Therefore, K-Means with GloVe 100D is considered a more balanced approach in terms of both coherence and topic readability.
Perbandingan IndoBERT dan IndoRoBERTa Untuk Analisis Sentimen Pada Film Dokumenter Dirty Vote Apriansyah, Fadhel Muhammad; Ramadhan, Teguh Ikhlas; Hidayat, Cepi Rahmat; Wijaya, Anggito Karta
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.v10i3.8607

Abstract

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify and categorize opinions or emotions in text. This study compares the performance of two Transformer-based models, IndoBERT and IndoRoBERTa, in analyzing sentiment toward the documentary film Dirty Vote. The research process includes data collection, text preprocessing, lexicon-based sentiment labeling, and model evaluation using K-Fold Cross-Validation. The results show that IndoBERT achieved an average accuracy of 99%, higher than IndoRoBERTa, which achieved 94%. IndoBERT also demonstrated better alignment with lexicon-based labeling in classifying positive, negative, and neutral sentiments. In terms of architecture, IndoBERT employs static masking, while IndoRoBERTa applies dynamic masking, leading to differences in the models' sensitivity to textual meaning. IndoBERT tends to provide more definitive classifications for opinions or strong criticisms, whereas IndoRoBERTa more frequently categorizes ambiguous comments as neutral sentiment. The conclusion of this study indicates that IndoBERT outperforms IndoRoBERTa in sentiment analysis of the documentary film Dirty Vote, both in terms of accuracy and consistency with lexicon-based labeling. These findings provide insights into the effectiveness of Transformer-based models for sentiment analysis in the Indonesian language and can serve as a reference for further NLP model development.

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