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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
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.
Pencarian Rute Terpendek untuk Pemetaan UMKM di Kecamatan Negeri Katon Menggunakan Algoritma A-Star Yulmaini, Yulmaini Yulmaini; Berlian, Agnes Tria
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.v10i3.8628

Abstract

In the regional economy, micro, small and medium enterprises (MSMEs) have a very significant role in driving economic growth and opening up employment opportunities for the community. However, the lack of information regarding business locations and optimal routes for MSMEs is still an obstacle in improving accessibility. The lack of information regarding the fastest route to the location of MSMEs leads to limited accessibility, especially for people who are unfamiliar with the area. Therefore, a more optimal strategy is needed to determine the best way to improve the distribution efficiency and mobility of MSMEs. The purpose of this research is to use the A-Star algorithm for mapping MSMEs to find the fastest route to the location of MSMEs. This research explicitly combines MSME spatial data with the implementation of the A-Star algorithm for route optimization. The results show that the A-Star algorithm is able to effectively speed up the route search process by taking into account the appropriate heuristic value. With the implementation of this algorithm, accessibility to MSMEs locations is significantly improved, allowing customers and businesses to easily find MSME locations with greater cost and time efficiency. Implementation of the A-Star algorithm in increase in MSME accessibility through optimal route efficiency.
Implementasi Naïve Bayes untuk Rekomendasi Pembelian Produk pada Aplikasi E-commerce Situngkir, Boy Betrand; Limbong, Endson Danielgar; Pandiangan, Very Andreas; siagian, Rivaldo calvin; Yennimar, Yennimar
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.v10i3.8880

Abstract

Electronic commerce (e-commerce) is a platform that influences buying and selling habits in Indonesia, with data from the Central Statistics Agency 2023 showing 31,753 e-commerce businesses using consumer review data as a determinant of product and service quality. This research aims to develop a sentiment-based product recommendation system using the Naïve Bayes algorithm. The research methodology includes collecting 1,287 data samples obtained from customer reviews using Web Scraper technology on the official MSI Official Store e-commerce platforms in the Tokopedia, Shopee, and Blibli applications. The results of data preprocessing yielded 921 clean data, and the Naïve Bayes Algorithm was applied as a classification model and system implementation in a website application. The data was then divided into 80% for training and 20% for testing. Model evaluation showed an accuracy of 82% for training data and 71% for testing data. These results indicate the effectiveness of the Naïve Bayes algorithm in forming a sentiment-based product recommendation system. This recommendation system helps users make more informed purchasing decisions based on consumer sentiment analysis. This research contributes to the development of intelligent recommendation systems that can improve user decision-making in the digital market

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