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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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Pipeline NLP End-to-End untuk Peringkasan Abstraktif dan Ekstraksi Entitas Berita Berbahasa Indonesia Berbasis Model Transformer Setia, Cuncun; Rukhviyanti, Novi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10030

Abstract

The rapid growth of online news content poses challenges for readers to capture the core information quickly and accurately. This research proposes and implements an automated end-to-end pipeline that integrates three main stages: data acquisition, abstractive text summarization, and Named Entity Recognition  (NER). The mT5 model is employed to generate coherent and concise summaries, while the BERT model is applied to extract key entities, including persons, organizations, and locations. The pipeline was evaluated using 100 news articles from the Egindo portal. Experimental results show that the system achieves an average text reduction of 62.47%, with a ROUGE-1 F1 score of 0.473. For NER tasks, the pipeline reached a Micro-F1 score close to 0.70, outperforming traditional approaches such as TextRank and CRF. These results demonstrate that the integration of Transformer-based models within a structured pipeline significantly improves summarization quality and entity extraction accuracy. The study contributes a practical NLP solution for the Indonesian language, providing a functional prototype that can be applied to online media analysis and media intelligence applications.
Pemanfaatan Teknologi Augmented Reality dengan Marker-Based Tracking sebagai Media Pengenalan Kabupaten Muara Enim Adeliani, Adeliani; Lestarini, Dinda; Seprina, Iin
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10051

Abstract

The development of digital technology has increased the demand for more interactive information media, including those used to present regional potential. Muara Enim Regency is rich in culture, industry, and tourism, all of which need to be introduced through more engaging media for both younger generations and the wider community. This study aims to develop an Augmented Reality–based application for introducing Muara Enim Regency using the Marker-Based Tracking method as a response to the need for more immersive and accessible information media. The development process follows the Multimedia Development Life Cycle (MDLC) method, which includes the Concept, Design, Material Collecting, Assembly, Testing, and Distribution phases. The application is implemented using Unity and Vuforia, integrating 3D objects, information panels, and an interactive quiz feature. Functional testing through Black-box Testing shows that all features operate according to specifications without significant issues. User Acceptance Testing (UAT) produced results categorized as very good, indicating that the application is positively received in terms of operational ease, informational clarity, stability, and interaction experience. Therefore, this application is considered suitable as an alternative medium for introducing the potential of Muara Enim Regency and has promising opportunities for further development through additional content and enhanced interactivity.
Smart Finance: Desain dan Implementasi Sistem Keuangan Cerdas Real-Time Berbasis IoT untuk UMKM Wardana, Bendra; Sitompul, Pretty Naomi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10006

Abstract

The development of Internet of Things (IoT) technology and real-time data analytics provides opportunities to improve financial management efficiency for Micro, Small, and Medium Enterprises (MSMEs). However, most MSMEs in Indonesia still rely on manual bookkeeping, which is inefficient, prone to errors, and limits access to formal financing. This study aims to design and implement Smart Finance, an IoT-based intelligent financial system capable of processing transaction data automatically in real time. The research method includes system requirement identification, system design and device integration, application implementation, and system performance testing. The system was developed as a web-based application integrated with IoT devices such as ESP32-CAM to support automatic transaction recording, cash flow visualization, and digital financial report generation. The testing results indicate that the system can automatically record transactions with good accuracy, provide real-time financial dashboards, and deliver transaction notifications, thereby helping MSME owners monitor their financial conditions more quickly and transparently. The main contribution of this study lies in integrating IoT devices with a web-based financial recording system that enables automatic and real-time transaction recording, an approach that is still rarely implemented in MSME financial management. Although challenges related to internet connection stability remain, the developed system demonstrates potential in improving efficiency, transparency, and the quality of financial decision-making among MSMEs. This study concludes that Smart Finance can serve as a practical and adaptive digital financial solution to support the sustainability and competitiveness of MSMEs in the digital era.
Penerapan Metode Canny Edge Untuk Deteksi Pelat Nomor Kendaraan Area Parkir PLN Mabar Hakim nainggolan, Ihsanul; Fakhriza, M.
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10121

Abstract

Vehicle license plate detection is an essential component in modern parking management, particularly in institutional areas like PLN Mabar, which necessitate fast and accurate identification systems. This study focuses on applying the Canny Edge Detection method to accurately identify the edges of vehicle license plates under specific environmental settings, specifically lighting conditions of 100115 lux and a camera height of 40 cm, evaluated across various threshold levels. Widely regarded as an optimal edge detection algorithm, the Canny Edge method offers significant advantages, including high-precision edge detection, robust noise interference minimization, and the generation of clear object boundaries. The research findings demonstrate that this method delivers excellent performance for vehicle detection in parking facilities when operating under controlled lighting and camera parameters. Specifically, the test results reveal that within a threshold range of 50 to 500, the system achieves a flawless 100% detection accuracy. This highlights the method's effectiveness in capturing crucial object edges under the tested conditions. Conversely, increasing the threshold beyond 500 leads to a gradual decline in system accuracy, dropping to 20% at a threshold of 9001000. This decline indicates that excessively high threshold values cause the system to discard vital contours necessary for accurate detection. Ultimately, the system successfully detects license plate edges with a high success rate and stable processing times, proving its viability for practical implementation within the vehicle identification system at the PLN Mabar parking area.
Sistem Informasi Keuangan dengan Prediksi Pendapatan Menggunakan Regresi Linier Malika, Sela; Putri, Raissa Amanda
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10037

Abstract

Financial management and forecasting are critical aspects in supporting decision-making within an organization, particularly amid the increasing demand for fast and accurate data analysis. In general, many companies in Indonesia still face challenges in utilizing historical financial data to optimally predict revenue. This issue is also encountered by a company that continues to rely on manual record-keeping using spreadsheet-based systems, which makes it difficult to conduct analysis and forecast future financial conditions. This study aims to implement a linear regression method to predict revenue based on historical financial transaction data. The methodology employed follows the CRISP-ML(Q) framework, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The modeling process is carried out by developing a linear regression model using independent and dependent variables. The results indicate that the constructed linear regression model is capable of generating revenue predictions with a relatively low error rate, thereby effectively representing patterns within the historical data. Model evaluation using error metrics demonstrates that the model performs adequately within the context of the dataset used. In conclusion, the linear regression method is effective for revenue prediction and can support data-driven decision-making processes. Future research is recommended to enhance the model by incorporating more complex variables and applying alternative prediction methods to improve accuracy.
Pemodelan Topik Komentar Terhadap Aplikasi Allstat BPS Tahun 2017-2025 Simanungkalit, Gabriella Elisabeth; Shafira, Hervira Nur; Nooraeni, Rani
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9350

Abstract

Penelitian ini dilatarbelakangi oleh meningkatnya kebutuhan akan data statistik yang mudah diakses melalui aplikasi mobile, salah satunya adalah aplikasi AllStat BPS. Tujuan dari penelitian ini adalah untuk menganalisis sentimen dan mengidentifikasi topik utama dalam ulasan pengguna aplikasi AllStat BPS pada periode 2017–2025. Metode yang digunakan mencakup analisis sentimen berbasis lexicon dengan kamus InSet dan klasifikasi menggunakan algoritma Naive Bayes, Random Forest, dan Support Vector Machine (SVM). Pemodelan topik dilakukan dengan pendekatan Latent Dirichlet Allocation (LDA). Hasil penelitian menunjukkan bahwa model Random Forest memberikan performa klasifikasi terbaik dengan akurasi pada data latih sebesar 88,16% dan nilai kappa 0,8046. Selain itu, LDA berhasil mengidentifikasi delapan topik utama dari ulasan pengguna, dengan Topik 1 memiliki nilai koherensi tertinggi (0,1784) yang mengindikasikan kekuatan semantik antar kata dalam topik tersebut. Topik-topik ini kemudian dipetakan ke dalam kerangka kualitas perangkat lunak berdasarkan standar ISO/IEC 25010, dengan aspek Functional Suitability dan Performance Efficiency sebagai topik dominan. Kesimpulan dari penelitian ini adalah bahwa kombinasi metode Random Forest dan LDA efektif dalam mengklasifikasikan sentimen serta menggambarkan fokus isu dalam ulasan pengguna aplikasi AllStat BPS.
Deep Learning-Based Sentiment Analysis of Islamic Boarding School Google Reviews Using IndoBERT Variants and XLM-RoBERTa syarifuddin, Ahmad
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10021

Abstract

Online reviews on platforms like Google Maps have become a crucial data source for analyzing public opinion and consumer behavior, including in the context of selecting religious educational institutions, specifically pesantren (Islamic boarding schools). This study aims to perform sentiment analysis to measure public perception towards pesantren located across the island of Java. The data were collected via web scraping, yielding a total of 8,577 reviews, which subsequently underwent essential text preprocessing steps including cleansing, case folding, tokenization, stopword removal, and stemming. The prepared dataset was then partitioned using the Stratified Train-Test Split method into 70% for training and 30% for testing.The research evaluated the performance of three pre-trained language models IndoBERT Base, IndoROBERTa Small, and XLM-RoBERTa, which were fine-tuned using the Focal Loss function. The training strategy prioritized saving the best model based on the neutral F1-score.The final evaluation on the unseen test data demonstrated that the IndoBERT Base model significantly outperformed the others, achieving the highest overall accuracy of 0.92 (92%). This strong balance confirms the model's excellent generalization ability, indicating no significant overfitting and successful mitigation of classification bias. The findings validate IndoBERT Base as the optimal model for sentiment classification of pesantren reviews. Future research is recommended to shift focus toward building a larger, more diverse dataset to further enhance model generalizability.
Prediksi Hasil Panen Karet di Gunung Tua Menggunakan Support Vector Machine Siregar, Siti Khairunnisa; Putri, Raissa Amanda; Furqan, Muhammad
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10040

Abstract

Penelitian ini bertujuan untuk memprediksi hasil panen karet di wilayah Gunung Tua, Kabupaten Padang Lawas Utara, dengan menggunakan algoritma Support Vector Machine (SVM). Produksi karet dipengaruhi oleh berbagai faktor musiman dan kondisi lingkungan yang menyebabkan fluktuasi hasil panen, sehingga menyulitkan perencanaan bagi petani maupun instansi terkait. Penelitian ini menerapkan pendekatan supervised learning dengan metode Support Vector Regression (SVR) untuk memodelkan prediksi hasil panen karet berdasarkan data produksi historis yang diperoleh dari instansi pertanian setempat. Tahapan penelitian meliputi pengumpulan data, prapemrosesan, normalisasi data, pelatihan model, dan pengujian. Evaluasi kinerja model dilakukan menggunakan Root Mean Square Error (RMSE) sebagai indikator tingkat kesalahan prediksi. Hasil penelitian menunjukkan bahwa model SVM mampu memprediksi hasil panen karet dengan nilai RMSE sebesar 191 dan tingkat akurasi sebesar 96,2%, yang menunjukkan bahwa model memiliki performa yang baik dalam menangkap pola data produksi. Dengan demikian, algoritma Support Vector Machine dapat dimanfaatkan sebagai alat pendukung pengambilan keputusan dalam perencanaan dan pengelolaan produksi pertanian karet
Kombinasi Model ARIMA dan KNN Dalam Peramalan Harga Produk Wijaya, Guntur
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10163

Abstract

This study proposes a product price forecasting model for PT ABC by integrating the Autoregressive Integrated Moving Average (ARIMA) model and the K-Nearest Neighbor (KNN) method into a hybrid predictive approach. The company faces recurring challenges related to product price fluctuations and stock availability caused by unstable market conditions and irregular supply distribution. To address these issues, a data-driven forecasting model is required to support inventory planning and price stabilization strategies. The dataset used in this study consists of historical cement purchase records from January 2023 to September 2025, obtained from the company’s ERP system. The research process includes data cleansing, transformation, monthly price aggregation, and the application of ARIMA, KNN, and a hybrid ARIMA–KNN model designed to improve forecasting accuracy. The evaluation results indicate that the hybrid ARIMA–KNN model outperforms the standalone ARIMA model in short-term price forecasting. Based on three performance metrics, the hybrid model achieved a Mean Absolute Error (MAE) of 1604.94, a Root Mean Square Error (RMSE) of 2299.37, and a Coefficient of Determination (R²) of 0.2881. These results suggest that while the model captures a portion of price variability, it still faces limitations in modeling non-linear fluctuations and sudden extreme changes. Nevertheless, the hybrid approach demonstrates improved stability by reducing extreme prediction variations, maintaining trend continuity, and generating smoother prediction curves that more closely align with actual price movements. This research contributes practically by providing PT ABC with a forecasting tool to support future price estimation, improve inventory management, and maintain market price stability. Additionally, the findings offer a foundation for future research on advanced non-linear and deep learning–based forecasting models.
Analisis Emosi Komentar Pengguna TikTok terhadap Film Jumbo Menggunakan Metode Naive Bayes Panggabean, Trisatin; Putri, Raissa Amanda
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10036

Abstract

TikTok has become a widely used social media platform where users actively express opinions through comment features. This study aims to classify the emotions contained in TikTok user comments on the Indonesian animated film Jumbo using the Naive Bayes Classifier method. The dataset consisted of 1,341 comments collected from the official Visinema Pictures account using the Apify Web Scraper. The collected data were processed through several preprocessing stages, including case folding, tokenization, normalization, stopword removal, and stemming using the Sastrawi library. Emotion labeling was performed based on the Indonesian NRC EmoLex lexicon by categorizing comments into three emotional classes: angry, happy, and sad. Feature extraction was conducted using the TF-IDF weighting method to generate relevant text representations and identify dominant terms in each emotional category. The dataset was divided into 80% training data and 20% testing data to evaluate the model performance. The experimental results show that the Naive Bayes model achieved an accuracy of 78.81%. The emotion distribution indicates that anger was the most dominant class with 904 comments, followed by happy with 415 comments, and sad with 22 comments. The model demonstrated the best performance in the anger class, achieving 100% recall, 75% precision, and an F1-score of 85.71%. However, the classification performance for minority classes, particularly happy and sad, still requires improvement. This research contributes to the development of text mining-based emotion analysis and provides insights into audience emotional responses that may support film evaluation and marketing strategies.

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