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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 471 Documents
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Analisis Efektivitas Fine-Tuning dan Prompt Engineering Berbasis Llama 3.1 pada Deteksi Depresi di Media Sosial Muhammad Ikhsan Asagaf; Junta Zeniarja
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10170

Abstract

Detecting depression has become an important concern in addressing mental health issues. According to WHO, more than 300 million people suffer from depression. Large Language Models offer great potential to address this issue, however the full fine-tuning process is often hampered by heavy computational requirements, and LLMs that are not specifically configured for a particular context can result in biased and inaccurate outcomes. This study aims to analyze the effectiveness of Prompt Engineering and Fine-Tuning using QLoRA in improving the accuracy of depression detection. Utilizing the Llama-3.1-8B-Instruct model on social media datasets, this research compares model performance in two scenarios consist of the application of direct prompting strategies on the base model and the application of QLoRA fine-tuning. Evaluation results demonstrate that the Chain-of-Thought strategy improved baseline accuracy from 81.4% to 84.4%, but still exhibited significant bias towards the 'Severe' class. In contrast, the QLoRA Fine-Tuning approach proved superior, achieving 92.4% accuracy with balanced F1-Scores across classes, effectively eliminating detection bias in the 'Minimum' class. These findings confirm that while prompting techniques can enhance baseline performance, QLoRA provides a more accurate, stable, and objective solution for depression detection tasks.
Deteksi Edema Paru Pada Citra Chest X-ray Menggunakan YOLOv5n Dengan Optimasi Hyperparameter Berbasis Grey Wolf Optimizer Nasrudin Affandi Prasetyo; Cinantya Paramita; Amiq Fahmi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10375

Abstract

Pulmonary edema is a lung disorder characterized by fluid accumulation in the alveolar and interstitial spaces, which disrupts gas exchange and reduces oxygen levels in the blood. Chest X-ray (CXR) imaging is commonly used for pulmonary edema assessment because it is fast and widely available; however, interpretation of CXR images heavily depends on radiologist expertise and may result in diagnostic variability, particularly in healthcare facilities with limited radiology resources. This study aims to develop an automated pulmonary edema detection system based on deep learning to support more consistent analysis of CXR images. The proposed method utilizes the YOLOv5n model as a lightweight object detection architecture due to its computational efficiency and suitability for resource-constrained environments. To improve detection performance and training stability, hyperparameters of the YOLOv5n model are optimized using the Grey Wolf Optimizer (GWO). The model is trained and evaluated using annotated CXR images, and its performance is assessed using precision, recall, mAP@0.5, and mAP@0.5–0.95 metrics. Experimental results show that the integration of GWO improves detection accuracy and model stability compared to the baseline configuration. The proposed framework demonstrates the potential to support pulmonary edema screening by providing efficient and consistent analysis of CXR images and can function as a decision-support tool for assisting medical personnel in early disease detection.
Penerapan Fuzzy Sugeno untuk Deteksi Overload Host pada Dynamic VM Consolidation M Naufal Adrian Pratama Putra; Chaerul Umam
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10130

Abstract

Perkembangan Cloud Computing mendorong pembangunan data center berskala besar yang terdiri dari ribuan host fisik dan mengakibatkan peningkatan konsumsi energi listrik secara signifikan. Tingginya konsumsi energi ini berdampak langsung pada biaya operasional dan efisiensi lingkungan, sehingga diperlukan strategi manajemen sumber daya yang lebih adaptif dan hemat energi. Dynamic Virtual Machine (VM) consolidation merupakan salah satu pendekatan efektif untuk mengurangi pemborosan energi dengan cara memigrasikan VM. Proses ini melibatkan beberapa tahapan penting, salah satunya adalah host overload detection yang berperan menentukan kapan sebuah host berada pada kondisi kelebihan beban. Penelitian ini mengusulkan penggunaan metode Fuzzy Sugeno sebagai mekanisme deteksi host overload untuk menangani ketidakpastian dan fluktuasi beban kerja pada lingkungan cloud. Metode yang diusulkan diuji melalui simulasi menggunakan CloudSim versi 7 dengan workload PlanetLab. Evaluasi dilakukan dengan membandingkan konsumsi energi, jumlah migrasi VM, pelanggaran SLA, dan degradasi performa terhadap metode deteksi bawaan CloudSim. Hasil pengujian menunjukkan bahwa metode Fuzzy Sugeno mampu meningkatkan efisiensi energi data center secara signifikan dibandingkan metode pembanding, meskipun menghasilkan peningkatan frekuensi migrasi VM dan pelanggaran SLA. Temuan ini menunjukkan adanya trade-off antara efisiensi energi dan kualitas layanan, sehingga metode Fuzzy Sugeno lebih sesuai untuk skenario data center yang memprioritaskan penghematan energi.
Analisis Korelasi Curah Hujan dan Lahan Terbangun Terhadap Luasan Genangan Banjir di Kabupaten Demak Mikael Arvito Kurnia Adi; Agusta Praba Ristadi Pinem; Nurtriana Hidayati
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10392

Abstract

Demak Regency is a low-lying coastal area that faces two types of pressures due to natural phenomena and massive human activities. The purpose of this study is to examine changes in built-up land and measure the level of spatial relationship between rainfall and built-up land area with flood inundation area in Demak Regency during the period 2020 to 2024. The method used in this study is spatial quantitative based on cloud computing using the Google Earth Engine platform that utilizes CHIRPS data for rainfall, Dynamic World for built-up land, and Sentinel-1 for flood inundation area. The findings of this study indicate that the annual rainfall trend has a low level of correlation with a coefficient of determination ranging from 1% to 7% while changes in built-up land show a high level of correlation with a Pearson correlation value between -0.52 to -0.58 which indicate that the reduction of non-built-up land due to land conversion is directly propotional  to the expansion of inundation which contributes to the variation of flood inundation by around 34%. The increase in flood inundation area reaching ±6,970 Ha in 2024 amid normal rainfall confirms that the decline in environmental capacity occurs due to excessive land conversion. This study concludes that managing the risk of increased flooding in Demak Regency requires strict integration of spatial planning policies, not just relying on a meteorology-based early warning system.
Perbandingan Performa CNN, Bi-LSTM, dan LSTM Untuk Analisis Sentimen Komentar Berbahasa Melayu Bengkulu Julia Purnama Sari; Funny Farady Coastera; Niska Ramadani; Esti Asmareta Ayu
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10376

Abstract

Bengkulu Malay is a regional language in Indonesia widely used in digital communication, particularly on social media. Comments in Bengkulu Malay reflect various sentiments toward local issues; however, sentiment analysis remains challenging due to limited data and unique linguistic characteristics. This study aims to evaluate the performance of three deep learning architectures—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM)—in classifying the sentiment of Bengkulu Malay text comments. The three models were tested using specific preprocessing approaches, including tokenization and stopword removal tailored to the structure of the Bengkulu Malay language. The results indicate that the Bi-LSTM model outperforms both CNN and LSTM in analyzing the sentiment of Bengkulu Malay comments. This is evidenced by the consistent dominance of Bi-LSTM across all datasets, achieving a peak accuracy of 91% on the Q1 dataset, while the LSTM and CNN models lagged behind by a significant margin. Given its stable performance and superior accuracy, the Bi-LSTM model proves to be the most effective architecture and is recommended for sentiment analysis of Bengkulu Malay comments. 
Anomaly-Based Network Intrusion Detection Using Isolation Forest on the Imbalanced UNSW-NB15 Dataset Syifaurachman Syifaurachman; Samso Supriyatna; Muhamad Ihsan Ashari
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10378

Abstract

The increasing complexity of network traffic and the rapid evolution of cyber threats require adaptive intrusion detection systems capable of identifying anomalous behavior in large-scale network environments. Traditional signature-based detection methods are effective in recognizing known attack patterns but are limited in detecting emerging or zero-day threats. Consequently, anomaly-based approaches using machine learning have become an important research direction in modern intrusion detection systems. One of the widely used unsupervised algorithms for anomaly detection is Isolation Forest, which identifies anomalies by isolating observations through random partitioning mechanisms.This study investigates the capability of the Isolation Forest algorithm for intrusion detection using the UNSW-NB15 dataset under imbalanced data conditions. The dataset consists of 2,540,044 network traffic records with approximately 87% normal traffic and 13% attack traffic. A quantitative experimental approach was applied, including data preprocessing, model development using Isolation Forest, and performance measurement using several evaluation metrics, namely Accuracy, Precision, Recall, F1-score, Receiver Operating Characteristic Area Under the Curve (ROC-AUC), and Precision-Recall Area Under the Curve (PR-AUC).The experimental results show that the model achieved an overall accuracy of 79.3% and a ROC-AUC value of 0.6302, indicating moderate capability in distinguishing between normal and malicious traffic. However, the PR-AUC value of 0.1710 reveals limited sensitivity in detecting minority attack instances under highly imbalanced conditions. These findings highlight that evaluation of intrusion detection systems under imbalanced data should not rely solely on accuracy-based metrics. Isolation Forest can serve as a baseline anomaly detection mechanism, but additional strategies are required to improve detection sensitivity in real-world intrusion detection environments.
Adaptasi Profil Proyeksi Horisontal dengan Stripping Vertikal untuk Segmentasi Baris Teks Miring Arab Jawi Ade Jamal; Iin Suryaningsih; Arif Supriyanto
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10085

Abstract

Abstract – Line segmentation in handwritten Arab Jawi scripts poses a significant challenge for automatic character recognition systems due to high variations in writing slant. The primary problem arises when using the standard Horizontal Projection Profile (HPP) method, where text skewness causes an overlap between peaks and valleys in the projection graph, leading to inaccurate or failed line separation. This research aims to develop a line segmentation solution for skewed Arab Jawi text by adapting the HPP method through a vertical stripping technique. The methodology involves dividing the document image into several narrow vertical columns or strips, followed by independent horizontal projection calculations for each strip. Local line separation points from each strip are then sequentially connected to form a dynamic separation path that follows the original inclination angle of the text. Research findings demonstrate that this approach successfully separates skewed text lines perfectly without cutting through characters, while also effectively managing variations in manuscript colour degradation and inconsistent line sizes. In conclusion, the modification of HPP with vertical stripping proves to be effective and computationally efficient as a pre-processing stage for ancient Jawi manuscripts. This method offers a balance between the simplicity of classical algorithms and the robustness required to handle handwriting complexity, making it highly potential for integration into the development of broader Optical Character Recognition systems to support the preservation of historical Southeast Asian manuscripts
Perbandingan Algoritma C4.5 dan Random Forest dalam Klasifikasi Kekeringan Tembakau Berbasis Electronic Nose Rahmat Tegar Patriot Lambang; Dimas Saputra; Muh. Mashdarul Hilmi Aufa; Ifnu Wisma Dwi Prastya
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10057

Abstract

Penilaian kualitas tembakau masih banyak dilakukan secara manual oleh tenaga ahli, sehingga rentan terhadap subjektivitas dan inkonsistensi antarpenilai. Masalah ini dapat menghambat proses kontrol mutu, terutama ketika volume produksi tinggi dan diperlukan keputusan cepat serta akurat. Penelitian ini bertujuan untuk mengembangkan pendekatan klasifikasi kualitas tembakau berbasis pembelajaran mesin yang terintegrasi dengan perangkat electronic nose (E-Nose) sebagai solusi yang lebih objektif dan terukur. Sebanyak 375 sampel tembakau yang mewakili empat tingkat kekeringan dikumpulkan dan diolah menggunakan metode Interquartile Range (IQR) untuk menghilangkan outlier serta Moving Average untuk mereduksi noise pada sinyal sensor MQ-4, MQ-7, dan MQ-135. Dua algoritma klasifikasi, yaitu C4.5 dan Random Forest, diterapkan dan dievaluasi menggunakan stratified 10-fold cross-validation untuk memperoleh estimasi performa yang stabil. Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 93%, nilai Cohen’s Kappa 0.9259, MCC 0.9262, balanced accuracy 0.945, dan cross-entropy log loss 0.4291. Temuan ini memperlihatkan bahwa integrasi E-Nose dengan teknik ensemble learning mampu meningkatkan keandalan proses identifikasi kualitas tembakau. Secara keseluruhan, penelitian ini menyimpulkan bahwa sistem klasifikasi berbasis E-Nose dan pembelajaran mesin dapat dijadikan dasar pengembangan teknologi otomasi penilaian tembakau yang lebih cepat, konsisten, dan siap diterapkan pada skala industri
Comparative Evaluation of VGG16, MobileNetV2, and ResNet50 for Pediatric Pneumonia Classification Using Grad-CAM Rendra Gunawan; Cinantya Paramita; Suryanti Chan
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10373

Abstract

Pneumonia ranks among the deadliest respiratory infections, particularly affecting young children, where rapid and precise detection proves essential for prompt intervention and averting severe outcomes. This research examines the deployment of three leading deep learning models VGG16, MobileNetV2, and ResNet50. This research categorizes pediatric chest X-rays into normal, bacterial pneumonia, and viral pneumonia classes using a dataset of children's chest radiographs (ages 1-5 years) from Guangzhou Hospital, enhanced through preprocessing, normalization, and augmentation techniques to boost model robustness. Model performance was assessed via accuracy, precision, recall, specificity, F1-score, G-Mean, and AUC metrics for thorough evaluation, revealing accuracies between 79% and 82% with VGG16 leading, followed by ResNet50 and MobileNetV2. Grad-CAM visualizations effectively highlighted key diagnostic regions on X-rays, offering clinicians valuable insights into infection sites identified by the models, and advancing automated chest X-ray systems for precise bacterial/viral pneumonia detection with supportive interpretability.
Segmentasi Kepuasan Mahasiswa Terhadap Dosen Menggunakan K-Means Clustering dan Identifikasi Faktor Dominan Dengan Random Forest Restu Rakhmawati; Suamanda Ika Novichasari; Imam Adi Nata; Fadhila Syahida Wibowo; Zharifa Nur Majidah; Meily Adenia
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10395

Abstract

This study aims to analyze student satisfaction patterns regarding lecturer teaching performance by integrating K-Means Clustering and Random Forest algorithms. The research data includes 5,260 observations analyzed based on service quality dimensions and academic attributes. The results using the Elbow Method and Silhouette Score established three optimal clusters representing Very Satisfied (score 4.84), Moderately Satisfied (score 4.04), and Dissatisfied (score 3.04) segments. Furthermore, the Random Forest algorithm demonstrated an accuracy of 47% on 1,576 test data and successfully identified that Semester Credit Load (SKS) is the most dominant determinant influencing satisfaction, with an importance value of 47.46%. A unique finding shows that students with the highest academic load (average 20.81 SKS) are actually in the Very Satisfied segment. This study concludes that more intensive student academic engagement correlates positively with appreciation for lecturer teaching quality. These results provide strategic guidance for university management to improve services based on student academic profiles.

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