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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 17 Documents
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Search results for , issue "Vol 10, No 4 (2025)" : 17 Documents clear
Segmentasi Citra Daun Tomat untuk Klasifikasi Penyakit Tanaman Menggunakan Support Vector Machine (SVM) Azli, Puteri Amelia; Minarni, Minarni; Syahrani, Anna; Swara, Ganda Yoga; Anisya, Anisya
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9404

Abstract

Tomatoes are one of the most widely grown crops worldwide. In Indonesia, particularly in West Sumatra, tomato production has declined. This is due to extreme weather conditions and plant disease outbreaks. One solution to help with early identification of tomato plant diseases is through digital image-based classification. This process involves several important stages, starting from image acquisition, preprocessing, segmentation, feature extraction, and classification. However, the quality of classification is highly dependent on the effectiveness of segmentation in separating leaf objects from the background. This study proposes a method for segmenting tomato leaf images based on a combination of color thresholding techniques, morphological operations, contour filtering, and bitwise masking to ensure that only the leaf parts are processed further. After undergoing the segmentation process, images are extracted based on color characteristics in HSV space and GLCM texture, then further processed using an SVM algorithm with an RBF kernel. The dataset used consists of 4000 tomato leaf images with an 80% training and 20% testing data division scheme, accompanied by 5-fold cross validation. The model achieved an accuracy of 96.97% on the training data and 93.75% on the testing data. The results show that segmentation methods using color thresholding, morphology, contours, and bitwise masking can help improve the consistency of extracted features, thereby potentially supporting more stable classification performance.
Penerapan Linear Discriminant Analysis Untuk Meningkatkan Kinerja Algoritma Support Vector Machine Gusrianty, Gusrianty; Fenly, Fenly; Jollyta, Deny; Erlin, Erlin; Putri, Ramalia Noratama; Oktariana, Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8772

Abstract

Obesity is a complex chronic disease influenced by various factors, such as genetic, environmental, and lifestyle, which is characterized by excess body weight due to the excessive accumulation of body fat. With the rapid advancement of technology and digitalization across all sectors, data has become increasingly vital, as large datasets generate valuable information. However, a key challenge in data analysis is addressing redundancy, noise, and high dimensionality, which can affect the performance of machine learning algorithms. This study aims to investigate the effectiveness of combining Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) in enhancing the accuracy and efficiency of high-dimensional data classification, particularly in predicting obesity levels. LDA is employed to reduce data dimensionality while retaining the most relevant features, whereas SVM is utilized as the classification algorithm to predict obesity levels based on patterns identified within the dataset. The research was conducted using a dataset consisting of 779 training samples and 195 testing samples. The results reveal that the combination of LDA and SVM achieved a classification accuracy of up to 99%, with a 50% reduction in data dimensionality and a computation speed of 0,0696 second. Moreover, computation time was significantly reduced, indicating that LDA not only facilitates data simplification but also improves the overall efficiency of the classification process.
Design Thinking untuk Perancangan UI Website Seller Toko: Studi Kasus PT Vetencode Pradani Abadi Hamzah, Shela Septiani; Lattu, Arny; Permana, Muhamad Anton
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8839

Abstract

Competition in the digital era is a significant challenge for traditional shops in neighborhood communities (RT/RW), particularly those with limited access to and understanding of digital technology needed to compete in online markets. This study aims to design a seller website user interface (UI) using the Design Thinking approach through five stages: Empathize, Define, Ideate, Prototype, and Test. Data collection was conducted through interviews, observations, and literature studies to ensure that the design aligns with the needs of small-scale business owners. The UI prototype was developed in the form of low-fidelity and high-fidelity wireframes and tested through usability testing based on the Heuristic Evaluation method by three internal evaluators. The results showed that 60% of the usability principles revealed no issues (severity 0), while the remaining 40% only indicated cosmetic issues (severity 0.33–1.00). The overall average score of 0.23 reflects a very good level of usability without requiring major improvements. Therefore, applying Design Thinking successfully produced a user-friendly UI tailored to neighborhood-level sellers in navigating digital transformation.
Comparison of IndoBERT and Bi-LSTM Models for Indonesian Law Violation Text Classification Pramana, Made Wahyu Adwitya; Putri, Desy Purnami Singgih; Purnawan, I Ketut Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8795

Abstract

Legal violations in Indonesia, particularly those under the Criminal Code (KUHP) and the Information and Electronic Transactions Law (UU ITE), are often difficult for the general public to interpret due to the complexity of legal language and article structures. This research aims to build a multilabel classification model that can automatically identify relevant legal articles from user-provided case descriptions. Two models were developed and compared: Bidirectional Long Short-Term Memory (Bi-LSTM) and IndoBERT. Using a manually labeled dataset, both models were evaluated through accuracy, F1-score, and Hamming Loss metrics, as well as 5-fold cross-validation. The results showed that IndoBERT outperformed Bi-LSTM with an average accuracy of 97% and a Hamming Loss of 0.027. However, t-test analysis revealed no statistically significant difference in F1-scores, indicating that both models have comparable effectiveness in capturing multiple labels. A confusion matrix analysis further identified patterns of misclassification in semantically similar articles. This study demonstrates the potential of NLP and deep learning to support legal awareness and provide the public with easier access to legal information.
Optimasi Fuzzy Logic Menggunakan Genetic Algorithm (GA) dalam Menentukan Program Diet dan Bulking Pramuditya, Benno; Prabowo, Ary
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9459

Abstract

The increasing demand for accurate and personalized diet and bulking programs highlights the need for a reliable decision support system (DSS). This study aims to develop a fuzzy logic–based DSS optimized with a Genetic Algorithm (GA) to recommend diet, bulking, or maintenance programs tailored to individual conditions. The methodology involved designing fuzzy sets, formulating IF–THEN rules, applying the Mamdani inference method, and optimizing fuzzy parameters using GA. Data were collected from 50 adult respondents, and the system was tested using 10 input scenarios validated by fitness experts. The results revealed that the fuzzy system without GA achieved only 38% agreement with expert recommendations, whereas GA optimization significantly improved accuracy to 82%. Furthermore, GA refined membership functions and eliminated irrelevant rules, producing a more streamlined yet precise system. The web-based interface facilitated user interaction and interpretation of results, ensuring practical usability. In conclusion, integrating fuzzy logic with GA enhanced the accuracy and adaptability of the system for determining diet and bulking programs, establishing it as a promising decision-making tool that can be further expanded with additional personalization variables in the future.
Prediksi Kebutuhan Beras Di Jawa Timur Menggunakan Metode Gated Recurrent Unit (GRU) Hozairi, Hozairi; Muhsi, Muhsi; Putri, Nadira Hijriani
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8790

Abstract

Food security is a strategic issue that affects economic stability and community welfare, especially in ensuring the availability of rice as a staple food in East Java. Uncertainty in food planning can cause an imbalance between rice production and consumption. Consequently, a precise forecast technique is necessary to aid decision-making. The objective of this research is to forecast or predict rice needs using the Gated Recurrent Unit (GRU) model to support more effective food management. The research methods include Min-Max Scaling normalization, and data division into 80% training and 20% testing. The GRU model has two main layers with 64 and 32 neuron units, The system was trained for 100 epochs with a batch size of 32 using the Adam optimizer and the MSE loss function. The evaluation results show high performance with MAE 0.0103, MSE 0.0001, RMSE 0.0116, and R² 0.9935, indicating low error and good generalization. The Training and Validation Loss graph shows a stable learning model without overfitting. This model can be a reliable prediction tool in food planning. Implementation of the model can help the government maintain the balance of rice supply and optimize agricultural policies.
OPTIMASI PERFORMA ALGORITMA DOUBLE EXPONENTIAL SMOOTHING HOLT UNTUK PERAMALAN JUMLAH MAHASISWA BARU MENGGUNAKAN MODIFIED GOLDEN SECTION Kurniawati, Iin; Rusdah, Mrs
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9301

Abstract

Penelitian ini membahas tentang perencanaan Universitas Buana Perjuangan Karawang dalam menghadapi peningkatan jumlah pendaftar mahasiswa baru. Berdasarkan penelitian sebelumnya, algoritma double exponential smoothing (holt) telah terbukti efektif dalam meramalkan jumlah mahasiswa baru. Namun, algoritma tersebut membutuhkan waktu lama untuk menentukan parameter terbaik. Data yang digunakan adalah jumlah mahasiswa dari tahun 2015 hingga 2023 yang dibagi berdasarkan pergelombang pemdaftaran. Pengujian dilakukan berdasarkan Mean Squared Error (MSE) dan Root Mean Squared Error (RMSE). Kontribusi penelitian ini adalah optimasi nilai alhpa dan betha pada algoritma double exponential smoothing untuk menghasilkan peramalan jumlah mahasiswa baru dengan tingkat eror terkecil. Berdasarkan hasil penelitian yang telah dilakukan, bahwa teknik modified golden section terbukti bisa mengoptimalkan nilai alpha dan beta pada metode double exponential smoothing untuk peramalan jumlah mahasiswa baru di Universitas Buana Perjuangan Karawang. Hal tersebut dibuktikan dengan nilai MSE dan RMSE pada hasil perbandingan metode double exponential smoothing dengan optimasi dan tanpa optimasi menunjukkan adanya kesamaan. Adapun nilai MSE yaitu 0,05876 dan RMSE yaitu 0,19137.  Sehingga, metode tersebut terbukti optimal dapat mengurangi perhitungan berulang-ulang yaitu cukup dengan iterasi ke 19 telah didapatkan nilai alpha dan beta terbaik.

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