Garuda - Garba Rujukan Digital

SISTEM INFORMASI E-LIBRARY MENGGUNAKAN METODE WEB ENGINEERING Ihsan Alvindra; Cindi Wulandari; Bunga Intan
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.454

A digital library information system (E-Library) offers a solution to the challenges of library data management—previously handled manually—which often resulted in disorganized records and difficulties in retrieving information regarding books and members. This study aims to design and develop an E-Library information system for SMP Negeri Purwodadi using the Web Engineering method, encompassing user requirements analysis, system design, implementation, and testing. The Laravel framework serves as the core technology for building a robust and efficient backend system, while Black Box Testing ensures the system operates in accordance with user needs. The results demonstrate that the system enables students and staff to access book data, search collections, and manage member information digitally via a user-friendly interface. Testing confirms that the system meets functionality and reliability standards. This development fosters a more integrated, efficient, and secure library management process, thereby enhancing the quality of school library services and supporting digital transformation in education.

PREDIKSI HARGA SAHAM BANK NEGARA INDONESIA (BNI) BERDASARKAN HISTORICAL STOCK PRICE MENGGUNAKAN METODE GATED RECURRENT UNIT (GRU) Arina Kanuri; Elmayati Elmayati; Bunga Intan
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.705

The volatile nature of stock price movements necessitates a prediction method capable of accurately modeling time-series data patterns. This study aims to predict the stock price of Bank Negara Indonesia (Persero) Tbk (BNI) using the Gated Recurrent Unit (GRU) method, based on historical data from the 2020–2025 period. The dataset includes closing prices, trading volume, moving averages (MA_7 and MA_21), volatility, and high-low ratios. The research process encompasses preprocessing, feature engineering, Min-Max normalization, the creation of sequential data with a time step of 30, and the splitting of data into training and testing sets. The GRU model is trained using the Adam optimizer, incorporating ReduceLROnPlateau and Early Stopping mechanisms to enhance training stability and mitigate the risk of overfitting. Model performance is evaluated using MSE, RMSE, MAE, R², and MAPE metrics. The results demonstrate that the GRU model yields effective predictions, achieving a MAPE of 3.91%, an RMSE of 211.21, and an R² of 0.746. Stock price predictions for the upcoming 10 days indicate a trend of gradual price decline. These findings suggest that the GRU method is effective for predicting BNI stock prices based on historical data and holds potential for supporting investment decision-making, while acknowledging external factors that influence market conditions.

Two-stage Transfer Learning for Chilli Leaf Disease Classification using CNN and Vision Transformer Rudi Kurniawan; Samsuryadi Samsuryadi; Fatma Susilawati Mohamad; Bunga Intan; Nelly Khairani Daulay
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7674

Chili cultivation is considerably affected by leaf diseases, making timely and reliable disease identification essential for maintaining agricultural productivity. While deep learning has demonstrated strong capabilities in plant disease recognition, many existing studies primarily focus on modifying network architectures rather than optimizing the training process. This study introduces a two-stage transfer learning framework that combines frozen-backbone adaptation with controlled fine-tuning to enhance classification performance. The experiments utilized a publicly available dataset comprising 12,000 augmented chili leaf images representing six disease categories. Several deep learning architectures were investigated, including DenseNet121, EfficientNetB0, MobileNetV2, ResNet152V2, and the Vision Transformer (ViT-B16). The experimental results indicate that the proposed two-stage training strategy improves classification performance consistently across the evaluated architectures. Among the models, ViT-B16 obtained the highest test accuracy of 99.61%, together with stable macro-averaged precision, recall, and F1-score values. Furthermore, the confusion matrix and ROC analysis revealed strong discrimination among the disease classes, with only limited errors occurring between visually similar categories. These findings highlight the importance of optimizing the training schedule when adapting pretrained models for plant disease recognition. Overall, the proposed framework provides an effective and scalable approach for chili leaf disease classification while offering an alternative to approaches that primarily rely on architectural modifications.

PENERAPAN METODE TREND PARABOLIC PROJECTION UNTUK PREDIKSI JUMLAH PENGUNJUNG BANK BNI WILAYAH KOTA LUBUKLINGGAU Cici Melika; Harma Oktafia Lingga Wijaya; Bunga Intan
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 12 No. 3 (2026): Volume 12 No. 3, September 2026 Produce
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v12i3.19198

This study aims to analyze and predict the number of visitors at Bank BNI using the Trend Parabolic Projection method. The data used in this research consist of historical visitor data which were analyzed to identify visitor growth trends over time. The research process included data collection, data processing, parabolic regression parameter calculation, data visualization, and forecasting for the next 12 months. The results show that the Trend Parabolic Projection method is capable of representing the data trend effectively, with model parameters becoming more stable as additional data are incorporated. Based on the forecasting results, the number of Bank BNI visitors in 2026 is predicted to gradually increase from approximately 655 visitors at the beginning of the year to around 693 visitors at the end of the year. These findings indicate that the proposed method can provide valuable insights into future visitor trends and can be used as a reference for operational planning and service quality improvement at Bank BNI.

Penerapan machine learning untuk mendeteksi serangan mirai infection pada jaringan komputer menggunakan dataset gotham 2025 Ravi Kumar Saidala; Priyanti; Bunga Intan; Sri Tita Faulina
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i3.3460

The expansion of networks increases the likelihood of cyberattacks, particularly those involving Mirai infections. This study aims to design and analyze a machine learning model capable of identifying Mirai infection attack anomalies within network infrastructure using the gotham2025 dataset. This dataset encompasses both normal traffic and various forms of Mirai infection attacks, such as ingress tool transfers, file downloads, Mirai C&C communication, reporting, and Merlin activity. Data imbalance was addressed using balancing methods—specifically the SMOTE technique—to prevent model bias. Algorithms such as Random Forest and Support Vector Machine (SVM) were implemented and evaluated based on accuracy, precision, recall, F1-score, confusion matrix, MCC, Kappa, ROC-AUC. The results demonstrate that Random Forest achieved 100% accuracy, while SVM attained 99.95%. Overall, both algorithms exhibited the most consistent performance in detecting the various forms of Mirai infection attacks. This research contributes to the development of machine learning-based intrusion detection systems aimed at enhancing network security.

Penerapan machine learning untuk mendeteksi serangan mirai infection pada jaringan komputer menggunakan dataset gotham 2025 Ravi Kumar Saidala; Priyanti; Bunga Intan; Sri Tita Faulina
Jurnal Teknologi Informasi Mura Vol. 18 No. 3 (2026): Jurnal Teknologi Informasi Mura SEPTEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i3.3460

The expansion of networks increases the likelihood of cyberattacks, particularly those involving Mirai infections. This study aims to design and analyze a machine learning model capable of identifying Mirai infection attack anomalies within network infrastructure using the gotham2025 dataset. This dataset encompasses both normal traffic and various forms of Mirai infection attacks, such as ingress tool transfers, file downloads, Mirai C&C communication, reporting, and Merlin activity. Data imbalance was addressed using balancing methods—specifically the SMOTE technique—to prevent model bias. Algorithms such as Random Forest and Support Vector Machine (SVM) were implemented and evaluated based on accuracy, precision, recall, F1-score, confusion matrix, MCC, Kappa, ROC-AUC. The results demonstrate that Random Forest achieved 100% accuracy, while SVM attained 99.95%. Overall, both algorithms exhibited the most consistent performance in detecting the various forms of Mirai infection attacks. This research contributes to the development of machine learning-based intrusion detection systems aimed at enhancing network security.

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