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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) International Journal of Artificial Intelligence Research
Hapsani, Anggi Gustiningsih
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Computer Science & IT Electrical & Electronics Engineering

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Data Encryption and Security in Data Storage Management Information System Using Blowfish Algorithm Putri, Mayang Anglingsari; Trihapningsari, Denisha; Hapsani, Anggi Gustiningsih; Putri, Chandra Sina
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 16, No 2 (2024): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v16i2.28893

Abstract

Documents and archives is an important part of BPAD (Badan Perpustakaan Arsip dan Dokumentasi) Malang because the main task is the management of archives. But in its management, mail archiving, as well as the approval letter is still done manually and there is no security of its data. With this system is expected to help to manage and document all messages are in BPAD (National Library of Archives and Documentation) Malang. Both incoming and outgoing mail. Letters contained in the system is also secured by means of encryption using the Blowfish algorithm to secure data archive important letters so that data is safe and away from the risk of this manipulation. Aplication can simplify the process of collecting data, archive storage and secure image archive in poor districts BPAD use blowfish algorithm so that data is safe and away from the risk of misuse.
Comparison Various Analytical Approaches to Find The Most Efficient and Effective Method for Peak Hour Identification Hapsani, Anggi Gustiningsih; Putri, Mayang Anglingsari
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.29193

Abstract

The Peak hour sales identification is essential to manage staff, inventory, and service capacity in coffee shop operations. This study compares an exploratory heatmap with two forecasting models, linear regression and Seasonal ARIMA (SARIMA) using six months of hourly transaction data from a coffee shop (1 March–17 August 2024) . The heatmap offers rapid visual recognition of high traffic periods but provides no predictive capability. For prediction, this study trained a linear regression and a SARIMA specification tuned by standard diagnostics; model performance was assessed on a held out set using MAE, RMSE, and MAPE. Linear regression yielded RMSE = 6.68, MAE = 5.40, and MAPE = 138.06%, indicating inadequate fit for intraday demand dynamics. In contrast, SARIMA achieved RMSE = 0.828, MAE = 0.557, and MAPE = 40.34%, substantially reducing error by explicitly modeling autocorrelation and recurrent seasonal cycles. The results show that seasonality aware time series modeling delivers actionable, interpretable forecasts for near term operational planning (such as staffing and product preparation). Overall, the proposed pipeline, heatmap for rapid situational awareness plus SARIMA for prediction, constitutes a practical baseline for peak hour identification in small scale retail.
Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction Sari, Yuita Arum; Hapsani, Anggi Gustiningsih; Adinugroho, Sigit; Hakim, Lukman; Mutrofin, Siti
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3183.967 KB) | DOI: 10.29099/ijair.v4i2.165

Abstract

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.
Co-Authors Lukman Hakim Mayang Anglingsari Putri, Mayang Anglingsari Putri, Chandra Sina Sigit Adinugroho Siti Mutrofin Trihapningsari, Denisha Yuita Arum Sari