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INDONESIA
Journal of Informatics and Information Security
Published by Universitas Bhayangkara Jakarta Raya
ISSN : -     EISSN : 27224058     DOI : https://doi.org/10.31599
Core Subject : Science,
Computer Science & IT
Jurnal ini berisi tentang karya ilmiah hasil penelitian bidang ilmu komputer yang bertemakan: Artificial Intelligence, Blockchain Technology, Business Intelligence, Cloud Computing, Computer Architecture, Computer Vision, Database Systems, Deep Learning, Human Computer Interaction, Digital Forensic, Internet of Things, IT Security, Machine Learning, Networking, Semantic Web, Sistem Terdistribusi, Systems Engineering, Wireless Network.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 116 Documents
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Implementasi Forward Chaining pada Sistem Pakar Diagnosa Kerusakan Laptop Berbasis Web: Studi Kasus Layanan Servis Laptop Dimas Permadi; Dwipa Handayani; Prio Kustanto; Muhammad Yasir; Achmad Noeman; Agus Hidayat
Journal of Informatic and Information Security Vol. 6 No. 2 (2025): Desember 2025
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/paw2f574

Abstract

Laptop damage diagnosis at PT Tsurtech Solution is currently carried out manually, which can take more time and requires the technician's experience to identify problems accurately. This research aims to design and implement a web-based expert system to assist in diagnosing laptop damage based on symptoms inputted by users. The system is developed using the Forward Chaining algorithm, a data-driven reasoning method that matches facts (symptoms) with a set of predefined rules to conclude the type of damage. The system is built using the Waterfall development method, assisted by UML diagrams, and implemented with PHP programming language and MySQL database. Blackbox Testing is used to evaluate the system’s functionality. The results show that the system can be used as a supporting tool to help both users and technicians perform initial laptop damage diagnosis more quickly and efficiently.    
Sistem Administrasi dan Pelayanan Warga Berbasis Web dengan Algoritma Queue di RW 10 Rawabugel Kota Bekasi Wahyu Syafriadi; Achmad Noeman; Mayadi
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/njw11q18

Abstract

The administrative and community service processes in RW 10 Rawabugel, Bekasi City, are still carried out manually, resulting in delays in service, long queues, and difficulties in managing community data and archives. Therefore, a system is needed to improve the efficiency and quality of service. This study aims to design and develop a web-based administrative and community service system by applying the Queue algorithm using the First Come First Served (FCFS) method. This system allows residents to submit service requests online and monitor the status of their requests in real time, while RW officials can manage data and archives in a structured and computerized manner. The use of the Queue algorithm serves to automatically and fairly arrange the order of services based on the time of submission. The results of the study show that the developed system has succeeded in increasing service efficiency, reducing queue backlogs, and facilitating the management of resident administrative data. This system is expected to be a solution in improving the quality of administrative services in the RW environment.
Sistem Prediksi Permintaan Barang Berat dengan Memanfaatkan Data Historis Penjualan Menggunakan Long Short-Term Memory (LSTM)TERM MEMORY (LSTM) Ginda Maruli Andi Siregar; Khairul Anam; Saiyaratul Mawaddah
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/w0zqt762

Abstract

Inventory management is an important aspect in maintaining the effectiveness of business operations, particularly in building material stores where demand fluctuations can affect stock availability. Inaccurate inventory planning may lead to overstock or stockout conditions, resulting in increased operational costs and reduced customer satisfaction. This study aims to develop a demand forecasting system for building materials using the Long Short-Term Memory (LSTM) method based on historical sales data at Toko Bangunan Beu Sukses. The dataset used consists of daily sales data from January 2024 to October 2025 covering six products, namely steel, cement, paint, pipes, zinc roofing, and plywood. Data preprocessing was performed through logarithmic transformation, differencing, normalization using MinMaxScaler, and sequence formation using the sliding window method. The LSTM model was trained and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation results indicate that the proposed model achieved a high level of forecasting accuracy, with all products obtaining MAPE values below 2%. Furthermore, the developed model was successfully integrated into a web-based application to support inventory management and decision-making processes. The results demonstrate that the LSTM method can effectively predict building material demand and support more efficient inventory management.   
Sistem Informasi Monitoring Kolektibilitas dan Manajemen Penagihan Piutang Koperasi Berbasis Web Mochammad Guntur Ramadhan; Dani Yusuf; Dwi Budi Srisulistiowati
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/r4vt2b04

Abstract

This research was motivated by the conventional receivable management process at Koperasi Konsumen Serba Usaha Desa Bersinar. The use of basic spreadsheet software for payment recording led to limitations in the real-time monitoring of members' collectability status and the absence of an early warning system for late payments, which hampered the cooperative's cash flow. The objective of this study was to design and develop a web-based information system for collectability monitoring and debt collection management. The system development employed the Waterfall method and Model-View-Controller (MVC) architecture, utilizing PHP and MySQL. The novelty of this system lies in the implementation of an automated classification function for five levels of collectability status (Current, Special Mention, Substandard, Doubtful, and Loss) by utilizing conditional logic and date manipulation based on the calculation of days past due (DPD). The functional testing results using the Black Box Testing method indicated that all application features operated in accordance with user requirement specifications. The implementation of this system assisted the cooperative in automating receivable risk monitoring and provided a technological blueprint to prevent the risk of non-performing loans in similar institutions. 
Analisis Klasterisasi Pelanggan Layanan Pijat Menggunakan Algoritma K-Means Clustering Pada Griya Sehat Faza Depok Wildanul Jannah; Adi Muhajirin; Rafika Sari
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/prvjnr35

Abstract

Customer segmentation is a crucial element in strengthening data-driven marketing strategies, especially for Micro, Small, and Medium Enterprises (MSMEs) operating in the service sector. Customer data management at Griya Sehat Faza Depok is still conducted conventionally without adopting an analytical approach to identify consumer profiles. This limitation hinders the optimization of marketing strategies and efforts to maintain customer loyalty. This study groups home massage service customers using the K-Means Clustering algorithm. The CRISP-DM (Cross Industry Standard Process for Data Mining) framework, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment, is used in this study. Customer transaction data from January to December 2025 serves as the dataset for analysis. Five variables are used in the clustering process: visit frequency, treatment type, total expenditure, service duration, and transportation costs representing customer distance. Data preprocessing stages include data cleaning, categorical data encoding, aggregation, and normalization using the Min-Max method. The optimal number of clusters is determined using the Elbow Method, while the quality of clustering results is evaluated using the Davies-Bouldin Index (DBI). The analysis results show the formation of four customer groups with a DBI value of 0.647, indicating good clustering quality. Each group exhibits distinct behavioral characteristics, enabling the identification of high-value customers, loyal customers, regular customers, and low-value customers. These findings offer practical recommendations for developing targeted marketing strategies, improving customer retention, and supporting more efficient therapist allocation at Griya Sehat Faza Depok.
Studi Klasifikasi Presisi Kesesuaian Lahan Pertanian Menggunakan Algoritma Extreme Gradient Boosting (XGBoost) Benardus Gunawan Sudarsono; Allan Desi Alexander
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/b7c8py98

Abstract

The application of artificial intelligence technology in the agricultural sector is the main foundation in the paradigm shift towards sustainable precision agriculture. This study presents a comprehensive analysis of the application of the Extreme Gradient Boosting (XGBoost) algorithm to predict the suitability of crop types based on soil chemical characteristics and macro-environmental conditions. Model evaluation was conducted using the benchmark dataset Crop Recommendation Dataset accessed through the Kaggle platform. This dataset has a perfect class balance with a total of 2,200 samples evenly divided into 22 agricultural commodities. The developed predictive model evaluates seven soil and climate biophysical parameters, namely nitrogen, phosphorus, potassium, air temperature, relative humidity, soil acidity (pH), and rainfall intensity. The test results show that the XGBoost algorithm ranks top in classification accuracy with values ​​ranging from 99.31% to 99.77%, surpassing other ensemble algorithms such as Random Forest as well as traditional models such as Decision Tree and Naive Bayes. Feature contribution analysis demonstrates that climate parameters (rainfall and humidity) act as primary ecological filters at the macro-level, while soil macronutrient (NPK) ratios serve as secondary determinants at the crop-specific micro-level. Overall, this boosting-based ensemble approach offers high accuracy and robustness to data outliers, making it a highly reliable agronomic decision-making tool for supporting sustainable land productivity.

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