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Subhanjaya Angga Atmaja
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JuSTISe: Journal Data Science, Technology, Informatics and Security
Published by Universitas Kebangsaan Republik Indonesia
ISSN : -     EISSN : 31634230     DOI : -
Core Subject :
JuSTISe: Journal Data Science, Technology, Informatics and Security adalah jurnal ilmiah nasional yang ditinjau oleh sejawat (peer-reviewed) dan diterbitkan oleh Universitas Kebangsaan Republik Indonesia. Jurnal ini berfokus pada publikasi hasil penelitian berkualitas tinggi di bidang ilmu data (data science), teknologi informasi, rekayasa informatika, keamanan siber, dan sistem digital. JuSTISe bertujuan menjadi wadah yang kredibel bagi akademisi, peneliti, dan praktisi untuk menyebarluaskan hasil penelitian asli serta artikel tinjauan yang berkaitan dengan kecerdasan buatan, analitik big data, rekayasa perangkat lunak, keamanan jaringan, dan sistem informasi. Jurnal ini menekankan pada penelitian teoritis maupun terapan yang berkontribusi terhadap kemajuan teknologi dan inovasi di era digital. Seluruh naskah yang dikirimkan akan melalui proses penelaahan sejawat (peer-review) yang ketat untuk memastikan kualitas dan integritas akademik. JuSTISe menerima naskah dari penulis nasional maupun internasional, dan seluruh artikel diterbitkan dalam bahasa Inggris untuk menjangkau audiens global. Jurnal ini diterbitkan dua kali dalam setahun (Juni dan Desember) dan berkomitmen untuk mendukung pengembangan ilmu pengetahuan serta kolaborasi penelitian di bidang informatika, ilmu data, dan keamanan siber.
Arjuna Subject : -
Articles 39 Documents
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Penerapan Teknologi Application Programming Interface (API) MikroTik untuk Monitoring Virtual Local Area Network (VLAN) di SMK Al-Munawaroh Cianjur Aris Suhendra; Deni Suprihadi
Journal Data Science, Technology, Informatics and Security Vol 2 No 2 (2024): Journal Data Science, Technology, Informatics and Security (Desember 2024)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v2i2.3984

Abstract

This study aims to enhance network control at SMK Al-Munawwarah Cianjur by implementing MikroTik API technology, focusing specifically on the Control aspect of the PIECES method. This aspect includes user access management, data security, and access control to network resources. The research begins with an analysis of the existing network control system, followed by identifying the need for more effective control mechanisms. To improve network control, a MikroTik API-based solution is developed to optimize VLAN configuration and network monitoring. This solution is implemented with the expectation of addressing existing weaknesses in the network management system, as well as providing greater ease and flexibility in network surveillance and access control. The results of the study demonstrate that the use of MikroTik API significantly enhances VLAN configuration efficiency, user access management, and network monitoring within the school environment. This implementation also successfully improves the security and integrity of data transmitted across the network, creating a more secure and controlled network environment. Thus, the findings of this research can serve as a reference for the development of network control systems in other educational institutions with similar needs.
Sistem Peringatan Dini Bencana Banjir Berbasis Mikrokontroler ATmega16 dengan Buzzer dan Web-Based Alfi febriawan Febriawan; Deni Suprihadi
Journal Data Science, Technology, Informatics and Security Vol 3 No 2 (2025): Journal Data Science, Technology, Informatics and Security (Desember 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i2.3988

Abstract

Flooding is one of the natural disasters that often occurs due to high rainfall and overflowing rivers. Sumbersari Village, especially in Sapan Village, often experiences flooding due to the overflowing Citarum River. Therefore, a flood detection tool is needed to provide early warning to the community in order to reduce the risk and losses due to flooding. This study aims to design and develop an Arduino Uno-based flood early warning system with a fuzzy approach. This system uses an HC-SR04 ultrasonic sensor to measure water levels and a raindrop detection sensor to detect rain intensity. Data from the sensor is processed by the Arduino Uno microcontroller and displayed via a buzzer and a web-based platform. The test results show that this tool has high accuracy in monitoring water levels and providing real-time warnings to the community around the river flow. It is hoped that this system can be an effective solution in flood disaster mitigation.
Optimalisasi Pengelolaan Data Vendor Menjadi Dashboard Interaktif Menggunakan Google Data Studio di PT LEN Industri (Persero) Adam Husain; Siti Nurhayati
Journal Data Science, Technology, Informatics and Security Vol 2 No 2 (2024): Journal Data Science, Technology, Informatics and Security (Desember 2024)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v2i2.3989

Abstract

The manual management of vendor data at PT Len Industri (Persero) has led to limitations in access and operational efficiency. To address these challenges, an interactive dashboard utilizing Google Data Studio was developed as a digitalization solution, enabling a more systematic data visualization, real-time access, and enhanced transparency.The implementation results indicate that the dashboard accelerates data retrieval processes, provides visually comprehensible displays, and improves management decision-making efficiency. Furthermore, the automation features help minimize errors in data management and enhance operational accountability. Thus, the use of an interactive dashboard has proven to be effective in optimizing vendor data management and supporting improved company operational performance.
Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes Classification di Kelurahan Padasuka Ciomas Bogor Muhammad Lutfi; Nana Suryana; Isep Saepudin; Adam Husain; Sri Handayani
Journal Data Science, Technology, Informatics and Security Vol 3 No 1 (2025): Journal Data Science, Technology, Informatics and Security (Juni 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i1.4265

Abstract

Life is characterized by symptoms of growth and development. The health status of each individual is different. In this case, one of the efforts to improve health status is to improve nutritional status. Nutritional status is a state of the body related to food consumption patterns and the use of nutrients that are tailored to the body's needs. Improving nutritional status is useful for increasing body resistance and making normal growth. In actualizing the daily nutritional status of children under five at the posyandu, it is usually obtained through anthropometric measurements, namely by using the BW/U index or body weight compared to age to determine nutritional status. However, in anthropometric measurements, it was found that there was confusion in the determination of nutritional quality, so that in order to get accurate results, a data mining method was needed, namely the Naive Bayes Classification (NBC) Algorithm which would be implemented in the study. This research is expected to help posyandu cadres in Padasuka sub-district, Ciomas sub-district, Bogor district in determining the nutritional status of toddlers better and more accurately.
Implementasi Algoritma K-Nearest Neighbor dan Naive Bayes dalam Memprediksi Status Seleksi pada PPDB Rifqi Maulana Adam; Deni Suprihadi
Journal Data Science, Technology, Informatics and Security Vol 3 No 1 (2025): Journal Data Science, Technology, Informatics and Security (Juni 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i1.4298

Abstract

Abstracts, This study aims to implement the K-Nearest Neighbor (KNN) and Naive Bayes algorithms to predict the selection status of New Student Admissions (PPDB) at the junior high school level in Cianjur Regency. PPDB is an annual agenda that plays a crucial role in determining the transition of students to higher education levels. However, the selection process often poses challenges, particularly due to limited information and subjectivity in decision-making by students and parents. This research adopts a quantitative approach by utilizing historical registration data from 20 public junior high schools in Cianjur Regency. The research procedure includes data collection, preprocessing, implementation of the KNN and Naive Bayes algorithms, and evaluation using the Confusion Matrix. The results indicate that both algorithms are capable of predicting students’ acceptance status through the zoning and achievement tracks with accuracy levels above 85%. Naive Bayes demonstrates advantages in computational efficiency, while KNN provides greater flexibility in handling variations in data. The developed prediction system is expected to assist students and parents in determining the most suitable school objectively and support schools and education authorities in providing data-driven recommendations. Furthermore, this study reinforces findings from previous research, emphasizing the potential of data mining as an effective approach to support educational selection processes and decision-making.
RESTful API dengan Dukungan AES-GCM dan XChaCha20-Poly1305 dalam Pengelolaan Data Identitas Penduduk (Studi Kasus Desa Galudra) Restu Oktafiandi; Deni Suprihadi
Journal Data Science, Technology, Informatics and Security Vol 3 No 2 (2025): Journal Data Science, Technology, Informatics and Security (Desember 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i2.4302

Abstract

The management of citizen identity data plays a critical role in governmental administration, including in Galudra Village, Cugenang District, Cianjur Regency. Traditionally, data recording has relied on Microsoft Excel, which, while adequate in the early stages, becomes inefficient as the population grows and the demand for fast, accurate, and secure services increases. This study develops a RESTful API integrated with AES-GCM and XChaCha20-Poly1305 cryptographic algorithms to enhance both security and efficiency in managing resident data. AES-GCM is employed to secure stored data, whereas XChaCha20-Poly1305 is applied to protect data during transmission. The system was developed using the waterfall model, with blackbox testing applied to validate its functionality. The implementation results indicate that the system effectively accelerates data processing and safeguards sensitive information. Network monitoring with Wireshark confirmed that all transmitted data is well-encrypted, making it inaccessible in its original form. Therefore, this solution not only addresses efficiency and security challenges at the village level but also aligns with Law Number 27 of 2022 concerning Personal Data Protection, and serves as a reference for implementing secure information technology in local government environments.
Smart HVAC: Monitoring dan Kontrol Berbasis IoT dengan Fuzzy Logic Mamdani Esa Aprillah; Yasri; Subhanjaya Angga Atmaja
Journal Data Science, Technology, Informatics and Security Vol 2 No 2 (2024): Journal Data Science, Technology, Informatics and Security (Desember 2024)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v2i2.4309

Abstract

The development of the Internet of Things (IoT) has driven the implementation of more efficient automation systems, including HVAC (Heating, Ventilation, and Air Conditioning) control. This study designs and implements an IoT-based HVAC monitoring and control system using a NodeMCU ESP8266, DHT11 sensors, relays, webcams, and the Mamdani Fuzzy Logic algorithm integrated with the Blynk application. The case study was conducted in the CV. Sukses Berkarya office, which requires stable temperature and humidity. The system is able to read temperature-humidity data, process it using fuzzy logic, and automatically control the air conditioning. Furthermore, YOLOv8 integration with the webcam is used to detect the number of people in the room as an additional parameter. Test results show that the system can maintain the room temperature within the ideal range of 18–30°C, reduce energy waste, and facilitate remote monitoring and control. This study demonstrates that the combination of IoT, fuzzy logic, and visual detection can produce an adaptive, efficient, and user-friendly HVAC system.
Prototipe Sistem Penampungan Air Otomatis Berbasis IoT dengan Logika Fuzzy untuk Pertanian Cerdas Adhin Luthfi Indrawan; Oscar Hadikaryana; Subhanjaya Angga Atmaja
Journal Data Science, Technology, Informatics and Security Vol 3 No 1 (2025): Journal Data Science, Technology, Informatics and Security (Juni 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i1.4310

Abstract

The development of Internet of Things (IoT) technology has brought significant changes to modern agricultural systems, particularly in terms of water efficiency. This research developed a prototype of an IoT-based automatic water storage system capable of adjusting water distribution according to environmental conditions in real time. The system was built using a NodeMCU ESP8266 as a control center and several sensors, including a soil moisture sensor, a rain sensor, an ultrasonic sensor, a temperature and humidity sensor, and a water flow sensor. The integration of these various sensors allows the system to detect land conditions comprehensively. Mamdani fuzzy logic is used as a decision-making method by considering variables such as soil moisture, weather conditions, and temperature. A rule-based system approach is added to provide more flexible decision rules. The sensor readings are processed by a microcontroller and then sent to the Blynk application so farmers can monitor and control the system via smartphone. Test results show that the system can adapt to various conditions. In dry soil with high temperatures, the pump automatically activates until the moisture threshold is reached. Conversely, when rain is detected or the soil is wet, the system delays watering to avoid waste. Water discharge data is also recorded in real time so that water use can be better controlled. This research demonstrates that the integration of IoT and Mamdani fuzzy logic can improve water distribution efficiency, reduce waste, and support smart farming practices. The developed system has the potential to be widely applied to agricultural land to help farmers manage water resources efficiently and sustainably.
Aplikasi Mobile Multi-Algoritma AI untuk Bisnis Konveksi Julian Utami; Oscar Hadikaryana; Iim Abdurrohim
Journal Data Science, Technology, Informatics and Security Vol 2 No 2 (2024): Journal Data Science, Technology, Informatics and Security (Desember 2024)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v2i2.4311

Abstract

Small and medium-sized garment enterprises often struggle with manual production recording and payroll estimation, leading to inefficiency. This study develops a mobile application integrating multiple artificial intelligence algorithms to improve business prediction accuracy. The research employed a Research and Development (R&D) method with a Rapid Application Development (RAD) approach. Eight years of historical production and payroll data were analyzed using three algorithms: Linear Regression, Random Forest Regression, and KMeans Clustering. The results indicate that the application enhanced recording efficiency, Random Forest outperformed Linear Regression on fluctuating data, and K-Means effectively recommended the best-performing employees. In conclusion, the system contributes to digitalization and data-driven decisionmaking in the garment sector.
Sistem Jemuran Otomatis dengan IoT dan Gemini AI Nadila Amalia Wibowo; Oscar Hadikaryana
Journal Data Science, Technology, Informatics and Security Vol 3 No 2 (2025): Journal Data Science, Technology, Informatics and Security (Desember 2025)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v3i2.4313

Abstract

Unpredictable weather often disrupts household activities, particularly in drying clothes. Sudden rain may cause clothes to get wet again, requiring constant user supervision. This research aims to design and implement an automatic clothesline system based on the Internet of Things (IoT), integrating a rain sensor, webcam, and Gemini AI with the zero-shot image classification method. The system relies on two main inputs: a rain sensor to detect raindrops, and a webcam capturing sky images classified by Gemini AI into sunny, light cloudy, heavy cloudy, and rainy. These inputs are processed using a rule-based algorithm on the ESP32 microcontroller, which controls the servo motor to retract or extend the clothesline automatically. In addition to the automatic mode, the system is equipped with a manual control mode accessible through the Blynk mobile application, allowing users to monitor and control the system remotely. Testing results show that the rain sensor accurately detects water presence, while Gemini AI can classify weather conditions responsively. Integration testing confirms that the clothesline operates effectively in both automatic and manual modes, while providing real-time weather condition notifications.

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