cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tonni Limbong
Contact Email
jurnal.means@gmail.com
Phone
+6281267058001
Journal Mail Official
tonni.budidarma@gmail.com
Editorial Address
http://ejournal.ust.ac.id/index.php/Jurnal_Means/about/editorialTeam
Location
Kota medan,
Sumatera utara
INDONESIA
MEANS (Media Informasi Analisa dan Sistem)
Published by Universitas Katolik Santo Thomas
ISSN : 25486985     EISSN : 25993089     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal MEANS berdiri sejak Tahun 2016 dengan SK dari LIPI yaitu p-ISSN : 2548-6985 (Print) dan e-ISSN : 2599-3089 (Online) Terbit dua kali setiap Tahunnya yaitu Periode I Bulan Juni dan Periode II Bulan Desember Hasil Plagirisme Maksimal 25%, Lebih dari 25% Artikel Tidak Bisa Publish. Ruang lingkup publikasi ini adalah untuk bidang Ilmu Komputer seperti : 1. Sistem Informasi 2. Manajemen dan Audit Sistem Informasi 3. Aplikasi Dekstop 4. Web dan Mobile 5. Sistem Pendukung Keputusan 6. Sistem Pakar 7. Pembelajaran berbasis Komputer 8. Kriptografy 9. Bidang Ilmu Koputer yang lainnya.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
 1   2 
Search results for , issue "Volume 10 Nomor 1" : 12 Documents clear
Pengembangan Sistem Peramalan Permintaan Menggunakan Algoritma Support Vector Regression Untuk Optimalisasi Safety Stock Berbasis Web (Studi Kasus: JG Motor Sukabumi) Fatalifi, Amerjid Ghulamson; Somantri, Somantri; Kharisma, Ivana Lucia
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to develop a web-based system utilizing Support Vector Regression (SVR) to predict motor vehicle spare part demand and optimize safety stock levels at JG Motor Sukabumi. The inventory management faces challenges such as fluctuating demand, supply delays, and overstock/stockout risks. To address these issues, SVR is chosen for its ability to handle non-linear and complex data, providing more accurate predictions than conventional methods. This research employs a descriptive quantitative approach with semi-experimental methods to test the SVR model's effectiveness and web-based system validity. The system features monthly demand prediction, safety stock calculation, historical data visualization, and interactive analytical reports. Development involves user requirement analysis, two-year historical sales data collection, data preprocessing, SVR model training with parameter optimization, and Flask-based integration. Black Box Testing ensures primary functions, such as input validation, prediction processing, and stock recommendation outputs, operate correctly. Results indicate the SVR model achieves high accuracy, reflected by low Mean Absolute Error (MAE) values. The web-based system is user-friendly for managers and operational staff to monitor demand and manage inventory efficiently. Moreover, the system supports strategic decision-making, enhancing JG Motor Sukabumi's operational efficiency and competitiveness in the automotive market.
Desain dan Implementasi Sistem Kontrol Suhu dan Kelembaban di Greenhouse dengan Pendekatan Fuzzy Tjahyanti, Luh Putu Ary Sri; Pratama, Putu Aditya; Prabawa, Putu Shantiawan; Gitakarma, Made Santo
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to develop an automatic control system for temperature and humidity in a greenhouse using the NodeMCU ESP 8266 microcontroller in the context of Smart Farming. Optimal greenhouse environmental control is crucial for increasing crop productivity. The system collects temperature and humidity data from sensors, with control using Fuzzy logic rules designed in Matlab. Test results show that the NodeMCU functions well at an average voltage of 4.7 V. The soil moisture sensor provides readings corresponding to soil conditions: dry (12%-14%), wet (66%-71%), and after watering (39%-42%). The DHT11 sensor recorded an average temperature of 33°C and air humidity of 55%. This system provides an automatic solution responsive to environmental changes, creating optimal conditions for plant growth, while improving efficiency and productivity in modern agriculture. This research contributes to the development of sustainable smart farming.
Implementasi Metode Double Diamond Design Process pada UI/UX Aplikasi Manajemen Kesehatan dan Gizi Personal (WellFits) Fatimah, Fitri Asri Nur; Voutama, Apriade
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

WellFits is an innovative digital solution for personal health and nutrition management, focusing on calorie deficit, stunting prevention in pregnant women, and nutrition consultation with experts. This study applies the Double Diamond Design Process to identify user needs and develop relevant features. The development process includes problem exploration, needs definition, solution development, and implementation with iterative eval_uation based on user feedback. Usability testing results showed an average score of 8.04 out of 10, indicating high satisfaction and ease of use. With features for tracking nutritional intake and expert consultations.
Sistem Informasi Kelayakan TORA Berbasis Web di Kabupaten Cianjur Dongoran, Awaluddin; Januriana, Andi Moch; Firmansyah, Devie; Wahyono, Eko Budi
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Agrarian reform is a strategic program aimed at improving land ownership distribution and enhancing community welfare. One crucial stage in this program is determining the eligibility of Tanah Objek Reforma Agraria (TORA), which requires an objective and structured selection method. This study develops a web-based system that implements the Simple Additive Weighting (SAW) method to assess TORA eligibility more efficiently and transparently. The SAW method is chosen because it provides systematic calculations by considering various established criteria, such as land legal status, land use, and social and economic potential. This system enables policymakers to manage and analyze data more effectively, thereby supporting accurate and fair decision-making in the implementation of agrarian reform. The study results indicate that a web-based approach using the SAW method can improve accuracy and efficiency in determining TORA eligibility, offering a more structured solution for agrarian reform programs in Indonesia.
Evaluasi Sistem Pelacakan Resi Pada Website Shopee Express Menggunakan Pendekatan Pieces Salamudin, Salamudin; Sutabri, Tata
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study uses the PIECES framework, which has six important aspects: Performance, Information, Economy, Control, Efficiency, and Service, to assess the shipment tracking system on the Shopee Express website. Direct observation and system testing serve as the foundation for the study's qualitative descriptive methodology. The system's accuracy in displaying real-time tracking information and its performance on stable networks are demonstrated by the results. However, a number of drawbacks are discovered, including restricted service responsiveness on sluggish connections, no progress feedback during data loading, and no input validation. The system lacks user assistance features like history tracking and interactive guidance, despite being cost-effective and available without a login. The PIECES framework is a useful tool for determining web-based logistic tracking systems' advantages and shortcomings.
Analisis Perbandingan Kinerja Algoritma Klasifikasi Data Menggunakan Metode K-NN, Naive Bayes, dan Decision Tree pada Dataset UCI Iris Octavianto, Muhammad Dicky Azhary; Subtari, Tata
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Data classification is one of the important techniques in data mining and machine learning, which is widely used to group data into certain classes. This study aims to analyze and compare the performance of three classification algorithms, namely K-Nearest Neighbor (K-NN), Naive Bayes, and Decision Tree, in classifying Iris data from the UCI Machine Learning Repository. This dataset consists of 150 data with four feature attributes and three target classes. Testing was carried out using the cross-validation method with a k-fold approach of 10 folds. The results of the performance evaluation were measured using the metrics of accuracy, precision, recall, and f1-score. Based on the test results, the K-NN algorithm showed the highest accuracy rate of 96.67%, followed by Decision Tree at 95.33%, and Naive Bayes at 94.00%. These findings indicate that choosing the right classification algorithm can affect the success rate in the data classification process.
The Impact of Aurora Supercomputer in Scientific Area and the Development Analysis Okafor, Ngozi Lilian; Gunawan, Stella Putri; Zhafirah, Rafdah; Sujatmiko, Devlin Wen; Aldila, Amalia Shifa; Scantya, Miranti Andhita; Supriyono, Lawrence Adi; Putranto, Kartiko Eko
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Supercomputers are crucial in solving complex scientific and industrial computing due to its tremendous computational power in enabling large- scale simulations, scientific research, and advancement in various scientific fields. This study is conducted on Aurora supercomputer, a powerful exa-scale supercomputer designed for intricate computing tasks, such as climate modeling, intense simulations, and AI and machine learning. By making use of the literature review approach, we analyze the capabilities and impact of Aurora on the scientific environment. Our research suggests that Aurora is capable of significantly enhancing performance on processing data, surpassing supercomputers such as Frontier and Fugaku. Furthermore, we discuss Aurora's impact on driving groundbreaking research across multiple scientific domains and its real-world applications such as drug discovery driven by AI and machine learning. The result highlights that Aurora marked a remarkable milestone in revolutionizing computational research and further research can show the true power of the Aurora supercomputer
Pengembangan Sistem Informasi Posyandu Balita Bintang Timur Pada RW 17 Kelurahan Sendangmulyo Kota Semarang Werdhiningsih, Alana Sharfina; Februariyanti, Herny
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Posyandu is a primary healthcare service that plays an essential role in monitoring child growth and maternal health in communities. However, manual record-keeping still used by “Bintang Timur” Posyandu in RW 17, Sendangmulyo Subdistrict, Semarang City, has led to delays in data processing and reporting. This study aims to design and develop a web-based information system to enhance the efficiency and accuracy of Posyandu data management. The system was deveIoped using the WaterfaIl model and built with PHP, Laravel, and MySQL technologies. The results show that the system effectively manages child data, visit records, immunizations, and generates digital reports. The implementation of this system accelerates data entry, minimizes errors, and facilitates easier access to information for cadres and related parties. Therefore, the system is expected to support more effective, transparent, and sustainable healthcare services.
Sistem Pengontrol Kecepatan Mobil Ambulans Berbasis IoT Menggunakan NodeMCU ESP8266 Laia, Juniarti; Nainggolan, Winner Parluhutan
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research introduces an IoT-based speed control system tailored for ambulance vehicles using the NodeMCU ESP8266 microcontroller. The system employs an optocoupler sensor to detect wheel rotation speed and transmits real-time data via Wi-Fi to the Blynk application. The objective is to ensure that the ambulance maintains safe operating speeds, while simultaneously activating alerts for the driver whenever thresholds are exceeded. Performance evaluations were conducted across GPRS, 3G, and 4G networks to assess reliability and responsiveness. The findings reveal a response latency of approximately 1 second on a 4G connection, and up to 3 seconds under GPRS. The proposed system exhibited strong network stability over a 24-hour test period and delivered accurate speed monitoring throughout. The novelty of this work lies in the seamless integration of real-time monitoring and alert mechanisms without reliance on manual intervention. This solution has potential application in improving the operational safety and efficiency of emergency medical transport services in Indonesia.
Performance Analysis of Neural Networks With Backpropagation on Binary and Multi-Class Data Classification Abdul Tahir; Irdam, Irdam; Sirama , Sirama
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Neural networks represent a widely adopted paradigm within the domain of machine learning, employed for a multitude of classification endeavors, encompassing image recognition and natural language processing. This investigation seeks to elucidate the influence of varying neuron quantities in hidden layers on the efficacy of neural networks in both binary and multi-class classification endeavors. The research utilizes a dataset procured from images depicting characters and digits, which were transformed into binary format via a thresholding methodology. The neural network architectures comprise one and two hidden layers, which are trained employing the backpropagation algorithm in conjunction with the Adam optimizer. The evaluation of the models is conducted through metrics such as accuracy, loss curves, and confusion matrices. Findings reveal that the configuration featuring two hidden layers with 40 sampai 99 neurons achieves the pinnacle accuracy of 99.64 percent alongside optimal loss stability. Furthermore, models incorporating a single hidden layer exhibited commendable accuracy, thereby indicating that a reduced number of neurons can proficiently encapsulate data complexity in less demanding tasks. This research underscores the criticality of selecting suitable neural network configurations contingent upon data complexity and classification objectives, while advocating for further investigation into regularization strategies to enhance performance.

Page 1 of 2 | Total Record : 12

 1   2 

Filter by Year

2024 2025


Reset
Filter By Issues
All Issue Volume 10 Nomor 1 Volume 9 Nomor 2 Volume 9 Nomor 1 Volume 8 Nomor 2 Volume 8 Nomor 1 Volume 7 Nomor 2 Volume 7 Nomor 1 Volume 6 Nomor 2 Volume 6 Nomor 1 Volume 5 Nomor 2 Volume 5 Nomor 1 Volume 4 Nomor 2 Volume 4 Nomor 1 Volume 3 Nomor 2 Volume 3 Nomor 1 Volume 2 Nomor 2 Volume 2 Nomor 1 Volume 1 Nomor 2 More Issue