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All Journal Jurnal Sistem Informasi Kaputama (JSIK) Majalah Ilmiah Kaputama El-Mujtama: Jurnal Pengabdian Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Mathematics and Technology (MATECH) International Journal of Health, Engineering and Technology Indonesian Journal of Education And Computer Science Indonesian Journal of Science, Technology, and Humanities Nusantara Journal of Multidisciplinary Science Jurnal Penelitian Teknologi Informasi dan Sains Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Systematic Literature Review Journal Router : Jurnal Teknik Informatika dan Terapan Intelligent Systems and Robotics
Syari, Milli Alfhi
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

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Integration of Critical Thinking in Intelligent Algorithms for Hoax Detection on Social Media Platforms Milli Alfhi Syari; Hermansyah Sembiring; Muhammad Fadlan Siregar
Systematic Literature Review Journal Vol. 1 No. 4 (2025): October: Systematic Literature Review Journal
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/slrj.v1i4.229

Abstract

The rapid growth of social media as a primary channel for information dissemination has triggered a significant surge in the distribution of hoaxes, potentially damaging social order, instigating mass disinformation, and threatening national security. This research aims to design an intelligent algorithm for hoax detection by integrating a critical thinking approach into Natural Language Processing (NLP)-based text processing. The algorithmic model is built using a combination of linguistic features, argument logic, and cognitive indicators such as the detection of unsubstantiated claims, identification of source bias, and evidence testing. To ensure accountability and transparency of the system, an Explainable AI (XAI) approach is applied so that classification results can be understood by non-technical users. The research results show that integrating critical thinking significantly improves detection accuracy to 93.1%, with an increase in precision and recall for detecting hoaxes based on emotional narratives. Beyond technical aspects, this model aligns with the mandate of Law of the Republic of Indonesia Number 11 of 2008 concerning Information and Electronic Transactions (ITE Law), particularly Article 28 paragraph (1), which prohibits the dissemination of false and misleading news that harms the public. Therefore, this system is not only scientifically relevant but also supports law enforcement and strengthens digital literacy in the post-truth era. These findings are expected to be a strategic contribution to the development of an ethical, critical, and responsible digital ecosystem.
Motorcycle Credit Purchase Decision Support System With Additive Ratio Assesstment (ARAS) Method Jecika Azzahra; Yani Maulita; Milli Alfhi Syari
International Journal of Health Engineering and Technology Vol. 1 No. 2 (2022): IJHE-JULY 2022
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (785.558 KB) | DOI: 10.55227/ijhet.v1i2.23

Abstract

In the era of globalization as it is today, life is felt to be growing rapidly, the number of people's needs for private vehicles to facilitate all daily activities. The development of increasingly advanced technology makes us to know the importance of the use of computers in the process of processing data quickly and practically. CV. Aneka Teknik is a company engaged in motorcycle sales services by way of cash and credit. This company cooperates with several Leasing. So far, the company only accepts application files from consumers without testing files from consumers whether they deserve credit or not.  This makes leasing difficult to handle so that the process of granting credit becomes slow. To overcome this, the author makes an application for a decision support system in determining the application of prospective customers to obtain credit facilities. After CV. Aneka Teknik processes incoming consumer files, so the files that are eligible to get credit are sent or submitted to leasing. The highest score in the ranking obtained by the ARAS method decision support system in the highest position is A9 with the name Apriandi Alfa Reza Saragih with a value of 0.1538 who has the right to be selected as a motorcycle loan recipient
Pendeteksian Kebocoran pada Jaringan Pipa Berbasis Internet of Things (IoT) dengan Notifikasi dan Lokalisasi Sumber Kebocoran Adityo Razzaqqi; Husnul Khair; Milli Alfhi Syari
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 3 (2025): September : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i2.609

Abstract

This study aims to design and develop a pipeline leakage detection system based on the Internet of Things (IoT) that provides real-time notifications and determines the location of leaks with high accuracy. Pipeline leakage is a serious issue, as it can lead to water wastage, environmental damage, and high maintenance costs. Therefore, a system that can detect leaks quickly and accurately is crucial for improving the efficiency of pipeline infrastructure management. The system developed in this study uses an ESP32 microcontroller, Waterflow sensor, and GPS module. The ESP32 microcontroller serves as the central processing unit that processes the data received from the Waterflow sensor and the GPS module. The Waterflow sensor detects changes in water flow that indicate a leak in the pipeline. When an abnormal reduction in flow is detected, the sensor sends a signal to the microcontroller. The GPS module then provides location coordinates, pinpointing the exact location of the leak, allowing the maintenance team to quickly address the issue. Additionally, the system is integrated with the Blynk application, which enables remote monitoring through a mobile device. The Blynk application provides a user interface that facilitates the monitoring of pipeline status and delivers notifications whenever a leak is detected. Testing results show that the IoT-based leakage detection system is capable of identifying leaks and sending real-time information with good accuracy. With this system, the process of identifying and addressing pipeline leaks can be done faster and more efficiently, ultimately reducing the losses caused by leakage. The system also offers a more effective solution for pipeline maintenance, improving the reliability of water distribution systems and reducing water resource wastage.
Design and Implementation of a Deep Reinforcement Learning Framework for Autonomous Navigation in Dynamic Unstructured Robotic Environments with Real Time Obstacle Avoidance Milli Alfhi Syari; Zira Fatmaira; Syofyan Anwar syahputra
Intelligent Systems and Robotics Vol. 1 No. 1 (2026): February: Intelligent Systems and Robotics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/isr.v1i1.29

Abstract

Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.
Application of the Learning Vector Algorithm Quantization On Smart Barcodes Andre Andre; Achmad Fauzi; Milli Alfhi Syari
Indonesian Journal of Education And Computer Science Vol. 1 No. 2 (2023): INDOTECH - August 2023
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v1i2.41

Abstract

The implementation of the Learning Vector Quantization (LVQ) algorithm on smart barcodes aims to enhance efficiency and accuracy in recognizing and tracking product data. In this context, barcodes serve as visual representations containing crucial product information. The LVQ algorithm is employed to optimize the classification and matching processes of barcode data with precise references. Through repeated training, this algorithm adapts learning vectors to better recognize barcode variations. In this study, researchers analyze the impact of LVQ algorithm implementation on smart barcode systems concerning identification accuracy, computational efficiency, and adaptability to changes. Experimental results demonstrate the significant benefits of applying barcodes to inventory systems in overall stock management and business efficiency. By utilizing barcode technology, the processes of tracking and recording product data become faster, more accurate, and automated. Barcode usage minimizes human errors, optimizes time, and reduces operational costs. By combining the intelligence of the LVQ algorithm with the potential of barcodes, this research illustrates a crucial advancement in the technology integration domain for the development of more sophisticated and effective systems
Application of the K-Means Algorithm in Traffic Violations In Langkat District (Case Study: Langkat Police) Elisa Puspita Sari; Yani Maulita; Milli Alfhi Syari
Indonesian Journal of Education And Computer Science Vol. 1 No. 2 (2023): INDOTECH - August 2023
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v1i2.50

Abstract

Societal activities are intertwined with traffic, and people prefer using vehicles. The lack of education and limited understanding of traffic regulations have led to numerous violations. The increasing number of traffic violations has resulted in a rise in traffic violation data. The abundance of traffic violation data has led to data accumulation within institutions. Therefore, data processing through data mining utilizing the K-Means Algorithm is deemed necessary. Research findings have unveiled a cluster of traffic violation data that stands out as the highest and most frequent during processing: the age group of 17 to 25 years, involving Honda Vario 150 vehicles, and evidence of violations related to driver's licenses (SIM) and vehicle registration certificates (STNK). Test results on three clusters from a dataset of 502 traffic violation records reveal the following: Cluster 1 comprises traffic violation data pertaining to individuals aged 26 to 45 years, using Honda CBR 250 vehicles, and violations tied to driver's licenses (SIM) and vehicle registration certificates (STNK). Cluster 2 includes traffic violation data concerning individuals aged 26 to 45 years, utilizing Suzuki Nex vehicles, and violations involving driver's licenses (SIM) as well as carrying more than one passenger. Cluster 3 involves traffic violation data associated with individuals aged 17 to 25 years, employing Honda Vario 150 vehicles, and violations linked to driver's licenses (SIM
Perancangan Lampu Pintar Berbasis Internet of Things (IoT) Menggunakan NodeMCU Dan Blynk Fahmi Aulia Sirait; Akim M. H. Pardede; Milli Alfhi Syari
Indonesian Journal of Education And Computer Science Vol. 1 No. 3 (2023): INDOTECH - December 2023
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v1i2.61

Abstract

This study focuses on the design of a smart lamp adopting the Internet of Things (IoT) concept using NodeMCU and the Blynk platform. This smart lamp is designed to provide users with more flexible and convenient control through the use of the internet network. NodeMCU, a development module based on the ESP8266 microcontroller, is employed as the core of the smart lamp to connect it to the Wi-Fi network. In the design phase, the smart lamp system is implemented with the capability to be controlled through the Blynk application downloadable to the user's smartphone device. Users can control the lamp, adjust its brightness, and change the light color according to preferences through the intuitive Blynk interface. Integration with the Blynk platform allows remote access and real-time monitoring of the smart lamp's status.The test results demonstrate that the designed smart lamp can effectively communicate with the Blynk application through the Wi-Fi network. The responsive control functionality and the ability to adjust light colors and brightness provide a satisfying user experience. By combining IoT technology and the Blynk platform, this study produces a tangible example of a smart lamp implementation that enhances the convenience and comfort of managing room lighting
Rancang Bangun Sistem Kontrol Suhu Pada Ternak Ayam Berbasis Internet of Things (IoT) Alwa Hul Karim; Hotler Manurung; Milli Alfhi Syari
Nusantara Journal of Multidisciplinary Science Vol. 2 No. 8 (2025): NJMS - Maret 2025
Publisher : PT. Inovasi Teknologi Komputer

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

Abstract

Penelitian ini merupakan perancangan dan bangun alat berbasis Internet of Things yang diimplementasikan pada kandang ayam dengan rentang usia 8-15 hari yaitu dengan pemantauan suhu otomatis yang divisualkan pada Blynk secara real-time.Penelitian ini menggunakan sistem kontrol suhu dengan sensor DHT 11 sebagai pendeteksi suhu dan menggunakan NodeMCU ESP8266 sebagai mikrokontroller. Sistem kontrol suhu kandang ayam menggunakan internet untuk terhubung dengan aplikasi Blynk yang menampilkan data suhu menggunakan gauge chart. Penelitian ini juga membantu dalam penekanan angka kematian pada anak ayam usia dini terkhusus pada usia rentan yaitu dibawah 15 hari. Dengan adanya sistem kontrol suhu pada kandang ayam menggunakan Internet of Things juga membantu peternak ayam skala rumahan maupun skala besar
Digital Image Security Implementation With Uses Super Encryption Algorithm Myszkowski And The Algorithm Paillier Cryptosystem EVAPIONA; Achmad Fauzi; Milli Alfhi Syari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.262

Abstract

This study aims to implement digital image security by applying two encryption algorithms, namely the Myszkowski algorithm and the Paillier Cryptosystem algorithm. Digital images are a very important form of data and are used frequently in a variety of applications, so protecting their security is a major concern. The encryption method proposed in this study uses a combination of the Myszkowski algorithm to randomize image pixels and the Paillier Cryptosystem algorithm to perform symmetric key encryption. At the experimental stage, qualitative and quantitative analysis was carried out on the performance of the encryption implemented on digital images. Testing is carried out by comparing the level of security and encryption speed of the two algorithms used. In addition, size analysis of encrypted images was also performed to evaluate the efficiency of the proposed system. The results of the study show that the use of a combination of the Myszkowski algorithm and the Paillier Cryptosystem algorithm provides a high level of security for digital images. In addition, the efficiency of this system has also been proven in producing efficient encryption image sizes, so that it can be implemented in image-based applications that require a higher level of security.
Co-Authors ACHMAD FAUZI Adityo Razzaqqi Ahmad Fauzi Aji Tyo Syahputra Al Dimas Sausan Roidoh Alwa Hul Karim Andre Andre Anjellita Sundari Sumarsono Buaton, Relita Diky Jaswa Dimas Aditya Elisa Puspita Sari EVAPIONA Fahmi Aulia Sirait Fatmaira, Zira Fauzi, Achmad GOESTI MESKANA PELAWI PELAWI Hermansyah Sembiring Hotler Manurung Jecika Azzahra Khair, Husnul Marto Marto Martoga Mahulae Muhammad Fadlan Siregar Novriyenni Novriyenni Novriyenni Nurhafieza Nurhafieza Pardede, Akim Manaor Hara Putri Mayang Sari Putri Purwani, Dea Nanda Rahmawati, Rahmawati Rendy Aginta Rusmin Saragih, Rusmin Safna Safitri Septian, Refli Sihombing, Marto Sri Rezeki Syofyan Anwar Syahputra Yani Maulita  Enno Loria