Article Per Year (5 Year)
p-Index From 2021 - 2026
3.668
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer Jurnal Inotera Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Eduscience (JES) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Said Fadlan Anshari
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

Published : 26 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 26 Documents
Search

 1   2   3 
Contagion Analysis of Plantation Commodity Producing Regions in Aceh Province Using Bayesian Inference Juliawati; Mukti Qamal; Said Fadlan Anshari
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

The commodity-producing region is one of the plantation sectors with significant potential for economic growth in Aceh Province. The spread level between commodities owned by regions within the network is called “contagion,” which means that one commodity will influence a region, leading to a greater focus on that commodity within the network, and a region will influence other regions. With the diversity of commodities across various areas, a comprehensive analysis and visualization of the network formed among commodity producing regions are conducted using a Social Network Analysis (SNA) approach. Thus, Bayesian inference can reveal the network of each region that has relationships among the variables used to form a graph with the desired representation. This network analysis result can provide an overview of Aceh Province's plantation data through the network graph visualization among commodity-producing regions and the network graph of commodity production levels by region. Keywords: Aceh; Contagion Analysis; Social Network Analysis
Full Automation and Control System Based on IoT in a Greenhouse (Case Study: Faculty of Agriculture, Malikussaleh University M Ishlah Buana Angkasa; Rizal Tjut Adek; Said Fadlan Anshari
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

This study aims to develop a full automation and control system based on the Internet of Things (IoT), implemented in a greenhouse to support real-time monitoring of temperature, soil moisture, and water levels in the tank. The system is designed using the ESP32-WROOM microcontroller as the core for data communication with various sensors, including the DHT22 sensor for air temperature and humidity, a soil moisture sensor for soil moisture, and a JSN-SR04T sensor for water level. The developed system connects to Firebase as a cloud data platform, enabling remote monitoring via a specially designed mobile application. Testing shows that the system works efficiently in supporting automated plant growth, reducing manual intervention, and increasing productivity. This system allows students and faculty in the Faculty of Agriculture at Malikussaleh University to more easily conduct research and teaching activities related to modern agricultural technology.
IoT-Integrated Home Energy Management System with Real-Time Monitoring and Solar Panel Optimization Mhd Firza Ryzaaldy; Muhammad Fikry; Said Fadlan Anshari
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v4i.949

Abstract

In this study, an IoT-integrated Home Energy Management System (HEMS) was developed using solar panels as the primary energy source. The system employs an ESP32 microcontroller as the core controller, equipped with DHT22, LDR, and INA219 sensors to monitor temperature, humidity, light intensity, voltage, and current. Real-time sensor data is presented on a web interface, allowing users to monitor system status and control devices like fans and lights either manually or automatically. The system demonstrated stable performance with a control response time of under one second and effective energy management aligned with environmental conditions. However, a key limitation was the limited capacity of the 10 Wp solar panel, particularly during low sunlight periods. To address this, enhancements such as improved load management or increased solar panel capacity are recommended. The system successfully implemented real-time monitoring and automated control, activating the fan at temperatures above 30 degrees Celsius and turning on lights when light intensity is below 1000 lux. This research highlights the potential of IoT technology in achieving efficient and sustainable home energy management.
Implementation of the Naïve Bayes Method in a Web-Based Fish Species Classification System Rizki Suwanda; Muhammad Fikry; Said Fadlan Anshari
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

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

Abstract

The current fish resources are abundant, and the discovery of new species has increased the variety of fish in the ocean. These fish are categorized into three groups: demersal, pelagic, and reef fish, each with unique characteristics of their respective groups. The manual classification process for large datasets requires a long time and involves complex procedures. With the advent of data and information technology, it is now possible to recognize and identify several fish species found in the ocean, which can be classified into the three groups. To simplify this classification process, a web-based system has been developed to classify fish into these groups. The data to be processed in this research will be classified using the Naive Bayes method to address this issue. This technique utilizes large datasets to extract information that was previously unknown or inaccessible, and it can provide accurate information for various purposes. The data for this study will be collected from various internet references and direct data obtained from fish landing sites (TPI) in Lhokseumawe and North Aceh. Additionally, a literature review method will be used to complement the data analysis process. The development of the web-based system will be implemented to facilitate the classification of fish species based on the existing data.
The Influence of Google Lens-Assisted Discovery Learning Model on Improving Students' Mathematical Connections Hidayatsyah Hidayatsyah; Muhammad Fikry; Said Fadlan Anshari; Sudirman Sudirman
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

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

Abstract

This study aims to analyze the effect of the Discovery Learning learning model assisted by Google Lens on improving students' mathematical connections. This study used a quasi-experimental method with a Non-equivalent Control Group Design involving high school/vocational high school students in Lhokseumawe City. The sample consisted of an experimental group using Google Lens and a control group using conventional learning. The results showed a significant increase in mathematical connection skills in the experimental group compared to the control group. Students' perceptions of the use of Google Lens were also positive, with indicators of increased learning motivation and engagement. These findings provide implications for the implementation of technology in mathematics learning.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.
Co-Authors Al-Ghiyats, Said Ananda, Ria Angkasa, M. Ishlah Buana Arnawan Hasibuan Bustami Bustami Chan, Fajar Ricaldi Dahlan Abdullah Dani Ramdani Dinda, Dinda Fadlisyah Fadlisyah Fahrezi, Irgi Fanita, Fanita Hidayatsyah Hidayatsyah Husna, Nur Fazri Juliawati Khairi, Abil Lidya Rosnita Limbong, Hendra Putranta M Ishlah Buana Angkasa Maulana, O.K. Muhammad Majid Mhd Firza Ryzaaldy Muhammad Daud Muhammad Fikry Mukti Qamal Mukti Qamal Munirul Ula Nisak, Rahmatin Pohan, Fadlyani Putri Agustina Dewi Putri, Nazirah Allisya Rini Meiyanti Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal, Reyhan Achmad Rizki Suwanda Rizki, Agus Maula Rizki, Sukma Rizky Putra Fhonna Rozzi Kesuma Dinata Sahputra, Irfan Samudera, Brucel Duta Sandy, Cut Lika Mestika Sartika, Tiara Sudirman Sudirman Sujacka Retno Sukiman, T. Sukma Achriadi Sukma Rizki Syibral Malasyi, Syibral Tulus Setiawan Wardina Ningsih Yasin, Fijri Ahmad Zalfie Ardian Zara Yunizar Zarayunizar Zarayunizar