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All Journal Jurnal Ilmiah Kursor JURIKOM (Jurnal Riset Komputer) SEMAR Jurnal Computer Science and Information Technology (CoSciTech) Sistem Pendukung Keputusan dengan Aplikasi Jurnal Informatika Medis (J-INFORMED) IJENSET
Muhammad Shodiq
Universitas Muhammadiyah, Lamongan
Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Veterinary

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GREY FORECASTING MODEL IMPLEMENTATION FOR FORECAST OF CAPTURED FISHERIES PRODUCTION muhammad shodiq; Budi Warsito; Rachmat Gernowo
Jurnal Ilmiah Kursor Vol 9 No 4 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i4.170

Abstract

The increasing need for fish causes problems related to production in the fisheries sector. In fisheries production all information related to (fishing ground) is well known, but on the other hand it is not easy to predict the amount of production due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply Grey forecasting model or GM (1,1) to predict fisheries production. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data of captured fish from 2010 to 2018 to analyze calculations using the GM model (1,1). The results showed that the Grey forecasting model or GM (1.1) produced accurate forecasts with an ARPE error value of 9.60% or the accuracy of the forecast model reached 90.39%.
Grey Forecasting Model Untuk Peramalan Harga Ikan Budidaya Muhammad Shodiq; Bagus Dwi Saputra
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5120

Abstract

Price is an important factor to consider because it determines the profit or loss from selling a product. The difficulty of controlling the volatility of fish prices is related to many factors, including stock availability, natural factors, and the level of demand. One way to solve the problem of fish price volatility is to predict fish prices in the future. The purpose of this study is to apply the gray forecasting method to forecasting fish prices, especially in the aquaculture industry. Gray forecasting is a method for creating forecasting models with a small amount of data that provides accurate forecasts. This study uses daily data on prices of Tilapia fish for the period of June 2022 for analysis of gray forecasting calculations. The results show that gray forecasting provides very accurate predictions with aa mafe value of 2.39% of the price of Tilapia fish
Smart Technology of CO2 Monitoring as Prevention of Acute Respiratory Infection Disease Using Artificial Intelligence Algorithm Muhammad Shodiq; Agus Priyono; M. Cahyo Kriswantoro3
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.7709

Abstract

Air pollution is a serious environmental problem that can affect human health because it contains toxic gases, one of which is carbon dioxide (CO2). This toxic gas can cause Acute Respiratory Infection (ARI). ARI is an acute infection of the respiratory tract that can cause death. One effort to prevent ARI is to monitor CO2 gas as a trigger for ARI. This study develops intelligent technology for monitoring CO2 concentration using a rule-based artificial intelligence algorithm by utilizing Internet of Things technology integrated with telegrams to provide warnings. Rule-based systems are part of artificial intelligence that have advantages and limitations that need to be considered before deciding whether it is the right technique to use in solving existing problems. This study uses daily data taken by CO2 gas sensors from 07.00 - 16.15 WIB with a data collection range of 15 minutes with a total of 38 data samples taken. The results of the study show that this rule-based algorithm is able to classify CO2 concentrations according to the rules that have been made. In addition, from the data taken, 42% are in the safe category, 50% are in the alert category and 8% are in the danger category, each of which has an effect on health. The system that was built can also send danger notifications via telegram
IMPLEMENTASI GREY MODEL (1,N) UNTUK SISTEM PERAMALAN JUMLAH TANGKAPAN IKAN Shodiq, Muhammad; Ayuningsih, Ekatri; Ramanda, Febri
Jurnal Informatika Medis Vol. 1 No. 1 (2023): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v1i1.1194

Abstract

The increasing need for fish causes problems related to number of fish catches in the fisheries sector. In fish catches amount, all information related to fishing ground is well known, but on the other hand it is not easy to predict the number of fish catches due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply grey forecasting model GM (1.N) to forecast the number of fish catches. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data on monthly number of fish catches and wave height in the year of 2016 to 2018 to analyze calculations using the GM (1.N) models. The study was conducted with 36 time series data. The result showed that the MAPE on the GM (1.N) model of 57% in the experiment with 36 data.
SISTEM INFORMASI PENERIMAAN CALON TENAGA KERJA INDONESIA PDA PT DEWI PENGAYOM BANGSA KABUPATEN PATI Ramanda, Febri; Shodiq, Muhammad; Nur, Delyardi
Jurnal Informatika Medis Vol. 1 No. 1 (2023): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v1i1.1196

Abstract

One form of service in PT Dewi protector Nations Pati regency receive prospective workers to some of Taiwan, Hongkong, Singapore often have difficulty including data management is still done manually, the amount of data they accumulate, requiring a lot of space to save the files prospective migrants and the length of the search data. The system developed is expected to address the various issues of labor recruitment Indonesia. The system is designed with modeling UML (Unified Modeling Language) and programming languages PHP. The result of this design produces Candidate Information System Acceptance Indonesian Workers at PT Dewi protector Nation Pati regency.
PREDIKSI JUMLAH PENYAKIT INFEKSI SALURAN PERNAPASAN AKUT (ISPA) MENGGUNAKAN SIMPLE MOVING AVERAGE Priyono, Agus; Shodiq, Muhammad; Ramanda, Febri
Jurnal Informatika Medis Vol. 1 No. 2 (2023): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v1i2.1646

Abstract

Health problems in Indonesia are still a topic that really needs to be developed and researched considering that disease problems in Indonesia are diverse and contribute to high death rates. Acute Respiratory Infection (ARI) is a disease that often occurs in society and is considered normal or not dangerous, but can cause death. A group of diseases included in ISPA are, Pneumonia, Influenza, and Respiratory Syncytial Virus (RSV). ISPA disease in Indonesia contributes to the highest number of deaths, so there is a need for action or policy that can control ISPA disease in the future. The aim of this research is to apply the simple moving average method to predict ARI disease. This method is simple in prediction but has optimal results in some use cases. This research uses annual data from 2007 to 2022 for the calculation method. The research results show that the simple moving average method provides accurate prediction results with a MAPE value of 11% for predicting the number of ISPA cases. It is hoped that the results of this research can determine policies for controlling ARI diseases Keywords: Acute Respiratory Infection, Prediction, Simple Moving Average
METODE RANDOM FOREST UNTUK MEMUDAHKAN KLASIFIKASI DIAGNOSIS PENYAKIT MENTAL Priyono, Agus; Shodiq, Muhammad; Alvinsyah, Dwi Putra; Hidayah, Septina Alfiani
Jurnal Informatika Medis Vol. 2 No. 1 (2024): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v2i1.2119

Abstract

Mental health is important in the development of every individual. A bad mentality can prevent a person from developing, making a person easily stressed, hopeless, and even attempt suicide and commit crimes. Currently there are quite a lot of case related to mental health which are caused by many factors such as economic, social and medical. Reflecting on this fact, there is a need for rapid mental health detection so that immediate intervention can be carried out. This needs to be done so that the patient's condition can improve. This research focuses on diagnosing mental illness by utilizing machine learning. The method used is random forest which in several studies has been proven to produce good accuracy. Random forest performs machine learning on the attributes contained in the dataset combined with K-Fold Cross Validation so that each patient can be evaluated. Next, a tuning process is also carried out to test the parameters contained in the method. After the tuning process was carried out, the best parameters obtained were n-estimator of 30, maximum depth of 4, minimum sample leaf of 2, and minimum sample split of 10. From the combination of these parameters, accuracy is 90.83%, recall is 90.83 %, and precision of 93.25%.
Smart Agricultural Governance: Methodologically Approached Web Based Automatic Monitoring and Irrigation Using Soil Moisture and Ultrasonic Sensors Syaifuddin, Ahmad; Saputra, Bagus Dwi; Shodiq, Muhammad
Indonesian Journal of Engineering, Science and Technology Vol. 1 No. 2 (2024): VOL. 01 NO. 02 (DECEMBER 2024)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v1i2.1029

Abstract

Agricultural productivity in Lamongan, particularly in Kedungpring District, is critically challenged by manual land processing and recurring droughts that severely impact chili crop yields. The persistent lack of efficient monitoring and irrigation technologies exacerbates agricultural sustainability concerns. This study aims to develop an automated, IoT-based irrigation management system that optimizes water use efficiency for chili crops through advanced sensor technologies and fuzzy logic processing. Utilizing an ESP8266 microcontroller integrated with soil moisture and ultrasonic sensors, the research employs the Takagi-Sugeno fuzzy logic method to process real-time environmental data. The system dynamically monitors soil moisture levels and water resources, enabling precise irrigation control. Fuzzy calculations generated a solenoid valve operation time of 321 seconds, classified as moderate, demonstrating the methodology's potential for accurate irrigation management. The developed automated monitoring system successfully demonstrates the potential of IoT technologies in addressing agricultural challenges, providing real-time data visualization and intelligent irrigation decision-making. By integrating sensor technologies with fuzzy logic processing, the research offers a promising solution to improve water resource management and potentially enhance crop productivity in drought-prone agricultural regions.   Keywords- Fuzzy Methode; Smart Agriculture; Soil Moisture; Takagi-Sugeno; Ultrasonic.  
Design of Rule Based Algorithm Based on IoT (Internet of Things) and Water Level Sensor to Monitor the Tide of Bengawan Solo zu fahim, wahyu; Handoyo, Eko; Shodiq, Muhammad
Indonesian Journal of Engineering, Science and Technology Vol. 1 No. 2 (2024): VOL. 01 NO. 02 (DECEMBER 2024)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v1i2.1030

Abstract

Laren Village in Laren District, Lamongan Regency, is traversed by the Bengawan Solo River, which is crucial for agriculture, fisheries, industry, and domestic needs. However, excessive use poses risks of pollution and flooding. Floods can cause material losses, infrastructure damage, environmental contamination, disease outbreaks, traffic disruption, and water scarcity. Effective mitigation measures are vital to reduce these risks. Internet of Things (IoT)-based technology offers an effective solution for flood risk mitigation. IoT enables real-time monitoring and decision-making using rule-based algorithms that analyze data patterns, set conditions, and automatically respond to potential threats. On August 6, 2024, observation data showed Bengawan Solo's water level ranged between 119 cm and 170 cm, mostly categorized as "Safe." However, the last two measurements, at 117 cm and 119 cm, shifted the status to "Caution," signaling a need for vigilance. The average water level of 151.7 cm indicated an overall "Safe" condition, yet the sudden drop underscores the importance of continuous monitoring. IoT systems with rule-based algorithms can detect real-time changes, enabling swift risk mitigation. This technology enhances safety, supports environmental sustainability, and preserves the Bengawan Solo River's ecosystem.   Keywords¾ Flood, IoT, Rule Based Algorithm, Telegram, Ultrasonic Sensor, 
Design and Implementation of The Dude Mikrotik Server Using Telegram Bot as Monitoring Client Network and Internet Connection Pratama, Ahmad Bagus Putra; Handoyo, Eko; Shodiq, Muhammad
Indonesian Journal of Engineering, Science and Technology Vol. 2 No. 1 (2025): VOL. 02 NO. 01 (JUNE 2025)
Publisher : Universitas Muhammadiyah Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38040/ijenset.v2i1.1033

Abstract

Network management and monitoring are crucial, especially in educational environments such as SMK Wachid Hasjim Maduran, which has a network with various devices and connected clients. The high number of users accessing the network often leads to bandwidth issues. This research aims to implement The Dude, a MikroTik-based network monitoring application, to detect and address bandwidth problems and monitor the status of client devices (up/down). The system is equipped with a notification feature that sends real-time information to network administrators via Telegram regarding the network's condition. Monitoring is conducted using The Dude, which can map all devices in the network and monitor their performance and status. When a bandwidth issue occurs or a client device goes down, the system automatically sends a notification to Telegram. This allows administrators to take the necessary actions promptly without always being in front of the computer. The results of this implementation show that utilizing The Dude in combination with Telegram notifications is effective in real-time network monitoring, reducing downtime, and improving response to network issues. This system is expected to be an efficient solution for network management in educational environments and can be adopted by other institutions facing similar challenges.   Keywords - MikroTik, Monitoring, Telegram Notification, The Dude.
Co-Authors Abdullah Hakim Gymnastiar Agus Priyono Agus Priyono Agus Priyono Agustina, Rohmatin Ahmad Faris Rachmad Putra Ahmad Syaifuddin Alvinsyah, Dwi Putra Ayuningsih, Ekatri Bagus Dwi Saputra Budi Warsito Eko Handoyo Fatahillah Akrom Febri Ramanda Hanif Azhar Ramadhan Heri Ardiansyah Hidayah, Septina Alfiani M Ainul Mahbubillah M. Ari Prayogo M. Cahyo Kriswantoro Muhammad Labib Jundillah Muhammad Rizky Rahmadani Neni Purwati Nur, Delyardi Pratama, Ahmad Bagus Putra Rachmat Gernowo Ramanda, Febri Riendy Riendy SAPUTRA, BAGUS DWI zu fahim, wahyu Zufar Faiil Haq