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Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30318750     EISSN : 30318742     DOI : 10.61132
Core Subject : Science,
Computer Science & IT
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer memuat naskah hasil-hasil penelitian di bidang Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Arjuna Subject : Teknik (semua kategori) - Teknik Mesin
Articles 201 Documents
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Implementation of the Naive Bayes Algorithm on Malaria Data Set Using Rapid Milner Ridwan Andri Prasetio; Gergorius Kopong Pati; Katarina Yunita Riti
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 5 (2024): Oktober : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i5.386

Abstract

Medical record data can be used as a benchmark and comparison in the health business to ascertain the rate at which a disease is developing in a given area. It would be beneficial, though, if this data could be transformed into useful information, like illness forecasts. Infectious diseases like malaria are common in tropical and subtropical regions. West Sumba Regency is the region with the highest number of malaria cases, and this figure rises year. Of the different Puskesmas labor locations, Lolo Wano Health Center has the largest number of positive cases of malaria. In order to apply information system technology and prevent malaria early, research was done at the Lolo Wano Community Health Center to predict malaria using the Naïve Bayes approach. This is because the Community Health Center does not currently have a malaria prediction system. Six of the 16 features in the patient dataset—a total of 27 patient data—were malaria symptoms. When there are suitable illness indicators, positive predictions are produced using the outcomes of Naïve Bayes computations. Before the patient proceeds with a direct medical evaluation, these anticipated results may be utilized as a provisional approximation. Naïve Bayes, Center, Prediction, Malaria
Sistem Pakar untuk Mendiagnosa Penyakit Tanaman Cabai Menggunakan Metode Forward Chaining : Studi Kasus Pertanian Maju Berbasis Web di Kabupaten Ende Paulus Apostolus Wangga; Friden Elefri Neno
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 5 (2024): Oktober : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i5.435

Abstract

Chilli farming is an important sector in Ende District, but plant disease problems are often an obstacle that hampers productivity. Farmers often have difficulty recognizing the symptoms of disease in chili plants and determining appropriate treatment steps. To overcome this problem, this research aims to design and build a web-based expert system that can help diagnose chili plant diseases using the Forward Chaining method. This expert system was developed by collecting knowledge from agricultural experts and literature related to chili plant diseases, as well as applying the Forward Chaining method for the reasoning process. Users, especially farmers, can enter the symptoms experienced by chili plants into the system, then the system will produce a disease diagnosis and appropriate treatment recommendations based on these symptoms. This research uses a case study at Maju Tani Agriculture in Ende District to ensure that the expert system developed is relevant to local conditions. This system is implemented in the form of a web-based application so that it can be accessed easily by farmers using devices connected to the internet. The test results show that this expert system can provide accurate and efficient diagnoses, as well as assist farmers in making decisions to overcome chili plant disease problems. It is hoped that this system can increase agricultural productivity and reduce the risk of losses due to plant diseases.
Rancang Bangun Boiler Panci Presto untuk Setrika Uap dengan Penambahan Komponen Indikator Level Air Muhamad Zainudin; Ziyadatul Husna
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.449

Abstract

This research was motivated by several laundries that use pressure cooker steam iron boilers for the ironing process. However, there are still users who find it difficult to estimate water level. If the water in the pressure cooker dries up or runs out whitout knowing it, then this can be dangerous and have a bad impact both on the user and in terms of the quality of the pot. So the aim of this research is to modify the pressure cooker steam iron boiler by adding a water level indicator whose function is to determine the water capacity during the ironing process, so that it is hoped that it can minimize work accidents. The method use is descriptive quantitative. From the design process, the tool was then tested to determine the starting time for using the iron using pressure variations of 15 psi, 20 psi, and 22 psi. From the research results, it was found that the design process was carried out through several stages, including the measurement process, drilling process, and assembly process. The test results started that the optimal pressure was 20 psi, because at this pressure the steam produced was optimal with a heating time of a 58 minutes.
Pemanfaatan Jaringan Saraf Tiruan untuk Prediksi Curah Hujan di Sumatera Utara Arizka Anggraini; Lailan Sofinah Harahap
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.457

Abstract

The use of Artificial Neural Networks (JST) for weather prediction is one of the innovative approaches in climate data analysis. This study aims to apply JST in predicting weather, especially rainfall and the number of rainy days in the North Sumatra region. Historical weather data obtained from BMKG Region I for 2022-2023 is used as input to train the JST model. With a training process that involves processing rainfall data, this model is expected to provide accurate predictions regarding weather patterns. The results of this research can help in agricultural sector planning, disaster risk mitigation, and natural resource management. JST has proven to be effective in identifying dynamic and complex weather patterns, so it has the potential to be used in long-term weather prediction.
Perancangan Model Jaringan Syaraf Tiruan untuk Memprediksi Penyakit Demam Berdarah Menggunakan Algoritma Hebb Rule Adinda Tarisyah Hsb; Mazayah Tsaqofah; Lailan Sofinah Harahap
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.483

Abstract

Dangeu dengue fever or what we often call dengue fever is a disease transmitted by the Aedes aegypti mosquito and caused by the dengue virus. This disease can potentially cause serious complications if it does not receive proper treatment. In this research, the author uses the application of artificial neural networks with the Hebb rule approach to predict the risk level of dengue fever. Predictions are made based on factors such as weather conditions, population density and historical case data that influence this disease. The Hebb rule is used in this research because of its ability to strengthen connections between neurons based on the input patterns they receive, so it is hoped that it can produce more accurate predictions. Test results show that this method has a fairly high level of accuracy in predicting the pattern of dengue fever cases in an area. This research indicates that the application of artificial neural networks with the Hebb rule can be an effective tool for related parties in taking preventive measures to minimize the number of dengue cases in the future.
Penyelesaian Permasalahan Pengadaan Stok di Toko Grosir Naek Menggunakan Logika Fuzzy dengan Metode Tsukamoto Juwita Sari; Intan Widya Saputri Nst; Lailan Sofinah Harahap
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.484

Abstract

Stock management is an important aspect in the operations of a grocery store. By knowing when to add stock, shop owners can optimize the availability of goods without excess stock. In this research, the fuzzy logic method is applied to determine stock procurement decisions based on sales data and remaining stock. The results of the research show that fuzzy logic can be used to assist in making stock procurement decisions with a fairly high level of accuracy.
Deteksi Wajah dalam Foto Menggunakan Teknologi Visi Komputer Supiyandi Supiyandi; Tegar Ardiansyah; Sri Putri Balqis; Jundi Haqqoni; Salsa Nabila Iskandar
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.490

Abstract

This study discusses the implementation of computer vision technology for face detection in photos using two sample images with variations in lighting and face pose. The developed system combines the Viola-Jones algorithm and Convolutional Neural Networks (CNN) to enhance resilience against lighting and face orientation variations. Experimental results show high accuracy even with only two sample images. This research also develops preprocessing techniques to handle extreme lighting conditions and demonstrates efficient implementation using Python and OpenCV.
Rancang Bangun Sistem Kontrol Irigasi Otomatis Berbasis IoT untuk Tanaman Stevia Budy Gunawan; Arbi Alfian Mas’ud; Khasanul Khakim; Muhammad Febriyanda Wiryawan; Reza Rachmat Setyabudi
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.492

Abstract

This research focuses on developing an IoT-based automatic irrigation control system for stevia plants to optimize plant growth and water usage efficiency. The system integrates ESP32 microcontroller with soil moisture sensors, DS18B20 temperature sensors, and DHT11 environmental sensors for comprehensive monitoring. Using Research and Development (R&D) methodology with an experimental approach, the system was designed and implemented to automatically control irrigation based on soil moisture levels. The results demonstrate that the system successfully maintains optimal soil moisture by activating the pump when moisture levels fall below 38% and deactivating it above 40%. Real-time monitoring through the Blynk platform enables remote observation and control of environmental parameters. The integration of multiple sensors with IoT technology provides an efficient solution for stevia plant irrigation management, offering potential applications in smart agriculture.
Pemanfaatan Medan Elektromagnetik dalam Teknologi Pengobatan Modern Novaldi Ramdani Reza; Rovino Alghafari; Errisa Zulqa Deswana; Muhammad Rifqi; Diyajeng Luluk Karlina
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.496

Abstract

The utilization of electromagnetic fields (EMF) in modern medical technology has become a significant focus of research. This study aims to explore the therapeutic effects of EMF, especially at low frequencies (ELF) and Pulsed Electromagnetic Fields (PEMF), in enhancing the healing of various medical conditions. The method used is a literature study by analyzing various sources from Google Scholar, Science Direct, and PubMed. The results showed that EMF therapy is effective in relieving pain, accelerating tissue healing, and improving the quality of life of patients with musculoskeletal disorders and fractures. Despite the many benefits, it is important to consider the potential health risks of long-term exposure to EMF. This study recommends the development of strict regulations and training for medical personnel to ensure the safe and effective use of EMF.
Evaluasi Kinerja Model RNN & LSTM untuk Prediksi Magnitude Gempa di Indonesia Fazira, Rara; Yudistira, Dimas; Sofinah Harahap, Lailan
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.498

Abstract

Indonesia di kawasan Cincin Api Pasifik, yang dikenal memiliki aktivitas seismik yang sangat tinggi dengan ribuan gempa bumi yang terjadi setiap tahunnya. Penelitian ini bertujuan untuk menganalisis kinerja Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM) dalam memprediksi magnitudo gempa bumi menggunakan data historis yang diambil dari Kaggle. Data tersebut mencakup rentang waktu dari November 2008 hingga September 2022, yang telah melalui proses normalisasi serta perpecahan menjadi data pelatihan dan pengujian. Model evaluasi kinerja dilakukan dengan menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Pada uji coba pertama, LSTM menunjukkan performa terbaik dengan nilai MAE 0.6226 dan RMSE 0.7731 pada data pengujian, lebih baik dibandingkan RNN yang mencatatkan MAE 0.6271 dan RMSE 0.7831. Sebaliknya, pada uji coba kedua, RNN unggul dengan nilai MAE 0.5583 dan RMSE 0.7008, sementara LSTM memiliki MAE 0.5822 dan RMSE 0.7132. Hasil ini menunjukkan bahwa LSTM lebih cocok untuk menangani pola data temporal yang kompleks, sedangkan RNN lebih andal pada dataset dengan pola yang lebih sederhana. Penelitian ini diharapkan dapat menjadi pijakan dalam pengembangan sistem prediktif untuk mitigasi risiko bencana gempa bumi di Indonesia.

Page 8 of 21 | Total Record : 201

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