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All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi JUPADAI : Jurnal Pengabdian Kepada Masyarakat Computer & Science Industrial Engineering Journal
Andi Maslan
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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IMPLEMENTASI SISTEM MONITORING ASAP ROKOK MENGGUNAKAN SMARTPHONE BERBASIS IOT Nanang Setiyawan; Andi Maslan
Computer Science and Industrial Engineering Vol 10 No 4 (2024): Comasie
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v10i4.8609

Abstract

Cigarette smoke not only reduces indoor air quality but is very dangerous for the health of both those who smoke and those around them. The chemicals contained in cigarette smoke can cause disease to attack the body. Several actions to reduce the impact of cigarette smoke, such as the government's policy of making pictures of mouth cancer on cigarette packs, writing and pictures prohibiting smoking are often ignored by smokers so they are still seen smoking in public places. Previous research showed that monitoring systems in the form of SMS text messages and website-based applications were still deemed to be less effective. Therefore, in this research, a cigarette smoke detector was built in a closed room using the MQ-2 sensor and the SHARP GP2Y1010AUOF smoke sensor as a smoke detector and controlled using Arduino Uno and NodeMCU V3. Sensor reading values are sent using an internet connection then stored in real time in the Firebase database and displayed on the Android smartphone application. If the sensor detects cigarette smoke, the application provides a notification on the application and a buzzer sounds on the device.
ANALISIS DAN PERANCANGAN KEAMANAN DATA TEKS MENGGUNAKAN ALGORITMA KRIPTOGRAFI SECURE HASH ALGORITHM Suhendra, Irwan; Andi Maslan
Computer Science and Industrial Engineering Vol 10 No 5 (2024): Comasie
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v10i5.8611

Abstract

Data security is a crucial aspect in today's digital era, especially with the increasing exchange of information over the network, so network security seeks to secure from physical and logical dangers and threats that can steal personal data. This research aims to analyse and design a text data security system by utilising the Secure Hash Algorithm (SHA) cryptographic algorithm in order to increase the level of security and integrity of text data through the application of reliable cryptographic technology. The research method used is Secure Hash Algorithm (SHA) 256 to analyse text data, and later the text data will be processed using the SHA 256 application to find out how it works and its implementation on securing text data. The results for how SHA works have a predetermined initial value for each hash literacy generated from several prime numbers, the message to be hashed is divided into smaller blocks, each of which has a length of 512 bits and padding is done so that the length of the text can be divided by 512 which later for each text block is processed repeatedly for 64 rounds, after all text blocks are processed, the resulting hash value is a series of bits with a certain length. The results for the SHA implementation steps are first to take the text data to be secured, then convert the text data into ASCII/Unicode format, run the SHA algorithm to find the hash value, save the hash value into the database, then verify the authenticity of the text data, run the SHA algorithm on the data and compare the hash value generated with the hash value previously stored, if the result is the same, then the text data is original. Recommendations that can be given are to explore and develop the blockhchain side and improve the SHA 256 application in the testing section.fritzing.
IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PENJUALAN PRODUK TERLARIS PADA PETSHOP MENGGUNAKAN ALGORITMA NAIVE BAYES Gaho, Ibrani; Andi Maslan
Computer Science and Industrial Engineering Vol 11 No 2 (2024): Comasie Vol 11 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v11i2.9041

Abstract

In recent years, the petshop industry has seen a significant increase. This is due to people's growing awareness of pet welfare and the need for specialized products for them. Product sales in petshops are not only influenced by customer preferences, but also by the diversity of pet breeds, which makes data collection and product stock management more complex. Therefore, predicting the products that are most in demand by customers is important. Data mining, as a part of computer science that focuses on extracting information from data, offers an effective way to analyze patterns and trends of product sales in petshops. In this study, Naive Bayes algorithm is used to predict the best-selling products in petshop. RapidMiner software was used to process the data in this study. Data processing with RapidMiner resulted in a prediction accuracy of 90.41%. Class precision for the prediction of hot-selling products is 88.24%, while for non-selling products is 90.00%. Class recall for the prediction of hot-selling products reaches 90.91%, while for products that are not in demand reaches 92.31%.
IMPLEMENTASI DEEP LEARNING DALAM SISTEM ABSENSI SISWA DENGAN FACE RECOGNITION Ari Alparisi; Andi Maslan
Computer Science and Industrial Engineering Vol 11 No 3 (2024): Comasi Vol 11 No 3
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v11i3.9103

Abstract

In an era where information technology is pervasive, education is impacted significantly. One technology being adopted is face recognition for student attendance authentication, which is more resistant to forgery and manipulation than methods like RFID cards. It offers high accuracy and can function under various conditions, making it effective and efficient. Student attendance is crucial for the effectiveness of the learning process. Traditional methods have limitations in accuracy, speed, and convenience. Institutions have shifted to technology-based methods such as mobile applications or RFID devices, which still require physical interaction. Face recognition, with deep learning, promises to streamline the attendance process by enhancing accuracy and efficiency. Deep learning processes complex data, such as facial images, with high accuracy. Integrating face recognition with deep learning can address challenges like pose variation, facial expressions, and lighting conditions. The objectives of this research aim to make a substantial contribution to the current development of student attendance technology, improving administrative processes in educational institutions.
PREDIKSI PENYAKIT DIABETES MENGGUNAKAN ALGORITMA REGRESI LOGISTIK Sugianto; Andi Maslan
Computer Science and Industrial Engineering Vol 11 No 4 (2024): Comasi Vol 11 No 4
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v11i4.9177

Abstract

The annual count of individuals afflicted with diabetes is rising. As to a survey carried out by the International Diabetes Federation (IDF), the global diabetes population is approximated to be 537 million by 2021, and this number is projected to increase further to exceed 780 million by 2045. The study's primary goal is to diagnose and forecast whether or not a patient has diabetes. The approach makes use of logistic regression, a statistical tool for modelling individual classifications of diabetes presence or absence. According to the diabetes risk prediction results, 43% of respondents gave consideration to the condition. Consequently, it has been demonstrated that normalisation enhances the accuracy of diabetes risk prediction using logistic regression methods. Based on the variables included, it is anticipated that the predictions made by this model will serve as a guide for the general public in understanding healthy living and diabetes prevention.
Co-Authors Ari Alparisi Azan Rahman Gaho, Ibrani Nanang Setiyawan Pangaribuan, Hotma Rabei Raad Ali Al-Jawr Sitohang, Sunarsan Sugianto Suhendra, Irwan Umar Faruq