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Journal of Information Technology and Computer Engineering
Published by Universitas Andalas
ISSN : 25991663     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Journal of Information Technology and Computer Engineering (JITCE) is a scholarly periodical. JITCE will publish research papers, technical papers, conceptual papers, and case study reports. This journal is organized by Computer System Department at Universitas Andalas, Padang, West Sumatra, Indonesia.
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Articles 7 Documents
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Search results for , issue "Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering" : 7 Documents clear
Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things Mumuh Muharam; Melda Latif; Baharuddin Baharuddin; Ibnum Richaflor
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.53-62.2020

Abstract

False alarm in fire detection can cause a huge loss. False alarm is generated by unwanted signal of smoke detector such as outdoor smoke or smoking. Therefore, it is designed a system that can reduce false alarm. The purposed system is built based on three components, those are sensors, actuators and data communication. Sensors are smoke, flame and camera sensor. Smoke sensor is used as the first thing to sense a signal from the system that warns the system there is a fire. Flame sensor and camera are used to confirm that a signal of fire whether false alarm or not. Internet of Things (IoT) is applied to control the system. The result show that the system is applicable.
Implementasi Smart Home Pada Pendeteksi Dini Kebakaran Menggunakan Forward Chaining Irawan Dwi Wahyono; Mochammad Bagus Priyantono
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.63-71.2020

Abstract

Fire is a disaster that can occur due to human negligence. So we need a system that functions to minimize the occurrence of fires by having a working concept to detect fires. This study aims to develop a fire detection system using the forward chaining method. In this detection system applying Artificial Intelligence where there are parameters of temperature, gas, the presence of fire, and the presence of water. This system also applies the Smart Home concept to detect fires early where there are sensor devices used by DHT 11, FLAME and MQ2. the data obtained from the sensor will be processed by the NodemCU Esp-8266 microcontroller. If there is an indication that caused a fire, the system immediately sends a warning via telegram. The results of this study obtained a precision of .94%, recall 93.6% and an accuracy of 96%.
Machine Learning Application for Classification Prediction of Household’s Welfare Status Nofriani Nofriani
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.72-82.2020

Abstract

Various approaches have been attempted by the Government of Indonesia to eradicate poverty throughout the country, one of which is equitable distribution of social assistance for target households according to their classification of social welfare status. This research aims to re-evaluate the prior evaluation of five well-known machine learning techniques; Naïve Bayes, Random Forest, Support Vector Machines, K-Nearest Neighbor, and C4.5 Algorithm; on how well they predict the classifications of social welfare statuses. Afterwards, the best-performing one is implemented into an executable machine learning application that may predict the user’s social welfare status. Other objectives are to analyze the reliability of the chosen algorithm in predicting new data set, and generate a simple classification-prediction application. This research uses Python Programming Language, Scikit-Learn Library, Jupyter Notebook, and PyInstaller to perform all the methodology processes. The results shows that Random Forest Algorithm is the best machine learning technique for predicting household’s social welfare status with classification accuracy of 74.20% and the resulted application based on it could correctly predict 60.00% of user’s social welfare status out of 40 entries.
Otomatisasi Pengoperasian Alat Elektronik Berdasarkan Hasil Prediksi Algoritma Long Short Term Memory Afriansyah Afriansyah; Ade Irawan
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.83-89.2020

Abstract

Excessive use of household electricity is one of the causes of the largest amount of national electricity consumption coming from households. One way to reduce the amount of household electricity consumption is to automate the operation of electronic devices. This research proposes utilizing Long Short Term Memory (LSTM) algorithm to predict the habit of operating an electronic device. The prediction is then applied to automate the operation of that by exploiting the time series data from the usage. A series of experiments are conducted to capture the data of operating a manual lamp. Then, an LSTM model is built by training the data. The experiment results show the prediction accuracy of 99,28% and Root Mean Square Error of 0,091. Furthermore, the LSTM model is used to automatically operate a lamp in a month. The electricity cost from the automation is 36,38% lower than the manual.
Predicting Survival of Heart Failure Patients Using Classification Algorithms Oladosu Oyebisi Oladimeji; Olayanju Oladimeji
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.90-94.2020

Abstract

Heart failure is a situation that occurs when the heart is unable to pump enough blood to meet the needs of other organs in the body. It is responsible for the annual death of approximately 17 million people worldwide. Series of studies have been done to predict heart failure survival with promising results. Hence, the purpose of this study is to increase the accuracy of previous works on predicting heart failure survival by selecting significant predictive features in order of their ranking and dealing with class imbalance in the classification dataset. In this study, we propose an integrated method using machine learning. The proposed method shows promising results as it performs better than previous works and this study confirms that dealing with imbalanced dataset properly increases accuracy of a model. The model was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at predicting if a patient will survive heart failure. Attention may be focused on mainly serum creatinine, ejection fraction, smoking status and age.
Rancang Bangun Sistem Reservasi Ruangan Menggunakan Near Field Communication (NFC) Berbasis Mikrokontroller Rahmad Fadhil; Mohammad Hafiz Hersyah
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.95-104.2020

Abstract

Current technological developments also help in the ordering system. Simplifying the reservation system with information technology is one of the innovations made to help users of the room more easily in booking a room. The system designed consists of hardware and software connected to book a room based on, UID, usage time and to open the door of the room. Hardware includes Arduino Mega, NFC tags, NFC readers, relays, solenoids, buzzers, and LEDs. The software includes a Mysql website and database. The system will store user data, date, shift, length of usage and type of room booked by the user. NFC tags will be used by the customer to open the door to the room by getting closer to the NFC reader. This system aims to facilitate the process of borrowing space without having to undergo a convoluted process.
Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image Pulung Hendro Prastyo; Amin Siddiq Sumi; Annis Nuraini
JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.02.105-109.2020

Abstract

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

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