Journal of Information Technology and Computer 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.
Articles
180 Documents
<![CDATA[Optimalisasi Peta Pengendalian Penduduk untuk Diintegrasikan pada Organisasi Perangkat Daerah (OPD) di Kabupaten Bandung Barat ]]>
<![CDATA[edsryan puspadianis Ramitha Satya putri]]>;
<![CDATA[Muhammad Rozahi Istambul]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 01 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.01.49-52.2020
<![CDATA[Demographic maps can present statistical data on demographic education levels, population stages, family stages, education levels, age, and number of lives in families based on participation in the National Health Insurance (JKN). Where every Regional Apparatus Organization (OPD) in West Bandung Regency that requires population control data needs a data integration and performance optimization, so that in each Regional Apparatus Organization (OPD) in work there is no more duplication of work. In this study, it is expected to be able to map all information about population control data to help the community and Regional Apparatus Organizations (OPD) in West Bandung Regency in order to obtain information clearly from population control data based on the results of family data collection which will make it easier for Regional Apparatus Organizations (OPD) related in order to help the entire community in West Bandung Regency. In this case a solution will be made by utilizing the QGIS application in solving problems where for population control maps can be integrated in the Regional Apparatus Organization (OPD) in West Bandung Regency.]]>
<![CDATA[Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things ]]>
<![CDATA[Mumuh Muharam]]>;
<![CDATA[Melda Latif]]>;
<![CDATA[Baharuddin Baharuddin]]>;
<![CDATA[Ibnum Richaflor]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.53-62.2020
<![CDATA[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.]]>
<![CDATA[Implementasi Smart Home Pada Pendeteksi Dini Kebakaran Menggunakan Forward Chaining ]]>
<![CDATA[Irawan Dwi Wahyono]]>;
<![CDATA[Mochammad Bagus Priyantono]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.63-71.2020
<![CDATA[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%.]]>
<![CDATA[Machine Learning Application for Classification Prediction of Household’s Welfare Status ]]>
<![CDATA[Nofriani Nofriani]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.72-82.2020
<![CDATA[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.]]>
<![CDATA[Otomatisasi Pengoperasian Alat Elektronik Berdasarkan Hasil Prediksi Algoritma Long Short Term Memory ]]>
<![CDATA[Afriansyah Afriansyah]]>;
<![CDATA[Ade Irawan]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.83-89.2020
<![CDATA[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.]]>
<![CDATA[Predicting Survival of Heart Failure Patients Using Classification Algorithms ]]>
<![CDATA[Oladosu Oyebisi Oladimeji]]>;
<![CDATA[Olayanju Oladimeji]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.90-94.2020
<![CDATA[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.]]>
<![CDATA[Rancang Bangun Sistem Reservasi Ruangan Menggunakan Near Field Communication (NFC) Berbasis Mikrokontroller ]]>
<![CDATA[Rahmad Fadhil]]>;
<![CDATA[Mohammad Hafiz Hersyah]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.95-104.2020
<![CDATA[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.]]>
<![CDATA[Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image ]]>
<![CDATA[Pulung Hendro Prastyo]]>;
<![CDATA[Amin Siddiq Sumi]]>;
<![CDATA[Annis Nuraini]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.4.02.105-109.2020
<![CDATA[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.]]>
<![CDATA[Prediksi Energi Listrik Kincir Angin Berdasarkan Data Kecepatan Angin Menggunakan LSTM ]]>
<![CDATA[Muhammad Qubaisy Andiyantama]]>;
<![CDATA[Iffah Zahira]]>;
<![CDATA[Ade Irawan]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 5 No 01 (2021): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.5.01.1-7.2021
<![CDATA[Fossil energy is well known as the most energy resource consumed by humans. However, the exploitation leads to damage both in the process of taking raw materials and in the use of those. Furthermore, the amount has become decreasing nowadays. Renewable energy could solve the energy crisis. One kind of renewable energy that has been successfully used by a human is by utilizing wind turbines. However, there are still many problems in its implementation and usage. One of the problems is the unstable generated electricity that is caused by instability of the wind speed. Inappropriate plans for utilizing wind turbines in such areas with varying wind speed could harm renewable energy investment. Therefore, forecasting the wind speed is necessary to anticipate the stability and embrace optimal produced energy. This study proposes the Long Short Term Memory (LSTM) algorithm to predict the generated energy by using the wind speed dataset. Thus, wind turbines can be utilized effectively and efficiently in the right area with sufficient average wind speed.]]>
<![CDATA[Implementasi Forward Chaining dan Certainty Factor pada Aplikasi Konsultasi Kecantikan ]]>
<![CDATA[Bunga Ratna Sari]]>;
<![CDATA[Sabar Rudiarto]]>
<![CDATA[ JITCE (Journal of Information Technology and Computer Engineering) Vol 5 No 01 (2021): Journal of Information Technology and Computer Engineering ]]>
Publisher : <![CDATA[Universitas Andalas ]]>
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DOI: 10.25077/jitce.5.01.8-17.2021
<![CDATA[Having skin that is free from problems is a dream for most women, especially in Indonesia. However, the problem is not all women are aware that in carrying out treatment or using a product, it must match the needs of each skin type. The rise of beauty salons that operate but are not under the auspices of specialist doctors and also cheap beauty products that are claimed to solve skin problems in an instant makes women easily tempted. This of course can have a big negative impact when the maintenance procedures performed are not up to standard, and there are dangerous ingredients in the products used. For this reason, consulting with a specialist or the expert is indispensable. Researchers used the forward chaining method and certainty factors in building this system which is expected to be used in determining facial skin problems and their solutions based on case studies at the XYZ beauty clinic. The Forward Chaining method is used to draw conclusions based on the facts entered. While certainty factor is used to calculate the trust value from the existing conclusions. This method requires 2 main values in performing calculations, namely MB (measurement of belief) and MD (measurement of disbelief). Based on the research that has been done, the system was successfully built and gave an accuracy value of 97.35%.]]>
