JITCE (Journal of Information Technology and Computer Engineering) 2020-10-23T09:12:22-04:00 Editor JITCE Open Journal Systems <p><strong>JITCE (Journal of Information Technology and Computer Engineering)</strong>&nbsp;is a scholarly periodical. JITCE will publish research papers, technical papers, conceptual papers, and case study reports. This journal is published by<a href=""> Computer System Department</a> at&nbsp;<a href="" target="_blank" rel="noopener">Universitas Andalas</a>, Padang, West Sumatra, Indonesia.</p> <p>One volume of JITCE consisted of two editions, which are published in March and September each year. Articles are written in Bahasa Indonesia (Indonesian language) OR English. Abstracts&nbsp;<strong>must be in English</strong>.</p> Pencegahan Kesalahan Alarm dalam Sistem Pendeteksi Dini Kebakan dan Pemadaman Berbasis Internet of Things 2020-10-23T09:10:44-04:00 Mumuh Muharam Melda Latif Baharuddin Baharuddin Ibnum Richaflor <table width="0"> <tbody> <tr> <td width="662"> <p>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. &nbsp;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.</p> </td> </tr> </tbody> </table> 2020-09-30T03:01:59-04:00 ##submission.copyrightStatement## Implementasi Smart Home Pada Pendeteksi Dini Kebakaran Menggunakan Forward Chaining 2020-10-23T09:12:22-04:00 Irawan Dwi Wahyono Mochammad Bagus Priyantono <table width="828"> <tbody> <tr> <td width="492"> <p>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%.</p> </td> </tr> </tbody> </table> 2020-09-30T00:00:00-04:00 ##submission.copyrightStatement## Machine Learning Application for Classification Prediction of Household’s Welfare Status 2020-10-23T09:12:06-04:00 Nofriani Nofriani <p>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.</p> 2020-09-30T00:00:00-04:00 ##submission.copyrightStatement## Otomatisasi Pengoperasian Alat Elektronik Berdasarkan Hasil Prediksi Algoritma Long Short Term Memory 2020-10-23T09:11:47-04:00 Afriansyah Afriansyah Ade Irawan <p>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.</p> 2020-09-30T00:00:00-04:00 ##submission.copyrightStatement## Predicting Survival of Heart Failure Patients Using Classification Algorithms 2020-10-23T09:11:26-04:00 Oladosu Oyebisi Oladimeji Olayanju Oladimeji <p>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&nbsp;on mainly serum creatinine, ejection fraction, smoking status and age. </p> 2020-09-30T00:00:00-04:00 ##submission.copyrightStatement##