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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 6 Documents
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Search results for , issue "Vol. 5 No. 1 (2022)" : 6 Documents clear
Darknet, Malware, KNN, Forensik Prediksi Jaringan TOR dan VPN menggunakan Algoritma K-Nearest Neighbour pada Trafik Darknet Aay Ramdan ramdan; Nur Widyasono; Husni Mubarok
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.167

Abstract

Proses network forensic untuk menganalisis malware telah dilakukan peneliti sebelumnya dengan menerapkan metode manual diantaranya metode Anomali Behaviour pada file capture trafik jaringan. Network forensik tersebut memerlukan proses yang lebih lama dan tidak akurat dengan hasil yang diinginkan. Perkembangan articial intelligence berkembang pesat pada setiap bidang teknologi dapat memberikan peluang terhadap bidang malware analisis dan digital forensik agar dapat melakukan proses analisis lebih cepat dan tepat terutama penggunaan Machine Learning. Trafik darknet merupakan jaringan internet yang didalamnya terdapat berbagai ancaman kejahatan cyber. Penelitian terhadap analisis malware terutama klasifikasi trafik darknet dengan menggunakan algoritma machine learning telah banyak dilakukan, namun hasil yang didapat berupa pengukuran kinerja pada setiap algoritma machine learning terhadap proses analisis malware tanpa adanya pembaruan dataset ataupun implementasi dalam sebuah aplikasi. Pembaruan dataset sangat diperlukan agar analisa malware dapat mengidentifikasi perkembangan malware terbaru dan implementasi dilakukan agar dapat diketahui kinerja dari sebuah algoritma yang diterapkan, oleh karena hal tersebut dalam penelitian ini akan dibahas tentang proses analisis ancaman malware pada trafik darknet dengan menggunakan algoritma machine learning yaitu K-Nearest Neighbour untuk memprediksi sebuah ancaman serangan malware dengan dataset CICDarknet 2020. Hasil pengukuran performa dataset menggunakan KNN memiliki nilai akurasi 96,17% dengan menerapkan pemilihan fitur dengan information gain.
Oksimeter Militer Pemantau Stres Prajurit TNI Berbasis Internet of Military Things Dananjaya Ariateja; Iqbal Ahmad Dahlan; Uvi Desi Fatmawati
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.174

Abstract

In carrying out their duties, TNI soldiers often experience pressure and threats that attack both physically and psychologically. This can trigger stress. Uncontrolled stress will cause disease disorders such as arrhythmias and hypoxemia. We offer a solution by building an Internet of Military Things (IoMT) based military oximeter for soldier stress monitoring. The proposed tool is real-time and portable, can monitor heart rate (BPM) and blood oxygen saturation (SpO2) when soldiers are on duty in conflict areas. This military oximeter is equipped with notifications and alarms that are integrated with applications installed on smartphones, so commanders can monitor the condition of their soldiers directly and view their health history. Based on the test results, obtained an accuracy of 99.7% and 99.88% for measuring heart rate and oxygen saturation in the blood. This military oximeter can be used as a medical aid to monitor the health condition of soldiers while on duty.
Prototype Penyiraman Otomatis Berbasis IOT untuk Multi Zona Tanaman Hias Hafiyyan Putra Pratama; Dewi Indriati Hadi Putri; Sudjani
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.180

Abstract

The greenhouse concept for the cultivation of ornamental plants can optimize the conditioning of a good planting environment because it can protect plants from direct rain, can distribute temperature, divide sunlight evenly and regulate the intensity of watering plants regularly. The important thing to pay attention to in the cultivation of ornamental plants is the intensity of watering plants. Regular watering of plants can be optimized with the help of Internet of Things (IoT) technology so that the process of watering plants becomes a smart watering system. In this study, automatic watering of ornamental plants was divided into 3 zones based on the characteristics of humidity, watering time, and required water quantity. The smart watering system for 3 zones is implemented in the form of a prototype including a monitoring system based on the MQTT protocol
Utilization of Artificial Intelligence in Facing the Covid-19 Pandemic: Systematic Literature Review Muhamad Rizky; Aang Subiyakto
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.184

Abstract

Abstract— COVID-19 virus is a virus that attacks the respiratory system which has become a pandemic, because it has spread to various countries and the world. Of course, this impact has brought difficulties in various sectors of human life. Artificial Intelligence (AI) is a computer system designed to follow human actions and thought patterns, so that they can have intelligence like humans. The presence of Artificial intelligence as a technology is able to play a role in producing innovations that are useful in dealing with the COVID-19 pandemic for human life. The purpose of this study was to find out how to use artificial intelligence in the face of the COVID-19 pandemic. The method used in this research is a literature study method. The results of this study are that artificial intelligence can be used in dealing with COVID-19 to help overcome problems that exist in various fields of human life.
Penggunaan Digital Signature Untuk Absensi Pada Universitas Muhammadiyah Purworejo Widatama Krisna; Hamid Jumasa Muhammad; Danti Puspitaningrum
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.188

Abstract

Abstract— The student attendance system carried out at the Muhammadiyah University of Purworejo currently still uses paper for both students and lecturers. The use of this paper allows errors to occur when the lecturer recapitulates student attendance data. In addition, the attendance process that still uses paper is considered less effective and efficient. Therefore, it is necessary to create an information system that can apply digital signatures at the Purworejo Muhammadiyah University to prevent Purworejo Muhammadiyah University students outside of lectures or being absent from being able to fill attendance. With this system, it is hoped that this digital signature can be used in academic information systems and support online learning
Pemodelan Machine Learning : Analisis Sentimen Masyarakat Terhadap Kebijakan PPKM Menggunakan Data Twitter Syafrial Fachri Pane; Jenly Ramdan
Jurnal Sistem Cerdas Vol. 5 No. 1 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i1.191

Abstract

Abstract— In this pandemic era, the government is forced to implement policies that can reduce the daily positive rate of COVID-19. One of these policies is known as PPKM. It is unclear when the pandemic will end, causing data phenomena to be scattered on social media, one of which is Twitter. Therefore, in this study, we conducted an analysis of sentiment originating from tweets from Twitter social media users in the Jakarta area regarding the government's policy, namely PPKM in the face of the COVID-19 pandemic. In this research, we use a Machine Learning approach, namely LSTM. This modeling produces a classification of positive and negative sentiments. The dataset used is 3000 tweets with a time period of September - November 2021. At the preprocessing stage, the data that are ready to be used for modeling are 2176. The results of this study get an accuracy of 0.943. So the model that we propose, namely LSTM, has succeeded in classifying a satisfactory sentiment with a positive number of 92% and a negative 8% of 2176 sentiments, so it can be concluded that the PPKM policy implemented by the Indonesian government in the DKI Jakarta area is said to be effective or positive.

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