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Optimasi Penjadwalan Mata Kuliah Menggunakan Metode Algoritma Genetika dengan Teknik Tournament Selection Sari, Yuslena; Alkaff, Muhammad; Wijaya, Eka Setya; Soraya, Syarifah; Kartikasari, Dany Primanita
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 1: Februari 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3621.855 KB) | DOI: 10.25126/jtiik.2019611262

Abstract

AbstrakBagi sebuah perguruan tinggi, penjadwalan perkuliahan merupakan suatu kegiatan yang sangat penting   untuk   dapat   terlaksananya   proses belajar mengajar   yang   baik.  Dimana   dalam   proses  belajar mengajar dapat dilakukan oleh semua pihak yang terkait, bukan hanya bagi dosen yang mengajar, tetapi juga bagi mahasiswa yang mengambil mata kuliah. Dalam penyusunan jadwal, ada beberapa variabel yang mempengaruhi yaitu: ruangan yang tersedia, jumlah mata kuliah yang diselenggarakan, waktu yang ada dan ketersediaan dosen yang mengajar. Oleh karena itu tujuan dari penelitian ini adalah merancang suatu sistem yang dapat membuat atau menyusun   jadwal    perkulihaan    secara  teroptimasi. Metode dalam proses pembuatan jadwal perkuliahan secara otomatis pada penelitian ini menggunakan metode algoritma genetika dengan teknik seleksi turnamen. hasil pengujian sistem dapat memberikan kemudahan dan kecepatan kepada user atau Program Studi Teknologi Informasi dalam proses pembuatan atau penyusunan jadwal untuk    perkuliahan,    yaitu hanya diperlukan waktu sekitar 14,7 menit dibandingkan dengan proses manual yang memerlukan waktu sekitar 2 (dua) hari.AbstractFor a college, the university course timetabling is is an activity that’s very important for the implementation of good teaching and learning process. In  teaching  and  learning  process  can be done    by    all    related    parties,   not    only    for Lecturers who teach, but also for students who take the course. In the preparation of the schedule, there are several variables that affect the: the available space, the number of courses held, the time available and the availability of lecturers  who  teach. Therefore, the  purpose  of this research is to design a system that can create or arrange optimization schedule optimally. Methods in the process of making university course   timetabling   automatically   in   this study using genetic algorithm method with tournament selection.
CLASSIFICATION OF STUDENT STUDY PERIOD USING NEURAL NETWORK BACKPROPAGATION ALGORITHM BASED ON ENTRY PATH (CASE STUDY: FACULTY OF ENGINEERING, UNIVERSITAS LAMBUNG MANGKURAT) Eka Setya Wijaya; Mochamad Fajar Al-Amin
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 1 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i1.210

Abstract

Comparison of Arima Model with The Addition of Linear Quadratic Estimation Algorithm for Prediction The Spread of Covid-19 in Kotabaru District Eka Setya Wijaya; Bara Nugraha Putra Suryana
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.211

Abstract

Coronavirus disease 2019 (Covid-19) has been declared by WHO as a pro-longed global pandemic which has caused signif- icant public health problems, deaths and economic losses, therefore it is necessary to carry out prevention and control ef- forts to break the chain of transmission of Covid-19. One effort that can be done is to estimate the additional number of posi- tive cases of Covid-19, so that the number of isolation rooms and the need for medical personnel can be estimated. In this study the prediction of an increase in the number of positive cases of Covid-19 was carried out using the Linear Quadratic Estimation (Kalman Filter) approach based on the state space model formed from the ARIMA model (0,1,4). Based on train- ing data from March 23, 2020 to April 4, 2023, the best time series model is the ARIMA model (0,1,4) which was chosen based on the smallest AIC value and satisfies the residual test hypothesis
Network Security Analysis with Hybrid Intrusion Detection System, Firewall, and Attacker Log Visualisation Sulthan Alfarisy; Eka Setya Wijaya; Muhammad Fajrian Noor; Muhammad Bahit
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 10 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v10i1.462

Abstract

The current digital era brings convenience to people in various industries, including access to information that can be obtained from various sources on the Internet. However, the freedom of the Internet has also led to an increase in cybercrime, which has become a serious problem. According to a monitoring report from the National Cyber and Crypto Agency (BSSN), Indonesia experienced a total of around 2.4 billion cyberattack anomalies between January 2021 and August 2022. With so many cases, an effective system is needed to detect, prevent, and monitor computer networks. This research applies a hybrid Intrusion Detection System (IDS) system that uses OSSEC and Suricata, and uses Elastic Stack for log management for server monitoring. The results show that this hybrid IDS system is able to detect all types of attacks tested, including port scanning, brute force, SQL injection, and denial of service (DoS). In addition, this system can also block attack access by utilising firewall features such as Iptables. The detection results of the hybrid IDS were successfully visualised using Elastic Stack, demonstrating the effectiveness of the system in improving computer network security.