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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer Infotech Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT SMARTICS Journal Technologia: Jurnal Ilmiah Journal of Applied Engineering and Technological Science (JAETS) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Indonesia Berdaya Batara Wisnu : Indonesian Journal of Community Services Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) JUSTE (Journal of Science and Technology)
Wijaya, Eka Setya
Universitas Lambung Mangkurat
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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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.
Identifikasi Sentimen pada Data Teks Media Sosial Melalui Pendekatan Pembelajaran Terawasi Bahit, Muhammad; Sari, Yuslena; Baskara, Andreyan Rizky; Wijaya, Eka Setya; Yunus, Harry Pratama; Armelia, Alysa
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i2.16032

Abstract

Analisis sentimen pada data teks media sosial menjadi penting untuk memahami opini publik, sehingga penelitian ini bertujuan untuk mengidentifikasi sentimen pada data teks media sosial melalui pendekatan pembelajaran terawasi. Dataset yang digunakan terdiri dari tweet dan ulasan produk yang telah dilabeli sentimen positif maupun negatif. Proses penelitian dilakukan melalui beberapa tahapan, yaitu prapemrosesan data (Removal of Stopwords, Lemmatization and Word Stemming, Spell Correction), Ekstraksi Fitur (N-Grm, Word count dan Tf-Idf Vectorizer) serta penerapan algoritma Multinomial Naive Bayes, dan Support Vector Machine (SVM). Hasil penelitian menunjukkan bahwa penghapusan stopwords menurunkan kinerja model, sehingga tetap menggunakan stopwords. Stemming dan lemmatization juga tidak memberikan pengaruh terhadap kinerja model, sedangkan spell correction dapat meningkatkan akurasi sekitar 2% tetapi dengan trade-off waktu komputasi yang tinggi. Pada tahap ekstraksi fitur, TF-IDF menghasilkan akurasi yang lebih tinggi dibandingkan Word Count. Algoritma Multinomial Naive Bayes menghasilkan akurasi sebesar 79,73% dengan AUC-ROC sebesar 0,85. Sedangkan SVM dengan kernel linear mendapatkan hasil terbaik dengan akurasi 82% dan AUC-ROC 0,88, lebih tinggi daripada RBF kernel yang hanya mencapai akurasi 77,79% dan AUC-ROC 0,82. Hasil penelitian ini dapat disimpulkan bahwa SVM dengan kernel linear lebih sesuai untuk klasifikasi teks berdimensi tinggi.
PENERAPAN METODE COLLABORATIVE FILTERING BERBASIS ITEM UNTUK PERSONALISASI REKOMENDASI PRODUK PADA TOKO ONLINE Maulida, Mutia; Wathani, Muhammad Rais; Wijaya, Eka Setya; Fikri, Muhammad Rizal
Technologia : Jurnal Ilmiah Vol 17, No 2 (2026): Technologia (April)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v17i2.23007

Abstract

Perkembangan e-commerce menuntut pelaku usaha untuk memberikan pengalaman belanja yang lebih personal. UD. THJ, sebuah usaha dagang di bidang tekstil (karpet dan gorden), menghadapi kendala dalam membantu pelanggan menemukan produk yang sesuai di antara banyaknya pilihan stok. Penelitian ini bertujuan membangun sistem informasi penjualan online yang mengintegrasikan sistem rekomendasi menggunakan metode Item-Based Collaborative Filtering. Metode ini bekerja dengan menganalisis kemiripan antar produk berdasarkan pola rating atau pembelian pengguna. Sistem dikembangkan menggunakan model Waterfall dan bahasa pemrograman PHP dengan framework CodeIgniter. Hasil penelitian menunjukkan bahwa sistem mampu memberikan rekomendasi produk yang relevan kepada pelanggan berdasarkan histori preferensi mereka, sehingga mempermudah proses pengambilan keputusan pembelian dan meningkatkan potensi penjualan pada UD. THJ.
Innovation in Crop Nutrition Planning Based on Rainfall Prediction Using Singular Spectrum Analysis and Boosting to Optimize Agricultural Management Yuslena Sari; Mambang Mambang; Muhammad Zulfadhilah; Subhan Panji Cipta; Muhammad Nursandi; Finki Dona Marleny; Ricardus Anggi Pramunendar; Sunardi Sunardi; Eka Setya Wijaya; Aurelia Monica Sari; Muhammad Alkaff
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/z3mgdv08

Abstract

The high variability of rainfall in tropical climates presents a major challenge for agricultural management, as weather uncertainty often leads to inefficient fertilization practices due to nutrient loss. This study aims to develop a robust framework for rainfall prediction, which can inform a flexible and precise crop nutrient scheduling system. Utilizing an hourly rainfall dataset (n=6,624) obtained from IoT sensors, the research proposes an approach that integrates Singular Spectrum Analysis (SSA) for signal decomposition and noise reduction with Gradient Boosting algorithms (LightGBM and XGBoost). Spline interpolation was employed to handle missing data, while SSA served to disentangle deterministic trends from random noise, enabling the models to perform more effectively on the refined dataset. Empirical evaluation demonstrates that the SSA-XGBoost hybrid model achieves superior performance, with an RMSE of 0.0057 and an R² of 0.8278, significantly outperforming the SSA-LightGBM model (R² 0.2879), which struggled to capture non-linear patterns within this dataset. The high predictive accuracy of the SSA-XGBoost model facilitates the implementation of responsive nutrient management strategies, wherein fertilizer application can be deferred during forecasted periods of high rainfall to prevent runoff and environmental pollution. This research contributes to the field of hydroinformatics by demonstrating the effectiveness of combining SSA and XGBoost as a cost-efficient yet high-performance solution for mitigating climate-related risks in tropical wetland agriculture.
Co-Authors Ahmad Rivaldy Akhmad Rivaldy Akhmad Rojali Al-Amin, Mochamad Fajar Andreyan Rizky Baskara Armelia, Alysa Aurelia Monica Sari Awaliyah, Aisyah Bara Nugraha Putra Suryana Dany Primanita Kartikasari Deni Setiawan Fajar Zulkarnain, Andry Fikri, Muhammad Rizal Finki Dona Marleny Gunawan Gunawan Gusti Nizar Syafi'i Husnul Khatimi Kunti Nastiti Liling tryasmono Mambang Maulida, Muti'a Maulida, Mutia Misnariyani Mochamad Fajar Al-Amin Muhamad Reza Anwar Muhammad Alkaff Muhammad Alkaff Muhammad Andri Firdaus Muhammad Bahit Muhammad Bahit Muhammad Fajrian Noor Muhammad Nursandi Muhammad Syauqi Al Fath Muhammad Zainal Abidin Muhammad Zulfadhilah Mutia Maulida Muti`a Maulida Nadiya Hijriana, Nadiya Noval Noval Nurul Fathanah Mustamin Ricardus Anggi Pramunendar Rico Silas Dwi Kasanda Samsul Hadi Subhan Panji Cipta Sulthan Alfarisy Sunardi, Ph.D., Sunardi Suriazdin, Syahrul Alam Syarifah Soraya Taufiq Abrory Triana Rifkah Wathani, Muhammad Rais Yunus, Harry Pratama Yuslena Sari, Yuslena Yusri Yusri Zaenuddin Zaenuddin, Zaenuddin