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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Techno: Jurnal Penelitian Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Journal of Information Systems Engineering and Business Intelligence Info-Teknik KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jukung (Jurnal Teknik Lingkungan) JTERA (Jurnal Teknologi Rekayasa) Jurnal Komtika (Komputasi dan Informatika) ILKOM Jurnal Ilmiah Astonjadro Jurnal Ilmu Komputer dan Bisnis JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Buletin Profesi Insinyur Jurnal Sains Komputer dan Teknologi Informasi Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) JUSTE (Journal of Science and Technology) Journal of Advanced Health Informatics Research Jurnal Jalan Jembatan INSTALL: Information System and Technology Journal Jurnal Komtika (Komputasi dan Informatika)
Yuslena Sari, Yuslena
Dosen Program Studi Teknologi Informasi Fakultas Teknik Universitas Lambung Mangkurat, Indonesia
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The Regression Analysis Data for E-Sport Athletes Prediction using OSEMN Framework: Analisis Regresi Data Prediksi Atlet E-Sport Menggunakan Kerangka OSEMN Prastya, Septyan Eka; Adla, Musyfia; Nugraha, Bayu; Sari, Yuslena
INSTALL: Information System and Technology Journal Vol 1 No 1 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i1.542

Abstract

In the fast-growing E-Sports industry, athlete performance is the key to achieving success and winning. Therefore, analyzing the factors that contribute to the performance of E-Sports athletes is essential in order to optimize their performance in competition. This study aims to analyze the relationship between age, number of training hours, and experience playing in competition with rank, kill death ratio (KDA), and the number of wins of E-Sports athletes using the OSEMN approach (Obtain, Scrub, Explore, Model, Interpret, and Communicate). The data was obtained from 300 professional or non-professional E- Sports athletes, over the past three years who were involved in various competitions. Independent variables included age, number of training hours, and experience playing in competitions, while the dependent variables included rank, KDA, and number of wins. Data was collected, processed and explored and then analyzed using multiple linear regression methods. This study succeeded in applying the regression analysis method using the OSEMN framework, identifying relevant variables, and developing effective data collection and processing methods. This model has the potential to provide accurate predictions of E- Sport athlete performance data. However, it is still important to consider other factors such as business context, comparison with other models, and cross- validation to confirm the reliability of the prediction results.
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.
Sistem Pakar Berbasis Android untuk Mendeteksi Jenis Perilaku ADHD pada Anak Alkaff, Muhammad; Khatimi, Husnul; Sari, Yuslena; Darmawan, Puja; Primananda, Rakhmadhany
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2464.534 KB) | DOI: 10.25126/jtiik.2019621265

Abstract

ADHD (Attention Deficit Hyperactivity Disorder) adalah gangguan perkembangan otak pada anak yang mengakibatkan meningkatnya aktifitas motorik sehingga menyebabkan penderitanya menjadi hiperaktif, impulsif dan inatentif. Kondisi ini sering memperlihatkan tingkah laku yang tidak wajar seperti selalu bergerak tanpa tujuan, selalu gelisah, atau tidak bisa duduk dengan tenang. Gangguan ADHD terbagi menjadi tiga jenis yaitu Hiperaktif, Inatentif dan Impulsif. Salah satu cara untuk mendiagnosa jenis ADHD yang diderita oleh anak adalah dengan konseling. Tujuan dari penelitian ini adalah membangun sebuah sistem pakar yang dapat membantu memberikan kesimpulan tentang jenis penyakit ADHD yang diderita oleh anak serta tingkat keyakinan diagnosisnya. Penelitian ini menggunakan metode Dempster-Shafer untuk melakukan perhitungan terhadap nilai keyakinan suatu diagnosa. Hal ini dilakukan dengan cara membandingkan setiap nilai keyakinan dari 2 gejala awal yang terjadi pada anak untuk selanjutnya dibandingkan lagi dengan nilai keyakinan dari gejala-gejala lainnya. Sehingga mengerucut pada suatu gejala yang mengacu kepada suatu jenis dari ADHD disertai dengan nilai keyakinannya seperti layaknya diagnosa seorang pakar psikologi anak. Dalam penelitian ini dibangun sistem pakar berbasis Android dengan basis pengetahuan dari 3 orang pakar untuk memudahkan orang tua anak dalam mendiagnosa gejala-gelaja yang mungkin diderita oleh anaknya. Hasil pengujian sistem terhadap pakar dengan persentase rata-rata sebesar 93,3% dari 3 orang pakar, menunjukan bahwa sistem pakar yang telah dibuat mampu mendiagnosa jenis perilaku ADHD yang diderita oleh anak-anak disertai dengan nilai tingkat keyakinan diagnosisnya.Abstract ADHD (Attention Deficit Hyperactivity Disorder) is a brain development disorder in children resulting in increased motor activity causing the sufferer to become hyperactive, impulsive and inattentive. This condition often shows unnatural behavior like always moving aimlessly, always restless, or unable to sit quietly. ADHD disorders divided into three types, namely Hyperactive, Inattentive and Impulsif. One way to diagnose the type of ADHD suffered by children is by counseling. The purpose of this study is to build an expert sistem that can help provide conclusions about the kind of ADHD that the children had and the diagnosis level of confidence. This research uses Dempster-Shafer method to perform the calculation of confidence value of diagnosis. This is done by comparing each of the confidence values of the two early symptoms that occur in the child to furthermore compare with the belief value of the other symptoms. Therefore, conical to a symptom that refers to a type of ADHD accompanied by the value of the diagnosis beliefs, just like the diagnosis of a child psychologist. In this study, an Android-based expert system with a knowledge base from three experts is built to facilitate the child's parents in diagnosing symptoms that may be suffered by his son. The experimental test of the system with the mean percentage of 90% from 3 experts, indicates that the expert system that has been made can diagnose the type of ADHD behavior suffered by the children accompanied by the value of the diagnosis confidence level.
Perbandingan Metode Pembobotan Tf-Rf Dan Tf-Idf Dikombinasikan Dengan Weighted Tree Similarity Untuk Sistem Rekomendasi Buku Sari, Yuslena; Baskara, Andreyan RIzky; Prakoso, Puguh Budi; Royani, Noorhanida
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 6: Desember 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935709

Abstract

Unit Pusat Terpadu Perpustakaan merupakan perpustakaan pusat yang ada di Universitas Lambung Mangkurat. Perpustakaan ini mempunyai sistem pencarian buku namun sistem tersebut belum adanya fitur rekomendasi buku sehingga anggota menjadi kesulitan dalam melakukan pencarian buku yang sesuai dengan keinginan anggota. Oleh karena itu, dengan adanya rekomendasi buku atau saran buku yang lain dapat menjadi alternatif untuk membantu anggota dalam melakukan pencarian buku yang sesuai. Dalam penelitian ini menggunakan perbandingan pembobotan kata TF-IDF dan TF-RF dengan weighted tree similarity sebagai pengukur kemiripan diantara beberapa data dengan parameter tree yang sudah ditentukan dan dilakukan perbandingan perhitungan dengan menghitung tf-idf dengan tf-rf menggunakan perhitungan excel mendapatkan nilai yang berbeda antara tf-idf dengan tf-rf, pembobotan tf-idf dapat mengukur kemiripan antara dokumen dan kata kunci buku yang paling mirip dengan buku yang dianggap paling relevan. Sehingga anggota memasukan kata kunci kemudian akan menemukan kemiripan buku dari kata kunci yang dimasukan sebelumnya namun untuk pembobotan tf-rf memberikan kata kunci dari setiap kategori. Hasil perbandingan yang di dapat yaitu 96% untuk tf-idf dan 98% untuk tf-rf. Sistem ini menggunakan bahasa pemrograman python dengan web framework django. AbstractThe Central Integrated Library Unit is the central library at Lambung Mangkurat University. This library has a book search system but the system does not have a book recommendation feature so that members find it difficult to search for books that match the wishes of members. Therefore, the existence of book recommendations or other book suggestions can be an alternative to assist members in searching for suiTabel books. In this study using a comparison of the weighting of the words TF-IDF and TF-RF with weighted tree similarity as a measure of the similarity between several data and a comparison of calculations is carried out by calculating tf-idf with tf-rf using excel calculations to get different values between tf-idf and tf -rf, tf-idf weighting can measure the similarity between documents and keywords of the book that is most similar to the book that is considered the most relevant. So that members enter keywords and then find the similarity of books from the keywords entered previously but for weighting tf-rf provides keywords from each category. The comparison results obtained are 76% for tf-idf and 80% for tf-rf. This system uses the python programming language with the django web framework.
Penerapan Metode K-Means Berbasis Jarak untuk Deteksi Kendaraan Bergerak Sari, Yuslena; Baskara, Andreyan Rizky; Prakoso, Puguh Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022945768

Abstract

Deteksi kendaraan bergerak adalah salah satu elemen penting dalam aplikasi Intelligent Transport System (ITS). Deteksi kendaraan bergerak juga merupakan bagian dari pendeteksian benda bergerak. Metode K-Means berhasil diterapkan pada piksel cluster yang tidak diawasi untuk mendeteksi objek bergerak. Secara umum, K-Means adalah algoritma heuristik yang mempartisi kumpulan data menjadi K cluster dengan meminimalkan jumlah kuadrat jarak di setiap cluster. Dalam makalah ini, algoritma K-Means menerapkan jarak Euclidean, jarak Manhattan, jarak Canberra, jarak Chebyshev dan jarak Braycurtis. Penelitian ini bertujuan untuk membandingkan dan mengevaluasi implementasi jarak tersebut pada algoritma clustering K-Means. Perbandingan dilakukan dengan basis K-Means yang dinilai dengan berbagai parameter evaluasi yaitu MSE, PSNR, SSIM dan PCQI. Hasilnya menunjukkan bahwa jarak Manhattan memberikan nilai MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 dan PCQI = 0.79 terbaik dibandingkan dengan jarak lainnya. Sedangkan untuk waktu pemrosesan data memperlihatkan bahwa jarak Braycurtis memiliki keunggulan lebih yaitu 0.3 detik. AbstractDetection moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detection moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE = 1.328 , PSNR = 21.14, SSIM = 0.83 and PCQI = 0.79 values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages 
Ground Coverage Classification in UAV Image Using a Convolutional Neural Network Feature Map Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Sari, Yuslena
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.206-216

Abstract

Background: To understand land transformation at the local level, there is a need to develop new strategies appropriate for land management policies and practices. In various geographical research, ground coverage plays an important role particularly in planning, physical geography explorations, environmental analysis, and sustainable planning. Objective: The research aimed to analyze land cover using vegetation density data collected through remote sensing. Specifically, the data assisted in land processing and land cover classification based on vegetation density. Methods: Before classification, image was preprocessed using Convolutional Neural Network (CNN) architecture's ResNet 50 and DenseNet 121 feature extraction methods. Furthermore, several algorithm were used, namely Decision Tree, Naí¯ve Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Results: Classification comparison between methods showed that using CNN method obtained better results than machine learning. By using CNN architecture for feature extraction, SVM method, which adopted ResNet-50 for feature extraction, achieved an impressive accuracy of 85%. Similarly using SVM method with DenseNet121 feature extraction led to a performance of 81%. Conclusion: Based on results comparing CNN and machine learning, ResNet 50 architecture performed the best, achieving a result of 92%. Meanwhile, SVM performed better than other machine learning method, achieving an 84% accuracy rate with ResNet-50 feature extraction. XGBoost came next, with an 82% accuracy rate using the same ResNet-50 feature extraction. Finally, SVM and XGBoost produced the best results for feature extraction using DenseNet-121, with an accuracy rate of 81%.   Keywords: Classification, CNN Architecture, Feature Extraction, Ground Coverage, Vegetation Density.
Identifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN) Sari, Yuslena; Alkaff, Muhammad; Arif Rahman, Muhammad
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.4605

Abstract

Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.
Co-Authors Adla, Musyfia Ahmad Rivaldy Ahmad Shalludin Akhmad Rivaldy Andreyan Rizky Baskara Ariefah Khairina Islahati ARIF RAHMAN, MUHAMMAD Bambang Lareno, Bambang Bayu Nugraha Chastine Fatichah Dany Primanita Kartikasari Darmawan, Puja Dewi Astria Faroek Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Endi Gunawan Fardian, Muhammad Reza Ferry Pratama Finki Dona Marleny Firmansyah, Muhammad Ilham Gusti Nizar Syafi'i Halimah Halimah Hani Pratiwi Husnul Khatimi Irfan Prasetia Johan Wahyudi Johan Wahyudi Mambang Maulidia Arafah Maulidiya, Erika Maya Amalia Meila Izzana, Meila Muhammad Adetya Ashari Muhammad Alkaff Muhammad Andri Firdaus Muhammad Arif Rahman Muhammad Baballe Ahmad Muhammad Faidhorrahman Muhammad Syauqi Al Fath Mutia Maulida Muti`a Maulida Nanik Suciati Noor Razikin Novitasari Novitasari Novitasari Novitasari Nurul Fathanah Mustamin Nurul Qamaria Prakoso, Puguh Budi Prastya, Septyan Eka Pratiwi, Hani Puguh Budi Prakoso Puguh Budi Prakoso Purwono, Purwono Rahman, Muhammad Arif Rakhmadhany Primananda, Rakhmadhany Rama Aria Megantara Ricardus A P, Ricardus A Ricardus Anggi Pramunendar Rico Silas Dwi Kasanda Royani, Noorhanida Rudy Ansari Rusiana, Novi Subhan Panji Cipta Syarifah Soraya Wenny Puspita Wijaya, Eka Setya