Articles
<![CDATA[Perbandingan Metode Adaptive Boosting dan Extreme Gradient Boosting Untuk Prediksi Hasil Pertandingan Liga Spanyol ]]>
<![CDATA[Muhammad Rohid Saputro]]>;
<![CDATA[Umi Mahdiyah]]>;
<![CDATA[Daniel Swanjaya]]>
<![CDATA[ Nusantara of Engineering (NOE) Vol 7 No 1 (2024): Volume 7 No. 1 Tahun 2024 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/noe.v7i1.20882
<![CDATA[Sepakbola merupakan olahraga yang paling terkenal di seluruh dunia dengan hampir 4 miliar pengagum dari berbagai belahan bumi. Negara besar di Eropa memiliki kompetisi sepakbola yang terstruktur dan memiliki tingkatan kompetisi yang lengkap. Algoritma AdaBoost dan XGBoost merupakan metode machine learning yang dapat digunakan untuk mengatasi suatu permasalahan yang berhubungan dengan deret dan situasi peramalan. Perlunya mengetahui prediksi kemenangan tim pertandingan sepak bola Liga Spanyol selalu menjadi pembahasan yang tidak pernah dilewatkan oleh penggemar sepak bola, oleh karena itu prediksi sangat berguna untuk para penggemar sepakbola dan pelatih tim sepak bola dapat mengantisipasi suatu kejadian yang mendatang. Misalnya, penggemar ataupun pelatih tim sepak bola Liga Spanyol dapat memperkirakan kemenangan tim pada masa mendatang. Data yang digunakan menggunakan dataset statistik pertandingan 2 tingkat teratas Liga Spanyol selama 4 musim yaitu pada musim 2018/2019 sampai musim 2021/2022 yang didapat dari www.football-data.co.uk. Pengujian menggunakan metode AdaBoost memperoleh tingkat akurasi sebesar 64,02%, dan metode XGBoost memperoleh tingkat akurasi sebesar 61,79%. Dari hasil pengujian yang telah dilakukan pada dataset Liga Spanyol musim 2018/2019 sampai 2021/2022, menunjukkan bahwa metode AdaBoost memperoleh hasil yang lebih baik jika dibandingkan dengan metode XGBoost.]]>
<![CDATA[Game Edukasi Pengenalan Gamelan Jawa Sebagai Media Pembelajaran ]]>
<![CDATA[Arie Putra, Zamima Daffa Rizki]]>;
<![CDATA[Irawan, Rony Heri]]>;
<![CDATA[Mahdiyah, Umi]]>
<![CDATA[ Nusantara of Engineering (NOE) Vol 7 No 1 (2024): Volume 7 No. 1 Tahun 2024 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/noe.v7i01.20891
<![CDATA[Perkembangan teknologi informasi diberbagai bidang membuat siapa saja dapat dengan mudah untuk mengakses informasi. Perkembangan teknologi yang diminati dikalangan anak-anak dan orang dewasa salah satunya adalah game. Game yang awalnya hanya untuk bersenang-senang, sekarang bisa dibuat untuk media pembelajaran yang menarik dan interaktif. Dalam dunia Pendidikan kemajuan teknologi juga semakin berkembang, misalnya dalam media pembelajaran. Pemanfaatan teknologi informasi di bidang pendidikan dapat memberikan solusi dan mempermudah dalam proses pembelajaran, contohnya seperti game edukasi. bahkan game yang akan peneliti rancang sekarang. Pembuatan game tidak hanya ditujukan sebagai sarana hiburan, tetapi juga untuk sarana pembelajaran. Dari permasalahan yang ada, peneliti tertarik untuk merancang game edukasi pengenalan gamelan jawa sebagai media pembelajaran digital yang baik dan menarik. Hasil penelitian yang dilakukan berupa game edukasi “Pengenalan Gamelan Jawa”, didalam game tersebut akan membahas tentang pengenalan gamelan jawa. Hasil uji coba Blackbox pada game “Pengenalan Gamelan Jawa” semua berjalan lancar, mulai dari fungsi tombol dan gameplay.]]>
<![CDATA[Implementasi Metode Transformasi Wavelet Diskrit Dengan K-Nearest Neighbor Untuk Klasifikasi Penyakit MataKata ]]>
<![CDATA[Pamungkas, Danar Puta]]>;
<![CDATA[Alghozali, Muhammad Attiqi]]>;
<![CDATA[Pamungkas, Danar Putra]]>;
<![CDATA[Mahdiyah, Umi]]>
<![CDATA[ Nusantara of Engineering (NOE) Vol 7 No 2 (2024): Volume 7 Nomor 2 - 2024 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/noe.v7i02.22889
<![CDATA[The eye is an organ in humans that functions as seeing objects around with the reflection of light received by the retina. This sense of vision can be affected by diseases that often occur, including cataracts, as well as other diseases such as glaucoma and retinal disease. The eye disease will interfere with the activities of the sufferer and can also attack his psyche. In examining and ensuring that eye diseases can be done by utilizing technology, with the development of technology to identify eye diseases can be done. Through an image of the patient's retinal fundus, the image can be processed using the image processing method. By combining image processing with classification methods from machine learning, images can be processed until they are identified in the class. This study was conducted with the aim of classifying eye diseases using the discrete wavelet transformation method with K-nearest neighbor, obtaining an accuracy level of 61% in the classification of a class. These results indicate that the classification can be done quite well, but in the results obtained, not all classes can classify well. Using datasets from Kaggle 300 normal eye datasets, 100 cataract eye datasets, 101 glaucoma eye datasets, and 100 retinal disease eye datasets, there are 4 classes of retinal fundus images. The retinal fundus is an image obtained as a result of capturing using a tool called the Ophthalmoscope where this tool helps illuminate and magnify the image in the eye to produce a capture of the retinal fundus.]]>
<![CDATA[Pelatihan Pengolahan Bawang Merah dengan Alat Pebmo Pada Kelompok Petani Bawang Merah Di Desa Sekoto Kabupaten Kediri ]]>
<![CDATA[Indrawati, Elsanda Merita]]>;
<![CDATA[Munawi, Hisbullah Ahlis]]>;
<![CDATA[Suwardono, Agus]]>;
<![CDATA[Santoso, Rachmad]]>;
<![CDATA[Manikta, M. Dewi]]>;
<![CDATA[Mahdiyah, Umi]]>;
<![CDATA[Nadliroh, Kuni]]>;
<![CDATA[Nevita, Ary Permatadeny]]>
<![CDATA[ Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 3 No 2 (2020): Volume 3 Nomor 2 Tahun 2020 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/ja.v3i2.13823
<![CDATA[Sekoto Village is the largest onion producer center in Kediri Regency. The aim of this service activity is to provide training on processing low quality shallots into high quality fried onion products using an Automatic Shallot Processing Machine (PEBMO) that is practical, effective and efficient. The activity material provided was about the marketing of shallots and the processing of low quality shallots starting from stripping of shallots, chopping onions, and draining oil using an Automatic Shallot Processing Machine (PEBMO). Community service activities (PKM) which include (1) observation; (2) design and provision of tools (PEBMO) to shallot farmers; (3) socialization; (4) training and practice; (5) monitoring. This activity is useful to increase the income of shallot farmers, this is because low quality shallots are processed into high-quality fried onion products with a stable selling price, so that shallot farmers will not lose if the yields produced have low quality.]]>
<![CDATA[Optimasi Prediksi Harga Ayam Boiler Berdasarkan Time Series Data dan Kondisi Eksisting Menggunakan Decision Tree ]]>
<![CDATA[Niswatin, Ratih Kumalasari]]>;
<![CDATA[Setiawan, Ahmad Bagus]]>;
<![CDATA[Mahdiyah, Umi]]>
<![CDATA[ Nusantara of Engineering (NOE) Vol 8 No 02 (2025): Volume 8 Nomor 2 - 2025 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/noe.v8i02.24878
<![CDATA[Predicting the price of boiler chicken is important in the chicken farming industry. Business actors need to have accurate price estimates as a reference in planning production and sales. However, precise and accurate price predictions can be challenging because they are influenced by various factors such as market conditions, demand, supply, and other factors. Therefore, this research was conducted to develop a boiler chicken price prediction method that can optimize prediction results by utilizing time series data and existing conditions using the Decision Tree C.45 algorithm. The aim of this research is to optimize boiler chicken price predictions based on time series data and existing conditions using the Decision Tree C.45 algorithm. By processing time series data and analyzing existing conditions, it is hoped that a more accurate prediction model can be obtained and can provide better results in predicting the price of boiler chicken. Apart from that, by implementing the Decision Tree C.45 algorithm, this research also aims to test the effectiveness of this algorithm in predicting the price of boiler chicken. The result of this research is a system that can accurately predict the price of boiler chicken, so that it can be used as an important basis for making decisions regarding determining the price of boiler chicken and inventory management.]]>
<![CDATA[Hybrid Ensemble Learning Sistem Keamanan Jaringan Untuk Meningkatkan Performa Deteksi Anomali ]]>
<![CDATA[Irawan, Rony Heri]]>;
<![CDATA[Irawan, Rony Heri Irawan]]>;
<![CDATA[Nico Adi Saputra]]>;
<![CDATA[Umi Mahdiyah]]>
<![CDATA[ Nusantara of Engineering (NOE) Vol 8 No 02 (2025): Volume 8 Nomor 2 - 2025 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/noe.v8i02.25617
<![CDATA[Serangan siber seperti zero-day attacks dan APT menjadi tantangan serius bagi sistem deteksi intrusi jaringan, terutama yang masih mengandalkan metode berbasis tanda tangan. Penelitian ini bertujuan merancang sistem deteksi anomali jaringan berbasis hybrid ensemble learning dengan menggabungkan algoritma Isolation Forest, K-Means, dan Random Forest menggunakan metode majority voting. Proses penelitian meliputi preprocessing data, pelatihan dan evaluasi model menggunakan dataset publik CSE-CIC-IDS2018. Evaluasi dilakukan dengan metrik akurasi, precision, recall, F1-score, dan AUC. Hasil menunjukkan bahwa pendekatan hybrid ini meningkatkan akurasi deteksi hingga 99,9% dan menurunkan false positive secara signifikan dibanding pendekatan tunggal. Sistem yang diusulkan terbukti lebih adaptif dan efisien dalam mengidentifikasi berbagai pola serangan siber, serta memberikan kontribusi terhadap pengembangan teknologi keamanan jaringan yang lebih andal.]]>
<![CDATA[Pengukuran Kemiripan Makna Menggunakan Cosine Similarity dan Basis Data Sinonim Kata ]]>
<![CDATA[Sanjaya, Ardi]]>;
<![CDATA[Setiawan, Ahmad Bagus]]>;
<![CDATA[Mahdiyah, Umi]]>;
<![CDATA[Farida, Intan Nur]]>;
<![CDATA[Prasetyo, Aprisa Risky]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.25126/jtiik.2024106864
<![CDATA[Penelitian ini bertujuan untuk memberikan alternatif dalam menguji kemiripan makna antar 2 kalimat. Pembentukan database sinonim kata dilakukan dengan mengelompokkan kata berdasar sinonim atau yang memiliki kesamaan arti. Masing-masing kelompok kata diberikan ID unik. Selanjutnya setiap kelompok kata dipecah untuk diuraikan menjadi kata tunggal, disimpan pada tabel kata dengan melabeli ID kata dan ID sinonim. ID sinonim didasarkan pada ID unik pada tabel sinonim. Dalam pengujian kemiripan makna, masing-masing kalimat akan di urai menjadi kata dan tiap-tiap kata akan dicocokkan berdasarkan tabel kata dengan acuan ID sinonim. ID Sinonim yang didapat kemudian dilakukan pengukuran jarak vektor dan kemiripan menggunakan rumus cosine similarity. Berdasarkan pengujian dan analisa yang telah dilakukan, dari 25 pengujian didapati 24 nilai kemiripan mengalami peningkatan prosentase. Hal tersebut dikarenakan penggunaan ID yang didasarkan pada kelompok kata dan irisan saat proses pembobotan mampu meningkatkan nilai kemiripan. Rata-rata nilai kemiripan pada penggunaan ID sebagai vektor hitung adalah 94,48% dan rata-rata nilai kemiripan pada metode atau alur pembanding adalah sebesar 69,96%. AbstractThis study aims to provide an alternative in testing the similarity of meaning between 2 sentences. The formation of a word synonym database is done by grouping words based on synonyms or those that have the same meaning. Each group of words is assigned a unique ID. Furthermore, each group of words is broken down to be broken down into single words, stored in the word table labeled word ID and synonym ID. Synonym ID is based on the unique ID in the synonym table. In testing the similarity of meaning, each sentence will be broken down into words and each word will be matched based on the word table with synonym ID references. The synonym ID obtained is then measured by measuring the vector distance and similarity using the cosine similarity formula. Based on the tests and analyzes that have been carried out, out of 25 tests it was found that 24 similarity values experienced an increase in the percentage. This is because the use of ID based on word groups and slices during the weighting process can increase the similarity value. The average similarity value in the use of ID as a calculating vector is 94.48% and the average similarity value in the comparison method or plot is 69.96%.]]>
<![CDATA[The Relationship of Offline Learning with Discrete Mathematics Learning Interests After the Pandemic ]]>
<![CDATA[Wahyuniar, Lilia Sinta]]>;
<![CDATA[Harini, Dwi]]>;
<![CDATA[Mahdiyah, Umi]]>;
<![CDATA[Rochana, Siti]]>
<![CDATA[ Journal of Instructional Mathematics Vol. 3 No. 1 (2022): Ethics and Mathematical Ability in Online-Mediated Learning Environments ]]>
Publisher : <![CDATA[Pendidikan Matematika STKIP Kusuma Negara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37640/jim.v3i1.1378
<![CDATA[This article aims to determine the relationship between offline learning and interest in learning discrete mathematics after the pandemic. The research method used is quantitative. The sampling technique in this study used purposive sampling. Data collection in this study used a questionnaire with a Likert scale of 1 to 5. The variables in this study consisted of variable X, namely offline learning and variable Y, namely interest in learning. Each variable consists of 4 indicators. The result of this research is that there is a relationship between offline learning and students' interest in learning in discrete mathematics courses of 0.801. So it can be said that the relationship between offline learning and learning interest is very strong. This is because in offline learning students can interact directly with the lecturer so that discrete mathematics material that has not been understood can be directly asked during learning. Offline learning can also encourage interest in learning so that students are more enthusiastic and enthusiastic about participating in discrete mathematics lectures.]]>
<![CDATA[Application of Problem-Based Learning Model to Improve Problem Solving Ability ]]>
<![CDATA[Rochana, Siti]]>;
<![CDATA[Wahyuniar, Lilia Sinta]]>;
<![CDATA[Mahdiyah, Umi]]>
<![CDATA[ Journal of Instructional Mathematics Vol. 3 No. 2 (2022): Connecting Mathematical Concepts in Learning and Solving Problems ]]>
Publisher : <![CDATA[Pendidikan Matematika STKIP Kusuma Negara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37640/jim.v3i2.1542
<![CDATA[This study aims to improve the problem solving ability of class VIII junior high school students. This research was conducted after the preliminary study it was found that problem solving abilities were still relatively low. Observational shows that more than 50% of class VIII students of SMP Muhammadiyah 3 Depok have poor problem solving skills. Based on the theory put forward some expert of experts, the researchers decided to apply the problem-based learning model. This study uses classroom action research design with a target of 50% of students having a problem solving ability score above the Kriteria Ketuntasan Minimal (KKM). There are four stage of this research, namely planning, implementation, observation and reflection. The data collection technique using the test and non-test techniques. Data analysis using descriptive analysis of quantitative and qualitative analysis descriptive. From the results of the study, it was found that 77% of students had problem solving scores above the KKM. Besides that, it was found that student were able to work on the questions in a more structured manner and could better understand the questions given.]]>
<![CDATA[The Influence of Mathematical Logical Intelligence on Student Learning Outcomes in Linear Algebra Courses ]]>
<![CDATA[Wahyuniar, Lilia Sinta]]>;
<![CDATA[Mahdiyah, Umi]]>;
<![CDATA[Rochana, Siti]]>;
<![CDATA[Harini, Dwi]]>
<![CDATA[ Journal of Instructional Mathematics Vol. 4 No. 2 (2023): Enhancing Mathematics Learning through Innovative Pedagogies ]]>
Publisher : <![CDATA[Pendidikan Matematika STKIP Kusuma Negara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37640/jim.v4i2.1871
<![CDATA[The purpose of this study was to determine the effect of mathematical logical intelligence on student learning outcomes in Linear Algebra courses. The research method used is quantitative. The sampling technique used in this study was simple random sampling. Data collection in this study used a mathematical logical intelligence test of 20 multiple choice questions and a test of student learning outcomes in linear algebra courses of 5 description questions. The variables in this study consisted of independent variables and dependent variables. The independent variable in this study is mathematical logical intelligence (X), while the dependent variable in this study is student learning outcomes in Linear Algebra courses (Y). The instrument test in this study consisted of validity test, distinguishing power, difficulty level and reliability test. The classical assumption test in this study consists of normality test and linearity test. The data analysis technique used simple linear regression. The result of this study is that there is an influence between mathematical logical intelligence on student learning outcomes in linear algebra courses of 0.527 or 53%. While the other 47% is influenced by other variables outside this study.]]>
