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All Journal Jurnal Informatika dan Teknik Elektro Terapan Applied Technology and Computing Science Journal Joutica : Journal of Informatic Unisla
Mustain Mustain
Universitas Islam Lamongan
Computer Science & IT Control & Systems Engineering Engineering Social Sciences

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SISTEM PENDUKUNG KEPUTUSAN PENJURUSAN SISWA PADA SMA PANCAMARGA 1 LAMONGAN MENGGUNAKAN METODE FUZZY Ahmad Fathoni; Mustain Mustain; Retno Wardhani
Joutica Vol 3, No 1 (2018)
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.528 KB) | DOI: 10.30736/jti.v3i1.202

Abstract

must know what factors affect the performance of teachers. The results of the analysis will be beneficial to make the education development program optimally and it is very necessary to advance the quality of education. Students need special attention from the government and society therefore the government regulates the improvement of the quality of teachers or teachers nationally through Law No. 20 of 2003 on National education system. In order to enforce the law, the government issued Regulation No. 19 of 2004 on the National Education standard. Therefore developed sebi student decision support system on the sma pancamarga 1 Lamongan using fuzzy.
ASPECT BASED SENTIMENT ANALYSIS DATA KUESIONER DI RUMAH SAKIT MUHAMMADIYAH LAMONGAN MENGGUNAKAN ALGORITMA K-NN. Mustain Mustain Mustain
Joutica Vol 6, No 2 (2021)
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (633.586 KB) | DOI: 10.30736/jti.v6i2.677

Abstract

Kesulitan untuk mengorganisir data kuesioner yang bersifat konvensional melatarbelakangi penelitian ini. Oleh karena itu dibuat sistem yang memudahkan pengelompokan data kuesioner secara otomatis yang lengkap dengan sentimen yang terkandung didalamnya. Dataset yang digunakan dalam penelitian ini adalah data kuesioner rumah sakit Muhammadiyah lamongan. Penelitian ini hanya menangani kuesioner yang berbentuk teks. Data dengan fisik kertas direkap kemudian diinput ke database lengkap dengan kategori unit kerja dan sentiment. Selanjutnya dataset tersebut di dilakukan pre-prosesing yang meliputi penanganan negasi case folding, tokenizing, filtering dan stemming. Sebagai data uji komentar dari kuesioner akan dilakukan pre-prosesing selanjutnya dihitung tingkat kemiripan document dengan menggunakan metode K- Nearest Neighbor dan Vector Space Model. Jumlah data yang ditangani mempengaruhi performa system terutama dari akurasi dan kecepatan pada saat proses klasifikasi. Hasil dari sistem yang dibuat berupa ranking dokumen yang paling mirip dengan dataset berdasarkan urutan nilai cosine similarity. Ujicoba klasifikasi berdasarkan kelas kategori menghasilkan nilai akurasi 91 %. Ujicoba berdasarkan Kelas Sentimen sebesar 94 %.dari kombinasi keduanya system berhasil mendapat akurasi sebesar 86 %
Classification Of Determination Of Sweet Siwalan Fruit Based On Color Feature Using Svm Support Vector Machine Method Mustain mustain; Munif Munif; Kurnia yahya
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3678

Abstract

Siwalan fruit is usually called a fruity fruit, not everyone knows about this fruit because this fruit only exists in certain regions in Indonesia. This fruit is very nutritious because it contains a lot of nutrients and has a sweet taste. But ordinary people determine the level of sweetness by means of manually tasting it directly. And with this research can help the community to distinguish between sweet and non-sweet siwalan, a system that can help classify siwalan fruit based on color is designed. With the SVM (Support Vector Machine) method and the method used is a learning machine method that can find the best hyperplane that separates 2 classes in the input space. It can be seen that the overall test data of 20 data from 3 taste siwalan (sweet, medium, lacking), the testing data obtained the highest level of accuracy of 90%, and can be said that SVM has a better level of accuracy.
Analysis of the K-Nearest Neighbor Algorithm to Determine the Prediction of Tofu Production Munif Munif; Mustain Mustain; Kurnia Yahya
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3677

Abstract

The tofu factory in Kedungpring does not yet have a prediction system to estimate the number of tofu that will be predicted for the next month. As a result, companies cannot meet market demand in a timely and appropriate amount. Therefore, it is necessary to make a prediction system to determine the amount of tofu production in Kedungpring District. In this research, the application of K-Nearest Neighbor Algorithm Analysis Application to determine the prediction of the number of Web-Based production to make it easy to predict the number of tofu production. The system functional test results show that all features in the application are able to run properly and functionally. Testing the accuracy of the prediction system K-Nearest Neighbor algorithm to determine the prediction of the number of web-based tofu production that can produces a MAPE of 0.68%
IMPLEMENTASI NODE.JS DAN PYTHON UNTUK SISTEM REKOMENDASI LAPTOP Danang Bagus Reknadi; Mochammad Fajrin; Munif Munif; Mustain Mustain
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.5112

Abstract

Laptop adalah komputer pribadi yang kecil dan ringan, sehingga portabel. Dengan banyaknya brand dan spesifikasi yang ada saat ini, calon pembeli sering kebingungan memilih laptop yang sesuai dengan kebutuhan mereka. Kebanyakan orang lebih familiar dengan aplikasi yang digunakan sehari-hari. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan mengembangkan sistem rekomendasi laptop. Sistem ini akan membantu calon pembeli memilih laptop yang sesuai dengan kebutuhan spesifikasi minimum aplikasi yang akan digunakan. Metode yang digunakan adalah Content Based Filtering, yang memberikan rekomendasi berdasarkan profil preferensi pengguna dan deskripsi item. Algoritma yang diterapkan adalah Euclidean Distance dan Cosine Similarity. Hasil penelitian menunjukkan bahwa Euclidean Distance dapat merekomendasikan laptop berdasarkan spesifikasi yang paling mendekati kebutuhan aplikasi, sementara Cosine Similarity dapat memberikan rekomendasi laptop dengan spesifikasi serupa saat melihat detail dari hasil rekomendasi Euclidean Distance. Pengujian dengan 100 kali uji data menghasilkan rata-rata precision sebesar 98.4% pada Cosine Similarity.
Co-Authors Ahmad Fathoni Danang Bagus Reknadi Kurnia Yahya Kurnia yahya Mochammad Fajrin MUNIF ROCHMAWANTO Retno Wardhani