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All Journal Jurnal Transformatika Jurnal Informatika Universitas Pamulang Jurnal Informatika dan Rekayasa Elektronik Transformasi Jurnal Pengabdian Masyarakat Julia Jurnal
Arum, Dhika Malita Puspita
Universitas An Nuur
Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Other

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GENETIC ALGORITHM FOR FEATURE SELECTION IN NAÏVE BAYES IN LIFE RESISTANCE CLASSIFICATION ON BREAST CANCER PATIENT Dhika Malita Puspita Arum; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 1 No 01 (2021): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v1i01.14

Abstract

Breast cancer is the most common cancer in women's suffering and is the second leading cause of death for women (after lung cancer). More than one million cases and nearly 600,000 breast cancer deaths occur worldwide each year. Survival is generally defined as surviving patients over a period of time after the diagnosis of the disease. Accurate predictions about the likelihood of survival of breast cancer patients can allow doctors and healthcare providers to make more informed decisions about patient care. To classify the survival of breast cancer patients can do the utilization of data mining techniques with Naive Bayes algorithm. Naive Bayes is very simple and efficient but very sensitive to the features so from it the selection of the appropriate features is in need because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with some attribute selection procedures such as Genetic Algorithm. In this study the researchers proposed the Genetic Algorithm for Feature Selection on Naive Bayes so as to improve the accuracy of breast cancer survival classification results. In this study using a private dataset breast cancer patients. The results show that Naive Bayes Genetic Algorithm has a higher accuracy of 90% compared to Naive Bayes with 86% accuracy 
PENGGUNAAN ALGORITMA FP-GROWTH UNTUK MENENTUKAN PAKET PENJUALAN PADA TOKO PERLENGKAPAN KONVEKSI SRI BUSANA Andri Triyono; Dhika Malita Puspita Arum; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.16

Abstract

Consumers of the Sri Busana convection shop are mostly tailors, both home and convection tailors, which are pretty large, especially in Grobogan district. The increasing number of fashion businesses or tailors in Grobogan district makes data on goods and sales at the sri busana convection shop increase because the sri busana convection shop always strives to meet the needs of tailors or home convection. In overcoming the problem of finding more efficient consumer patterns, an analysis of buying patterns is carried out. Consumer buying patterns were analyzed using Association rules and FP-Growth methods. With this algorithm, the process of determining consumer purchasing patterns consists of 2 product combinations with a support value of 50% and a confidence value of 100%. 3 product combinations with a support value of 40% and a confidence value of 80%. 4 product combinations with a support value of 40% and a confidence value of 80%. 
OPTIMIZATION OF PARTICLE SWARM OPTIMIZATION IN NAÏVE BAYES FOR CAESAREAN BIRTH PREDICTION Dhika Malita Puspita Arum; Andri Triyono; Eko Supriyadi; Rahmawan Bagus Trianto
Julia: Jurnal Ilmu Komputer An Nuur Vol 2 No 01 (2022): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v2i01.17

Abstract

The Maternal Mortality Rate (MMR) in 2017 according to the World Health Organization (WHO) is estimated to reach 296,000 women who die during and after pregnancy or childbirth. Caesarean birth is the last alternative in labor if the mother cannot give birth normally due to certain indications with a high risk, both for the mother and the baby. factors of a mother giving birth by caesarean section, such as placenta previa, hypertension, breech baby, fetal distress, narrow hips, and can also experience bleeding in the mother before the delivery stage. It is hoped that delivery by caesarean method can minimize problems for the baby and mother. Accurate prediction of the condition of the mother's pregnancy can enable d octors, health care providers and mothers to make more informed decisions regarding the management of childbirth. To predict caesarean births, data mining techniques using the Naive Bayes algorithm can be used. Naive Bayes is very simple and efficient but very sensitive to features, therefore the selection of appropriate features is very necessary because irrelevant features can reduce the level of accuracy. Naive Bayes will work more effectively when combined with several attribute selection procedures such as Particle Swarm Optimization. In this study, the researcher proposes a Particle Swarm Optimization algorithm for attribute weighting in Naive Bayes so as to increase the accuracy of Caesarean birth prediction results 
PENERAPAN SISTEM INFORMASI STOK BARANG BERBASIS APLIKASI UNTUK MENINGKATKAN EFISIENSI PENGELOLAAN INVENTARIS PADA TOKO SEMBAKO Agus Condro Wibowo; Dwi Kurniawan Aprilianto; Ahmad Yususf Mufarihin; Andri Triyono; Dhika Malita Puspita Arum
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 1 (2025): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i1.22

Abstract

Perkembangan teknologi informasi yang berkembang pesat memberikan dampak yang signifikan ke berbagai sektor, seperti pengelolaan inventaris di sektor ritel. penelitian ini bertujuan untuk menerapkan sistem informasi yang berbasis aplikasi untuk stock barang agar meningkatkan efisiensi pengelolaan inventaris pada toko. aplikasi yang di bangun di harapkan dapat mempermudah pemantauan stok barang, mempercepat proses pencatatan transaksi, serta meminimalisir kesalahan dalam mengelola data stock. metode yyang di gunakan dalam penelitian ini adalah pengembangan aplikasi berbasis perangkat lunak yang dapat memberikan hasil laporan inventaris secara akurat. hasil yang di harapkan dari penerapan sistem informasi ini adalah pengurangan tingkat kesalahan, serta pengelolaan stok yang lebih mudah dan cepat. Penelitian ini memiliki kontribusi dalam memberikan solusi bagi toko-toko untuk menghadapi tantangan pengelolaan inventaris yang lebih kompleks di era digital sekarang. 
Co-Authors Achmad Rizki Ramadhani Adri Tiyono Agus Condro Wibowo Agus Susilo Nugroho Ahmad Yususf Mufarihin Andri Triyono andri triyono Andri Triyono Dhika Malita Dwi Kurniawan Aprilianto Edi Widodo, Edi Eko Supriyadi Kartika Imam Santoso Mika Agustiana Nugroho, Agus Susilo Rahmawan Bagus Trianto susilo nugroho, agus Triyono, Andri