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All Journal SMATIKA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JMAI (Jurnal Multimedia & Artificial Intelligence) Jurnal Teknologi Informasi dan Komunikasi JiTEKH (Jurnal Ilmiah Teknologi Harapan) Jurnal ilmiah teknologi informasi Asia
Nur nafi'iyah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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ALGORITMA KNN UNTUK KLASIFIKASI KEMATANGAN BUAH APEL BERDASARKAN TEKSTUR Imam Wahyu Pratama; Nur Nafi'iyah; Masruroh
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol 11 No 1 (2020): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v11i1.149

Abstract

Abstract An apple is a fruit that is widely planted in mountainous or cold regions, for example in Malang. Apples are fruits that have many colors, there are green, yellow, and red colors. With a variety of colors that make consumers feel confused whether the apples to be eaten are sweet or sour. Because almost most consumers do not know the type of apple that will be purchased. Sometimes the type of apple that will be purchased is green, but because it does not know ripe or raw, it is wrong to choose. In order to help consumers in knowing the level of maturity of apple again, a system was made. With the aim to be able to classify the level of maturity of the apple again. So that the system created will display information whether the apple is more ripe or raw. The system will process the image of the apple again and take the GLCM texture features (intensity, contrast, energy, smoothness, entropy, skewness). And the process of determining fruit maturity using the KNN method. The system was built using the matlab tool, with 200 datasets, consisting of 130 training datasets, and 70 testing datasets. In applying the KNN algorithm to determine the maturity level of apples, the accuracy results are 51.4%. With output data that is not in accordance with the target number of 34 data and according to the target number of 36 data.
Sistem Pendukung Keputusan Pemilihan Kualitas Songkok Berdasarkan Bahan Baku Menggunakan Metode Naïve Bayes Mochamad Ainun Rozaq; Nur Nafi'iyah; Masruroh Masruroh
JMAI (Jurnal Multimedia & Artificial Intelligence) Vol. 3 No. 2 (2019): JMAI (Jurnal Multimedia dan Artificial Intelligence)
Publisher : LPPM Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.562 KB) | DOI: 10.26486/jmai.v3i2.95

Abstract

In determining the quality of skullcap with criteria including boss-bored, velvet, inner layers, layers of cloth. And there are several types of quality, namely super, premium, standard and low. The purpose of making a skullcap quality Determination system is to be able to help the production department in the production of skullcaps and marketing. Because if the process of determining the production is done using human labor, it is susceptible to fatigue and doubt because of the limitations of human capability. To reduce the obstacles that occur needed a desktop-based system that can help determine the quality of skull cap. The purpose of this research is to develop a decision support application system to determine the quality of songkok and how to determine the quality of songkok by using the naïve bayes method. The data used in this study were 300 training dataset lines and 54 testing dataset with 74,5% accuracy.
Algoritma Backpropagation untuk Memprediksi Korban Bencana Alam Nur Nafi'iyah; Ahmad Ahmad Salaffudin1; Nur Qomariyah Nawafilah
SMATIKA JURNAL Vol 9 No 02 (2019): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v9i02.400

Abstract

Indonesia is a country prone to natural disasters. Because Indonesia is a maritime country and its geographical area is Mount Merapi. In order to reduce victims of natural disasters or other disasters, we conducted research related to predictions of victims of natural disasters. The purpose of this study is to help the team or related parties in preparing themselves to deal with the victims of a growing natural disaster. The algorithm used in predicting victims of natural disasters is backpropagation. The data used in this study is the DIBI dataset taken from the Google dataset. The predicted impact was 5128 lines, 524 missing victims, 2653 injured, 941 lines dead. Each dataset with each category of disaster impacts, missing victims, injured victims, and death victims was made of 2 input variables. Input variables from each category are district code, and year and the output variable is the number of disaster victims. Neural network structure and architecture of this study, namely 2 input layer nodes, 2 hidden layer nodes, and 1 output layer node. From the architecture, training and testing were carried out, where the results of testing disaster impact data were 110 lines of MSE value of 0.0371, testing results of wounded victims data as much as 53 lines of MSE value of 0.0256, results of testing of missing victims as much as the 24 lines of the MSE value are 0.041, and the results of testing of the dead are 41 lines of the MSE value of 0.029.
Backpropagation untuk Memprediksi Jumlah Wisatawan Mancanegara ke Indonesia Kevin Aringgi Salim; Nur Nafi'iyah; Siti Mujilahwati
SMATIKA JURNAL Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.622

Abstract

Developing areas that have tourism potential is an effort to increase sources of income for villagers. Areas that have tourist areas can be a vehicle that attracts the attention of the public, both domestically and abroad. Tourists who come can provide income for tourist areas or the community. Therefore, predicting the number of incoming tourists can be predicted based on data from previous years. The goal is to make predictions to improve infrastructure and all needs for tourists. The purpose of this study is to apply the Backpropagation method to predict the number of foreign tourist visits to Indonesia. The dataset used in this study is 6000 lines and is divided into 4800 lines of training data, and 1200 lines of test data. The dataset is taken from the bps website, with the input variables being month, year, country of origin, tourist entrance to Indonesia, and the output variable being the number of tourists. The model of Backpropagation is evaluated by calculating MAE, and the architecture built is 4-9-1, 4 input layer nodes, 9 hidden layer nodes, and 1 output layer node. The test results of the MAE value of the Backpropagation method in predicting the number of tourists to Indonesia are 0.247.
Sistem Pakar Diagnosa Penyakit Kolesterol pada Remaja dengan Metode Certainty Factor Muhammad Busthomi; Nur Nafi'iyah; Nur Qomariyah Nawafilah
Jurnal Processor Vol 15 No 1 (2020): Processor
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.833 KB) | DOI: 10.33998/processor.2020.15.1.670

Abstract

Sistem Pakar adalah suatu sistem yang dapat menikuran keahlian pakar tertentu ke dalam komputer. Sistem pakar sering digunakan untuk mendiagnosa penyakit. Di mana penyakit yang didiagnosa hanya satu penyakit dengan beberapa jenis gejala. Alasan dibuat suatu sistem pakar untuk mendiagnosa penyakit salah satunya membantu pihak dokter yang masih belum mempunyai pengalaman kerja yang banyak, serta mengurangi tingkat kesalahan manusia. Dalam penelitian ini akan membuat suatu sistem untuk mendiagnosa penyakit kolesterol dengan 3 jenis penyakit, dan 8 gejala. Tujuan penelitian ini adalah membuat suatu aplikasi berbasis web untuk mendiagnosa penyakit kolesterol dengan menggunakan perhitungan nilai ketidakpastian (certainty factor). Jenis penyakit kolesterol yang didiagnosa terdapat 3, yaitu kolesterol LDL, kolesterol HDL, dan Disiplidemia. Dari masing-masing penyakit mempunyai gejala dan nilai CF. Sistem ini mempunyai gejala 8, yaitu sakit dan pegal di kepala, mudah mengantuk, pegal sampai pundak, mudah capek, kadar kolesterol di bawah 90 Mg/dl, kadar kolesterol di atas 120 Mg/dl, nyeri dada, kram kaki. Masing-masing gejala terhadap penyakit mempunyai nilai CF. Di mana nilai CF masing-masing gejala terhadap penyakit menggunakan nilai MB MD. Dan nilai MB MD didapatkan dari pakar dokter penyakit dalam. Hasil dari penelitian ini, yaitu aplikasi dapat digunakan untuk mendiagnosa penyakit kolesterol dengan nilai CF tertinggi. Kata Kunci: certainty factor, MB MD, kolesterol.
PERBANDINGAN METODE NORTH WEST CORNER DAN LEAST COST DALAM MEMINIMALKAN BIAYA PENGIRIMAN BARANG PADA CABANG PT.BORWITA CITRA PRIMA Sulistyowati Sulistyowati; Nur nafi'iyah; Purnomo Hadi Susilo
JiTEKH (Jurnal Ilmiah Teknologi Harapan) Vol 7 No 2 (2019): September 2019
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v7i02.59

Abstract

PT. Borwita Citra Prima adalah salah satu perusahaan yang bergerak di bidang food dan non food. Cabang PT. Borwita Citra Prima melakukan proses pengelolahan distribusi barang ke gudang cabang yang terletak di Bojonegoro dan Tuban. Dari Cabang PT.Borwita Citra Prima Babat dan dua cabang mendistribusikan ke agen-agen besar seperti YKH (Babat), Anik snack (Bojonegoro), Hafiz snack (Tuban) yang terletak di beberapa kota. Tujuan penelitian ini adalah mengetahui penyelesaian pengiriman barang menggunakan metode North West Corner dan Least Cost. Dari hasil penelitian ini dapat disimpulkan bahwa dengan adanya pengaplikasiaan metode North West Corner dan Least Cost pada pengiriman barang hasil produksi Cabang PT. Borwita Citra Prima dapat menentukan rute pengiriman yang biaya paling minimum. Sehingga dapat membantu pihak perusahaan khususnya bagian pengiriman barang dalam menentukkan rute pengiriman barang. Metode North West Corner dan Least Cost dapat menghitung biaya transportasi yang paling rendah dari satu Gudang penyimpanan (depo) menuju ke tempat tujuan. Metode Least Cost dapat menentukan rute dengan biaya transportasi pengiriman terendah.
Algoritma Kohonen dalam Mengubah Citra Graylevel Menjadi Citra Biner Nur Nafi'iyah
Jurnal Ilmiah Teknologi Informasi Asia Vol 9 No 2 (2015): Volume 9 Nomor 2 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pada pengolahan citra terdapat enam jenis operasi pengolahan, yaitu peningkatan kualitas citra, restorasi citra, kompresi citra, segmentasi citra, analisis citra, dan rekonstruksi citra. Pada umumnya informasi yang ada dalam suatu citra terletak pada strukturnya. Agar mudah memahami suatu citra dapat dilakukan dengan menyederhanakan struktur citra tersebut. Salah satu metode untuk menyederhanakan struktur citra adalah dengan melakukan proses segmentasi citra (image segmentation).Proses segmentasi citra merupakan proses dasar dan penting di dalam komputer visi. Segmentasi yang dilakukan pada citra harus tepat agar informasi yang terkandung di dalamnya dapat diterjemahkan dengan baik. Terdapat banyak metode dalam melakukan segmentasi pada citra. Beberapa teknik segmentasi citra: Thresholding (global thresholding dan lokal adaptif thresholding), Connected Component Labelling, dan Segmentasi Berbasis Clustering (Iterasi, K-means, fuzzy C-means, SOM).Penelitian ini akan mengubah citra berwarna menjadi citra biner (hitam dan putih). Proses mengubah citra menggunakan algoritma Kohonen. Dengan algoritma Kohonen citra dapat diubah dengan tepat.
Co-Authors Ahmad Ahmad Salaffudin1 Imam Wahyu Pratama Kevin Aringgi Salim Masruroh Masruroh Masruroh Mochamad Ainun Rozaq Muhammad Busthomi Nawafilah, Nur Qomariyah Purnomo Hadi Susilo, Purnomo Hadi Siti Mujilahwati Sulistyowati Sulistyowati