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Systemic: Information System and Informatics Journal
Published by Universitas Islam Negeri Sunan Ampel
ISSN : 24608092     EISSN : 25486551     DOI : -
Core Subject : Science,
Computer Science & IT
SYSTEMIC (Information System and Informatic Journal) publishes articles on information technology from various perspectives, including literature studies, laboratory studies, and field studies. The journal prioritizes studies related to the theme: -Information System -IT Governance and Management -Information Security -Artificial Intelligent and Its Application -Internet of Things.
Arjuna Subject : -
Articles 8 Documents
 1 
Search results for , issue "Vol. 6 No. 2 (2020): Desember" : 8 Documents clear
Analisis Usabilitas Pada Website iLab Universitas Gunadarma Menggunakan Metode Heuristic Evaluation Ahmad Apandi
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.918

Abstract

Gunadarma University's independent laboratory (iLab) is one of the laboratories that provides convenience in providing information through the website. The ease of accessing the website cannot be separated from the website's usability. Good usability is very influential for website users and must be met. In this study, the method used is heuristic evaluation. Heuristic is an evaluation method used to find errors in interface design. Based on the measurement of the iLab website using the heuristic evaluation method by Nielsen, it was found that ten aspects studied got one and two values, which means that the iLab website has several deficiencies that do not cause a problem or in other words are not a problem and do not interfere with users when accessing the iLab website. The highest severity rating of 2.00 is in the aspect of helping user recognize, diagnose, and recover from errors.
Implementasi Perbandingan Algoritma Apriori Dan FP-Growth Untuk Mengetahui Pola Pembelian Konsumen Pada Produk Panel Di PT Surya Multi Perkasa Movinko Diego Armando Pratama Putra; Tresna Maulana Fahrudin; Natalia Damastuti
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.963

Abstract

Some companies have not used much consumer purchase transaction data as one of their sales strategies, this transaction data contains what items are often bought by consumers in one purchase transaction at a different time and structure. If the transaction data is analyzed and explored in more depth, the company will gain insight into consumer purchase patterns analysis and be profitable for the company. In this research, an analysis of consumer purchase transaction data was carried out using Apriori algorithm and FP-Growth, both of which are association rule method group that aims to determine consumer purchasing patterns. The data used in this study were obtained from panel product purchase transaction data at PT Surya Multi Perkasa Movinko. The transaction data consist of 23 types of product items and 492 transactions. The experimental results of this study showed that the best performance of Apriori algorithm with a support factor of 0.0054 and a confidence factor of 0.30 generating 12 association rules, while the best performance of FP-Growth algorithm with a supporting factor of 2 and a confidence factor of 0.7 generating 9 association rules.
Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP) Dinita Rahmalia; Mohammad Syaiful Pradana; Teguh Herlambang
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.967

Abstract

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.
Pengenalan Karakter Huruf Braille dengan Metode Convolutional Neural Network Muhammad Fahmi Herlambang; Asep Nana Hermana; Kurnia Ramadhan Putra
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.969

Abstract

Braille characters consists of 6 dots that are designed in such way to be a writing system to help blind people. However, learning or reading Braille characters isn’t an easy thing to do, because fingers sensitivity and understanding the writing system are needed to be able to read Braille. Therefore, there are some researches on Braille characters recognition with different methods and technologies, such as deep learning. The Convolutional Neural Network (CNN) is used. CNN method has been used in various recognition researches, such as face recognition, document analysis, image classification, etc. In this research, the CNN method is used to perform Braille characters recognition. The system performs the Braille character recognition process per character based on a model that has been trained using a dataset with the 26 Braille characters. The result of 81.54% accuracy is achieved for Braille character image acquisition with a smartphone with 0 to 4 degrees tilting and 30cm distance with training model using learning rate of 0.0001 and Adam optimizer.
Rancang Bangun Kontrol Keasaman pH Tanaman dalam Sistem Hidroponik menggunakan Kontrol PID Berbasis Android Ika Indriani Retna Wardani; Kunto Eko Susilo
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.970

Abstract

The more dense condition of the earth with humans, of course, affect the availability of land on the surface of the earth. Of course this affects the stability of farming especially in urban areas. Hydroponics is one of the smart solutions that can be taken to grow crops in narrow areas or even without land. hydroponics really need a controlled environment to avoid decreasing the quality of plants until the plants wither. One important parameter is the degree of acidity (pH) of the nutrient solution, where pH is very influential on the solubility and availability of nutrients in the nutrient solution. The development of the IoT (Internet of Things) currently covers various aspects, one of which is in agriculture, with the MQTT Broker protocol monitoring and measuring plant pH on a hydroponic system can be done remotely. The method used is to use PID control where the pH of the plant will reach a set point that is determined stably. The expected result is that it can help to control plant pH remotely through andoroid to prevent deterioration in plant quality.
Pemanfaatan Algoritma FP-Growth Untuk Menentukan Strategi Penjualan Pada Kedai Kopi Teras Garden Adrian Marvel Ugrasena; Achmad Zakki Falani
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.977

Abstract

Data mining is a process extracting data from the dataset. In this paper will try to apply data mining on data transaction in Kedai Kopi Teras Garden for sales strategy by creating recommendation item that suit to be sold by package system or the other words by to be sold simultaneously. This paper use association rule method with fp-growth algorithm to find customer purchase pattern on Kedai Kopi Teras Garden. The output of this paper will create some rules for recommendation item that can be sold as a package according to data that already collected and processed with association rule method. Data will be divided by 2, dry season data and rainy season data because there’s customer pattern change accordingly with the season.
Pemanfaatan Image Mining Untuk Klasifikasi Dan Prediksi Kematangan Tomat Menggunakan Metode Jaringan Saraf Tiruan Backpropagation Firdaus; Nori Sahrun
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.1038

Abstract

Classification and prediction of tomato maturity level are automated using the Backpropagation Neural Network method. The determination of maturity in agriculture is still applied manually. With the development of technology in the field of image mining, determining the maturity of tomatoes can be done automatically. The method used in making this system is an Artificial Neural Network. The algorithm used is backpropagation. The output of tomato ripeness consists of three categories, namely immature, half ripe and ripe. 60 training data and testing data were used. The backpropagation architecture in this study consists of 3 input layers, 4 hidden layers, and 1 output layer. The activation function used from input to hidden layer is binary sigmoid, while from hidden layer to output is the identity function (purelin). Image extraction in the form of RGB minimum value is useful as input. Processed and produces output maturity level and maturity prediction. The results of testing the training data system obtained an accuracy value of 96.67% and testing data of 90%.
Klasifikasi Text Judul Buku Perpustakaan Untuk Menentukan Kategori Buku Menggunakan K-Nearest Neighbor Muhamad Kadafi
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.1056

Abstract

The need for information in the form of books or scientific articles at the Library of UIN Raden Fatah Palembang continues to increase. To make it easier to find book information, one of which is by classifying books based on the type of category. In classifying library book data, the Nearest Neighbor Classifier method in data mining can be combined with text data extraction techniques to classify library book title text data. The purpose of this study was to classify the text title of library books using the Nearest Neighbor Classifier to determine the type of book category. This research method uses the Nearest Neighbor Classifier data mining classification technique. The results of this study are that the highest accuracy value is found at K = 12, which is 72.50%, and the model formed can be used to classify books with labels 2x0, 150, 2x2, 400, 020, 2x1, 657, 500, 375, 302.2, 800. and cannot be used for classifying books with class labels 070, 370, 330, 300, 600, 340, 700.

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