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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 104 Documents
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Sistem Pembelajaran Hukum Baca Al-Qur’an Menggunakan Algoritma LPC dan KNN Hafizh Achmad Dinan; Youllia Indrawaty N; Kurnia Ramadhan Putra
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
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.v6i1.927

Abstract

A Muslim must be able to read the verses of the Qur’an properly as taught by the Prophet, Muhammad. Reading the Qur'an in accordance with tajwid is obligatory for every Muslim, if someone reads the Qur'an without using tajwid, the law is sinful. The development of the application of learning the Tajwid of Qur’an is aimed at helping a Muslim to be good at reading the Qur’an that is good and right.Al-Fatihah is uses in this application. Learning the Tajwid of Qur’an Application is using Linear Predictive Coding (LPC) method as sound feature extraction and K-Nearest Neigbor as matching with training data. For testing the pronunciation of the 1st verse obtained data accuracy of 83.3%, the 2nd verse is 86.7%, the 3rd verse is 85%, the 4th verse is 80%, the 5th verse is 88.3%, the 6th verse is 93.3%.
Sistem Penunjang Keputusan Seleksi Atlet Berdasarkan Data Fisik Menggunakan Naïve Bayes yosia halundaka; Din Syamsudin; Aryo Nugroho
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
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.v6i1.935

Abstract

The purpose of this observation was to predict the passing rate of the physical test results of the participants of the martial arts athlete organized by KONI. The researchist made a classification based on data obtained from trainer of physical martial arts Kempo by using data mining. Data mining can be interpreted is a process extractcing new informartion was taken from big chunk of data for support the results of making decision support systems. Therefor using decision support systems which can help make a decision to choose the best athlet. In data mining there are many methods that can be used, one of them is the classification method by using alogarithms naive bayes. The results obtained from this observation is Correctly Classified Instances 94.4% with speed presentase 60% and 70% bring out Correctly Classified Instances 92.85%, while speed presentase 80% bring out Correctly Classified Instances 88.89%.
Sistem Penunjang Keputusan Pemasaran Produk X Menggunakan Metode K-Means Ach. Syuhbanul Yaumi; Zainul Zulfiqkar; Aryo Nugroho
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
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.v6i1.936

Abstract

The problem that is currently in store is the difficulty to find out which product x is currently in high demand or is most widely used by consumers so that inventory of product x can be met according to customer demand and does not occur out of stock. Therefore, in the research grouping with the K-Means method for marketing product x is one way to determine customer choices for product x consumed. In this study grouping data from questionnaires or questionnaires that are distributed in stores, then the data are grouped into 2 groups using one of the clustering algorithms, K-Means. The data used are data collected by 366 respondents of store customers. After the data is processed using one of the data mining methods, the K-Means algorithm, shows that cluster 1 is a type A consumer group with a percentage of 33%, while cluster 2 is a type B consumer group with a percentage of 67%.
Game Promosi Wisata Kota Malang “Kakang Mbakyu” Dengan Menggunakan Decission Tree dan Hierarchy Finite State Fathurrahman; Yunifa Miftachul Arif
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
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.v6i1.958

Abstract

Currently, the Tourism Sector in Indonesia is considered the most effective sector contributing to increasing the country's foreign exchange, Foreign exchange earnings were obtained from Indonesian tourism visits which surged and recorded as the highest compared to other countries in Southeast Asia, In particular in the city of Malang there was a significant increase in the number of tourists, based on data listed at www.malangkota.go.id in 2015 the number of tourists entering the city of Malang totaled 3,290,067 people, while 8,265 foreign tourists visited the following year to 3,987,074 for domestic tourists and 9,535 foreign tourists. In this research, Decission Tree algorithm was successfully implemented in the game by producing a time variable gain with a value of 2.01 and a gain point of 1.86 so that the time variable will be processed before the variable points to produce level jumps according to the ability of the player, and for the Hierarchy Finite State Machine, it proved to be successful with the NPC's moving behavior in accordance with the previously designed rules.
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%.

Page 8 of 11 | Total Record : 104

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