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INDONESIA
JURNAL SISTEM INFORMASI BISNIS
Published by Universitas Diponegoro
ISSN : 20883587     EISSN : 25022377     DOI : -
Core Subject : Economy, Science,
JSINBIS merupakan jurnal ilmiah dalam bidang Sistem Informasi bisnis fokus pada Business Intelligence. Sistem informasi bisnis didefinisikan sebagai suatu sistem yang mengintegrasikan teknologi informasi, orang dan bisnis. SINBIS membawa fungsi bisnis bersama informasi untuk membangun saluran komunikasi yang efektif dan berguna untuk membuat keputusan yang tepat waktu dan akurat. Business intelligence sebagai dasar pengembangan dan aplikasi SINBIS menjadi kerangka kerja teknologi informasi yang sangat penting untuk membuat agar organisasi dapat mengelola, mengembangkan dan mengkomunikasikan aset dalam bentuk informasi dan pengetahuan. Dengan demikian SINBIS merupakan kerangka dasar dalam pengembangan perekonomian berbasis pengetahuan.
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Articles 14 Documents
Search results for , issue "Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018" : 14 Documents clear
Sistem Auto Recommendation Objek Wisata Menggunakan Metode SAW Murti, Alif Catur; Chamid, Ahmad Abdul
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (397.406 KB) | DOI: 10.21456/vol8iss1pp9-16

Abstract

The tourism sector is a complex multidimensional sector, which has an influence over the interrelation of other sectors in tourism activities and now the tourism sector is concerned by the government. Every tourist who will visit the tourist sites must have some consideration. The fact that today is happening is a lot of tourists who are disappointed because the destination is not in accordance with what they want. Seeing that the media campaign that is currently done is still not efficient, so Auto Recommendation System is needed which is capable to do visualized in digital mapping, and the system built can show the automatic recommendation of tourist destinations in accordance with the wishes of tourists. Auto Recommendation System is a system that implements Simple Additive Weighting (SAW) method. The results of this research factors other than the value of weight given by tourists, is the value factor owned by each alternative for each criteria, because the higher the value owned by the higher the higher the value of the final ranking obtained by the alternative. The end result of the SAW method is in the form of alternative ranking of existing tourism objects. Based on the results of these rankings the system also produces output in the form of location maps along with detailed information of the tourism object. Religious tourism Sunan Kudus and Sunan Muria became the recommended tourist attraction with first and second priority.
Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes Jumeilah, Fithri Selva
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (658.869 KB) | DOI: 10.21456/vol8iss1pp92-98

Abstract

The large number of online sales transactions has increased the number of service users. One of the companies engaged in the delivery service in Indonesia is Tiki Nugraha Ekakurir or more known JNE. Currently, JNE service users reach 14.000.000 per month. JNE has used many media communications with its customers one of them with Twitter. The number of followers of JNECare is 108,000 and the number of tweets is 375,000. The number of comments for people who can be used to see what they think of JNE is an inseparable comment is a negative, positive or neutral category. To simplify the grouping of comments, the data will be classified using the Naïve Bayes method present in Rstudio. The amount data used on the internet is 1725 tweets. The data will be divided into allegations of 70% data training as much as 1208 data and 30% data testing or as many as 517 data. Before the data is classified the previous data must go through the process of preprocessing that is changing all the letters into lowercase and other letters other than letters and spaces (case folding), tokenizing words, and the removal of the word common (stopword remove). After the data is cleared the data will be labeled manually one by one and new data can be used for the training process to get the probability model for each category. Probailitas obtained by using Naïve bayes algorithm. Models obtained from the training will be used using data testing. The categories obtained from the test will be used to process the data used by using the confusion matrix and will calculate the accuracy, precision and recall. From the results of the classification of JNE comments obtained that Naïve Bayes was able to classify the data well. This is evidenced by the average percentage accuracy of 85%, 78% precision and 67% recall.
Identifikasi Huruf Kapital Tulisan Tangan Menggunakan Linear Discriminant Analysis dan Euclidean Distance Cahyani, Septa; Wiryasaputra, Rita; Gustriansyah, Rendra
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (766.518 KB) | DOI: 10.21456/vol8iss1pp57-67

Abstract

The human ability to recognize a variety of objects, however complex the object, is the special ability that humans possess. Any normal human will have no difficulty in recognizing handwriting objects between an author and another author. With the rapid development of digital technology, the human ability to recognize handwriting objects has been applied in a program known as Computer Vision. This study aims to create identification system different types of handwriting capital letters that have different sizes, thickness, shape, and tilt (distinctive features in handwriting) using Linear Discriminant Analysis (LDA) and Euclidean Distance methods. LDA is used to obtain characteristic characteristics of the image and provide the distance between the classes becomes larger, while the distance between training data in one class becomes smaller, so that the introduction time of digital image of handwritten capital letter using Euclidean Distance becomes faster computation time (by searching closest distance between training data and data testing). The results of testing the sample data showed that the image resolution of 50x50 pixels is the exact image resolution used for data as much as 1560 handwritten capital letter data compared to image resolution 25x25 pixels and 40x40 pixels. While the test data and training data testing using the method of 10-fold cross validation where 1404 for training data and 156 for data testing showed identification of digital image handwriting capital letter has an average effectiveness of the accuracy rate of 75.39% with the average time computing of 0.4199 seconds.
Deteksi dan Penggolongan Kendaraan dengan Kalman Filter dan Model Gaussian di Jalan Tol Waliulu, Raditya Faisal
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.907 KB) | DOI: 10.21456/vol8iss1pp1-8

Abstract

Monitoring systems are widely implemented in various sectors aimed at improving the security and productivity aspects. The research aims to detect moving objects in the form of video file tipefile (* .avi) 640x480 resolution and image class according to pixel area. Moving objects are given in the Region of Interest path for easy detection. Detection on moving objects using methods of Kalman filter and gaussian mixture model. There are two types of distribution, the distribution of Background and Foreground. The form of the Foreground distribution is filtered using Bit Large Object segmentation to obtain the dimensions of the vehicle and morphological operations. The feature extraction results from the vehicle are used for vehicle classification based on pixel dimension. Segmentation results are used by Kalman Filter to calculate the tracking of moving object positions. If the Bit Large Object segmentation is not found moving object, then it is continued on the next frame. The final results of system detection are calculated using Positive True validation, True Negative, False Positive, and False Negative by looking for the sensitivity and specificity of each morning, day and night conditions
Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika Dodu, A. Y. Erwin; Nugraha, Deny Wiria; Putra, Subkhan Dinda
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.225 KB) | DOI: 10.21456/vol8iss1pp99-106

Abstract

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.
Sistem Pendukung Keputusan Pemilihan Siswa Berprestasi Berbasis Website dengan Metode Simple Additive Weighting Pradana, Razqa Lathif; Purwanti, Dwi; Arfriandi, Arif
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (27.161 KB) | DOI: 10.21456/vol8iss1pp34-41

Abstract

Generally, the selection of outstanding students in every school still uses the report value as the reference and it is done manually. It is required a system that may select the outstanding students accordance with the criteria and done automatically. In this study, it was developed an automatic system for selecting outstanding students by using Simple Additive Weighting (SAW) concept. The criteria set by the school in the selection of outstanding students are the average of the first and the second semester score, the achievements on district, city, and national level, liveliness in the organization and extracurricular, and credit point of attitude. Method of investigation used in this study is R & D, including introduction study, the development of system consisting of the application of SAW method and designing waterfall method, and testing of system which done by testing the comparison of the result and respond of users. The result of black box testing showed that all functionality in the system run well and appropriate; while for the white box showed that all paths run in accordance with SAW method. For the result of the comparison testing showed that the validation level was 100%. The result of the users respond revealed that the average of teacher responds was 90% and the students respond was 80, 34%. Therefore it can be concluded that decision support system by using SAW method can determine the outstading students.
Segmentasi Thresholding untuk Pemilihan Kualitas Telur Asin Nurhayati, Oky Dwi; Afifah, Diana Nur; ., Nuryanto; Rustanti, Ninik
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.251 KB) | DOI: 10.21456/vol8iss1pp42-48

Abstract

Visually, choosing the quality of salted eggs by looking at egg shells is something that is very difficult to do. In addition, the lighting and the weakness of the senses of vision also becomes difficult to see the quality of salted eggs visually. So far, to determine a good salted egg, only known from the weight of eggs. Not all eggs that have mild density have poor quality. So far, suppliers often get eggs that have bad quality (broken) so that when processed will produce defective salted eggs. The goal achieved as an effort to improve the quality of this production is software design to know the quality of salted eggs. Quality selection technology involves image processing techniques such as gray imagery, histogram equalization, P-Tile segmentation, and first-order statistical feature extraction that serves to recognize the type of egg image quality. The results obtained with the application of image processing techniques have a fairly good accuracy to determine the quality of salted eggs into two good and bad conditions.
Analisis Prediksi Kebangkrutan Perusahaan Menggunakan Artificial Neural Network Pada Sektor Pertambangan Batubara Nurdini, Rizki Amalia; Priyadi, Yudi; ., Norita
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.321 KB) | DOI: 10.21456/vol8iss1pp107-114

Abstract

Indonesia?s coal mining industry has been decreased since the last five years and causing the financial performance of companies in the industry to deteriorate. The aim of this paper is to analyze the bankruptcy prediction on coal mining sector companies listed in Indonesia Stock Exchange (IDX) in 2012 ? 2016 using data mining prediction method that is artificial neural network model with three financial ratios as an input parameter. The financial ratios used are shareholder?s equity ratio, current ratio and return on assets. The results indicate that these ratios are very suitable to be used as an input parameter because it shows a quite significant difference in calculation results between bankrupted and non-bankrupted companies.The ANN training model used in the prediction process in this study resulted in the best training performance with the model architecture of 15 neurons on input layer and one hidden layer with 30 neurons in it. The training model produces training performance with the lowest MSE of 0,000000313 and the highest R of 99,9%. Bankruptcy prediction result using ANN showed that 7 (seven) coal mining sector companies are predicted to be bankrupt
Implementasi Metode Association Rule untuk Menganalisis Data Twitter tentang Badan Penyelenggara Jaminan Sosial dengan Algoritma Frequent Pattern-Growth Tamaela, Jemaictry; Sediyono, Eko; Setiawan, Adi
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (388.588 KB) | DOI: 10.21456/vol8iss1pp25-33

Abstract

BPJS services cannot be separated from criticism and complaints of the people in Indonesia. Twitter is one of the social media choose to share experiences related to things about BPJS. The information that is shared can be processed to gain new knowledge (knowledge discovery), which is related to public opinion about BPJS. Tweets collected from the national BJPS twitter are divided into words, then, specified words can be used as items to form the itemset. The association rule technique with the FP-Growth algorithm that is implemented in the application can process text data from Twitter to form the item set. Each item set contains a collection of tweets that are responses and the opinion of the community about an event or phenomenon related to BPJS services. The tree structure of FP-Growth simplifies the process of the validation because it can track and display the frequency of occurrence of each word and itemset, before and after branch pruning which is not included in the support value. The OSM API integration with the application in this study provides visual information about where the tweet comes from, so it can be used to generate itemset from a collection of tweets from a particular region.
Peningkatan Akurasi Prediksi Waktu Perbaikan Bug dengan Pendekatan Partisi Data Ridwan, Mochammad Arief; Rochimah, Siti
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (375.649 KB) | DOI: 10.21456/vol8iss1pp76-83

Abstract

Software developers need to have a plan in setting up software development costs. Software repairs in the system maintenance phase can be caused by bugs. Bugs are malfunctions that occur in software that does not meet the needs of the software. The software bug can have a fast or long time in the repair depending on the difficulty level. Developers can be assisted by predictive model recommendations and provide time-out considerations for bug fixes. Some research has been done on the predicted time of bug fixes using various existing classification algorithms with free datasets that can be accessed or downloaded from the software site. The classification of existing research uses several datasets of varying time ranges, the results obtained from a very variable time span are assessed to be further enhanced by partitioning over time ranges prior to classification. With partitions based on the repair timeframe, improved accuracy has been made with some classification methods. The results obtained after performing trials on multiple datasets are an increase for the majority of the datasets used. There is also a decrease in accuracy in some tests performed with a particular dataset.

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