JURNAL SISTEM INFORMASI BISNIS
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.
Articles
305 Documents
<![CDATA[Penerapan K-Nearest Neighbour dalam Penerimaan Peserta Didik dengan Sistem Zonasi ]]>
<![CDATA[Kurniawan, Denni]]>;
<![CDATA[Saputra, Ade]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 9, No 2 (2019): Volume 9 Nomor 2 Tahun 2019 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol9iss2pp212-219
<![CDATA[Admission of new students is a routine activity at the beginning of each new meeting year in all formal educational institutions. At the moment the acceptance of new students uses the zoning system and has been regulated by Permendikbud No. 20 in 2019. This zoning system will accept students where their residence enters through the user area with the school environment. With this Permendikbud the government hopes that there will be an evenness in the quality of education in all schools, so that schools will no longer get the title of superior and non-superior schools. But in a system, the zoning improves anxieties in the school environment. This research supports to help the participating school students will be accepted in accordance with the provisions of the Ministry of Education and Culture. In overcoming problems that arise in the school environment there needs to be a system that can overcome that problem. In this study using the K-Nearest Neighbor (K-NN) method. Where the K-NN method will do the classification of new learners' residence with the school. In determining the classification using the K-NN method used for zoning and non-zoning areas, it is seen based on the closest K value. In finding the optimal value in this study using the Rapidminer application. The optimal high-level test results at K 5 where the value of this K is 83.36%]]>
<![CDATA[Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM) ]]>
<![CDATA[Neneng, Neneng]]>;
<![CDATA[Adi, Kusworo]]>;
<![CDATA[Isnanto, Rizal]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 6, No 1 (2016): Volume 6 Nomor 1 Tahun 2016 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol6iss1pp1-10
<![CDATA[Texture is one of the most important features for image analysis, which provides informations such as the composition of texture on the surface structure, changes of the intensity, or brightness. Gray level co-occurence matrix (GLCM) is a method that can be used for statistical texture analysis. GLCM has proven to be the most powerful texture descriptors used in image analysis. This study uses the four-way GLCM 0o, 45o, 90o, and 135o. Support vector machine (SVM) is a machine learning that can be used for image classification. SVM has a high generalization capability without any requirement of additional knowledge, even with the high dimension of the input space. The data used in this study are the image of goat meat, buffalo meat, horse meat, and beef with shooting distance 20 cm, 30 cm and 40 cm. The result of this study shows that the best recognition rate of 87.5% was taken at a distance of 20 cm with neighboring pixels distance d = 2 in the direction GLCM 135o.]]>
<![CDATA[Sistem Pendukung Keputusan Untuk Pengadaan Fasilitas Hotel Menggunakan Metode TOPSIS ]]>
<![CDATA[Hendartie, Susi]]>;
<![CDATA[Surarso, Bayu]]>;
<![CDATA[Noranita, Beta]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 1, No 3 (2011): Volume 1 Nomor 3 Tahun 2011 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol1iss3pp143-152
<![CDATA[The development of hotel business to make consumers more critical to choose a hotel products and services. If the hotel facilities more complete, so interest of the consumer is higher to choose the hotel. This  research study intend to build a decision support system for the procurement of hotel facilities with TOPSIS method. This method uses the six alternative form of the data; hotel rooms (guest  room), karaoke, gift shop, a gym, spa and travel corner (travel tour information) and data of some criteria. This method was chosen because it is based on the best alternative concept, was not only has the shortest distance from the positive ideal solution, but also has  the longest distance  from  the  negative  ideal  solution.  TOPSIS  calculations  systems  have  been  done  the  comparison  of  final  value  using  excell calculation. Calculations that used in this research study is simple and produces alternative hotel rooms (guest room) with t he highest ranking as the ideal solution. TOPSIS method facilitates decision-makers in choosing the best alternative for the procurement of hotel facilities.Keywords : Decision support system; Hotel facilities; TOPSIS]]>
<![CDATA[Front Matter JSINBIS Volume 5 Nomor 2 Tahun 2015 ]]>
<![CDATA[M.Eng, Ph.D, Prof. Mustafid]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 5, No 2 (2015): Volume 5 Nomor 2 Tahun 2015 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol5iss2pp%p
<![CDATA[Front Matter JSINBIS Volume 5 Nomor 2 Tahun 2015]]>
<![CDATA[Sistem business intelligence untuk mendukung perguruan tinggi yang kompetitif ]]>
<![CDATA[Mustafid, Mustafid]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 3, No 1 (2013): Volume 3 Nomor 1 Tahun 2013 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol3iss1pp18-24
<![CDATA[Pengembangan perguruan tinggi saat ini tidak lagi bergantung pada megahnya kampus atau peralatan laboratorium, melainkan bergantung pada kecerdasan dalam pengelolaan modal intelektual berdasarkan sumberdaya yang dimiliki. Peranan modal intelektual menjadi sangat penting dan strategis dalam pengelolaan program bisnis akademik untuk mencapai keunggulan kompetitif. Sistem business intelligence sebagai elemen penting dalam perguruan tinggi difungsikan untuk membantu manajerial mengelola modal intelektual dalam proses pengambilan keputusan dalam rangka meningkatkan kinerja perguruan tinggi. Penelitian ini bertujuan untuk mengimplementasikan sistem business intelligence untuk mendukung pengelolaan program bisnis akademik kearah yang lebih kompetitif melalui sistem perencanaan, pengukuran dan meningkatkan kinerja berbasis modal intelektual. Modal intelektual perguruan tinggi dideskripsikan berdasarkan sumberdaya perguruan tinggi yang dimiliki, agar dalam pengelolaan modal intelektual menjadi lebih optimal. Penggunaan sistem business intelligence berbasis pada teknologi informasi bertujuan untuk mendesain mengukur, mengelola dan mengembangkan modal intelektual, antara lain berupa pengetahuan, skill dan keahlian sumberdaya manusia perguruan tinggi. Penggunaan indikator kinerja kunci perguruan tinggi dianalisis untuk memenuhi standar mutu perguruan tinggi yang kompetitif. Keywords : Business intelligence systems, intellectual capital, human capital, structural capitalal, key performance indicators.]]>
<![CDATA[Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes ]]>
<![CDATA[Jumeilah, Fithri Selva]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol8iss1pp92-98
<![CDATA[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.]]>
<![CDATA[Kombinasi Balanced Scorecard dan Objective Matrix Untuk Penilaian Kinerja Perguruan Tinggi ]]>
<![CDATA[Mahmudi, A Aviv]]>;
<![CDATA[Surarso, Bayu]]>;
<![CDATA[Subagio, Agus]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 4, No 1 (2014): Volume 4 Nomor 1 Tahun 2014 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol4iss1pp01-10
<![CDATA[Working Assessment is very essential aspect for a college to keep its excellent quality to face tight competition in either national or international level. The working assessment can be implemented to compare the result with organization strategy, and can also help to plan the upcoming strategy to achieve the final target of organization. The combination of Balanced Scorecard and OMAX is aimed at building the information system of working assessment in a college. Balanced Scorecard in the college is used to determine the strategic purposes, KPI and target, optimistic and pessimistic value. The scoring of each KPI uses AHP method; the scoring of KPI influences on general working score. The scoring of working assessment uses Objective Matrix (OMAX) method to know the total result indicator stated by the college, and can find out the total result indicator of each working criterion. The combination of BSC and OMAX can avoid the use of overwhelming data because this model focused on four perspectives, with the important key indicator of a college. Besides, the four perspectives can avoid the orientation of short-term target, because OMAX developed the more obvious frame of time that also focused on the long-term target. This combination was a good integration that can afford to modify hybrid model in determining the score card of a college. BSC changed into the form of OMAX that presented a target in the long period. This combination is also a good integration that can afford to modify a hybrid model in determining the scorecard of the college. BSC changes into the form of OMAX that presents the long-term target. Â Keywords: AHP; Balanced scorecard; OMAX; Work assessment; College]]>
<![CDATA[Ekstraksi Ciri Orde Pertama dan Metode Principal Component Analysis untuk Mengidentifikasi Jenis Telur Ayam Kampung dan Ayam Arab ]]>
<![CDATA[Nurhayati, Oky Dwi]]>;
<![CDATA[Eridani, Dania]]>;
<![CDATA[Ulinuha, Ajik]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 9, No 2 (2019): Volume 9 Nomor 2 Tahun 2019 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol9iss2pp133-140
<![CDATA[Chicken eggs become one of the animal proteins commonly used by people, especially in Indonesia. Eggs have high economic value and have diverse benefits and a high nutritional content. Visually to distinguish between domestic chicken eggs and arabic chicken eggs has many difficulties because physically the shape and color of eggs have similarities. This research was conducted to develop applications that were able to identify the types of domestic chicken eggs and Arab chicken eggs using the Principal Componenet Analysis (PCA) method and first order feature extraction. The application applies digital image processing stages, namely resizing image size, RGB color space conversion to HSV, contrast enhancement, image segmentation using the thresholding method, opening and region filling morphology operations, first order feature extraction and classification using the PCA method. The results of identification of types of native domestic chicken eggs and Arabic chicken eggs using the Principal Component Analysis method showed the results of 95% system accuracy percentage, consisting of 90% accuracy of success in the type of domestic chicken eggs and 100% accuracy of success in the type of Arabic chicken eggs.]]>
<![CDATA[Studi Implementasi Adaptive Neuro Fuzzy Inference System Untuk Menentukan Normalitas Kehamilan ]]>
<![CDATA[Rusdiana, Lili]]>;
<![CDATA[Sediyono, Eko]]>;
<![CDATA[Surarso, Bayu]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 5, No 2 (2015): Volume 5 Nomor 2 Tahun 2015 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol5iss2pp98-108
<![CDATA[Early detection of normality pregnancy is one of the ways to prevent more serious disorders in pregnancy. This thesis study the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the normality of pregnancy. The period of pregnancy and complaints during pregnancy are used as inputs and the normality of pregnancy as output. Data were analyzed using ANFIS method and using Sugeno FIS rules. The program simulation results show that the performance of ANFIS can be implemented to determine the normality of pregnancy. The learning results on different training with the highest level of accuracy of 77,5% can recognize the symptoms and 97.5% could identify the diagnosis to determine the normality of pregnancy. The system can provide the necessary information about the normality of pregnancy. The results show that ANFIS can be used to determine the normality of pregnancy.  ]]>
<![CDATA[Sistem Informasi Perpustakaan Berbasis Web Application ]]>
<![CDATA[Irawan, Yudie]]>;
<![CDATA[Mustafid, Mustafid]]>;
<![CDATA[Sugiharto, Aris]]>
<![CDATA[ JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 1, No 2 (2011): Volume 1 Nomor 2 Tahun 2011 ]]>
Publisher : <![CDATA[Universitas Diponegoro ]]>
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DOI: 10.21456/vol1iss2pp69-72
<![CDATA[Digital  library  system  contributes  the  development  of  digital  resource  digital  resource  that  can  be accessed  via the  Internet.  Librarymanagement system contributed to the development of automation membership data processing, circulation and cataloging. In this thesisis  to develop  a new  concept of  digital  library  systems  and  library  management  system  by  integrating  these  two systems  architecture. Integration  architecture  implemented  by  inserting  component  library  management  system  into  the  digital  library  system  architecture. Web application technology required for these components in order to be integrated with the digital library system components.  The newsystem  has  the advantage  of  this  development  application  utilization  of  borrowing,  membership  and  kataloging  to  a  sharable  over the internet,  so  applications  that  can  be used  together.  Information  can be  delivered  between the  library  catalog,  without  leaving the  digitallibrary function in the utilization of shared digital resources derived from uploading by each librarian.Keywords : Digital library system; Library management system; Web application]]>
