JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
Articles
472 Documents
<![CDATA[Kombinasi Penggunaan Model Warna Dalam Pendeteksian Letak Bibir pada Gambar digital Berwarna ]]>
<![CDATA[Sulaeman Santoso]]>;
<![CDATA[Erico Darmawan Handoyo]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.574
<![CDATA[Different color model has been used to detect the location of lips within color images. These color models have different advantages and weakness. This paper intends to compare several uses of color model (namely the rgb, normalized rbg, and the YCbCr color model that is used frequently on several lip localization technique) and try to propose a combination of color model to minimize post processing in lip localization. The proposed combination combines the normalized rgb color model, the YcbCr colod model and the intensity based rgb model to obtain lip area. The combination proved to provide better distinction with only slighty more calculation.]]>
<![CDATA[Analisis Manajemen Risiko Teknologi Informasi Penerapan Pada Document Management System di PT. JABAR TELEMATIKA (JATEL) ]]>
<![CDATA[Gilang Mochammad Husein]]>;
<![CDATA[Radiant Victor Imbar]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.575
<![CDATA[ADEL (Aplikasi Dokumen Elektronik) and NADINE (Naskah Dinas Elektronik) are the use of information technology (IT) document management system (DMS) electronic archive product that used by JATEL to enhance information quality and to establish an information dan document workflow so it becomes easier, faster, and well-maintained. Electronic archive IT systems are designed to increase the value and benefits of information and able to control the documents distribution and disposition simultaneously that can increase JATEL business value and support business process automation. Information technology can assist organization but also present risks that need to be managed properly. To anticipate or reduce the likelihood of risk, information technology risk management is required. The implementation of information technology risk management involve risk identification, risk assessment and risk treatment. Keywords — ADEL (Aplikasi Dokumen Elektronik), NADINE (Naskah Dinas Elektronik), risk identification, risk assessment, and risk treatment.]]>
<![CDATA[Penerapan Metode KMeans dan Cobweb Terhadap Analisis Prestasi Akademik Mahasiswa yang Mengikuti Kegiatan Kemahasiswaan ]]>
<![CDATA[Neil Casaandra Sudharmono]]>;
<![CDATA[Mewati Ayub]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.577
<![CDATA[The growth of an university can be seen with student’s academic records which can be used for determining students’ quality. Besides student’s academic records, other component that can be used to determine students’ quality is student’s participation in non-academic activities which most of universities have to develop their student’s softskill during their study period. Based on these data provision, we can analyze how non-academic activities affect student’s academic achievement. In this research, the data that used for analysis are students’ academic data and students’ activities data from Maranatha Christian University as case study. This analysis use one of data mining technique, which is clustering . The clustering method that used for this research are Kmeans andCobweb. Clustering method groups students and alumni that participate in student activities or not by looking and the student’s and alumni’s faculty, major, years of study, and if the student or alumni participate in student activites, we can analyze it with how many activities that the student and alumni had.]]>
<![CDATA[Extended Vector Space Model with Semantic Relatedness on Java Archive Search Engine ]]>
<![CDATA[Oscar Karnalim]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.578
<![CDATA[Byte code as information source is a novel approach which enable Java archive search engine to be built without relying on another resources except the Java archive itself [1]. Unfortunately, its effectiveness is not considerably high since some relevant documents may not be retrieved because of vocabulary mismatch. In this research, a vector space model (VSM) is extended with semantic relatedness to overcome vocabulary mismatch issue in Java archive search engine. Aiming the most effective retrieval model, some sort of equations in retrieval models are also proposed and evaluated such as sum up all related term, substituting non-existing term with most related term, logaritmic normalization, context-specific relatedness, and low-rank query-related retrieved documents. In general, semantic relatedness improves recall as a tradeoff of its precision reduction. We also proposed a scheme to take the advantage of relatedness without affected by its disadvantage (VSM + considering non-retrieved documents as low-rank retrieved documents using semantic relatedness). This scheme assures that relatedness score should be ranked lower than standard exact-match score. This scheme yields 1.754% higher effectiveness than our standard VSM.]]>
<![CDATA[Perbandingan Metode-Metode Klasifikasi untuk Indoor Positioning System ]]>
<![CDATA[Yuan Lukito]]>;
<![CDATA[Antonius R. Chrismanto]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.579
<![CDATA[Indoor Positioning System can provide position and navigation guidances inside a building. This paper discusses about systematic comparison between K-Nearest Neigbors and Naïve Bayes Classifier over WiFi-based Indoor Position System dataset. The dataset is collected using a custom Android Application, which able to receive and record WiFi signal strengths from the surrounding WiFi hotspots in UKDW campus. The dataset consists of 11658 Received Signal Strength (RSS) data from 41 public locations in UKDW campus. We use 10-folds cross validation and T-Test with 0.05 significance level to compare classification accuracy between K-Nearest Neigbors and Naïve Bayes classifier. Based on the experiment result, we can conclude that K-Nearest Neighbors classifier produces better classification accuracy (83.58%) than Naïve Bayes (61.52%).]]>
<![CDATA[Analisis dan Perancangan Aplikasi Penyusunan Jadwal Mengajar Sesuai Jadwal Kesediaan Mengajar Dosen Di Fakultas Teknologi Informasi (Studi Kasus : Jurusan Teknik Informatika) ]]>
<![CDATA[Meliana Christianti J.]]>;
<![CDATA[Robby Tan]]>;
<![CDATA[Oscar Karnalim]]>;
<![CDATA[Egie Imandha]]>;
<![CDATA[Tendy Cahyadi]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.580
<![CDATA[Faculty of Information Technology is one of the faculties at Maranatha Christian University. Currently, scheduling is still done manually. Scheduling process takes quite a long and exhaustive, as the secretary of the department needs to ask the willingness of faculty, and compare each lecturer teaching schedule so it does not clash with other lecturers. As part of Maranatha Christian University, the Faculty of Information Technology would like to shorten its preparation time to arrange faculty teaching schedule and to adjust to the schedule provided by each lecturer. This application will be developed with Java (desktop), PHP (web) programming language, and MySQL database. ]]>
<![CDATA[Perancangan Model Pengukuran Layanan Teknologi Informasi pada Perguruan Tinggi (Studi Kasus: Perguruan Tinggi X) ]]>
<![CDATA[Adelia Adelia]]>;
<![CDATA[Kridanto Surendro]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.581
<![CDATA[Information technology services within the organization need to be taken to ensure that the service providers can provide quality service and benefit for service user. The research model to measure information technology services provided to users of the service providers, especially in college. In the design of model, using the concepts included in the service intelligence and ISO 20000, Balanced Scorecard, Balanced Scorecard and IT Balanced Scorecard management of information technology services. Measurement services performed by using models generated from mapping the concepts used in the study. Colleges may use the measurement model is a college that has been using information technology in supporting the activities of the college. Measurements of IT service level emphasizes on IT services owned by the college and different type of IT services to users in the college]]>
<![CDATA[Aplikasi Optimalisasi Muat Barang Dengan Penerapan Algoritma Dynamic Programming Pada Persoalan Integer Knapsack ]]>
<![CDATA[Daniel Jahja Surjawan]]>;
<![CDATA[Irene Susanto]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.582
<![CDATA[In the era of globalization, the use of information technology become a necessity that can not be separated in running a business in all instances. The need of information technology is not only used as a supporter of the process, but also has become a major part of which should go well. It was felt also by the CV. Labatrans engaged in courier services, which often occurs excess or shortage even when the goods are loaded into a vehicle that will deliver. There has been no control of the goods to be delivered, so that sometimes exceed the limit load capacity of the vehicle. This cause a heap of goods in the warehouse, and the main problem consumers will be disappointed because the goods delivered are not accepted at the right promised time. Another problem occurs when consumers asked where the goods they are shipped, they want to know where their goods for some reason such as perishable goods or food. And usually the staff of the company can not give an answer because of limited information. The purpose of this research is to create an application that can handle tracking of goods delivered and the load optimization by applying Dynamic Programming Algorithms on the issue of Integer Knapsack]]>
<![CDATA[Analisa Nilai Lamda Model Jarak Minkowsky Untuk Penentuan Jurusan SMA (Studi Kasus di SMA Negeri 2 Tualang) ]]>
<![CDATA[Khairul Umam Syaliman bin Lukman]]>;
<![CDATA[Ause Labellapansa]]>
<![CDATA[ Jurnal Teknik Informatika dan Sistem Informasi Vol 1 No 2 (2015): JuTISI ]]>
Publisher : <![CDATA[Maranatha University Press ]]>
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DOI: 10.28932/jutisi.v1i2.583
<![CDATA[SMA Negeri 2 (SMAN 2) is located in Tualang. So far the data report student majors only stored in a database as a final report. Data from the report of the majors could be used as guidelines to determine the students' decision majors for the following year. To take advantage of the data stored in that particular database, we can use data mining disciplines. The method used to make the determination of students majoring done by using Nearest K-Nearest Neighbor (K-NN) algorithm. On the other hand, the method for calculating the distance between the data used models Minkowsky distance with a value of lambda (λ) as a parameter. Lambda values that were analyzed were lambda 1, 2 and 3. Lambda with the value of 1 can generate increasing accuracy in the 11th experiment or with a large amount of data equal to 276 data. Lambda 2 will produce increasing accuracy by the 16th experiment or with the number of training data equal to 356 data while lambda 3 can also produce accuracy continuously increasing by the 11th experiement or with the amount of training data equal to 276 data. The accuracy of the lambda value of 1 is better than lambda 2 and lambda 3. This was proven in 25 experiments at lambda 1 which produces the highest accuracy value for 20 times.Keywords — Classification, Data Mining, K-Nearest Neighbor, Lamda (λ), Minkowsky.]]>
