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Jurnal Teknologi Informasi Cyberku
Published by Universitas Dian Nuswantoro
ISSN : 19073380     EISSN : 27472183     DOI : -
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science
Jurnal Teknologi Informasi - Jurnal CyberKU is an open access journal, published by Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro. The journal is intended to be dedicated to the development of Information Technology related to Intelligent System, and Business Intelligence. Topics of interest include, but are not limited to: Artificial Intelligence, Machine Learning, Data Mining, Image Processing, Computer Vision, Text Processing, Signal Processing, Speech Recognition, Software Engineering, Decision Support System, IT Governance, eBusiness, Game Technology, Multimedia, eLearning, Computational Education, Computational Engineering, Mobile Computing, Internet of Things.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 67 Documents
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Penentuan Prioritas Penerima Dana Bantuan Operasional Pendidikan Lembaga Pendidikan Anak Usia Dini dengan Metode KNN, TOPSIS dan K-Means Diwahana Mutiara Candrasari; Abdul Syukur; Moch Arief Soeleman
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Education Operational Aid of the Early Childhood Education unit is the financial assistance which is provided to educational institutions, especially for those who engaged in non-formal education, which is used for the process of education in the curriculum of educational institutions in order to give the appropriate and adequate education for students. However, the reality has stated that there is a lot of financial irregularity and inaccuracy of data in the disbursement of the fund of education operational aidat the institute units of Early Childhood Education. On the other hand, there are many complaints that come from the institution itself due to the inaccuracy of data. This research was conducted by applying the KNN, K-Meansalgorithmand TOPSISmethods. The results of the experiment will be tested with two methods that is using cluster distance performance and K-Means cluster count performance. Meanwhile, to measure the level of ranking of TOPSIS method,the experiment will use the percentage calculation method between the experimental data with the implementation of the data to determine the accuracy of TOPSISmethods.
Klasifikasi Penerbitan Surat Keputusan Tunjangan Profesi Guru Menggunakan Naive Bayes Berbasis Information Gain Rani Pratikaningtyas; Purwanto Purwanto; Moch Arief Soeleman
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Sertifikasi guru merupakan salah satu upaya pemerintah untuk meningkatkan mutu pendidikan disertai dengan peningkatan kesejahteraan guru. Namun banyaknya penerima sertifikasi yang ternyata tidak cair berpengaruh kepada laporan anggaran belanja negara dan daerah. Penelitian ini bertujuan untuk melakukan seleksi fitur dengan cara memberi bobot pada setiap atribut dari data Penerbitan Surat Keputusan Tunjagan Profesi Guru di Kota Surakarta tahun 2015, menggunakan metode information gain untuk meningkatkan akurasi pada algoritma Naïve Bayes, sehingga dapat mengklasifikaasi penerbitan surat keputusan tunjangan profesi guru dengan baik. Information gain digunakan untuk memilih atribut khususnya dalam menangani data dengan dimensi tinggi. Sedangkan untuk proses klasifikasinya menggunakan algoritma Naïve Bayes yang merupakan teknik prediksi berbasis probabilistic sederhana. Adapun atribut yang digunakan dalam eksperiman ini adalah, NUPTK, Format Bayar, Jenis PTK, Jenis Kelamin, NIP, Status Kepegawaian, Kode Sertifikasi, Area Tugas, Jenjang, JJM Mengajar, Tugas Tambahan, Tugas Mengajar, Golongan, Nama Bank, Keputusan. Hasil Eksperimen dari metode Naïve Bayes didapatkan hasil akurasi sebesar 93,31% sedangkan setelah menggunakan seleksi fitur dengan information gain didapatkan hasil akurasi sebesar 96,11%. Sehingga mengalami peningkatan akurasi sebesar 2,80%.
Identifikasi Jumlah Bibit Bandeng Menggunakan Metode K-Means Berbasis HSV Color dan Morfologi Salman Suleman; Purwanto Purwanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Seed milkfish is one commodity of national food security. Availability of seed milkfish as one of the major production support in the cultivation of milkfish in ponds must be fulfilled. Factors seed availability is essential in improving the commodities which impact on improving the living standard of farmers' welfare milkfish seedling cultivation. Seed fish are difficult to identify because the object is small so that farmers banding should be extra seedlings in calculating the amount of seed milkfish contained in one container. Identification of seed milkfish (milkfish seeds) one way to find out information on the number of seeds in a container milkfish. This research proposes the identification number of seeds banding using the K-Means method based on the HSV Color and morphology preprocessing. This research begins with step preprocessing, do transformasi Color original image RGB to HSV and RGB to Grayscale by the threshold value the image of S and V the Color space (Color space) HSV and morphology, the next process then feature extraction based on the area and the latter process is counting the number of seeds that are recognized as banding objects based on the results of clustering using the K-Means method. Based on the results of testing milkfish Seed identification show reached 92.70% accuracy and error rate 7:30%.
Klasterisasi Kecerdasan Majemuk Siswa Berbasis Jaringan Syaraf Kohonen Guna Mendukung Adaptive Elearning Stefanus Santosa; Wiji Lestari Panjidang; Yonathan Purbo Santosa
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Learning strategies are often applied without considering the unique and different characteristics of the learner's intelligence. This causes students to have difficulty understanding the material, not focused, bored, decreased motivation, frustration, and various other learning difficulties. The efforts to create student-oriented learning strategies can be done with adaptive elearning. Adaptive elearning system requires recognition function to cluster the intelligence of the learner when learning takes place. This study shows that Kohonen's Artificial Neural Network can be used for mapping students based on multiple intelligences. The results showed that there were 8 clusters with different intelligence compositions. There is no cluster that has a single intelligence. Intrapersonal intelligence is almost owned by 90% of students, while the lowest is visual-spatial intelligence, which is only 23.33%. In order to create a learner-oriented learning process, this clustering method should be embedded in an adaptive elearning system.
Prediksi Pendapatan Penjualan Obat Menggunakan Metode Backpropagation Neural Network dengan Algoritma Genetika Sebagai Seleksi Fitur Nur Azise; Pulung Nurtantio Andono; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The hospital is one of the means of health services for the community, in which there are multiple units, one of which was the installation of a pharmaceutical is a source of revenue for the hospitals contributed by 40 – 60%. Each month the sale of drugs on pharmaceutical erratic installation (fluctuating) and have an impact on earning spharma ceutical installations specifically and at hospitals in General, i.e. against the determination of the lead in policy development and the development of hospitals inthe future. Therefore the forecast or prediction about the drug's sales revenue is urgently needed. Forecasting technique commonly used is the technique of forecastingwith Artificial neural Network method or so-called Artificial neural Network which hasthe best accuracy with the value error. However, the method of Artificial neural Network has a number of shortcomings, so it takes an optimization method, one of themwith Genetic Algorithm optimization methods. In this study using data on drug sales revenue installation hospital Elizabeth Situbondo. In the process of training and testing data in this study using the method of Backpropagation Neural Network and Genetic Algorithm to feature selection. On this panelitian proves that the method of Backpropagation Neural Network with genetic algorithm as a selection of the best RMSE value generating features of 0115. While the test results with the method of Backpropagation Neural Network without Genetic Algorithms as a value generating features selection RMSE 0152.
PENGENALAN VARIETAS MANGGA BERDASARKAN BENTUK DAN TEKSTUR DAUN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK Fathorazi Nur Fajri; Purwanto Purwanto; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 2 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Pada saat ini mangga Indonesia sangat diminati oleh orang asing terlebih untuk mangga kualitas unggul seperti mangga manalagi dan gadung. Akan tetapi tak jarang masyarakat tidak mengerti atau keliru mengenali varietas mangga yang mereka tanam. Selama ini identifikasi atau pengenalan varietas mangga dilakukan dengan menggunakan mata. Hal ini pun dibutuh keahlian atau pakar dalam membedakan varietas mangga tersebut. Akan tetapi orang yang ahli mempunyai keterbatasan, tidak semua varietas mangga dapat dikenali atau diidentifikasi. Terdapat beberapa usulan model yang telah dilakukan untuk mengindentifikasi mangga dengan citra digital akan tetapi akurasi yang dihasilkan masih kurang yaitu di bawah 80 %. Selain itu masing masing peneliti hanya menggunakan satu fitur citra yaitu fitur tekstur. Penelitian ini mengunakan dataset sebanyak 300 citra daun mangga, 150 citra daun mangga varietas manalagi dan 150 citra daun gadung. Metode yang digunakan pada penelitian ini yaitu Backpropagation Neural Network (BPNN) dengan menggunakan fitur bentuk dan tekstur daun mangga. Model BPNN yang paling optimal pada penelitian ini yaitu menggunakan hidden layer = 19, learning rate = 0.9, momentum = 0.9 dan epoch = 100 dengan hasil root mean squar error (RMSE) = 0.0018. Kemudian hasil dari pengujian menggunakan citra daun mangga menghasilkan tingkat akurasi 96 %.
ALGORITMA SUPPORT VECTOR MACHINE UNTUK MEMPREDIKSI NILAI UJIAN NASIONAL Emi Rizky; Purwanto Purwanto; Heribertus Himawan
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

In order to improve the quality of graduate education through an exam in order to compete in domestic, regional and international levels and therefore require the achievement of national standards through the National Examination (UN). produce test scores that boast with the title and can pass the National Exam, due to lack of graduates when the National Examination become routine issues annually. This problem is felt by students, parents, teachers, educational units and agencies associated with other national exams. By looking at the reasons we need a prediction to predict the value of the UN. Soft computing has several abilities one of which is a technique that can be used to predict the ability of students to acquire the methods of the National Examination Support Vector Machine (SVM) which is a branch of artificial intelligence where the processing system configuration information obtained performance model for the prediction of the National Examination the Root mean squared Error (RMSE) is the best for Indonesian was 0.713 + / - 0.173, English at 0586 + / - 0.066, and Mathematics by 0882 + / - 0188. configuration with predictions using a barometer. k-fold 10, C (cost) of 0.1 with kernel-type radial Indonesian subjects, k-fold 10, C (cost) of 0.3 with radial kernel type for the subjects of English and Mathematics.

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