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All Journal Jurnal Ilmiah Kursor Jurnal Informatika Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science Applied Technology and Computing Science Journal
Joan Santoso
Sekolah Tinggi Teknik Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Klasifikasi Helpdesk Menggunakan Metode Support Vector Machine Stefanie Hilda Kusumahadi; Hartarto Junaedi; Joan Santoso
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.1125

Abstract

The online helpdesk with ticketing system with the help of operators often experiences problems such as inappropriate delegation processes, the duration of the helpdesk waiting time to be delegated, even the helpdesk is missed to be handled. The ticket delegation checked manually by the operator has risks creating an error in delegating helpdesk tickets to inappropriate technicians. The helpdesk classification system is needed so that every incoming helpdesk ticket can be classified to the right technician according to the job description. The incoming Helpdesk is classified into 6 types of requests, namely multimedia, documentation, internet, server, hardware, software and miscellaneous. This helpdesk grouping is needed so that related technicians for each helpdesk can work and help the helpdesk according to their respective job descriptions. The Support Vector Machine method is used to classify text on the helpdesk. The use of Linear and Polynomial kernels produces an accuracy of 78%, the RBF or Gaussian kernel produces the highest accuracy of 81% while the Sigmoid kernel produces the smallest accuracy of 51%. The helpdesk classification results with the Support Vector Machine method can produce quite good accuracy.
Pemanfaatan Expert System Untuk Penentuan Kegawatdaruratan Pasien Balita Di IGD Andik Jatmiko; Joan Santoso; Hendrawan Armanto
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 1 No 2 (2018): December
Publisher : Unusa Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v1i2.854

Abstract

Triage merupakan suatu suatu prosedur yang dilakukan petugas di instalasi gawat darurat untuk menentukan tingkat kegawatan pasien, dalam hal ini seorang petugas medis senior yang di tunjuk bertugas memilah dan menentukan urutan pasien yang terlebih dahulu dilayani. Namun kenyataan di lapangan tidak semua petugas menguasai keahlian dalam menentukan tingkat kegawatan pasien. Kurangnya petugas senior yang mempunyai keahlian tersebut berakibat kecepatan pelayanan kurang maksimal, terlebih kasus balita yang mana merupakan kasus yang memerlukan perhatian khusus karena sistem kekebalaan dan daya tahan tubuhnya belum terbentuk sempurna. Dari beberapa faktor tersebut penulis ingin melalukan pemanfaatan expert system untuk penentuan kegawatdaruratan pasien balita di IGD Rumah Sakit Islam Jemursari Surabaya. Pemanfaatan sistem pakar ini dilakukan dengan menggunakan mesin inferensi backward chaining yang artinya proses pencarian dimulai dari fakta-fakta untuk selanjutnya menuju pada suatu kesimpulan. Manfaat penelitian ini adalah untuk meringankan kerja petugas medis dan bagi keluarga pasien dalam mengakses informasi tentang tingkat kegawatan pasien. Dalam implementasinya menggunakan metode backward chaining dengan menggunakan DBMS MySQL dan bahasa pemograman PHP.
Digit Classification of Majapahit Relic Inscription using GLCM-SVM Tri Septianto; Endang Setyati; Joan Santoso
Knowledge Engineering and Data Science Vol 1, No 2 (2018)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1094.878 KB) | DOI: 10.17977/um018v1i22018p46-54

Abstract

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.
HIDDEN MARKOV MODELS BASED INDONESIAN VISEME MODEL FOR NATURAL SPEECH WITH AFFECTION Endang Setyati; Mauridhi Hery Purnomo; Surya Sumpeno; Joan Santoso
Jurnal Ilmiah Kursor Vol 8 No 3 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i3.61

Abstract

In a communication using texts input, viseme (visual phonemes) is derived from a group of phonemes having similar visual appearances. Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such as speech recognition. For speech emotion recognition, a HMM is trained for each emotion and an unknown sample is classified according to the model which illustrate the derived feature sequence best. Viterbi algorithm, HMM is used for guessing the most possible state sequence of observable states. In this work, first stage, we defined system of an Indonesian viseme set and the associated mouth shapes, namely system of text input segmentation. The second stage, we defined a choice of one of affection type as input in the system. The last stage, we experimentally using Trigram HMMs for generating the viseme sequence to be used for synchronized mouth shape and lip movements. The whole system is interconnected in a sequence. The final system produced a viseme sequence for natural speech of Indonesian sentences with affection. We show through various experiments that the proposed, the results in about 82,19% relative improvement in classification accuracy.
Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit Tri Septianto; Endang Setyati; Joan Santoso
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 3, Year 2018 (July 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (256.306 KB) | DOI: 10.14710/jtsiskom.6.3.2018.106-109

Abstract

The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.
Co-Authors Andik Jatmiko Endang Setyati Hartarto Junaedi Hendrawan Armanto Mauridhi Hery Purnomo Stefanie Hilda Kusumahadi Surya Sumpeno Tri Septianto