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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) COPING (Community of Publishing in Nursing) JUTI: Jurnal Ilmiah Teknologi Informasi Creative Information Technology Journal SISFOTENIKA Jurnal Sains Dan Teknologi (SAINTEKBU) Jurnal Komputer Terapan CogITo Smart Journal Jurnal Eksplora Informatika specta: Journal of Photography, Arts, and Media JUTEKIN (Jurnal Manajemen Informatika) Jurnal Ilmiah Sinus CCIT (Creative Communication and Innovative Technology) Journal METIK JURNAL Technologia: Jurnal Ilmiah G-Tech : Jurnal Teknologi Terapan Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat CAHAYA tech Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA DEVICE Jurnal Pengabdian Seni Jurnal Ekonomi dan Teknik Informatika EXPLORE Techno Jurnal Permata Indonesia Panggung JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Bisnis Digital dan Sistem Informasi
Kusrini Kusrini
STMIK AMIKOM Yogyakarta
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : SISFOTENIKA

 1 
PREDIKSI MAHASISWA DROP OUT MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Siti Nurhayati; Kusrini kusrini; Emha Taufiq Luthfi
SISFOTENIKA Vol 5, No 1 (2015): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.86 KB) | DOI: 10.30700/jst.v5i1.25

Abstract

AbstrakTingginya tingkat keberhasilan mahasiswa dan rendahnya tingkat kegagalan mahasiswa dapat mencerminkan kualitas dari suatu perguruan tinggi. Salah satu indikator kegagalan mahasiswa adalah kasus drop out. Untuk mengatasi permasalah, dilakukan prediksi menggunakan metode support vector machine. Support Vector Machine berusaha mencari hyperplane yang optimal dimana dua kelas pola dapat dipisahkan dengan maksimal, parameter yang di gunakan pada Support Vector Machine hanya parameter kernel dalam satu parameter C yang memberikan pinalti pada titik data yang di klasifikasikan secara acak. Dalam Support Vector Machine bobot (w) dan bias (b) merupakan solusi global optium dari quadratic programming sehingga cukup dengan sekali running akan menghasilkan solusi yang akan selalu sama untuk pilihan kernel dan parameter yang sama. Melalui penerapan support vector machine diharapakan untuk mendapatkan parameter Support Vector Machine yang digunakan tepat untuk memperoleh margin terbaik dalam memprediksi mahasiswa drop out.Kata Kunci— prediksi drop out, kernel, support vector machine, unified modeling language
Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu Christin Nandari Dengen; Kusrini Kusrini; Emha Taufiq Luthfi
SISFOTENIKA Vol 10, No 1 (2020): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.866 KB) | DOI: 10.30700/jst.v10i1.484

Abstract

Students who are accepted every year are increasing, but not all students can graduate on time. In achieving graduation, of course, there are stages or processes that must be passed by each student such as following a number of courses, conducting fieldwork practices, real work lectures and final assignment seminars. These processes are carried out within a period of time determined by the University. For this reason, a prediction system for student graduation is needed in order to minimize students who graduate not on time. In predicting student graduation on time using 50 sample data for the 2013 graduation year with gender, IPK, graduation and toefl attributes. This study carried out the application of the CRISP-DM method with the C4.5 algorithm in predicting student graduation. The use of the C4.5 algorithm is supported by simulations carried out using the WeKa application and gets an accuracy value of 60%. With the existence of this research, it is expected to be able to help the Informatics Engineering Program at Universita Mulawarman so that students can graduate on time.
Sistem Pendukung Keputusan Menentukan Bibit Padi Terbaik Menggunakan Metode Gap Kompetensi Elvis Pawan; Agung Jasuma; Achmad Yusron Arif; Kusrini Kusrini
SISFOTENIKA Vol 10, No 1 (2020): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (348.674 KB) | DOI: 10.30700/jst.v10i1.511

Abstract

Government is preparing Indonesia to achieve food self-sufficiency, but we need know the main process that will hamper these ideals achieved if the production of paddy agriculture decreases, especially remote areas. This is due to lack of knowledge of farmers in determining which rice seeds are suitable for planting on agricultural land. This research is expected to help farmers specifically who do not understand the determination of good rice seeds. In addition, the results of this study can help the government realize food self-sufficiency in Indonesia. In designing this SPK using the Competence Gap method by including several criteria such as rice production per hectare, disease resistance, seed prices, planting limits, and land requirements, these criteria will be processed according to the rules of the Competence Gap method to produce the best seed recommendation. This study resulted in SPK, the main criteria used for disease resistance and the price of seeds given a priority value of 70%, another criterion of 30%. Based these criteria, three rice varieties recommended by DSS, Kalimasada, both Inpasari 42 GSR and the three types of Mapan P-05. The next researcher expected able to compare several methods and combine them into expert system for better accuracy.
Co-Authors Abdul Malik Zuhdi Abdul Rokhim Achmad Yusron Arif Agung Jasuma Agung Nugroho Agus Harjoko Aisha Alfani Aji Susanto Anom Purnomo Andayani Andayani Andi Sunyoto Andria Andria Anggit Dwi Hartanto Aprison Wolla Gole Ari Suhartanto Arif Fajar Solikin Arif Fajar Solikin Armadyah Amborowati Asro Nasiri Assani, Moh. Yushi Azis Wahyudi B, Isdayani Christin Nandari Dengen DHANI ARIATMANTO Dina Maulina Dwinda Etika Profesi Eka Wahyu Pujiharto Eka Yulia Sari Eko Purwanto Elfrida Ratnawati Elik Hari Muktafin Elvis Pawan Emha Taufiq Luthfi Erfan Tongalu Eva Oktaviani Fajar Dwi Insani Fareza Aditiyanto Nugroho Ferry Wahyu Wibowo Firmanda Fasya Hamada Zein HANIF AL FATTA Hasan, Nur Fitrianingsih Henderi . Heri Abijono Irfan Purwanto Jhoanne Fredricka Jimmy H Moedjahedy Junaidi Sabtu Kusnawi Kusnawi Lili Kartikawati M Rudyanto Arief M Vaizul Rahman M. Afriansyah M. Rudyanto Arief M. Syukri Mustafa Marta Ardiyanto Maykel Sonobe Mochamad Fadillah Abdullah Muchamat Zainal Arifin Muhamad Kurniawan Muhammad Agus Muljanto Muhammad Alfariz Muhammad Fahmi Muhammad Fajar Apriyanto Muhammad Firdaus Abdi Muhammad Rudyanto Arief Muhammad Salimy Ahsan Musthofa Galih Pradana Mutiara Dwi Anggraini Neno, Friden Elefri Norhikmah Norhikmah Pamekas, Bondan Wahyu Rahmat Saleh Sukur Retantyo Wardoyo Rifqi Hammad Ririn Putri Damaiyanti Riska Dwi Handayani Siti Nurhayati Siti Solekhah Sofyan Pariyasto Sri Hartati Sri Lestari Rahayu Sri Yanto Qodarbaskoro Wahyu Nur Alimyaningtias Widiyanto, Wahyu Wijaya Wira Dimuksa Wiwi Widayani Yeyen Dwi Atma Yustian Servanda Zenal Muttaqin Zeni Muhamad Noer