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All Journal Dinamika Informatika Journal of Computer Networks, Architecture and High Performance Computing Applied Science and Technology Research Journal
Sunggito Oyama, Sunggito
Universitas PGRI Yogyakarta (UPY)
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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ANALISIS KRITERIA SISTEM PENDUKUNG KEPUTUSAN BEASISWA BELAJAR BAGI GURU MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP) Oyama, Sunggito; Ernawati, Ernawati; Mudjihartono, Paulus
Dinamika Informatika Vol 5, No 1 (2015): Jurnal Dinamika Informatika
Publisher : Dinamika Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemberian beasiswa belajar bagi guru selama ini sangat di perlukan manfaatnya bagi guru yang sedang menempuh sarjana S1. Banyak kriteria yang dijadikan perhitungan untuk mendapatkan skor pemohon. Proses seleksi yang dilakukan adalah  dengan  memilah-milah  berkas  yang  dikumpulkan  oleh pendaftar beasiswa sembari mengecek database terkait status  beasiswa  dari  yang  bersangkutan. Dengan  jumlah pendaftar yang cukup banyak maka proses  seleksi  tersebut  menyita  banyak  waktu  karyawan  dan hasilnyapun kurang  valid. Untuk itulah dibutuhkan suatu sistem pendukung keputusan untuk memberi pertimbangan dalam  menyeleksi  beasiswa. Pada awal data  akan  Dianalisis kriteria penentu yang memiliki faktor paling besar dan kemudian di buat suatu sistem pendukung keputusan untuk menentukan penerima beasiswa dengan  metode    Analytical Hierarchy Process. Sistem  pendukung   keputusan   ini   akan mengurutkan  prioritas  penerima  beasiswa  sesuai  dengan kriteria  yang  ditentukan pengambil  keputusan. Pengambil keputusan dapat memberikan pandangan dan memasukkan penilaian berdasarkan pengalaman mereka. Dengan demikian, sistem pendukung keputusan ini mampu membantu pihak pengelola beasiswa untuk menentukan alternatif terbaik penerima beasiswa sesuai kondisi yang diharapkan. Hasil akhir di laporan dari pembuatan sistem pendukung keputusan ini adalah sebuah urutan prioritas pemberian beasiswa dari yang terbesar hingga yang terkecil.
ANALISIS KRITERIA SISTEM PENDUKUNG KEPUTUSAN BEASISWA BELAJAR BAGI GURU MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP) Oyama, Sunggito; Ernawati, Ernawati; Mudjihartono, Paulus
Dinamika Informatika Vol 5, No 1 (2015): Jurnal Dinamika Informatika
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemberian beasiswa belajar bagi guru selama ini sangat di perlukan manfaatnya bagi guru yang sedang menempuh sarjana S1. Banyak kriteria yang dijadikan perhitungan untuk mendapatkan skor pemohon. Proses seleksi yang dilakukan adalah  dengan  memilah-milah  berkas  yang  dikumpulkan  oleh pendaftar beasiswa sembari mengecek database terkait status  beasiswa  dari  yang  bersangkutan. Dengan  jumlah pendaftar yang cukup banyak maka proses  seleksi  tersebut  menyita  banyak  waktu  karyawan  dan hasilnyapun kurang  valid. Untuk itulah dibutuhkan suatu sistem pendukung keputusan untuk memberi pertimbangan dalam  menyeleksi  beasiswa. Pada awal data  akan  Dianalisis kriteria penentu yang memiliki faktor paling besar dan kemudian di buat suatu sistem pendukung keputusan untuk menentukan penerima beasiswa dengan  metode    Analytical Hierarchy Process. Sistem  pendukung   keputusan   ini   akan mengurutkan  prioritas  penerima  beasiswa  sesuai  dengan kriteria  yang  ditentukan pengambil  keputusan. Pengambil keputusan dapat memberikan pandangan dan memasukkan penilaian berdasarkan pengalaman mereka. Dengan demikian, sistem pendukung keputusan ini mampu membantu pihak pengelola beasiswa untuk menentukan alternatif terbaik penerima beasiswa sesuai kondisi yang diharapkan. Hasil akhir di laporan dari pembuatan sistem pendukung keputusan ini adalah sebuah urutan prioritas pemberian beasiswa dari yang terbesar hingga yang terkecil.
Decision Support System for Prospective Scholarship Recipients Using SMARTER and Forward Chaining Method Muhammad Fairuzabadi; Agustia Rizki, Joyanda; Oyama, Sunggito
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 2 No. 1 (2023): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (611.225 KB) | DOI: 10.31316/astro.v2i1.4647

Abstract

The purpose of this research is to design and build a web-based decision support system application to determine prospective scholarship recipients at MAN 2 Yogyakarta and test its reliability. The Decision Support System is a problem solving system with supporting tools. This system can solve problems using algorithm methods. One of the decision support system methods that can be applied to scholarship cases at MAN 2 Yogyakarta is the Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) method and forward chaining. The SMARTER method is a decision support method by determining the criteria and sub-criteria and their weight using the ROC (Rank Order Centroid). Meanwhile, the forward chaining method is a search method or a forward tracking technique that starts with existing information and combines rules to produce a conclusion or goal. The advantages of the DSS that have been developed are as follows. (1) The DSS built can be used by the scholarship selection team to recommend students who have the potential to get scholarships more quickly and objectively (2) The DSS for prospective scholarship recipients that was developed uses two methods of calculation, namely the Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Forward Chaining methods, so that the prediction results are better and faster. The SMART method emphasizes more detailed criteria and the Forward Chaining Method allows for a faster selection process.
The Detection of Bullying Against Indonesian National Team Players Using Support Vector Machine Oyama, Sunggito; Kumalasari, Desty Nur
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5701

Abstract

Detection is a process to check or conduct an examination of something using certain methods and techniques. Detection can be used for various problems, for example in detection bullying, especially on social media, is a significant problem with negative impacts on mental health, especially for public figures such as Indonesian National Team players. This study aims to detect bullying comments on the Instagram platform using the Support Vector Machine (SVM) algorithm. The research dataset consists of 3,100 comments collected from the official Indonesian National Team account, which are classified into bullying and non-bullying categories. The data preprocessing stages include case folding, tokenizing, normalization, removing stopwords, and stemming. The processed data was analyzed using the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature weighting before being classified using SVM with a linear kernel and Naïve Bayes. The results showed that SVM performed better with an accuracy of 89%, a bullying category precision reaching 93%, and a recall of 83%. Meanwhile, the Naïve Bayes method produced an accuracy of 79%, with a bullying category precision of 76% and a recall of 86%. The non-bullying category in Naïve Bayes has higher precision (84%) but lower recall (72%). Thus, SVM is proven to be more effective in detecting negative comments due to a better balance between precision and recall. However, challenges such as informal language variations and data imbalance remain obstacles in the development of this model. This study contributes to the development of cyberbullying detection technology and supports the creation of a healthier social media environment.
Decision Support System for Prospective Scholarship Recipients Using SMARTER and Forward Chaining Method Muhammad Fairuzabadi; Agustia Rizki, Joyanda; Oyama, Sunggito
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 2 No. 1 (2023): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v2i1.4647

Abstract

The purpose of this research is to design and build a web-based decision support system application to determine prospective scholarship recipients at MAN 2 Yogyakarta and test its reliability. The Decision Support System is a problem solving system with supporting tools. This system can solve problems using algorithm methods. One of the decision support system methods that can be applied to scholarship cases at MAN 2 Yogyakarta is the Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) method and forward chaining. The SMARTER method is a decision support method by determining the criteria and sub-criteria and their weight using the ROC (Rank Order Centroid). Meanwhile, the forward chaining method is a search method or a forward tracking technique that starts with existing information and combines rules to produce a conclusion or goal. The advantages of the DSS that have been developed are as follows. (1) The DSS built can be used by the scholarship selection team to recommend students who have the potential to get scholarships more quickly and objectively (2) The DSS for prospective scholarship recipients that was developed uses two methods of calculation, namely the Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Forward Chaining methods, so that the prediction results are better and faster. The SMART method emphasizes more detailed criteria and the Forward Chaining Method allows for a faster selection process.
Co-Authors Agustia Rizki, Joyanda Ernawati - Ernawati Ernawati Kumalasari, Desty Nur Muhammad Fairuzabadi Paulus Mudjihartono