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All Journal Jurnal Script Syntax Literate: Jurnal Ilmiah Indonesia TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Innovation Research and Knowledge
I Wayan Aries Agetia
Politeknik Negeri Bali, Badung
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IMPLEMENTASI METODE TOPSIS PADA SPK PENENTUAN PENERIMA BEASISWA DI IST AKPRIND BERBASIS WEB Agetia, I Wayan Aries; ., Suraya; Rachmawati, Rr. Yuliana
Jurnal Script Vol 2, No 2 (2015): EDISI JUNI 2015
Publisher : Jurnal Script

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Abstract

ABSTRACTThe selection process of determining scholarship recipients candidates in Bagian Kemahasiswaan dan Alumni at Institut Sains dan Teknologi AKPRIND Yogyakarta only be done by calculating the average of all criteria to implement into the form of a Ms.Excel. During this time, there are still lacking proper selectors for selecting applicants because only rely on the manual method, so it’s can’t assess applicants objectively. And so we need a system that is able to assist and support the decision of selectors. This application is designed using diagams context and data flow diagrams, while in programming using PHP programming language with application PHP Data Object as a connection to the MySQL database and using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results of this design in the form of web-based applications which focused on decision making. The use of the method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in decision support system scholarship recipients Peningkatan Prestasi Akademik (PPA) and Bantuan Belajar Mahasisswa (BBM) is able to provide the best alternative based on the results of the highest ranking. Keywords: Scholarship, Decision Support Systems, TOPSIS (Technique For Others Preference by Similarity to Ideal Solution), PHP, MySQL
IMPLEMENTASI METODE TOPSIS PADA SPK PENENTUAN PENERIMA BEASISWA DI IST AKPRIND BERBASIS WEB I Wayan Aries Agetia; Suraya -; Rr. Yuliana Rachmawati
Jurnal SCRIPT Vol. 3 No. 1 (2015): Vol. 03 No. 01 Edisi Juni 2015
Publisher : Jurusan Informatika INSTITUT SAINS & TEKNOLOGI AKPRIND YOGYAKARTA

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Abstract

The selection process of determining scholarship recipients candidates in Bagian Kemahasiswaan dan Alumni at Institut Sains dan Teknologi AKPRIND Yogyakarta only be done by calculating the average of all criteria to implement into the form of a Ms.Excel. During this time, there are still lacking proper selectors for selecting applicants because only rely on the manual method, so it’s can’t assess applicants objectively. And so we need a system that is able to assist and support the decision of selectors. This application is designed using diagams context and data flow diagrams, while in programming using PHP programming language with application PHP Data Object as a connection to the MySQL database and using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The results of this design in the form of web-based applications which focused on decision making. The use of the method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in decision support system scholarship recipients Peningkatan Prestasi Akademik (PPA) and Bantuan Belajar Mahasisswa (BBM) is able to provide the best alternative based on the results of the highest ranking.
Implementasi Metode Ahp-Wp Dalam Sistem Pendukung Keputusan Pemilihan Tenaga Kependidikan Non PNS Teladan Di Universitas Udayana I Wayan Aries Agetia; I Made Candiasa; Dewa Gede Hendra Divayana
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (670.357 KB) | DOI: 10.36418/syntax-literate.v7i11.9434

Abstract

Universitas Udayana merupakan salah satu Perguruan Tinggi di Bali yang melaksanakan pemilihan tenaga kependidikan non PNS teladan setiap tahunnya. Sampai saat ini, masih ada unsur subjektifitas yang tinggi dalam pelaksanaannya, karena dikerjakan secara manual. Diperlukan penerapan sistem pendukung keputusan (SPK) dalam permasalahan tersebut, agar diperoleh hasil yang lebih obyektif. Untuk itu dilakukan penelitian untuk membangun SPK pemilihan tenaga kependidikan non PNS teladan di Universitas Udayana dengan melibatkan 95 orang subyek. Kriteria yang telibat dalam perankingan adalah nilai Sasaran Kinerja Pegawai (SKP), nilai orientasi pelayanan, nilai integritas, nilai komitmen, nilai disiplin, dan nilai kerja sama. Pembobotan kriteria dilakukan dengan metode Analitycal Hierarchy Process (AHP) dan perankingan dilakukan dengan metode Weighted Product (WP). Akurasi hasil perankingan ditentukan dengan metode Mean Absolute Percentage Error (MAPE). Akurasi hasil perankingan dengan kombinasi metode AHP-WP dibandingkan dengan kombinasi AHP-Fuzzy WP. Sistem pendukung keputusan pada penilitian ini dikembangkan dengan berbasis web menggunakan Bahasa pemrograman PHP dan database MySQL. Sistem pendukung keputusan diuji dengan metode blackbox testing untuk menguji keberhasilan eksekusi setiap menu yang tersedia pada sistem. Implementasi metode AHP dan WP dalam sistem pendukung keputusan menghasilkan perankingan pemilihan tenaga kependidikan non PNS teladan. Perhitungan akurasi dengan menggunakan metode MAPE menunjukkan persentase kesalahan pada perankingan metode WP untuk tahun 2019 adalah sebesar 0.0356%, tahun 2020 adalah sebesar 0,0225%, dan tahun 2021 adalah sebesar 3,49%. Sedangkan perhitungan akurasi pada perankingan metode fuzzy WP menunjukkan persentase kesalahan pada tahun 2019 sebesar 14,97%, tahun 2020 sebesar 15,92%, dan tahun 2021 sebesar 31,68%. Blackbox testing pada sistem pendukung keputusan pemilihan Tenaga Kependidikan Non PNS teladan menunjukkan sistem telah berjalan sesuai dengan yang dibutuhkan.
IMPLEMENTASI METODE WEIGHTED PRODUCT (WP) DALAM MENENTUKAN PEGAWAI BERPRESTASI DI UNIVERSITAS XYZ I Wayan Aries Agetia
Journal of Innovation Research and Knowledge Vol. 5 No. 1: Juni 2025
Publisher : Bajang Institute

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Abstract

Hingga saat ini, proses pemilihan Pegawai Berprestasi di Universitas XYZ masih dipengaruhi oleh unsur subjektivitas yang tinggi, karena dilakukan secara manual. Untuk mengatasi hal tersebut dan menghasilkan keputusan yang lebih objektif, diperlukan penerapan Sistem Pendukung Keputusan (SPK). Tujuan dari penelitian ini yaitu merancang dan mengimplementasikan SPK dalam pemilihan Pegawai Berprestasi di Universitas XYZ, yang melibatkan 95 subjek dalam prosesnya. Kriteria yang digunakan untuk pemeringkatan mencakup nilai integritas, nilai komitmen, nilai disiplin, nilai kerja sama, dan nilai Sasaran Kinerja Pegawai (SKP). Metode Weighted Product (WP) digunakan untuk proses pemeringkatan. Keakuratan hasil pemeringkatan kemudian diukur menggunakan metode Mean Absolute Percentage Error (MAPE). Implementasi metode WP dalam sistem SPK berhasil menghasilkan peringkat untuk pemilihan Pegawai Berprestasi. Perhitungan dengan metode MAPE menghasilkan persentase kesalahan yaitu 4,121663915%, yang mencerminkan tingkat akurasi yang cukup baik dalam proses pemilihan pegawai berprestasi.
Evaluasi Kinerja Algoritma Naïve Bayes, SVM, dan IndoBERT pada Analisis Sentimen Ulasan Pengguna Gojek Berbasis Text Mining I Wayan Aries Agetia; Ni Luh Eka Armoni; I Putu Ari Utama Irawan
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9617

Abstract

This study aims to evaluate and compare the performance of Naïve Bayes, Support Vector Machine (SVM), and IndoBERT algorithms in the task of sentiment classification of user reviews on the Gojek application. Data were collected through web scraping from the Google Play Store and subsequently labeled into three sentiment categories: negative, neutral, and positive. A quantitative approach with a descriptive-comparative design was employed. The research procedure consisted of data collection, text preprocessing, dataset splitting into training and testing sets, model development, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the IndoBERT algorithm achieved the best performance, with an accuracy of 92.46%, outperforming Naïve Bayes (87.94%) and SVM (86.43%). Furthermore, IndoBERT demonstrated greater consistency in precision, recall, and F1-score across all sentiment categories. In contrast, Naïve Bayes exhibited a tendency to misclassify certain classes, while SVM showed relatively stable performance, although it did not reach optimal results. These findings suggest that transformer-based approaches are more effective in capturing the contextual complexity of the Indonesian language. This study contributes by providing a comparative analysis of classical and transformer-based methods in Indonesian sentiment classification and offers empirical evidence of the superiority of transformer-based approaches in capturing linguistic contextual nuances in user reviews of digital applications.
Co-Authors Dewa Gede Hendra Divayana, Dewa Gede Hendra I Made Candiasa I Putu Ari Utama Irawan Ni Luh Eka Armoni RR. Yuliana Rachmawati Rr. Yuliana Rachmawati, Rr. Yuliana Suraya - Suraya ., Suraya