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All Journal Jurnal Simetris Jurnal technoscientia JIKO (Jurnal Informatika dan Komputer) Jurnal Teknologi Komputer dan Sistem Informasi Respati JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Intelligent Software Systems MANISE (Manajemen, Bisnis dan Ekonomi) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Danny Kriestanto, Danny
Program Studi Informatika, STMIK AKAKOM, Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : journal of intelligent software systems

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Lecture Scheduling Using Genetic Algorithm Method Liyan, Sur; Kriestanto, Danny; Ramadhan, Alfitra; Haries, Muhammad; Lukman, Lukman
Journal of Intelligent Software Systems Vol 3, No 2 (2024): December 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i2.1501

Abstract

Lecture scheduling at a university is a very important element, because it determines the progress of the lecture activity process. At the Indonesian Digital Technology University, the lecture scheduling process still uses Microsoft Excel, this is considered less than optimal because it takes a relatively long time, the process is long and requires a high level of accuracy, which is something that often becomes an obstacle in the scheduling process. The genetic algorithm is an algorithm that can be used to solve problems on a large scale and with a high level of complexity, such as lecture scheduling. Genetic algorithms have advantages over other optimization methods, namely that genetic algorithms can optimize problems with complex problems and a very wide search space. There are several stages in a genetic algorithm, namely: initial population initialization, fitness evaluation, selection, crossover and mutation. The results of this research show that scheduling lectures using the genetic algorithm method results in faster and more accurate results, because the process is carried out by the program by finding the best solution from each generation iteration and the process will stop when the required solution is obtained. Meanwhile, scheduling lectures using MS Excel takes longer because it is done manually with the help of the VLOOKUP formula and requires a high level of accuracy so that there are no conflicting lecture schedules. From the test results, using Python software with a genetic algorithm takes 0.609356 seconds with an accuracy level of 100%. Meanwhile, testing using MS Excel with VLOOKUP takes around 20 minutes with an accuracy rate of 95%.Keywords— Scheduling, Lectures, Genetic Algorithm
PERFORMANCE ANALYSIS OF LOGISTIC REGRESSION ALGORITHM IN OPINION SEGMENTATION OF INDOSAT NETWORK SERVICE REVIEWS Dwi, Sandy Ananda; Kriestanto, Danny; Anwar, Ajie Al Qadri; Ro'uf, Syahrur; Rochmana, Lintang Suci; Nugroho, Muhammad Agung
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2004

Abstract

In the era of the industrial revolution 4.0, where the use of network services has become a basic need and cannot be separated from daily activities, the massive number of network service users can be proven by the increasing number of people using digital platforms to search for information, express opinions or even just to communicate with each other, currently network services are available in the form of digital platforms that can be used to purchase network data packages or just to monitor the quality of network services, therefore this study aims to analyze user sentiment towards network services that have been launched by the Indosat provider based on the results of user reviews sourced from the digital platform using a machine learning approach and a logistic regression algorithm model to determine the segmentation of opinions that are widely expressed on the digital platform. The results of this study indicate that the logistic regression algorithm is able to analyze patterns of consumer characteristics with good accuracy in the algorithm model, and the results of the accuracy of the algorithm model in finding segmentation patterns in sentiment opinions reach an accuracy value of 85%, precision 81%, recall 77% and f1-score 79% to predict an opinion that has negative and positive sentiment during testing, then network speed, connection disruption and network data package prices are one of the factors that can influence an opinion regarding negative and positive sentiment.
Rule Based System to Support Decisions on Determining Employee Status (Lecturers) for Scholarship Student Graduates Sipayung, Hotma Sadariahta; Andriyani, Widyastuti; Purnomosidi Dwi Putranto, Bambang; Kriestanto, Danny
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1337

Abstract

Salah satu permasalahan yang terjadi di Universitas Teknologi Digital Indonesia (UTDI) adalah proses seleksi yayasan Dosen Tetap yang disebut-sebut baru untuk diterapkan kepada mahasiswa penerima calon beasiswa S2 di Magister Teknologi Informasi (MTI). UTDI Yogyakarta. Kriteria yang digunakan dalam aturan tersebut adalah Indeks Prestasi (IP) Semester 1, IP Semester 2, IP Semester 3, Indeks Prestasi Kumulatif (IPK), Makalah (karya ilmiah), Kerjasama, Disiplin, Komunikasi, Pra Tesis, Tesis, Nilai C. , dan Durasi Studi yang diperoleh dari MTI UTDI, selanjutnya akan menggunakan Algoritma C4.5 untuk menghasilkan pohon keputusan yang akan dipelajari aturan dalam sistem. Penelitian ini menggunakan kaidah yang diperoleh dari MTI UTDI oleh Ketua Program Studi (Kaprodi) yaitu 41 data latih dan 8 data uji. Menggunakan forward chaining sebagai metode dalam sistem pakar yang mencari solusi melalui permasalahan, kemudian menggunakan Algoritma C4.5 yang merupakan algoritma yang digunakan untuk membentuk pohon keputusan. Aturan yang terbentuk kemudian digunakan untuk memprediksi kelayakan lulusan beasiswa Magister menjadi Dosen Tetap, Dosen Kontrak, atau tidak memenuhi persyaratan. Hasil prediksi tersebut kemudian dievaluasi menggunakan Confusion Matrix dan memperoleh nilai akurasi sebesar 75%, Precision sebesar 77,78%, dan Recall sebesar 77,78%. Sehingga Algoritma C4.5 dengan menggunakan aplikasi RapidMiner cukup layak digunakan untuk mendukung pengambilan keputusan dalam pemilihan mahasiswa penerima beasiswa Magister yang akan diangkat menjadi Dosen Tetap, Dosen Kontrak maupun yang tidak memenuhi syarat sebagai Dosen di UTDI. Fakultas Teknologi Informasi
Co-Authors Agung Budi Prasetyo Agung Budi Prasetyo Agung Prasetyo Alif Benden Arnado Andriyani, Widyastuti Anwar, Ajie Al Qadri Dedy Ahmad Kurniawan Dini Fakta Sari, Dini Fakta Dwi, Sandy Ananda Femi Dwi Astuti Haries, Muhammad Hidayat, Herry Liyan, Sur Lukman Lukman Muhammad Taufik Irawan Muzakki, Akbar Rizqi Nugroho, Muhammad Agung Purnomosidi Dwi Putranto, Bambang Ramadhan, Alfitra Ro'uf, Syahrur Rochmana, Lintang Suci Sipayung, Hotma Sadariahta Sur Yanti, Sur