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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Fountain of Informatics Journal Tech-E Jurnal Komputasi Jurnal Teknoinfo Riau Journal of Empowerment Jurnal Tekno Kompak Building of Informatics, Technology and Science Community Development Journal: Jurnal Pengabdian Masyarakat JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Informatika dan Ilmu Komputer Bulletin of Informatics and Data Science Journal of Data Science and Information System Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science Journal of Information Technology Jurnal Komputasi
Dyah Ayu Megawaty
Universitas Teknokrat Indonesia
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Decision Support System Feasibility for Promotion using the Profile Matching Method Dyah Ayu Megawaty; Andris Silitonga
Journal of Data Science and Information Systems Vol. 1 No. 2 (2023): Volume 1 Number 2 May 2023
Publisher : Journal of Data Science and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/dimis.v1i2.36

Abstract

Human Resources (HR) is an important element in companies and agencies. In companies like PT. Petrogas Sinta Energi, with fairly good economic growth and being able to compete with other companies, a system is needed to evaluate each employee to fill a position. Decision Making System (DSS) Determination of job eligibility using the profile matching method by utilizing two aspects, namely the Aspect of Work Attitude and the Intellectual Aspect can assist the Company in measuring the eligibility of an employee to occupy a position. Technology or Website-based Decision Support Systems (DSS) can speed up and simplify the determination of employee eligibility assessments as well as computerized data that can be accessed online. The results of calculations using the profile matching method, the results of the decision support system on behalf of Asep with a value of 4.55 are obtained which are selected in rank 1.
Multi-Attribute Decision Making Seleksi Kandidat Ketua OSIS Menggunakan Metode SMART Chyntia Adelia Valentina Haris; Yesi Meliyana; Erna Novita; Dyah Ayu Megawaty
Journal of Data Science and Information Systems Vol. 1 No. 4 (2023): Volume 1 Number 4 November 2023
Publisher : Journal of Data Science and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/dimis.v1i4.80

Abstract

The selection process for candidates for Student Council President (Intra-School Student Organization) often involves various problems that need to be considered and overcome. There is a risk of non-objectivity in the assessment of candidates, either from the part of the selection committee or from the selection participants themselves. The purpose of this study is to apply the Simple Multi-Attribute Rating Technique (SMART) method in the selection of Student Council President candidates by the student affairs department before voting by students. The ranking results of candidates for student council president recommended 1st place with a final score of 0.738 with the name of the prospective candidate, Janice Lie, and 2nd place with a final score of 0.643 with the name of the prospective candidate, Branden Riski Dwi Kurniawan. Based on the application of the SMART method, 2 candidates for student council president are Janice Lie, and Branden Riski Dwi Kurniawan to carry out the next process in voting for the student council president election by the school students.
Design and Development of Lecture Planning System in Informatics Study Program Dyah Ayu Megawaty; Delsa Diana Saputri
Journal of Information Technology, Software Engineering and Computer Science Vol. 1 No. 2 (2023): Volume 1 Number 2 April 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v1i1.31

Abstract

In everyday life, the existence of the internet is increasingly becoming an important part, including for students. Students can easily find information via the internet, but the function of the internet is not only that, especially for students, one of which is to arrange schedules, and find out grade information online. Strategy greatly influences students in undergoing lectures, it can even affect student achievement and graduation time. Therefore, this research produces a system for managing student strategies in lectures, for example scheduling lectures. The system developed is the Lecture Planning System. The development model used in this study is a procedural model in the form of the Extreme Programming (XP) application development methodology. Extreme Programming includes planning and analysis, so it is very helpful in determining learning and planning strategies. The results of the test obtained a score of 100% which indicates that this lecture planning application system is very feasible to use.
Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection Faruk Ulum; Junhai Wang; Dyah Ayu Megawaty; Ari Sulistiyawati; Riska Aryanti; Sumanto Sumanto; Setiawansyah Setiawansyah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1810

Abstract

Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.
Penerapan Metode A New Additive Ratio Assessment (ARAS) pada Sistem Pendukung Keputusan Pemilihan Siswa PKL Terbaik Dyah Ayu Megawaty
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 3 No. 2 (2025): Volume 3 Number 2 June 2025
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v3i2.203

Abstract

Siswa Praktik Kerja Lapangan (PKL) adalah program pendidikan yang memberikan kesempatan kepada siswa untuk mendapatkan pengalaman kerja di lingkungan industri atau perusahaan. Melalui PKL, siswa dapat mengaplikasikan pengetahuan yang telah dipelajari di sekolah dalam konteks dunia nyata, serta memperoleh pemahaman tentang proses kerja dan tuntutan profesi tertentu. Masalah dalam penelitian ini yaitu kesulitan dalam menilai kualitas dan potensi siswa secara holistik. Evaluasi kinerja siswa sering kali hanya didasarkan pada pencapaian akademis atau pengalaman kerja sebelumnya, yang mungkin tidak sepenuhnya mencerminkan kemampuan dan kepribadian siswa di lingkungan kerja sebenarnya. Tujuan penelitian ini adalah untuk mengimplementasikan metode A New Additive Ratio Assessment dalam pengembangan sistem pendukung keputusan untuk memilih siswa PKL terbaik. Dengan menggunakan metode A New Additive Ratio Assessment, penelitian ini bertujuan untuk mengidentifikasi dan memperoleh peringkat yang optimal dari kandidat siswa berdasarkan sejumlah kriteria yang telah ditentukan sebelumnya. Melalui penerapan metode A New Additive Ratio Assessment menyediakan alat yang efektif bagi para pengambil keputusan, dalam memilih siswa PKL yang paling sesuai dengan kebutuhan dan persyaratan yang ditetapkan. Hasil rekomendasi menunjukkan peringkat 1 siswa PKL terbaik dengan nilai fungsi optimasi sebesar 0,9695 yaitu Siswa IS. Hasil ini dapat menjadi rekomendasi bagi perusahaan dalam menentukan siswa PKL terbaik dengan menerapkan metode A New Additive Ratio Assessment.
APLIKASI PERMAINAN SEBAGAI MEDIA PEMBELAJARAN PETA DAN BUDAYA SUMATERA UNTUK SISWA SEKOLAH DASAR Dyah Ayu Megawaty; Damayanti Damayanti; Zakaria Sani Assubhi; Maulana Aziz Assuja
Jurnal Komputasi Vol. 9 No. 1 (2021)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i1.2779

Abstract

Indonesia secara umum mempunyai banyak keanekaragaman budaya dari berbagai pulau. Pulau Sumatera termasuk dalam pulau-pulau terbesar di Indonesia. Seiring dengan perkembangan zaman, kebudayaan yang ada di Indonesia pada saat ini secara perlahan mulai terlupakan. Oleh karena itu tujuan dari penelitian ini adalah memberikan alternatif lain untuk mengenalkan pulau Sumatera pada pelajar sekolah dasar melalui aplikasi permainan sehingga siswa tidak mudah bosan dan tertarik untuk mengetahui sejarah masing-masing provinsi, rumah adat dan pakaian adatnya. Penelitian ini dikembangkan dengan menggunakan construct 2 dan metode pengembangan Multimedia Development Life Cycle. Hasil penelitian ini berupa aplikasi permainan pengenalan peta dan budaya Sumatera yang dapat dijalankan melalui smartphone berbasis android dengan disertakan latihan soal, untuk mengetahui sejauh mana kemampuan siswa mendalami materi yang didapat dari game ini. Aplikasi ini telah diuji kepada siswa sekolah dasar dengan rata-rata hasil 94% atau sangat baik.
Penerapan Metode K-Means Clustering dan Principal Component Analysis (PCA) untuk Pengelompokan Provinsi di Indonesia Berdasarkan Indikator Pendidikan Clinton Lumbantoruan; Dyah Ayu Megawaty
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.256

Abstract

Penelitian ini bertujuan untuk mengategorikan provinsi-provinsi di Indonesia berdasarkan indikator-indikator pendidikan, dengan harapan memberikan gambaran menyeluruh dan berbasis data mengenai kondisi pendidikan. Latar belakang studi ini beranjak dari adanya kesenjangan tingkat pendidikan di berbagai wilayah yang membutuhkan analisis mendalam dengan pendekatan data mining. Data yang digunakan diperoleh dari Badan Pusat Statistik (BPS) yang mencakup sejumlah indikator pendidikan, seperti rata-rata lama bersekolah, angka partisipasi kasar (APK), angka partisipasi murni (APM), angka partisipasi sekolahan (APS), serta persentase penduduk yang tidak pernah atau belum mengenyam pendidikan. Pendekatan yang diterapkan dalam penelitian ini adalah Principal Component Analysis (PCA) untuk reduksi dimensi dan K-Means Clustering untuk pengelompokan data. Langkah-langkah penelitian mencakup preprocessing data, normalisasi dengan menggunakan StandardScaler, reduksi dimensi melalui PCA, dan clustering dengan K-Means. Temuan dari penelitian ini menunjukkan bahwa dua komponen utama dari PCA mampu menerangkan hingga 79% variasi dalam data, sehingga bermanfaat dalam menyederhanakan dataset yang ada. Proses pengelompokan menghasilkan tiga kelompok provinsi dengan karakteristik pendidikan yang bervariasi, yaitu kategori tinggi, menengah, dan rendah. Penilaian menggunakan silhouette score mengindikasikan bahwa model ini memiliki kualitas pengelompokan yang baik. Temuan dari penelitian ini diharapkan dapat memberikan kontribusi dalam membantu pengambilan kebijakan pendidikan yang lebih tepat serta menjadi landasan untuk penelitian selanjutnya dengan menambahkan variabel yang lebih luas.
Perbandingan Kinerja Naïve Bayes, SVM, dan Random Forest dalam Klasifikasi Risiko Kehamilan Reva Ekalia; Dyah Ayu Megawaty
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9724

Abstract

Classifying pregnancy risk levels is a crucial aspect in supporting early detection of potential complications in pregnant women. However, most previous studies have focused on a single algorithm and relied solely on accuracy metrics, thus failing to provide a comprehensive picture of model performance in multiclass classification. Furthermore, performance comparisons between algorithms using more comprehensive evaluation approaches are still limited. This study aims to analyze and compare the performance of the Naïve Bayes, Support Vector Machine (SVM), and Random Forest algorithms in classifying pregnancy risk levels using the Maternal Health Risk Dataset from the UCI Machine Learning Repository, which consists of 1,014 data sets with six maternal health attributes. The methods used include data preprocessing, hyperparameter optimization using GridSearchCV, and model evaluation using Stratified K-Fold Cross Validation with k = 10. Model performance was measured using accuracy, precision, recall, and F1-score metrics to provide a more comprehensive evaluation. The results showed that the Random Forest algorithm had the best performance with an accuracy value of 0.8629, precision of 0.8704, recall of 0.8629, and F1-score of 0.8635, followed by SVM and Naïve Bayes. The superiority of Random Forest is due to its ability to combine several decision trees and capture non-linear relationships between features, resulting in more accurate and stable predictions. Thus, Random Forest is recommended as the most effective method in pregnancy risk classification based on maternal health data.
Co-Authors Ade Dwi Putra Alita, Debby Andi Nurkholis Andris Silitonga Ari Sulistiyawati Ari Sulistiyawati Arief Budiman Attohiroh Attohiroh Bustanul Ulum Chyntia Adelia Valentina Haris Clara Ananta Febrina Clinton Lumbantoruan Damayanti, Damayanti Debby Alita Delsa Diana Saputri Dewi Fatmawati Dewi Sri Wahyuni Dinda Titian Lestari Donaya Pasha Dyah Aminatun Erna Novita Erwanto Erwanto Farel Adrianto Tansir Faruk Ulum Fenty ariany Fikih Yuhada Sena Gilang Eko Prasetyo I Putu Hari Saftana Ida Nurwanti Imam Ahmad Indra Pratama Putra Isidorus Richardius Kuncoro Junhai Wang Lara Tastilia M Fabian Apriando Mandasari, Berlinda Maulana Aziz Assuja Mohamad Ghazi Rio Muhammad Arsi Muhammad Ativ Mustaqov Muhammad Najib Dwi Satria Muhammad Saefudin Neneng Neneng Nuralia Nuralia Parjito Parjito Priandika, Adhie Thyo Pustika, Reza Putri Sukma Dewi Putri Sukma Dewi Qadhli Jafar Adrian Reifco Harry Farrizqy Renhard Yudika Simanjuntak Reva Ekalia Rida Alifah Rifli Yanto Rillya Arundaa Riska Aryanti Rohmat Indra Borman Rubiyah Rubiyah Ryan Randy Suryono Samuel Hutagalung Sandy Purnama Setiawansyah Setiawansyah Sumanto Sumanto Susan Sagita Tenda, Edwin Wahyuni Dinasari Yesi Meliyana Yuri Rahmanto Yusra Fernando Zakaria Sani Assubhi