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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer Annual Research Seminar BAREKENG: Jurnal Ilmu Matematika dan Terapan PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Infomedia Riau Journal of Empowerment Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) CCIT (Creative Communication and Innovative Technology) Journal Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Informatika dan Rekayasa Komputer Journal of Embedded Systems, Security and Intelligent Systems
Desty Rodiah
Department Informatics Engineering, Faculty Of Computer Science, Sriwijaya University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Decision Support System for Selection of Outstanding Students Using the AHP and SAW Methods Sadana, Feron; Yunita, Yunita; Rodiah, Desty
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.61

Abstract

It is very important for outstanding students to be directed and guided to get coaching related to the development of each student's personal potential so that superior and quality students are created. The process of selecting outstanding students can get wrong decisions because the process of selecting outstanding students is based on subjectivity, this allows many selected outstanding students not to reach the desired standard and do not get the best candidates. Therefore, a decision support system was created that can carry out the calculation process for all selections for the selection of outstanding students. This final project will implement the AHP and SAW methods in forming a system. The stages are carried out by comparing feature weights with the AHP method. Then the next stage is to rank using the SAW method to get selected outstanding students. Of the 72 students who were selected from the school, they were then selected to become 20 outstanding students based on the highest-ranking order. Software testing is done by comparing the results of school calculations with system calculations. Based on the results of the tests carried out, an accuracy value of 80% was obtained.
Decision Support System For New Employee Selection Using AHP And TOPSIS Fahriza, Dicky; Abdiansyah, Abdiansyah; Rodiah, Desty
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.67

Abstract

There are so many prospective workers with the same educational background, but not necessarily in accordance with the required company position and not necessarily they have the same expertise. To minimize the occurrence of errors, it can be done by making a decision-making system (DSS) to provide these recommendations. In this study, the Analytical Hierarchy Process (AHP) and the Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method were used to provide recommendations for prospective new employees. The steps taken are to compare the importance of each criterion weight with the AHP method. Then the ranking stage is carried out using the TOPSIS method to get recommendations for selected employees. The data used in this study is primary data in the form of 70 data on prospective employees from PT Hutama Jaya Perkasa. From the 70 data then selected to be 36 prospective employees based on the order of the highest ranking. Software testing is done by comparing the results of system calculations and the results of company calculations. Based on the test results obtained an accuracy value of 94.4%.
Optimization of Tsukamoto FIS Using Genetic Algorithm for Rainfall Prediction in Banyuasin Regency Akbar, Muhammad Rafi; Miraswan, Kanda Januar; Rodiah, Desty; Buchari, Muhammad Ali; Marjusalinah, Anna Dwi
Sriwijaya Journal of Informatics and Applications Vol 5, No 2 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i2.118

Abstract

Indonesia, as a tropical country with high rainfall, heavily relies on accurate rainfall predictions for various critical purposes, including water resource management and extreme weather impact mitigation. One commonly used method is the Tsukamoto Fuzzy Inference System (FIS). However, implementing the Tsukamoto FIS often leads to high error rates. This is attributed to the difficulty in determining the boundaries of fuzzy variable membership functions. To address this issue, this research proposes an innovative approach by optimizing the boundaries of fuzzy membership functions using Genetic Algorithms (GA). The study resulted in a 49.02% reduction in the error rate, decreasing from 76.82% to 27.8%. This method significantly enhances rainfall prediction accuracy and contributes to the advancement of more sophisticated prediction methods. The optimization method proposed in this study also holds potential for application across various atmospheric science contexts.
Pengembangan Representasi Pengetahuan Ontologi Domain Bidang Ilmu Informatika Rodiah, Desty; Kanda Januar Miraswan; Junia Kurniati; Dellin Irawan; Vanya Terra Ardiani
Jurnal PROCESSOR Vol 19 No 2 (2024): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2024.19.2.1905

Abstract

Research in computer science, which often involves complex issues, frequently encompasses multiple sub-disciplines. The more research that applies multiple sub-disciplines, it becomes challanging to categorize the appropriate branches of knowledge related to the research. Therefore, a knowledge representation is needed to accurately depict these fields of study. This research develops an ontology that serves as a knowledge representation for computer science, comprising four sub-disciplines: graphics and visualization, natural language processing, distributed systems, and data science and pattern recognition.The ontology development is based on the grouping references from the Association for Computing Machinery (ACM). Using the Protégé software version 5.5.0, the development resulted in a matrix with 3,584 axioms, 837 logical axioms, 794 classes, and 1 equivalent class. Once the ontology was successfully developed, it underwent testing through query examinations, with four specific queries for each sub-discipline. The query testing utilized a filter based on keywords input by the user. The keywords used were graphics, words, security, and patterns. The ontology successfully provided answers based on the exploration of relationships between subclasses within the ontology.
Representasi Pengetahuan Ontologi untuk Klasifikasi Topik Penelitian pada Bidang Ilmu Informatika Rodiah, Desty; Miraswan, Kanda Januar; Kurniati, Junia; Irawan, Dellin; Ardani, Vanya Terra
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.244

Abstract

Research in informatics often involves multiple subdisciplines, making topic classification challenging. Typically, text classification in natural language processing requires training and testing. However, ontology-based classification eliminates the need for training data. Challenges in ontology-based classification include finding terms that lack similarity with ontology and ensuring accuracy in measuring data similarity with knowledge representation. To address this, the fastText method identifies term similarities between words and ontology, while the Wu-Palmer method measures semantic similarity and relationships within ontology. The research process includes preprocessing (Casefolding, Tokenizing, Stopword Removal, Lemmatization), Query Processing (Query Reduction, Duplicate Removal), Word Embedding with fastText, and Semantic Similarity measurement using Wu-Palmer. The dataset consists of 200 research studies from Fasilkom Unsri informatics students' final projects. The classification results show that 178 out of 200 topics were correctly classified, achieving an accuracy of 89.5%, demonstrating the system’s effectiveness.
Implementation of K-Means and TOPSIS Algorithm for Determining High School Student Majors Yunita, Yunita; Rodiah, Desty; Wahyuni, Putri Eka; Kurniati, Junia; Sarpanda, Dama Putra
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8358

Abstract

This study focuses on the implementation of the K-means algorithm to assist high school students in selecting majors that align with their interests and skills. Utilizing a dataset of 231 grade X students from 2022, the K-means algorithm successfully formed two distinct clusters. The results indicated an accuracy of 81.81% for the K-means clustering process, recall of 81.75%, precision of 77.87%, and specificity of 81.75%. Following this, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to rank the students within each cluster based on various weighted criteria. The TOPSIS method achieved a final ranking accuracy of 80.9%. The findings demonstrate the effectiveness of combining K-means and TOPSIS in facilitating informed decision-making for students regarding their academic paths.
Aplikasi QR-code untuk sistem daftar hadir: Solusi digitalisasi administrasi di SMA dan SMK Rodiah, Desty; Yusliani, Novi; Abdiansah; Utami, Alvi Syahrini; Miraswan, Kanda Januar; Marieska, Mastura Diana; Yunita; Rini, Dian Palupi
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 8 No 2 (2025)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v8i2.22696

Abstract

Kebijakan Merdeka Belajar dari Kemendikbud RI mendorong guru untuk menerapkan pendekatan pengajaran yang fleksibel dan adaptif melalui integrasi teknologi dalam kegiatan pembelajaran. Dalam konteks ini, program pengabdian kepada masyarakat memberikan pelatihan untuk mengembangkan sebuah aplikasi daftar hadir berbasis QR-Code menggunakan Python untuk guru SMA dan SMK. Aplikasi ini dirancang untuk mencatat kehadiran siswa secara cepat, tepat, dan efisien, serta mendukung kemudahan administrasi dan memberikan pengalaman langsung dalam penggunaan teknologi pemrograman. Kegiatan pengabdian ini menerapkan metode Participatory Action Research (PAR), yang meliputi lima tahap: To Know (menggali kebutuhan mitra melalui survei), To Understand (mengevaluasi pelatihan sebelumnya), To Plan (menyusun materi dan instrumen evaluasi), To Act (melaksanakan pelatihan melalui presentasi dan praktikum), dan To Change (melakukan evaluasi). Evaluasi dilakukan dengan pendekatan N-Gain dan skala Likert. Hasil N-Gain sebesar 20,90% menunjukkan efektivitas pelatihan yang kurang meskipun terdapat peningkatan nilai rata-rata sebesar 7,37 poin. Hal ini dipengaruhi oleh latar belakang peserta yang sudah berpengalaman, sehingga materi dan soal perlu dikembangkan lebih lanjut. Di sisi lain, hasil Likert menunjukkan persepsi peserta yang sangat positif. Kendala koneksi internet sempat memengaruhi praktikum, namun narasumber dan mahasiswa aktif membantu peserta yang mengalami hambatan tersebut.
Effect of Genetic Algorithm on Prediction of Heart Disease Stadium using Fuzzy Hierarchical Model Rini, Dian Palupi; Afandi, Defrian; Rodiah, Desty
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

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

Abstract

The Fuzzy Hierarchical Model method can be used to predict the stage of heart disease. The use of the Fuzzy Hierarchical Model on complex problems is still not optimal because it is difficult to find a fuzzy set that provides a more optimal solution. This method can be improved by changing the membership function constraints using Genetic Algorithm to get better predictions. Tests carried out using 282 heart disease patient data resulted in a Root Mean Squared Error (RMSE) value of 0.55 using the best Genetic Algorithm parameters, including population size of 140, number of generations of 125, and a combination of cross-over rate and mutation rate of 0.4 and 0.6 whereas the RMSE value generated by the Fuzzy Hierarchical Model before being optimized by the Genetic Algorithm was 0.89. These results indicate an increase in the predictive value of the Fuzzy Hierarchical Model after being optimized using the Genetic Algorithm.
CORRESPONDENCE ANALYSIS PADA HUBUNGAN FAKTOR-FAKTOR YANG MEMPENGARUHI PENDAPATAN PETANI KOPI PAGARALAM Irmeilyana, Irmeilyana; Ngudiantoro, Ngudiantoro; Rodiah, Desty
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 1 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1080.221 KB) | DOI: 10.30598/barekengvol15iss1pp179-192

Abstract

Pagaralam is one of the coffee-producing districts in South Sumatra (Sum-Sel). Pagaralam coffee farming is a hereditary business, where the majority of land processing is still traditional. This is related to working capital and farmers' income. This study aims to analyze the factors that affect the income of Pagaralam coffee farmers by using correspondence analysis. There are 30 variables or factors studied. Each variable is divided into several categories. The categories of each variable are described graphically with the categories of income variable. Primary data were obtained from 196 respondents who were selected based on purposive sampling technique. There are 13 factors that affect the income of respondents, namely: number of dependents, number of trees, age of the trees, number of female workers from outside the family, frequency of fertilization, frequency of herbicide application, production of harvest, production outside of harvest, gross income, minimum price of coffee beans, the maximum price of coffee beans, economic status and land productivity. There are 8 of the 13 factors that predominantly characterize the profile of net income level of Pagaralam coffee farmers. In general, the factor that must be considered in coffee farming is land productivity which is also related to production costs in land processing and crop production, as well as external factors regarding the market price of coffee.
Prediksi Tingkat Indeks Prestasi Kumulatif Akademik Mahasiswa dengan Menggunakan Teknik Data Mining Desiani, Anita; Yahdin, Sugandi; Rodiah, Desty
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722493

Abstract

Educational data mining (EDM) adalah suatu bidang aplikasi antara pendidikan dan komputer. Salah satu yang dapat dilakukan pada EDM adalah memprediksi tingkat prestasi mahasiswa. Tingkat indeks prestasi kumulatif (IPK) akademik mahasiswa sangat penting karena menentukan tingkat kelulusan dan kualiatas institusi pendidikan. Penelitian ini bertujuan untuk menganalisa atribut-atribut yang mempengaruhi tingkat indeks prestasi kumulatif (IPK) mahasiswa yang berasal dari faktor eksternal pada mahasiswa. Adapun atribut yang digunakan adalah 10 variabel atribut yaitu nilai TOEFL, pendidikan ayah, pendidikan ibu, pekerjaan ayah, pekerjaan ibu, asal daerah, tempat tinggal selama kuliah dan tingkat prestasi akademik yang dicapai. Hasil akurasi pengolahan dengan menggunakan Algoritma C4.5 adalah 75,18% dan Naive Bayes 74,47% menunjukkan bahwa model dan atribut yang digunakan baik untuk memprediksi tingkat IPK  mahasiswa. Algoritma C4.5 mampu menunjukkan atribut apa yang berpengaruh langsung pada tingkat IPK  mahasiswa yaitu Nilai TOEFL, jam belajar, pendidikan ayah, pekerjaan ayah, dan tempat tinggal mahasiswa.  Algoritma C4.5 tidak mampu  memperhitungkan peluang suatu klasifikasi jika jumlah  instan pada klasifikasi tersebut sangat sedikit pada kejadian data. Sebaliknya Naive Bayes tetap mampu memperhitungkan peluang kemunculan dan ketepatannya informasi yang dihasilkan  meski jumlah instan yang sedikit. Dalam penelitian ini data mahasiswa yang memiliki tingkat IPK cumlaude sangat sedikit, namun Naive Bayes tetap mampu mengukur Recall pada kelas ini sebesar 28,6% dan Precision sebesar 40%. 
Co-Authors Abdiansah Abdiansyah, Abdiansyah Afandi, Defrian Ahmad Nauvan Zikri Al Ghifran Akbar, Muhammad Rafi Al-Filambany, Muhammad Gibran Allsela Meiriza, Allsela Alvi Syahrini Utami Angga Adiningrat Mulyanata Anita Desiani Ardani, Vanya Terra Ari Firdaus Arti Dian Nastiti Buchari, Muhammad Ali Defrian Afandi Dellin Irawan Dian Palupi Rini Dian Palupi Rini Dicky Ahmad Rizaldi Dinda Lestarini Dite Geovanni Erwin Erwin Erwin, Erwin Fahriza, Dicky Febrivia, Pretty Fujianti Firdaus Firdaus Fitria Khoirunnisa Geovanni, Dite Irawan, Dellin Irmeilyana Jambak, Muhammad Ihsan Junia Kurniati Junia Kurniati, Junia Kanda Januar Miraswan Luh Sri Mulia Eni M. Fachrurrozi . Mardiana Mardiana Marjusalinah, Anna Dwi Mastura Diana Marieska Mudinillah, Adam Muhammad Akmal Shidqi Muhammad Gibran Al-Filambany Muhammad Naufal Rachmatullah Ngudiantoro . Novi Yusliani Nur Hamidah Oktaria Permata Sari Osvari Arsalan Qurrota ‘Aini Muthmainnah Rizki Kurniati Rusdi Efendi Sadana, Feron Samsuryadi Samsuryadi Saparudin Saparudin Sarifah Putri Raflesia Sarpanda, Dama Putra Shidqi, Muhammad Akmal Sri Indra Maiyanti Sri Indra Maiyanti Sugandi Yahdin Vanya Terra Ardiani Wahyuni, Putri Eka Winda Agusthia Yunita YUNITA Yunita Yunita Yunita Yunita Yunita Yunita